diff --git a/README.md b/README.md index c849198..efd0875 100644 --- a/README.md +++ b/README.md @@ -11,8 +11,7 @@ https://www.coursera.org/learn/machine-learning 本项目包含课程中的所有课后作业以及笔记，目前正在将手写笔记进行电子化。 1. 笔记(notes)都为中文，为了便于复习和扩充等，尽量会按照视频目录，以及视频内容进行提炼整理。 - -2. 所有课后作业(assignments)都已提交并通过 Coursera 编程测验。仅供参考，请勿抄袭。 +2. 遵循[荣誉准则][honor code]，现已移除已通过的课后编程作业的源代码，之后会改为指导笔记形式陆续更新。 For Andrew Ng's machine learning course on Coursera. @@ -26,11 +25,11 @@ You can read it by Typora or any other similar markdown editor. 1. **在线阅读地址**：http://scruel.gitee.io/ml-andrewng-notes/ -2. 直接在 GitHub Page 上阅读，需安装 Chrome 插件 —— [GitHub with MathJax][1]（部分公式仍无法正常解析）。 +2. 直接在 GitHub Page 上阅读，需安装 Chrome 插件 —— [GitHub with MathJax][GitHub with MathJax]（部分公式仍无法正常解析）。 3. 下载 .html 网页文件及 笔记图片 image 文件夹，浏览器打开阅读。 -4. 笔记源码(.md)基于支持 LaTeX 的 markdown 编辑器 [Typora][2]，其他类似的编辑器也可以阅读及编辑，注意其他编辑器可能会产生排版问题。 +4. 笔记源码(.md)基于支持 LaTeX 的 markdown 编辑器 [Typora][Typora]，其他类似的编辑器也可以阅读及编辑，注意其他编辑器可能会产生排版问题。 @@ -39,12 +38,16 @@ You can read it by Typora or any other similar markdown editor. 方便网络不方便的同学学习 -[百度网盘下载](https://pan.baidu.com/s/1mkmnRIC) | 密码：fdzc（包含视频，讲义，以及编程作业题原题压缩包） +[百度网盘下载][baidupan] | 密码：fdzc（包含视频，讲义，以及编程作业题原题压缩包） -[B站在线中英双语](http://www.bilibili.com/video/av9912938?bbid=F8173D95-FF96-47EF-B7F4-0779D698B8051978infoc) | [B站在线英文版1](https://www.bilibili.com/video/av17624209/?from=search&seid=15848135050308500663) | [B站在线英文版2](https://www.bilibili.com/video/av17624412/?from=search&seid=15848135050308500663) +[B站在线中英双语][bilibili_zh] | [B站在线英文版1][bilibili_en1] | [B站在线英文版2][bilibili_en2] 当然，还是建议克服一下困难，最好能上 Coursera 网站学习，官网安排的贴心小测试可以检验你对知识的掌握情况。 + + +如果遇到 Coursera 上提交编程作业失败的问题，以 [submitWithConfiguration.m](https://github.com/scruel/ML-AndrewNg-Notes/blob/master/assignments/submitWithConfiguration.m) 覆盖编程作业中 lib 文件夹下的同名文件即可解决。 + ## Thanks - Coursera 官网 @@ -55,18 +58,24 @@ You can read it by Typora or any other similar markdown editor. 注：由于手写笔记成稿时间较早，有所参考无法一一致谢，在此统一表示谢意！ ## License -[![Creative Commons License](https://i.creativecommons.org/l/by-nc/4.0/88x31.png)][3] +[![Creative Commons License](https://i.creativecommons.org/l/by-nc/4.0/88x31.png)][CC BY-NC 4.0] -This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][3]. +This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][CC BY-NC 4.0]. -[知乎文章](https://zhuanlan.zhihu.com/p/32781741) +[知乎文章][zhihu] By: Scruel -[1]: https://chrome.google.com/webstore/detail/ioemnmodlmafdkllaclgeombjnmnbima -[2]: https://typora.io/ -[3]: http://creativecommons.org/licenses/by-nc/4.0/ \ No newline at end of file +[zhihu]: https://zhuanlan.zhihu.com/p/32781741 +[baidupan]: https://pan.baidu.com/s/1mkmnRIC +[bilibili_zh]: http://www.bilibili.com/video/av9912938?bbid=F8173D95-FF96-47EF-B7F4-0779D698B8051978infoc +[bilibili_en1]: https://www.bilibili.com/video/av17624209/?from=search&seid=15848135050308500663 +[bilibili_en2]: https://www.bilibili.com/video/av17624412/?from=search&seid=15848135050308500663 +[GitHub with MathJax]: https://chrome.google.com/webstore/detail/ioemnmodlmafdkllaclgeombjnmnbima +[Typora]: https://typora.io/ +[honor code]: https://www.coursera.org/learn/machine-learning/supplement/nh65Z/machine-learning-honor-code +[CC BY-NC 4.0]: http://creativecommons.org/licenses/by-nc/4.0/ \ No newline at end of file diff --git a/assignments/convert(MNIST2Matlab).m b/assignments/convert(MNIST2Matlab).m deleted file mode 100644 index 0e5e668..0000000 --- a/assignments/convert(MNIST2Matlab).m +++ /dev/null @@ -1,99 +0,0 @@ -% Version 1.000 -% -% Code provided by Ruslan Salakhutdinov and Geoff Hinton -% -% Permission is granted for anyone to copy, use, modify, or distribute this -% program and accompanying programs and documents for any purpose, provided -% this copyright notice is retained and prominently displayed, along with -% a note saying that the original programs are available from our -% web page. -% The programs and documents are distributed without any warranty, express or -% implied. As the programs were written for research purposes only, they have -% not been tested to the degree that would be advisable in any important -% application. All use of these programs is entirely at the user's own risk. - -% This program reads raw MNIST files available at -% http://yann.lecun.com/exdb/mnist/ -% and converts them to files in matlab format -% Before using this program you first need to download files: -% train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz -% t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz -% and gunzip them. You need to allocate some space for this. - -% This program was originally written by Yee Whye Teh - -% Work with test files first -fprintf(1,'You first need to download files:\n train-images-idx3-ubyte.gz\n train-labels-idx1-ubyte.gz\n t10k-images-idx3-ubyte.gz\n t10k-labels-idx1-ubyte.gz\n from http://yann.lecun.com/exdb/mnist/\n and gunzip them \n'); - -f = fopen('t10k-images-idx3-ubyte','r'); -[a,count] = fread(f,4,'int32'); - -g = fopen('t10k-labels-idx1-ubyte','r'); -[l,count] = fread(g,2,'int32'); - -fprintf(1,'Starting to convert Test MNIST images (prints 10 dots) \n'); -n = 1000; - -Df = cell(1,10); -for d=0:9, - Df{d+1} = fopen(['test' num2str(d) '.ascii'],'w'); -end; - -for i=1:10, - fprintf('.'); - rawimages = fread(f,28*28*n,'uchar'); - rawlabels = fread(g,n,'uchar'); - rawimages = reshape(rawimages,28*28,n); - - for j=1:n, - fprintf(Df{rawlabels(j)+1},'%3d ',rawimages(:,j)); - fprintf(Df{rawlabels(j)+1},'\n'); - end; -end; - -fprintf(1,'\n'); -for d=0:9, - fclose(Df{d+1}); - D = load(['test' num2str(d) '.ascii'],'-ascii'); - fprintf('%5d Digits of class %d\n',size(D,1),d); - save(['test' num2str(d) '.mat'],'D','-mat'); -end; - - -% Work with trainig files second -f = fopen('train-images-idx3-ubyte','r'); -[a,count] = fread(f,4,'int32'); - -g = fopen('train-labels-idx1-ubyte','r'); -[l,count] = fread(g,2,'int32'); - -fprintf(1,'Starting to convert Training MNIST images (prints 60 dots)\n'); -n = 1000; - -Df = cell(1,10); -for d=0:9, - Df{d+1} = fopen(['digit' num2str(d) '.ascii'],'w'); -end; - -for i=1:60, - fprintf('.'); - rawimages = fread(f,28*28*n,'uchar'); - rawlabels = fread(g,n,'uchar'); - rawimages = reshape(rawimages,28*28,n); - - for j=1:n, - fprintf(Df{rawlabels(j)+1},'%3d ',rawimages(:,j)); - fprintf(Df{rawlabels(j)+1},'\n'); - end; -end; - -fprintf(1,'\n'); -for d=0:9, - fclose(Df{d+1}); - D = load(['digit' num2str(d) '.ascii'],'-ascii'); - fprintf('%5d Digits of class %d\n',size(D,1),d); - save(['digit' num2str(d) '.mat'],'D','-mat'); -end; - -dos('rm *.ascii'); - diff --git a/assignments/machine-learning-ex1/ex1.pdf b/assignments/machine-learning-ex1/ex1.pdf deleted file mode 100644 index dda3e96..0000000 Binary files a/assignments/machine-learning-ex1/ex1.pdf and /dev/null differ diff --git a/assignments/machine-learning-ex1/ex1/computeCost.m b/assignments/machine-learning-ex1/ex1/computeCost.m deleted file mode 100644 index 6abeb9e..0000000 --- a/assignments/machine-learning-ex1/ex1/computeCost.m +++ /dev/null @@ -1,21 +0,0 @@ -function J = computeCost(X, y, theta) -%COMPUTECOST Compute cost for linear regression -% J = COMPUTECOST(X, y, theta) computes the cost of using theta as the -% parameter for linear regression to fit the data points in X and y - -% Initialize some useful values -m = length(y); % number of training examples -% You need to return the following variables correctly - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the cost of a particular choice of theta -% You should set J to the cost. - -J = 1 / 2 / m * sum((X * theta - y) .^ 2); - - -%JVal = 1 / 2 / m * (X * theta - y)' * (X * theta -y); -%gradient = theta - 1 / m * (X' * (X * theta - y)); -% ========================================================================= - -end diff --git a/assignments/machine-learning-ex1/ex1/computeCostMulti.m b/assignments/machine-learning-ex1/ex1/computeCostMulti.m deleted file mode 100644 index bc01808..0000000 --- a/assignments/machine-learning-ex1/ex1/computeCostMulti.m +++ /dev/null @@ -1,22 +0,0 @@ -function J = computeCostMulti(X, y, theta) -%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables -% J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the -% parameter for linear regression to fit the data points in X and y - -% Initialize some useful values -m = length(y); % number of training examples - -% You need to return the following variables correctly -J = 0; - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the cost of a particular choice of theta -% You should set J to the cost. -J = 1 / 2 / m * sum((X * theta - y) .^ 2); -%J = 1 / 2 / m * (X * theta - y)' * (X * theta - y); - - - -% ========================================================================= - -end diff --git a/assignments/machine-learning-ex1/ex1/ex1.m b/assignments/machine-learning-ex1/ex1/ex1.m deleted file mode 100644 index f5239fd..0000000 --- a/assignments/machine-learning-ex1/ex1/ex1.m +++ /dev/null @@ -1,122 +0,0 @@ -%% Machine Learning Online Class - Exercise 1: Linear Regression - -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% linear exercise. You will need to complete the following functions -% in this exericse: -% -% warmUpExercise.m -% plotData.m -% gradientDescent.m -% computeCost.m -% gradientDescentMulti.m -% computeCostMulti.m -% featureNormalize.m -% normalEqn.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% -% x refers to the population size in 10,000s -% y refers to the profit in $10,000s -% - -%% Initialization -clear ; close all; clc - -%% ==================== Part 1: Basic Function ==================== -% Complete warmUpExercise.m -fprintf('Running warmUpExercise ... \n'); -fprintf('5x5 Identity Matrix: \n'); -warmUpExercise() - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% ======================= Part 2: Plotting ======================= -fprintf('Plotting Data ...\n') -data = load('ex1data1.txt'); -X = data(:, 1); y = data(:, 2); -m = length(y); % number of training examples - -% Plot Data -% Note: You have to complete the code in plotData.m -plotData(X, y); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =================== Part 3: Gradient descent =================== -fprintf('Running Gradient Descent ...\n') - -X = [ones(m, 1), data(:,1)]; % Add a column of ones to x -theta = zeros(2, 1); % initialize fitting parameters - -% Some gradient descent settings -iterations = 1500; -alpha = 0.01; - -% compute and display initial cost -computeCost(X, y, theta) - -% run gradient descent -theta = gradientDescent(X, y, theta, alpha, iterations); - -% print theta to screen -fprintf('Theta found by gradient descent: '); -fprintf('%f %f \n', theta(1), theta(2)); - -% Plot the linear fit -hold on; % keep previous plot visible -plot(X(:,2), X*theta, '-') -legend('Training data', 'Linear regression') -hold off % don't overlay any more plots on this figure - -% Predict values for population sizes of 35,000 and 70,000 -predict1 = [1, 3.5] *theta; -fprintf('For population = 35,000, we predict a profit of %f\n',... - predict1*10000); -predict2 = [1, 7] * theta; -fprintf('For population = 70,000, we predict a profit of %f\n',... - predict2*10000); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ============= Part 4: Visualizing J(theta_0, theta_1) ============= -fprintf('Visualizing J(theta_0, theta_1) ...\n') - -% Grid over which we will calculate J -theta0_vals = linspace(-10, 10, 100); -theta1_vals = linspace(-1, 4, 100);%从-1到4之间取100个数组成一个向量 - -% initialize J_vals to a matrix of 0's -J_vals = zeros(length(theta0_vals), length(theta1_vals)); - -% Fill out J_vals -for i = 1:length(theta0_vals) - for j = 1:length(theta1_vals) - t = [theta0_vals(i); theta1_vals(j)]; - J_vals(i,j) = computeCost(X, y, t); - end -end - - -% Because of the way meshgrids work in the surf command, we need to -% transpose J_vals before calling surf, or else the axes will be flipped -J_vals = J_vals'; -% Surface plot -figure; -surf(theta0_vals, theta1_vals, J_vals)%画出三维图形 -xlabel('\theta_0'); ylabel('\theta_1'); - -% Contour plot 轮廓图 -figure; -% Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100 -contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20)) -xlabel('\theta_0'); ylabel('\theta_1'); -hold on; -plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2); diff --git a/assignments/machine-learning-ex1/ex1/ex1_multi.m b/assignments/machine-learning-ex1/ex1/ex1_multi.m deleted file mode 100644 index 1b2d0f2..0000000 --- a/assignments/machine-learning-ex1/ex1/ex1_multi.m +++ /dev/null @@ -1,201 +0,0 @@ -%% Machine Learning Online Class -% Exercise 1: Linear regression with multiple variables -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% linear regression exercise. -% -% You will need to complete the following functions in this -% exericse: -% -% warmUpExercise.m -% plotData.m -% gradientDescent.m -% computeCost.m -% gradientDescentMulti.m -% computeCostMulti.m -% featureNormalize.m -% normalEqn.m -% -% For this part of the exercise, you will need to change some -% parts of the code below for various experiments (e.g., changing -% learning rates). -% - -%% Initialization - -%% ================ Part 1: Feature Normalization ================ - -%% Clear and Close Figures -clear ; close all; clc - -fprintf('Loading data ...\n'); - -%% Load Data -data = load('ex1data2.txt'); -X = data(:, 1:2); -y = data(:, 3); -m = length(y); - -% Print out some data points -fprintf('First 10 examples from the dataset: \n'); -fprintf(' x = [%.0f %.0f], y = %.0f \n', [X(1:10,:) y(1:10,:)]'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -% Scale features and set them to zero mean -fprintf('Normalizing Features ...\n'); - -[X mu sigma] = featureNormalize(X); - -% Add intercept term to X -X = [ones(m, 1) X]; - - -%% ================ Part 2: Gradient Descent ================ - -% ====================== YOUR CODE HERE ====================== -% Instructions: We have provided you with the following starter -% code that runs gradient descent with a particular -% learning rate (alpha). -% -% Your task is to first make sure that your functions - -% computeCost and gradientDescent already work with -% this starter code and support multiple variables. -% -% After that, try running gradient descent with -% different values of alpha and see which one gives -% you the best result. -% -% Finally, you should complete the code at the end -% to predict the price of a 1650 sq-ft, 3 br house. -% -% Hint: By using the 'hold on' command, you can plot multiple -% graphs on the same figure. -% -% Hint: At prediction, make sure you do the same feature normalization. -% - -fprintf('Running gradient descent ...\n'); - -% Choose some alpha value -alpha = 0.001; -num_iters = 4000; - -% Init Theta and Run Gradient Descent -theta = zeros(3, 1); -[theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters); -%[theta, J_history] = gradientDescentMulti(X, y, zeros(3, 1), 0.001, 4000); -%plot(1:numel(J_history), J_history, '-bc', 'LineWidth', 2); -%price = theta(1) + (1650 - mu(1)) / sigma(1) * theta(2) + (3 - mu(2)) / sigma(2) * theta(3) % You should change this -%price = theta(1) + (15 - mu(1)) / sigma(1) * theta(2) + (1 - mu(2)) / sigma(2) * theta(3) + (2 - mu(3)) / sigma(3) * theta(4) % You should change this - -fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ... - '(using gradient descent):\n $%f\n'], price); - -% Plot the convergence graph -figure; -plot(1:numel(J_history), J_history, '-b', 'LineWidth', 2); -xlabel('Number of iterations'); -ylabel('Cost J'); - -% Display gradient descent's result -fprintf('Theta computed from gradient descent: \n'); -fprintf(' %f \n', theta); -fprintf('\n'); - -% Estimate the price of a 1650 sq-ft, 3 br house -% ====================== YOUR CODE HERE ====================== -% Recall that the first column of X is all-ones. Thus, it does -% not need to be normalized. -price = 0; % You should change this - - -% ============================================================ - -fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ... - '(using gradient descent):\n $%f\n'], price); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ================ Part 3: Normal Equations ================ - -fprintf('Solving with normal equations...\n'); - -% ====================== YOUR CODE HERE ====================== -% Instructions: The following code computes the closed form -% solution for linear regression using the normal -% equations. You should complete the code in -% normalEqn.m -% -% After doing so, you should complete this code -% to predict the price of a 1650 sq-ft, 3 br house. -% - -%% Load Data -data = csvread('ex1data2.txt'); -X = data(:, 1:2); -y = data(:, 3); -m = length(y); - -% Add intercept term to X -X = [ones(m, 1) X]; - -% Calculate the parameters from the normal equation -theta = normalEqn(X, y); - -% Display normal equation's result -fprintf('Theta computed from the normal equations: \n'); -fprintf(' %f \n', theta); -fprintf('\n'); - - -% Estimate the price of a 1650 sq-ft, 3 br house -% ====================== YOUR CODE HERE ====================== -price = theta(1) + 1650 * theta(2) + 3 * theta(3); % You should change this - - -% ============================================================ - -fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ... - '(using normal equations):\n $%f\n'], price); - -%% ============= Part 4: Visualizing J(theta_0, theta_1) ============= -fprintf('Visualizing J(theta_0, theta_1) ...\n') - -% Grid over which we will calculate J -theta0_vals = linspace(-10, 10, 100); -theta1_vals = linspace(-1, 4, 100);%从-1到4之间取100个数组成一个向量 -theta2_vals = linspace(-1, 4, 100);%从-1到4之间取100个数组成一个向量 - -% initialize J_vals to a matrix of 0's -J_vals = zeros(length(theta0_vals), length(theta1_vals), length(theta1_vals)); - -% Fill out J_vals -for i = 1:length(theta0_vals) - for j = 1:length(theta1_vals) - t = [theta0_vals(i); theta1_vals(j)]; - J_vals(i,j) = computeCostMulti(X, y, t); - end -end - - -% Because of the way meshgrids work in the surf command, we need to -% transpose J_vals before calling surf, or else the axes will be flipped -J_vals = J_vals'; -% Surface plot -figure; -surf(theta0_vals, theta1_vals, J_vals)%画出三维图形 -xlabel('\theta_0'); ylabel('\theta_1'); - -% Contour plot 轮廓图 -figure; -% Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100 -contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20)) -xlabel('\theta_0'); ylabel('\theta_1'); -hold on; -plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2); diff --git a/assignments/machine-learning-ex1/ex1/ex1data1.txt b/assignments/machine-learning-ex1/ex1/ex1data1.txt deleted file mode 100644 index 0f88ccb..0000000 --- a/assignments/machine-learning-ex1/ex1/ex1data1.txt +++ /dev/null @@ -1,97 +0,0 @@ -6.1101,17.592 -5.5277,9.1302 -8.5186,13.662 -7.0032,11.854 -5.8598,6.8233 -8.3829,11.886 -7.4764,4.3483 -8.5781,12 -6.4862,6.5987 -5.0546,3.8166 -5.7107,3.2522 -14.164,15.505 -5.734,3.1551 -8.4084,7.2258 -5.6407,0.71618 -5.3794,3.5129 -6.3654,5.3048 -5.1301,0.56077 -6.4296,3.6518 -7.0708,5.3893 -6.1891,3.1386 -20.27,21.767 -5.4901,4.263 -6.3261,5.1875 -5.5649,3.0825 -18.945,22.638 -12.828,13.501 -10.957,7.0467 -13.176,14.692 -22.203,24.147 -5.2524,-1.22 -6.5894,5.9966 -9.2482,12.134 -5.8918,1.8495 -8.2111,6.5426 -7.9334,4.5623 -8.0959,4.1164 -5.6063,3.3928 -12.836,10.117 -6.3534,5.4974 -5.4069,0.55657 -6.8825,3.9115 -11.708,5.3854 -5.7737,2.4406 -7.8247,6.7318 -7.0931,1.0463 -5.0702,5.1337 -5.8014,1.844 -11.7,8.0043 -5.5416,1.0179 -7.5402,6.7504 -5.3077,1.8396 -7.4239,4.2885 -7.6031,4.9981 -6.3328,1.4233 -6.3589,-1.4211 -6.2742,2.4756 -5.6397,4.6042 -9.3102,3.9624 -9.4536,5.4141 -8.8254,5.1694 -5.1793,-0.74279 -21.279,17.929 -14.908,12.054 -18.959,17.054 -7.2182,4.8852 -8.2951,5.7442 -10.236,7.7754 -5.4994,1.0173 -20.341,20.992 -10.136,6.6799 -7.3345,4.0259 -6.0062,1.2784 -7.2259,3.3411 -5.0269,-2.6807 -6.5479,0.29678 -7.5386,3.8845 -5.0365,5.7014 -10.274,6.7526 -5.1077,2.0576 -5.7292,0.47953 -5.1884,0.20421 -6.3557,0.67861 -9.7687,7.5435 -6.5159,5.3436 -8.5172,4.2415 -9.1802,6.7981 -6.002,0.92695 -5.5204,0.152 -5.0594,2.8214 -5.7077,1.8451 -7.6366,4.2959 -5.8707,7.2029 -5.3054,1.9869 -8.2934,0.14454 -13.394,9.0551 -5.4369,0.61705 diff --git a/assignments/machine-learning-ex1/ex1/ex1data2 - 副本.txt b/assignments/machine-learning-ex1/ex1/ex1data2 - 副本.txt deleted file mode 100644 index 79e9a80..0000000 --- a/assignments/machine-learning-ex1/ex1/ex1data2 - 副本.txt +++ /dev/null @@ -1,47 +0,0 @@ -2104,3,399900 -1600,3,329900 -2400,3,369000 -1416,2,232000 -3000,4,539900 -1985,4,299900 -1534,3,314900 -1427,3,198999 -1380,3,212000 -1494,3,242500 -1940,4,239999 -2000,3,347000 -1890,3,329999 -4478,5,699900 -1268,3,259900 -2300,4,449900 -1320,2,299900 -1236,3,199900 -2609,4,499998 -3031,4,599000 -1767,3,252900 -1888,2,255000 -1604,3,242900 -1962,4,259900 -3890,3,573900 -1100,3,249900 -1458,3,464500 -2526,3,469000 -2200,3,475000 -2637,3,299900 -1839,2,349900 -1000,1,169900 -2040,4,314900 -3137,3,579900 -1811,4,285900 -1437,3,249900 -1239,3,229900 -2132,4,345000 -4215,4,549000 -2162,4,287000 -1664,2,368500 -2238,3,329900 -2567,4,314000 -1200,3,299000 -852,2,179900 -1852,4,299900 -1203,3,239500 diff --git a/assignments/machine-learning-ex1/ex1/ex1data2.txt b/assignments/machine-learning-ex1/ex1/ex1data2.txt deleted file mode 100644 index 79e9a80..0000000 --- a/assignments/machine-learning-ex1/ex1/ex1data2.txt +++ /dev/null @@ -1,47 +0,0 @@ -2104,3,399900 -1600,3,329900 -2400,3,369000 -1416,2,232000 -3000,4,539900 -1985,4,299900 -1534,3,314900 -1427,3,198999 -1380,3,212000 -1494,3,242500 -1940,4,239999 -2000,3,347000 -1890,3,329999 -4478,5,699900 -1268,3,259900 -2300,4,449900 -1320,2,299900 -1236,3,199900 -2609,4,499998 -3031,4,599000 -1767,3,252900 -1888,2,255000 -1604,3,242900 -1962,4,259900 -3890,3,573900 -1100,3,249900 -1458,3,464500 -2526,3,469000 -2200,3,475000 -2637,3,299900 -1839,2,349900 -1000,1,169900 -2040,4,314900 -3137,3,579900 -1811,4,285900 -1437,3,249900 -1239,3,229900 -2132,4,345000 -4215,4,549000 -2162,4,287000 -1664,2,368500 -2238,3,329900 -2567,4,314000 -1200,3,299000 -852,2,179900 -1852,4,299900 -1203,3,239500 diff --git a/assignments/machine-learning-ex1/ex1/featureNormalize.m b/assignments/machine-learning-ex1/ex1/featureNormalize.m deleted file mode 100644 index 6915923..0000000 --- a/assignments/machine-learning-ex1/ex1/featureNormalize.m +++ /dev/null @@ -1,40 +0,0 @@ -function [X_norm, mu, sigma] = featureNormalize(X) -%FEATURENORMALIZE Normalizes the features in X -% FEATURENORMALIZE(X) returns a normalized version of X where -% the mean value of each feature is 0 and the standard deviation -% is 1. This is often a good preprocessing step to do when -% working with learning algorithms. - -% You need to set these values correctly -X_norm = X; -mu = zeros(1, size(X, 2)); -sigma = zeros(1, size(X, 2)); - -% ====================== YOUR CODE HERE ====================== -% Instructions: First, for each feature dimension, compute the mean -% of the feature and subtract it from the dataset, -% storing the mean value in mu. Next, compute the -% standard deviation of each feature and divide -% each feature by it's standard deviation, storing -% the standard deviation in sigma. -% -% Note that X is a matrix where each column is a -% feature and each row is an example. You need -% to perform the normalization separately for -% each feature. -% -% Hint: You might find the 'mean' and 'std' functions useful. -% - -mu = mean(X_norm); -sigma = std(X_norm); -X_norm = (X_norm .- mu) ./ sigma; - - - - - - -% ============================================================ - -end diff --git a/assignments/machine-learning-ex1/ex1/gradientDescent.m b/assignments/machine-learning-ex1/ex1/gradientDescent.m deleted file mode 100644 index be26db2..0000000 --- a/assignments/machine-learning-ex1/ex1/gradientDescent.m +++ /dev/null @@ -1,31 +0,0 @@ -function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters) - %GRADIENTDESCENT Performs gradient descent to learn theta - % theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by - % taking num_iters gradient steps with learning rate alpha - - % Initialize some useful values - m = length(y); % number of training examples - J_history = zeros(num_iters, 1); - - for iter = 1:num_iters - - % ====================== YOUR CODE HERE ====================== - % Instructions: Perform a single gradient step on the parameter vector - % theta. - % - % Hint: While debugging, it can be useful to print out the values - % of the cost function (computeCost) and gradient here. - % - theta = theta - alpha / m * (X' * (X * theta - y)); - %X * theta 代表了hθ - %X' (hθ-y)表示将每一个X中的元素(n * m)与(hθ-y)(m * 1)相乘，从而得到新的theta矩阵(n * 1) - - - % ============================================================ - - % Save the cost J in every iteration - J_history(iter) = computeCost(X, y, theta); - - end - -end diff --git a/assignments/machine-learning-ex1/ex1/gradientDescentMulti.m b/assignments/machine-learning-ex1/ex1/gradientDescentMulti.m deleted file mode 100644 index 601a1a5..0000000 --- a/assignments/machine-learning-ex1/ex1/gradientDescentMulti.m +++ /dev/null @@ -1,31 +0,0 @@ -function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters) -%GRADIENTDESCENTMULTI Performs gradient descent to learn theta -% theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by -% taking num_iters gradient steps with learning rate alpha - -% Initialize some useful values -m = length(y); % number of training examples -J_history = zeros(num_iters, 1); - - for iter = 1:num_iters - - % ====================== YOUR CODE HERE ====================== - % Instructions: Perform a single gradient step on the parameter vector - % theta. - % - % Hint: While debugging, it can be useful to print out the values - % of the cost function (computeCostMulti) and gradient here. - % - theta = theta - alpha / m * (X' * (X * theta - y)); - %X * theta 代表了hθ - %X' (hθ-y)表示将每一个X中的元素(n * m)与(hθ-y)(m * 1)相乘，从而得到新的theta矩阵(n * 1) - - - % ============================================================ - - % Save the cost J in every iteration - J_history(iter) = computeCostMulti(X, y, theta); - - end - -end diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/AUTHORS.txt b/assignments/machine-learning-ex1/ex1/lib/jsonlab/AUTHORS.txt deleted file mode 100644 index 9dd3fc7..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/AUTHORS.txt +++ /dev/null @@ -1,41 +0,0 @@ -The author of "jsonlab" toolbox is Qianqian Fang. Qianqian -is currently an Assistant Professor at Massachusetts General Hospital, -Harvard Medical School. - -Address: Martinos Center for Biomedical Imaging, - Massachusetts General Hospital, - Harvard Medical School - Bldg 149, 13th St, Charlestown, MA 02129, USA -URL: http://nmr.mgh.harvard.edu/~fangq/ -Email: or - - -The script loadjson.m was built upon previous works by - -- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 - date: 2009/11/02 -- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 - date: 2009/03/22 -- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565 - date: 2008/07/03 - - -This toolbox contains patches submitted by the following contributors: - -- Blake Johnson - part of revision 341 - -- Niclas Borlin - various fixes in revision 394, including - - loadjson crashes for all-zero sparse matrix. - - loadjson crashes for empty sparse matrix. - - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson. - - loadjson crashes for sparse real column vector. - - loadjson crashes for sparse complex column vector. - - Data is corrupted by savejson for sparse real row vector. - - savejson crashes for sparse complex row vector. - -- Yul Kang - patches for svn revision 415. - - savejson saves an empty cell array as [] instead of null - - loadjson differentiates an empty struct from an empty array diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/ChangeLog.txt b/assignments/machine-learning-ex1/ex1/lib/jsonlab/ChangeLog.txt deleted file mode 100644 index 07824f5..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/ChangeLog.txt +++ /dev/null @@ -1,74 +0,0 @@ -============================================================================ - - JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave - ----------------------------------------------------------------------------- - -JSONlab ChangeLog (key features marked by *): - -== JSONlab 1.0 (codename: Optimus - Final), FangQ == - - 2015/01/02 polish help info for all major functions, update examples, finalize 1.0 - 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson - -== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ == - - 2014/11/22 show progress bar in loadjson ('ShowProgress') - 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact') - 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels - 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab - -== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ == - - 2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8 - 2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson - 2014/08/04 escape special characters in a JSON string - 2014/02/16 fix a bug when saving ubjson files - -== JSONlab 0.9.9 (codename: Optimus - beta), FangQ == - - 2014/01/22 use binary read and write in saveubjson and loadubjson - -== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ == - - 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang) - -== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ == - 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson - -== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ == - 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin) - -== JSONlab 0.9.0 (codename: Rodimus), FangQ == - - 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson - 2012/06/01 support JSONP in savejson - 2012/05/25 fix the empty cell bug (reported by Cyril Davin) - 2012/04/05 savejson can save to a file (suggested by Patrick Rapin) - -== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ == - - 2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS - 2012/01/25 patch to handle root-less objects, contributed by Blake Johnson - -== JSONlab 0.8.0 (codename: Sentiel), FangQ == - - 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab - 2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer - 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson - 2011/11/18 fix struct array bug reported by Mykel Kochenderfer - -== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ == - - 2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration - 2011/10/20 loadjson supports JSON collections - concatenated JSON objects - -== JSONlab 0.5.0 (codename: Nexus), FangQ == - - 2011/10/16 package and release jsonlab 0.5.0 - 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug - 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level - 2011/10/10 create jsonlab project, start jsonlab website, add online documentation - 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support - 2011/10/06 *savejson works for structs, cells and arrays - 2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/LICENSE_BSD.txt b/assignments/machine-learning-ex1/ex1/lib/jsonlab/LICENSE_BSD.txt deleted file mode 100644 index 32d66cb..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/LICENSE_BSD.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright 2011-2015 Qianqian Fang . All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are -permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, this list - of conditions and the following disclaimer in the documentation and/or other materials - provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND -FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS -OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -The views and conclusions contained in the software and documentation are those of the -authors and should not be interpreted as representing official policies, either expressed -or implied, of the copyright holders. diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/README.txt b/assignments/machine-learning-ex1/ex1/lib/jsonlab/README.txt deleted file mode 100644 index 7b4f732..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/README.txt +++ /dev/null @@ -1,394 +0,0 @@ -=============================================================================== -= JSONLab = -= An open-source MATLAB/Octave JSON encoder and decoder = -=============================================================================== - -*Copyright (C) 2011-2015 Qianqian Fang -*License: BSD License, see License_BSD.txt for details -*Version: 1.0 (Optimus - Final) - -------------------------------------------------------------------------------- - -Table of Content: - -I. Introduction -II. Installation -III.Using JSONLab -IV. Known Issues and TODOs -V. Contribution and feedback - -------------------------------------------------------------------------------- - -I. Introduction - -JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, -human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format -to represent complex and hierarchical data. It is as powerful as -[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely -used for data-exchange in applications, and is essential for the wild success -of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and -[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. - -UBJSON (Universal Binary JSON) is a binary JSON format, specifically -optimized for compact file size and better performance while keeping -the semantics as simple as the text-based JSON format. Using the UBJSON -format allows to wrap complex binary data in a flexible and extensible -structure, making it possible to process complex and large dataset -without accuracy loss due to text conversions. - -We envision that both JSON and its binary version will serve as part of -the mainstream data-exchange formats for scientific research in the future. -It will provide the flexibility and generality achieved by other popular -general-purpose file specifications, such as -[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly -reduced complexity and enhanced performance. - -JSONLab is a free and open-source implementation of a JSON/UBJSON encoder -and a decoder in the native MATLAB language. It can be used to convert a MATLAB -data structure (array, struct, cell, struct array and cell array) into -JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB -data structure. JSONLab supports both MATLAB and -[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone). - -------------------------------------------------------------------------------- - -II. Installation - -The installation of JSONLab is no different than any other simple -MATLAB toolbox. You only need to download/unzip the JSONLab package -to a folder, and add the folder's path to MATLAB/Octave's path list -by using the following command: - - addpath('/path/to/jsonlab'); - -If you want to add this path permanently, you need to type "pathtool", -browse to the jsonlab root folder and add to the list, then click "Save". -Then, run "rehash" in MATLAB, and type "which loadjson", if you see an -output, that means JSONLab is installed for MATLAB/Octave. - -------------------------------------------------------------------------------- - -III.Using JSONLab - -JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, -and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and -two equivallent functions -- loadubjson and saveubjson for the binary -JSON. The detailed help info for the four functions can be found below: - -=== loadjson.m === -
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          Franois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-
- -=== savejson.m === - -
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-
- -=== loadubjson.m === - -
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-
- -=== saveubjson.m === - -
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-
- - -=== examples === - -Under the "examples" folder, you can find several scripts to demonstrate the -basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you -will see the conversions from MATLAB data structure to JSON text and backward. -In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet -and validate the loadjson/savejson functions for regression testing purposes. -Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson -and loadubjson pairs for various matlab data structures. - -Please run these examples and understand how JSONLab works before you use -it to process your data. - -------------------------------------------------------------------------------- - -IV. Known Issues and TODOs - -JSONLab has several known limitations. We are striving to make it more general -and robust. Hopefully in a few future releases, the limitations become less. - -Here are the known issues: - -# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays; -# When processing names containing multi-byte characters, Octave and MATLAB \ -can give different field-names; you can use feature('DefaultCharacterSet','latin1') \ -in MATLAB to get consistant results -# savejson can not handle class and dataset. -# saveubjson converts a logical array into a uint8 ([U]) array -# an unofficial N-D array count syntax is implemented in saveubjson. We are \ -actively communicating with the UBJSON spec maintainer to investigate the \ -possibility of making it upstream -# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \ -files, however, it can parse all UBJSON files produced by saveubjson. - -------------------------------------------------------------------------------- - -V. Contribution and feedback - -JSONLab is an open-source project. This means you can not only use it and modify -it as you wish, but also you can contribute your changes back to JSONLab so -that everyone else can enjoy the improvement. For anyone who want to contribute, -please download JSONLab source code from it's subversion repository by using the -following command: - - svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab - -You can make changes to the files as needed. Once you are satisfied with your -changes, and ready to share it with others, please cd the root directory of -JSONLab, and type - - svn diff > yourname_featurename.patch - -You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at -the email address shown in the beginning of this file. Qianqian will review -the changes and commit it to the subversion if they are satisfactory. - -We appreciate any suggestions and feedbacks from you. Please use iso2mesh's -mailing list to report any questions you may have with JSONLab: - -http://groups.google.com/group/iso2mesh-users?hl=en&pli=1 - -(Subscription to the mailing list is needed in order to post messages). diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/jsonopt.m b/assignments/machine-learning-ex1/ex1/lib/jsonlab/jsonopt.m deleted file mode 100644 index 0bebd8d..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/jsonopt.m +++ /dev/null @@ -1,32 +0,0 @@ -function val=jsonopt(key,default,varargin) -% -% val=jsonopt(key,default,optstruct) -% -% setting options based on a struct. The struct can be produced -% by varargin2struct from a list of 'param','value' pairs -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% -% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $ -% -% input: -% key: a string with which one look up a value from a struct -% default: if the key does not exist, return default -% optstruct: a struct where each sub-field is a key -% -% output: -% val: if key exists, val=optstruct.key; otherwise val=default -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -val=default; -if(nargin<=2) return; end -opt=varargin{1}; -if(isstruct(opt) && isfield(opt,key)) - val=getfield(opt,key); -end - diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m b/assignments/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m deleted file mode 100644 index 42798c0..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m +++ /dev/null @@ -1,566 +0,0 @@ -function data = loadjson(fname,varargin) -% -% data=loadjson(fname,opt) -% or -% data=loadjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09, including previous works from -% -% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 -% created on 2009/11/02 -% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 -% created on 2009/03/22 -% Joel Feenstra: -% http://www.mathworks.com/matlabcentral/fileexchange/20565 -% created on 2008/07/03 -% -% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a JSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.FastArrayParser [1|0 or integer]: if set to 1, use a -% speed-optimized array parser when loading an -% array object. The fast array parser may -% collapse block arrays into a single large -% array similar to rules defined in cell2mat; 0 to -% use a legacy parser; if set to a larger-than-1 -% value, this option will specify the minimum -% dimension to enable the fast array parser. For -% example, if the input is a 3D array, setting -% FastArrayParser to 1 will return a 3D array; -% setting to 2 will return a cell array of 2D -% arrays; setting to 3 will return to a 2D cell -% array of 1D vectors; setting to 4 will return a -% 3D cell array. -% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}') -% dat=loadjson(['examples' filesep 'example1.json']) -% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); - -if(jsonopt('ShowProgress',0,opt)==1) - opt.progressbar_=waitbar(0,'loading ...'); -end -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end -if(isfield(opt,'progressbar_')) - close(opt.progressbar_); -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=data(j).x0x5F_ArraySize_; - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - if next_char ~= '}' - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - parse_char(':'); - val = parse_value(varargin{:}); - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' - break; - end - parse_char(','); - end - end - parse_char('}'); - -%%------------------------------------------------------------------------- - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim2=[]; - arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:}); - pbar=jsonopt('progressbar_',-1,varargin{:}); - - if next_char ~= ']' - if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:})) - [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos); - arraystr=['[' inStr(pos:endpos)]; - arraystr=regexprep(arraystr,'"_NaN_"','NaN'); - arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf'); - arraystr(arraystr==sprintf('\n'))=[]; - arraystr(arraystr==sprintf('\r'))=[]; - %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed - if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D - astr=inStr((e1l+1):(e1r-1)); - astr=regexprep(astr,'"_NaN_"','NaN'); - astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf'); - astr(astr==sprintf('\n'))=[]; - astr(astr==sprintf('\r'))=[]; - astr(astr==' ')=''; - if(isempty(find(astr=='[', 1))) % array is 2D - dim2=length(sscanf(astr,'%f,',[1 inf])); - end - else % array is 1D - astr=arraystr(2:end-1); - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]); - if(nextidx>=length(astr)-1) - object=obj; - pos=endpos; - parse_char(']'); - return; - end - end - if(~isempty(dim2)) - astr=arraystr; - astr(astr=='[')=''; - astr(astr==']')=''; - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf); - if(nextidx>=length(astr)-1) - object=reshape(obj,dim2,numel(obj)/dim2)'; - pos=endpos; - parse_char(']'); - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end - return; - end - end - arraystr=regexprep(arraystr,'\]\s*,','];'); - else - arraystr='['; - end - try - if(isoct && regexp(arraystr,'"','once')) - error('Octave eval can produce empty cells for JSON-like input'); - end - object=eval(arraystr); - pos=endpos; - catch - while 1 - newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1); - val = parse_value(newopt); - object{end+1} = val; - if next_char == ']' - break; - end - parse_char(','); - end - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - parse_char(']'); - - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr len esc index_esc len_esc - % len, ns = length(inStr), keyboard - if inStr(pos) ~= '"' - error_pos('String starting with " expected at position %d'); - else - pos = pos + 1; - end - str = ''; - while pos <= len - while index_esc <= len_esc && esc(index_esc) < pos - index_esc = index_esc + 1; - end - if index_esc > len_esc - str = [str inStr(pos:len)]; - pos = len + 1; - break; - else - str = [str inStr(pos:esc(index_esc)-1)]; - pos = esc(index_esc); - end - nstr = length(str); switch inStr(pos) - case '"' - pos = pos + 1; - if(~isempty(str)) - if(strcmp(str,'_Inf_')) - str=Inf; - elseif(strcmp(str,'-_Inf_')) - str=-Inf; - elseif(strcmp(str,'_NaN_')) - str=NaN; - end - end - return; - case '\' - if pos+1 > len - error_pos('End of file reached right after escape character'); - end - pos = pos + 1; - switch inStr(pos) - case {'"' '\' '/'} - str(nstr+1) = inStr(pos); - pos = pos + 1; - case {'b' 'f' 'n' 'r' 't'} - str(nstr+1) = sprintf(['\' inStr(pos)]); - pos = pos + 1; - case 'u' - if pos+4 > len - error_pos('End of file reached in escaped unicode character'); - end - str(nstr+(1:6)) = inStr(pos-1:pos+4); - pos = pos + 5; - end - otherwise % should never happen - str(nstr+1) = inStr(pos), keyboard - pos = pos + 1; - end - end - error_pos('End of file while expecting end of inStr'); - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct - currstr=inStr(pos:end); - numstr=0; - if(isoct~=0) - numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end'); - [num, one] = sscanf(currstr, '%f', 1); - delta=numstr+1; - else - [num, one, err, delta] = sscanf(currstr, '%f', 1); - if ~isempty(err) - error_pos('Error reading number at position %d'); - end - end - pos = pos + delta-1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - pbar=jsonopt('progressbar_',-1,varargin{:}); - if(pbar>0) - waitbar(pos/len,pbar,'loading ...'); - end - - switch(inStr(pos)) - case '"' - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'-','0','1','2','3','4','5','6','7','8','9'} - val = parse_number(varargin{:}); - return; - case 't' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true') - val = true; - pos = pos + 4; - return; - end - case 'f' - if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false') - val = false; - pos = pos + 5; - return; - end - case 'n' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null') - val = []; - pos = pos + 4; - return; - end - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m b/assignments/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m deleted file mode 100644 index 0155115..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m +++ /dev/null @@ -1,528 +0,0 @@ -function data = loadubjson(fname,varargin) -% -% data=loadubjson(fname,opt) -% or -% data=loadubjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/01 -% -% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a UBJSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.IntEndian [B|L]: specify the endianness of the integer fields -% in the UBJSON input data. B - Big-Endian format for -% integers (as required in the UBJSON specification); -% L - input integer fields are in Little-Endian order. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% obj=struct('string','value','array',[1 2 3]); -% ubjdata=saveubjson('obj',obj); -% dat=loadubjson(ubjdata) -% dat=loadubjson(['examples' filesep 'example1.ubj']) -% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); -fileendian=upper(jsonopt('IntEndian','B',opt)); -[os,maxelem,systemendian]=computer; - -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end - - -%% -function newdata=parse_collection(id,data,obj) - -if(jsoncount>0 && exist('data','var')) - if(~iscell(data)) - newdata=cell(1); - newdata{1}=data; - data=newdata; - end -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=double(data(j).x0x5F_ArraySize_); - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); % TODO - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - count=double(parse_number()); - end - if next_char ~= '}' - num=0; - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - %parse_char(':'); - val = parse_value(varargin{:}); - num=num+1; - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' || (count>=0 && num>=count) - break; - end - %parse_char(','); - end - end - if(count==-1) - parse_char('}'); - end - -%%------------------------------------------------------------------------- -function [cid,len]=elem_info(type) -id=strfind('iUIlLdD',type); -dataclass={'int8','uint8','int16','int32','int64','single','double'}; -bytelen=[1,1,2,4,8,4,8]; -if(id>0) - cid=dataclass{id}; - len=bytelen(id); -else - error_pos('unsupported type at position %d'); -end -%%------------------------------------------------------------------------- - - -function [data adv]=parse_block(type,count,varargin) -global pos inStr isoct fileendian systemendian -[cid,len]=elem_info(type); -datastr=inStr(pos:pos+len*count-1); -if(isoct) - newdata=int8(datastr); -else - newdata=uint8(datastr); -end -id=strfind('iUIlLdD',type); -if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,cid)); -end -data=typecast(newdata,cid); -adv=double(len*count); - -%%------------------------------------------------------------------------- - - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim=[]; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - if(next_char=='[') - dim=parse_array(varargin{:}); - count=prod(double(dim)); - else - count=double(parse_number()); - end - end - if(~isempty(type)) - if(count>=0) - [object adv]=parse_block(type,count,varargin{:}); - if(~isempty(dim)) - object=reshape(object,dim); - end - pos=pos+adv; - return; - else - endpos=matching_bracket(inStr,pos); - [cid,len]=elem_info(type); - count=(endpos-pos)/len; - [object adv]=parse_block(type,count,varargin{:}); - pos=pos+adv; - parse_char(']'); - return; - end - end - if next_char ~= ']' - while 1 - val = parse_value(varargin{:}); - object{end+1} = val; - if next_char == ']' - break; - end - %parse_char(','); - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - if(count==-1) - parse_char(']'); - end - -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr esc index_esc len_esc - % len, ns = length(inStr), keyboard - type=inStr(pos); - if type ~= 'S' && type ~= 'C' && type ~= 'H' - error_pos('String starting with S expected at position %d'); - else - pos = pos + 1; - end - if(type == 'C') - str=inStr(pos); - pos=pos+1; - return; - end - bytelen=double(parse_number()); - if(length(inStr)>=pos+bytelen-1) - str=inStr(pos:pos+bytelen-1); - pos=pos+bytelen; - else - error_pos('End of file while expecting end of inStr'); - end - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct fileendian systemendian - id=strfind('iUIlLdD',inStr(pos)); - if(isempty(id)) - error_pos('expecting a number at position %d'); - end - type={'int8','uint8','int16','int32','int64','single','double'}; - bytelen=[1,1,2,4,8,4,8]; - datastr=inStr(pos+1:pos+bytelen(id)); - if(isoct) - newdata=int8(datastr); - else - newdata=uint8(datastr); - end - if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,type{id})); - end - num=typecast(newdata,type{id}); - pos = pos + bytelen(id)+1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - switch(inStr(pos)) - case {'S','C','H'} - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'i','U','I','l','L','d','D'} - val = parse_number(varargin{:}); - return; - case 'T' - val = true; - pos = pos + 1; - return; - case 'F' - val = false; - pos = pos + 1; - return; - case {'Z','N'} - val = []; - pos = pos + 1; - return; - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos e1l e1r maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/mergestruct.m b/assignments/machine-learning-ex1/ex1/lib/jsonlab/mergestruct.m deleted file mode 100644 index 6de6100..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/mergestruct.m +++ /dev/null @@ -1,33 +0,0 @@ -function s=mergestruct(s1,s2) -% -% s=mergestruct(s1,s2) -% -% merge two struct objects into one -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% s1,s2: a struct object, s1 and s2 can not be arrays -% -% output: -% s: the merged struct object. fields in s1 and s2 will be combined in s. -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(~isstruct(s1) || ~isstruct(s2)) - error('input parameters contain non-struct'); -end -if(length(s1)>1 || length(s2)>1) - error('can not merge struct arrays'); -end -fn=fieldnames(s2); -s=s1; -for i=1:length(fn) - s=setfield(s,fn{i},getfield(s2,fn{i})); -end - diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/savejson.m b/assignments/machine-learning-ex1/ex1/lib/jsonlab/savejson.m deleted file mode 100644 index 7e84076..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/savejson.m +++ /dev/null @@ -1,475 +0,0 @@ -function json=savejson(rootname,obj,varargin) -% -% json=savejson(rootname,obj,filename) -% or -% json=savejson(rootname,obj,opt) -% json=savejson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript -% Object Notation) string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09 -% -% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array). -% filename: a string for the file name to save the output JSON data. -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.FloatFormat ['%.10g'|string]: format to show each numeric element -% of a 1D/2D array; -% opt.ArrayIndent [1|0]: if 1, output explicit data array with -% precedent indentation; if 0, no indentation -% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [0|1]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern -% to represent +/-Inf. The matched pattern is '([-+]*)Inf' -% and $1 represents the sign. For those who want to use -% 1e999 to represent Inf, they can set opt.Inf to '$11e999' -% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern -% to represent NaN -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSONP='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode. -% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs) -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a string in the JSON format (see http://json.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% savejson('jmesh',jsonmesh) -% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end - -whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -if(jsonopt('Compact',0,opt)==1) - whitespaces=struct('tab','','newline','','sep',','); -end -if(~isfield(opt,'whitespaces_')) - opt.whitespaces_=whitespaces; -end - -nl=whitespaces.newline; - -json=obj2json(rootname,obj,rootlevel,opt); -if(rootisarray) - json=sprintf('%s%s',json,nl); -else - json=sprintf('{%s%s%s}\n',nl,json,nl); -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=sprintf('%s(%s);%s',jsonp,json,nl); -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - if(jsonopt('SaveBinary',0,opt)==1) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - else - fid = fopen(opt.FileName, 'wt'); - fwrite(fid,json,'char'); - end - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2json(name,item,level,varargin) - -if(iscell(item)) - txt=cell2json(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2json(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2json(name,item,level,varargin{:}); -else - txt=mat2json(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2json(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -nl=ws.newline; -if(len>1) - if(~isempty(name)) - txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; - else - txt=sprintf('%s[%s',padding0,nl); - end -elseif(len==0) - if(~isempty(name)) - txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; - else - txt=sprintf('%s[]',padding0); - end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:})); - if(i1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=struct2json(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1)); -nl=ws.newline; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding0,nl); end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); - else - txt=sprintf('%s%s{%s',txt,padding1,nl); - end - if(~isempty(names)) - for e=1:length(names) - txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})); - if(e1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=str2json(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding1,nl); end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - if(isoct) - val=regexprep(item(e,:),'\\','\\'); - val=regexprep(val,'"','\"'); - val=regexprep(val,'^"','\"'); - else - val=regexprep(item(e,:),'\\','\\\\'); - val=regexprep(val,'"','\\"'); - val=regexprep(val,'^"','\\"'); - end - val=escapejsonstring(val); - if(len==1) - obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"']; - if(isempty(name)) obj=['"',val,'"']; end - txt=sprintf('%s%s%s%s',txt,padding1,obj); - else - txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']); - end - if(e==len) sep=''; end - txt=sprintf('%s%s',txt,sep); -end -if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end - -%%------------------------------------------------------------------------- -function txt=mat2json(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:})) - if(isempty(name)) - txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - else - txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - end -else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0) - numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']',''); - else - numtxt=matdata2json(item,level+1,varargin{:}); - end - if(isempty(name)) - txt=sprintf('%s%s',padding1,numtxt); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - else - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - end - end - return; -end -dataformat='%s%s%s%s%s'; - -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep); - if(size(item,1)==1) - % Row vector, store only column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([iy(:),data'],level+2,varargin{:}), nl); - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,data],level+2,varargin{:}), nl); - else - % General case, store row and column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,iy,data],level+2,varargin{:}), nl); - end -else - if(isreal(item)) - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json(item(:)',level+2,varargin{:}), nl); - else - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl); - end -end -txt=sprintf('%s%s%s',txt,padding1,'}'); - -%%------------------------------------------------------------------------- -function txt=matdata2json(mat,level,varargin) - -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -tab=ws.tab; -nl=ws.newline; - -if(size(mat,1)==1) - pre=''; - post=''; - level=level-1; -else - pre=sprintf('[%s',nl); - post=sprintf('%s%s]',nl,repmat(tab,1,level-1)); -end - -if(isempty(mat)) - txt='null'; - return; -end -floatformat=jsonopt('FloatFormat','%.10g',varargin{:}); -%if(numel(mat)>1) - formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]]; -%else -% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]]; -%end - -if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1) - formatstr=[repmat(tab,1,level) formatstr]; -end - -txt=sprintf(formatstr,mat'); -txt(end-length(nl):end)=[]; -if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1) - txt=regexprep(txt,'1','true'); - txt=regexprep(txt,'0','false'); -end -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],\n[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -txt=[pre txt post]; -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end - -%%------------------------------------------------------------------------- -function newstr=escapejsonstring(str) -newstr=str; -isoct=exist('OCTAVE_VERSION','builtin'); -if(isoct) - vv=sscanf(OCTAVE_VERSION,'%f'); - if(vv(1)>=3.8) isoct=0; end -end -if(isoct) - escapechars={'\a','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},escapechars{i}); - end -else - escapechars={'\a','\b','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\')); - end -end diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m b/assignments/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m deleted file mode 100644 index eaec433..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m +++ /dev/null @@ -1,504 +0,0 @@ -function json=saveubjson(rootname,obj,varargin) -% -% json=saveubjson(rootname,obj,filename) -% or -% json=saveubjson(rootname,obj,opt) -% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a Universal -% Binary JSON (UBJSON) binary string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/17 -% -% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array) -% filename: a string for the file name to save the output UBJSON data -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [1|0]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSON='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a binary string in the UBJSON format (see http://ubjson.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% saveubjson('jsonmesh',jsonmesh) -% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end -json=obj2ubjson(rootname,obj,rootlevel,opt); -if(~rootisarray) - json=['{' json '}']; -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=[jsonp '(' json ')']; -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2ubjson(name,item,level,varargin) - -if(iscell(item)) - txt=cell2ubjson(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2ubjson(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2ubjson(name,item,level,varargin{:}); -else - txt=mat2ubjson(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2ubjson(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); % let's handle 1D cell first -if(len>1) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) '[']; name=''; - else - txt='['; - end -elseif(len==0) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; - else - txt='Z'; - end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})]; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=struct2ubjson(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=[txt S_(checkname(name,varargin{:})) '{']; - else - txt=[txt '{']; - end - if(~isempty(names)) - for e=1:length(names) - txt=[txt obj2ubjson(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})]; - end - end - txt=[txt '}']; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=str2ubjson(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - val=item(e,:); - if(len==1) - obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),'']; - if(isempty(name)) obj=['',S_(val),'']; end - txt=[txt,'',obj]; - else - txt=[txt,'',['',S_(val),'']]; - end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=mat2ubjson(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) || jsonopt('ArrayToStruct',0,varargin{:})) - cid=I_(uint32(max(size(item)))); - if(isempty(name)) - txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ]; - else - if(isempty(item)) - txt=[S_(checkname(name,varargin{:})),'Z']; - return; - else - txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))]; - end - end -else - if(isempty(name)) - txt=matdata2ubjson(item,level+1,varargin{:}); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']',''); - txt=[S_(checkname(name,varargin{:})) numtxt]; - else - txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})]; - end - end - return; -end -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=[txt,S_('_ArrayIsComplex_'),'T']; - end - txt=[txt,S_('_ArrayIsSparse_'),'T']; - if(size(item,1)==1) - % Row vector, store only column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([iy(:),data'],level+2,varargin{:})]; - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,data],level+2,varargin{:})]; - else - % General case, store row and column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,iy,data],level+2,varargin{:})]; - end -else - if(isreal(item)) - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson(item(:)',level+2,varargin{:})]; - else - txt=[txt,S_('_ArrayIsComplex_'),'T']; - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})]; - end -end -txt=[txt,'}']; - -%%------------------------------------------------------------------------- -function txt=matdata2ubjson(mat,level,varargin) -if(isempty(mat)) - txt='Z'; - return; -end -if(size(mat,1)==1) - level=level-1; -end -type=''; -hasnegtive=(mat<0); -if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0))) - if(isempty(hasnegtive)) - if(max(mat(:))<=2^8) - type='U'; - end - end - if(isempty(type)) - % todo - need to consider negative ones separately - id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]); - if(isempty(find(id))) - error('high-precision data is not yet supported'); - end - key='iIlL'; - type=key(find(id)); - end - txt=[I_a(mat(:),type,size(mat))]; -elseif(islogical(mat)) - logicalval='FT'; - if(numel(mat)==1) - txt=logicalval(mat+1); - else - txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')]; - end -else - if(numel(mat)==1) - txt=['[' D_(mat) ']']; - else - txt=D_a(mat(:),'D',size(mat)); - end -end - -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end -%%------------------------------------------------------------------------- -function val=S_(str) -if(length(str)==1) - val=['C' str]; -else - val=['S' I_(int32(length(str))) str]; -end -%%------------------------------------------------------------------------- -function val=I_(num) -if(~isinteger(num)) - error('input is not an integer'); -end -if(num>=0 && num<255) - val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')]; - return; -end -key='iIlL'; -cid={'int8','int16','int32','int64'}; -for i=1:4 - if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1))) - val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')]; - return; - end -end -error('unsupported integer'); - -%%------------------------------------------------------------------------- -function val=D_(num) -if(~isfloat(num)) - error('input is not a float'); -end - -if(isa(num,'single')) - val=['d' data2byte(num,'uint8')]; -else - val=['D' data2byte(num,'uint8')]; -end -%%------------------------------------------------------------------------- -function data=I_a(num,type,dim,format) -id=find(ismember('iUIlL',type)); - -if(id==0) - error('unsupported integer array'); -end - -% based on UBJSON specs, all integer types are stored in big endian format - -if(id==1) - data=data2byte(swapbytes(int8(num)),'uint8'); - blen=1; -elseif(id==2) - data=data2byte(swapbytes(uint8(num)),'uint8'); - blen=1; -elseif(id==3) - data=data2byte(swapbytes(int16(num)),'uint8'); - blen=2; -elseif(id==4) - data=data2byte(swapbytes(int32(num)),'uint8'); - blen=4; -elseif(id==5) - data=data2byte(swapbytes(int64(num)),'uint8'); - blen=8; -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/blen)) data(:)']; - end - data=['[' data(:)']; -else - data=reshape(data,blen,numel(data)/blen); - data(2:blen+1,:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function data=D_a(num,type,dim,format) -id=find(ismember('dD',type)); - -if(id==0) - error('unsupported float array'); -end - -if(id==1) - data=data2byte(single(num),'uint8'); -elseif(id==2) - data=data2byte(double(num),'uint8'); -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)']; - end - data=['[' data]; -else - data=reshape(data,(id*4),length(data)/(id*4)); - data(2:(id*4+1),:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function bytes=data2byte(varargin) -bytes=typecast(varargin{:}); -bytes=bytes(:)'; diff --git a/assignments/machine-learning-ex1/ex1/lib/jsonlab/varargin2struct.m b/assignments/machine-learning-ex1/ex1/lib/jsonlab/varargin2struct.m deleted file mode 100644 index 9a5c2b6..0000000 --- a/assignments/machine-learning-ex1/ex1/lib/jsonlab/varargin2struct.m +++ /dev/null @@ -1,40 +0,0 @@ -function opt=varargin2struct(varargin) -% -% opt=varargin2struct('param1',value1,'param2',value2,...) -% or -% opt=varargin2struct(...,optstruct,...) -% -% convert a series of input parameters into a structure -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% 'param', value: the input parameters should be pairs of a string and a value -% optstruct: if a parameter is a struct, the fields will be merged to the output struct -% -% output: -% opt: a struct where opt.param1=value1, opt.param2=value2 ... -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -len=length(varargin); -opt=struct; -if(len==0) return; end -i=1; -while(i<=len) - if(isstruct(varargin{i})) - opt=mergestruct(opt,varargin{i}); - elseif(ischar(varargin{i}) && i -# name: email -# type: sq_string -# elements: 1 -# length: 16 -scruel@vip.qq.com - - -# name: token -# type: sq_string -# elements: 1 -# length: 16 -A3Wc4pI1qYjCUaRB - - diff --git a/assignments/machine-learning-ex1/ex1/warmUpExercise.m b/assignments/machine-learning-ex1/ex1/warmUpExercise.m deleted file mode 100644 index 524f568..0000000 --- a/assignments/machine-learning-ex1/ex1/warmUpExercise.m +++ /dev/null @@ -1,21 +0,0 @@ -function A = warmUpExercise() -%WARMUPEXERCISE Example function in octave -% A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix - -A = []; -% ============= YOUR CODE HERE ============== -% Instructions: Return the 5x5 identity matrix -% In octave, we return values by defining which variables -% represent the return values (at the top of the file) -% and then set them accordingly. - - - -A = eye(5); - - - -% =========================================== - - -end diff --git a/assignments/machine-learning-ex2/ex2.pdf b/assignments/machine-learning-ex2/ex2.pdf deleted file mode 100644 index b4cc7c2..0000000 Binary files a/assignments/machine-learning-ex2/ex2.pdf and /dev/null differ diff --git a/assignments/machine-learning-ex2/ex2/costFunction.m b/assignments/machine-learning-ex2/ex2/costFunction.m deleted file mode 100644 index 523b621..0000000 --- a/assignments/machine-learning-ex2/ex2/costFunction.m +++ /dev/null @@ -1,32 +0,0 @@ -function [J, grad] = costFunction(theta, X, y) -%COSTFUNCTION Compute cost and gradient for logistic regression -% J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the -% parameter for logistic regression and the gradient of the cost -% w.r.t. to the parameters. - -% Initialize some useful values -m = length(y); % number of training examples - -% You need to return the following variables correctly -J = 0; -grad = zeros(size(theta)); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the cost of a particular choice of theta. -% You should set J to the cost. -% Compute the partial derivatives and set grad to the partial -% derivatives of the cost w.r.t. each parameter in theta -% -% Note: grad should have the same dimensions as theta -% - -hx = sigmoid(X * theta); %hypothesis, m * 1 -J = 1 / m * sum(-y' * log(hx) - (1 .- y)' * log(1 -hx)); -grad = 1 / m * X' *(hx - y); - - - - -% ============================================================= - -end diff --git a/assignments/machine-learning-ex2/ex2/costFunctionReg.m b/assignments/machine-learning-ex2/ex2/costFunctionReg.m deleted file mode 100644 index 5e00863..0000000 --- a/assignments/machine-learning-ex2/ex2/costFunctionReg.m +++ /dev/null @@ -1,32 +0,0 @@ -function [J, grad] = costFunctionReg(theta, X, y, lambda) -%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization -% J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using -% theta as the parameter for regularized logistic regression and the -% gradient of the cost w.r.t. to the parameters. - -% Initialize some useful values -m = length(y); % number of training examples - -% You need to return the following variables correctly -J = 0; -grad = zeros(size(theta)); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the cost of a particular choice of theta. -% You should set J to the cost. -% Compute the partial derivatives and set grad to the partial -% derivatives of the cost w.r.t. each parameter in theta - -hx = sigmoid(X * theta); %hypothesis, m * 1 - -J = 1 / m * sum(-y' * log(hx) - (1 - y)' * log(1 - hx)) + lambda / (2 * m) * theta(2:end)' * theta(2:end); - -gradf = (1 / m) * (X(:, 1)' * (hx - y)); -gradb = (1 / m) * (X(:, 2:end)' * (hx - y)) + lambda * theta(2:end) / m; - -grad = [gradf;gradb]; - - -% ============================================================= - -end diff --git a/assignments/machine-learning-ex2/ex2/ex2.m b/assignments/machine-learning-ex2/ex2/ex2.m deleted file mode 100644 index 1cbf04a..0000000 --- a/assignments/machine-learning-ex2/ex2/ex2.m +++ /dev/null @@ -1,135 +0,0 @@ -%% Machine Learning Online Class - Exercise 2: Logistic Regression -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the logistic -% regression exercise. You will need to complete the following functions -% in this exericse: -% -% sigmoid.m -% costFunction.m -% predict.m -% costFunctionReg.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% Load Data -% The first two columns contains the exam scores and the third column -% contains the label. - -data = load('ex2data1.txt'); -X = data(:, [1, 2]); y = data(:, 3); - -%% ==================== Part 1: Plotting ==================== -% We start the exercise by first plotting the data to understand the -% the problem we are working with. - -fprintf(['Plotting data with + indicating (y = 1) examples and o ' ... - 'indicating (y = 0) examples.\n']); - -plotData(X, y); - -% Put some labels -hold on; -% Labels and Legend -xlabel('Exam 1 score') -ylabel('Exam 2 score') - -% Specified in plot order -legend('Admitted', 'Not admitted') -hold off; - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - -%% ============ Part 2: Compute Cost and Gradient ============ -% In this part of the exercise, you will implement the cost and gradient -% for logistic regression. You neeed to complete the code in -% costFunction.m - -% Setup the data matrix appropriately, and add ones for the intercept term -[m, n] = size(X); - -% Add intercept term to x and X_test -X = [ones(m, 1) X]; - -% Initialize fitting parameters -initial_theta = zeros(n + 1, 1); - -% Compute and display initial cost and gradient -[cost, grad] = costFunction(initial_theta, X, y); - -fprintf('Cost at initial theta (zeros): %f\n', cost); -fprintf('Gradient at initial theta (zeros): \n'); -fprintf(' %f \n', grad); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - -%% ============= Part 3: Optimizing using fminunc ============= -% In this exercise, you will use a built-in function (fminunc) to find the -% optimal parameters theta. - -% Set options for fminunc -options = optimset('GradObj', 'on', 'MaxIter', 400); - -% Run fminunc to obtain the optimal theta -% This function will return theta and the cost -[theta, cost] = ... - fminunc(@(t)(costFunction(t, X, y)), initial_theta, options); - -% Print theta to screen -fprintf('Cost at theta found by fminunc: %f\n', cost); -fprintf('theta: \n'); -fprintf(' %f \n', theta); - -% Plot Boundary -plotDecisionBoundary(theta, X, y); - -% Put some labels -hold on; -% Labels and Legend -xlabel('Exam 1 score') -ylabel('Exam 2 score') - -% Specified in plot order -legend('Admitted', 'Not admitted') -hold off; - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - -%% ============== Part 4: Predict and Accuracies ============== -% After learning the parameters, you'll like to use it to predict the outcomes -% on unseen data. In this part, you will use the logistic regression model -% to predict the probability that a student with score 45 on exam 1 and -% score 85 on exam 2 will be admitted. -% -% Furthermore, you will compute the training and test set accuracies of -% our model. -% -% Your task is to complete the code in predict.m - -% Predict probability for a student with score 45 on exam 1 -% and score 85 on exam 2 - -prob = sigmoid([1 45 85] * theta); -fprintf(['For a student with scores 45 and 85, we predict an admission ' ... - 'probability of %f\n\n'], prob); - -% Compute accuracy on our training set -p = predict(theta, X); - -fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - diff --git a/assignments/machine-learning-ex2/ex2/ex2_reg.m b/assignments/machine-learning-ex2/ex2/ex2_reg.m deleted file mode 100644 index b1fce3d..0000000 --- a/assignments/machine-learning-ex2/ex2/ex2_reg.m +++ /dev/null @@ -1,116 +0,0 @@ -%% Machine Learning Online Class - Exercise 2: Logistic Regression -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the second part -% of the exercise which covers regularization with logistic regression. -% -% You will need to complete the following functions in this exericse: -% -% sigmoid.m -% costFunction.m -% predict.m -% costFunctionReg.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% Load Data -% The first two columns contains the X values and the third column -% contains the label (y). - -data = load('ex2data2.txt'); -X = data(:, [1, 2]); y = data(:, 3); - -plotData(X, y); - -% Put some labels -hold on; - -% Labels and Legend -xlabel('Microchip Test 1') -ylabel('Microchip Test 2') - -% Specified in plot order -legend('y = 1', 'y = 0') -hold off; - - -%% =========== Part 1: Regularized Logistic Regression ============ -% In this part, you are given a dataset with data points that are not -% linearly separable. However, you would still like to use logistic -% regression to classify the data points. -% -% To do so, you introduce more features to use -- in particular, you add -% polynomial features to our data matrix (similar to polynomial -% regression). -% - -% Add Polynomial Features - -% Note that mapFeature also adds a column of ones for us, so the intercept -% term is handled -X = mapFeature(X(:,1), X(:,2)); - -% Initialize fitting parameters -initial_theta = zeros(size(X, 2), 1); - -% Set regularization parameter lambda to 1 -lambda = 1; - -% Compute and display initial cost and gradient for regularized logistic -% regression -[cost, grad] = costFunctionReg(initial_theta, X, y, lambda); - -fprintf('Cost at initial theta (zeros): %f\n', cost); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - -%% ============= Part 2: Regularization and Accuracies ============= -% Optional Exercise: -% In this part, you will get to try different values of lambda and -% see how regularization affects the decision coundart -% -% Try the following values of lambda (0, 1, 10, 100). -% -% How does the decision boundary change when you vary lambda? How does -% the training set accuracy vary? -% - -% Initialize fitting parameters -initial_theta = zeros(size(X, 2), 1); - -% Set regularization parameter lambda to 1 (you should vary this) -lambda = 100; - -% Set Options -options = optimset('GradObj', 'on', 'MaxIter', 400); - -% Optimize -[theta, J, exit_flag] = ... - fminunc(@(t)(costFunctionReg(t, X, y, 1)), initial_theta, options); - -% Plot Boundary -plotDecisionBoundary(theta, X, y); -hold on; -title(sprintf('lambda = %g', lambda)) - -% Labels and Legend -xlabel('Microchip Test 1') -ylabel('Microchip Test 2') - -legend('y = 1', 'y = 0', 'Decision boundary') -hold off; - -% Compute accuracy on our training set -p = predict(theta, X); - -fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100); - - diff --git a/assignments/machine-learning-ex2/ex2/ex2data.csv b/assignments/machine-learning-ex2/ex2/ex2data.csv deleted file mode 100644 index 97a3688..0000000 --- a/assignments/machine-learning-ex2/ex2/ex2data.csv +++ /dev/null @@ -1,100 +0,0 @@ -0,34.62365962,78.02469282 -0,30.28671077,43.89499752 -0,35.84740877,72.90219803 -1,60.18259939,86.3085521 -1,79.03273605,75.34437644 -0,45.08327748,56.31637178 -1,61.10666454,96.51142588 -1,75.02474557,46.55401354 -1,76.0987867,87.42056972 -1,84.43281996,43.53339331 -0,95.86155507,38.22527806 -0,75.01365839,30.60326323 -1,82.30705337,76.4819633 -1,69.36458876,97.71869196 -0,39.53833914,76.03681085 -1,53.97105215,89.20735014 -1,69.07014406,52.74046973 -0,67.94685548,46.67857411 -1,70.66150955,92.92713789 -1,76.97878373,47.57596365 -0,67.37202755,42.83843832 -1,89.67677575,65.79936593 -0,50.53478829,48.85581153 -0,34.21206098,44.2095286 -1,77.92409145,68.97235999 -1,62.27101367,69.95445795 -1,80.19018075,44.82162893 -0,93.1143888,38.80067034 -0,61.83020602,50.25610789 -0,38.7858038,64.99568096 -1,61.37928945,72.80788731 -1,85.40451939,57.05198398 -0,52.10797973,63.12762377 -1,52.04540477,69.43286012 -0,40.23689374,71.16774802 -0,54.63510555,52.21388588 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b/assignments/machine-learning-ex2/ex2/ex2data2.csv deleted file mode 100644 index a888992..0000000 --- a/assignments/machine-learning-ex2/ex2/ex2data2.csv +++ /dev/null @@ -1,118 +0,0 @@ -0.051267,0.69956,1 --0.092742,0.68494,1 --0.21371,0.69225,1 --0.375,0.50219,1 --0.51325,0.46564,1 --0.52477,0.2098,1 --0.39804,0.034357,1 --0.30588,-0.19225,1 -0.016705,-0.40424,1 -0.13191,-0.51389,1 -0.38537,-0.56506,1 -0.52938,-0.5212,1 -0.63882,-0.24342,1 -0.73675,-0.18494,1 -0.54666,0.48757,1 -0.322,0.5826,1 -0.16647,0.53874,1 --0.046659,0.81652,1 --0.17339,0.69956,1 --0.47869,0.63377,1 --0.60541,0.59722,1 --0.62846,0.33406,1 --0.59389,0.005117,1 --0.42108,-0.27266,1 --0.11578,-0.39693,1 -0.20104,-0.60161,1 -0.46601,-0.53582,1 -0.67339,-0.53582,1 --0.13882,0.54605,1 --0.29435,0.77997,1 --0.26555,0.96272,1 --0.16187,0.8019,1 --0.17339,0.64839,1 --0.28283,0.47295,1 --0.36348,0.31213,1 --0.30012,0.027047,1 --0.23675,-0.21418,1 --0.06394,-0.18494,1 -0.062788,-0.16301,1 -0.22984,-0.41155,1 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-0.13191,-0.51389,1 -0.38537,-0.56506,1 -0.52938,-0.5212,1 -0.63882,-0.24342,1 -0.73675,-0.18494,1 -0.54666,0.48757,1 -0.322,0.5826,1 -0.16647,0.53874,1 --0.046659,0.81652,1 --0.17339,0.69956,1 --0.47869,0.63377,1 --0.60541,0.59722,1 --0.62846,0.33406,1 --0.59389,0.005117,1 --0.42108,-0.27266,1 --0.11578,-0.39693,1 -0.20104,-0.60161,1 -0.46601,-0.53582,1 -0.67339,-0.53582,1 --0.13882,0.54605,1 --0.29435,0.77997,1 --0.26555,0.96272,1 --0.16187,0.8019,1 --0.17339,0.64839,1 --0.28283,0.47295,1 --0.36348,0.31213,1 --0.30012,0.027047,1 --0.23675,-0.21418,1 --0.06394,-0.18494,1 -0.062788,-0.16301,1 -0.22984,-0.41155,1 -0.2932,-0.2288,1 -0.48329,-0.18494,1 -0.64459,-0.14108,1 -0.46025,0.012427,1 -0.6273,0.15863,1 -0.57546,0.26827,1 -0.72523,0.44371,1 -0.22408,0.52412,1 -0.44297,0.67032,1 -0.322,0.69225,1 -0.13767,0.57529,1 --0.0063364,0.39985,1 --0.092742,0.55336,1 --0.20795,0.35599,1 --0.20795,0.17325,1 --0.43836,0.21711,1 --0.21947,-0.016813,1 --0.13882,-0.27266,1 -0.18376,0.93348,0 -0.22408,0.77997,0 -0.29896,0.61915,0 -0.50634,0.75804,0 -0.61578,0.7288,0 -0.60426,0.59722,0 -0.76555,0.50219,0 -0.92684,0.3633,0 -0.82316,0.27558,0 -0.96141,0.085526,0 -0.93836,0.012427,0 -0.86348,-0.082602,0 -0.89804,-0.20687,0 -0.85196,-0.36769,0 -0.82892,-0.5212,0 -0.79435,-0.55775,0 -0.59274,-0.7405,0 -0.51786,-0.5943,0 -0.46601,-0.41886,0 -0.35081,-0.57968,0 -0.28744,-0.76974,0 -0.085829,-0.75512,0 -0.14919,-0.57968,0 --0.13306,-0.4481,0 --0.40956,-0.41155,0 --0.39228,-0.25804,0 --0.74366,-0.25804,0 --0.69758,0.041667,0 --0.75518,0.2902,0 --0.69758,0.68494,0 --0.4038,0.70687,0 --0.38076,0.91886,0 --0.50749,0.90424,0 --0.54781,0.70687,0 -0.10311,0.77997,0 -0.057028,0.91886,0 --0.10426,0.99196,0 --0.081221,1.1089,0 -0.28744,1.087,0 -0.39689,0.82383,0 -0.63882,0.88962,0 -0.82316,0.66301,0 -0.67339,0.64108,0 -1.0709,0.10015,0 --0.046659,-0.57968,0 --0.23675,-0.63816,0 --0.15035,-0.36769,0 --0.49021,-0.3019,0 --0.46717,-0.13377,0 --0.28859,-0.060673,0 --0.61118,-0.067982,0 --0.66302,-0.21418,0 --0.59965,-0.41886,0 --0.72638,-0.082602,0 --0.83007,0.31213,0 --0.72062,0.53874,0 --0.59389,0.49488,0 --0.48445,0.99927,0 --0.0063364,0.99927,0 -0.63265,-0.030612,0 diff --git a/assignments/machine-learning-ex2/ex2/ex2test.csv b/assignments/machine-learning-ex2/ex2/ex2test.csv deleted file mode 100644 index 30c0ae3..0000000 --- a/assignments/machine-learning-ex2/ex2/ex2test.csv +++ /dev/null @@ -1,5 +0,0 @@ -0,98.83450677,10.6943068 -1,65.31925747,65.58935318 -0,70.23870632,48.51428198 -1,80.366756,70.9601479 -0,60.46852179,60.5943071 diff --git a/assignments/machine-learning-ex2/ex2/ex2test.txt b/assignments/machine-learning-ex2/ex2/ex2test.txt deleted file mode 100644 index ba6c5ec..0000000 --- a/assignments/machine-learning-ex2/ex2/ex2test.txt +++ /dev/null @@ -1,5 +0,0 @@ -98.83450676630196,10.69430680250754,0 -65.31925746917527,65.58935317747915,1 -70.23870631569862,48.51428198012956,0 -80.36675600171273,70.96014789746954,1 -60.46852178591112,60.59430710452014,0 \ No newline at end of file diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/AUTHORS.txt b/assignments/machine-learning-ex2/ex2/lib/jsonlab/AUTHORS.txt deleted file mode 100644 index 9dd3fc7..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/AUTHORS.txt +++ /dev/null @@ -1,41 +0,0 @@ -The author of "jsonlab" toolbox is Qianqian Fang. Qianqian -is currently an Assistant Professor at Massachusetts General Hospital, -Harvard Medical School. - -Address: Martinos Center for Biomedical Imaging, - Massachusetts General Hospital, - Harvard Medical School - Bldg 149, 13th St, Charlestown, MA 02129, USA -URL: http://nmr.mgh.harvard.edu/~fangq/ -Email: or - - -The script loadjson.m was built upon previous works by - -- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 - date: 2009/11/02 -- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 - date: 2009/03/22 -- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565 - date: 2008/07/03 - - -This toolbox contains patches submitted by the following contributors: - -- Blake Johnson - part of revision 341 - -- Niclas Borlin - various fixes in revision 394, including - - loadjson crashes for all-zero sparse matrix. - - loadjson crashes for empty sparse matrix. - - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson. - - loadjson crashes for sparse real column vector. - - loadjson crashes for sparse complex column vector. - - Data is corrupted by savejson for sparse real row vector. - - savejson crashes for sparse complex row vector. - -- Yul Kang - patches for svn revision 415. - - savejson saves an empty cell array as [] instead of null - - loadjson differentiates an empty struct from an empty array diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/ChangeLog.txt b/assignments/machine-learning-ex2/ex2/lib/jsonlab/ChangeLog.txt deleted file mode 100644 index 07824f5..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/ChangeLog.txt +++ /dev/null @@ -1,74 +0,0 @@ -============================================================================ - - JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave - ----------------------------------------------------------------------------- - -JSONlab ChangeLog (key features marked by *): - -== JSONlab 1.0 (codename: Optimus - Final), FangQ == - - 2015/01/02 polish help info for all major functions, update examples, finalize 1.0 - 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson - -== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ == - - 2014/11/22 show progress bar in loadjson ('ShowProgress') - 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact') - 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels - 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab - -== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ == - - 2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8 - 2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson - 2014/08/04 escape special characters in a JSON string - 2014/02/16 fix a bug when saving ubjson files - -== JSONlab 0.9.9 (codename: Optimus - beta), FangQ == - - 2014/01/22 use binary read and write in saveubjson and loadubjson - -== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ == - - 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang) - -== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ == - 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson - -== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ == - 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin) - -== JSONlab 0.9.0 (codename: Rodimus), FangQ == - - 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson - 2012/06/01 support JSONP in savejson - 2012/05/25 fix the empty cell bug (reported by Cyril Davin) - 2012/04/05 savejson can save to a file (suggested by Patrick Rapin) - -== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ == - - 2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS - 2012/01/25 patch to handle root-less objects, contributed by Blake Johnson - -== JSONlab 0.8.0 (codename: Sentiel), FangQ == - - 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab - 2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer - 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson - 2011/11/18 fix struct array bug reported by Mykel Kochenderfer - -== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ == - - 2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration - 2011/10/20 loadjson supports JSON collections - concatenated JSON objects - -== JSONlab 0.5.0 (codename: Nexus), FangQ == - - 2011/10/16 package and release jsonlab 0.5.0 - 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug - 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level - 2011/10/10 create jsonlab project, start jsonlab website, add online documentation - 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support - 2011/10/06 *savejson works for structs, cells and arrays - 2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/LICENSE_BSD.txt b/assignments/machine-learning-ex2/ex2/lib/jsonlab/LICENSE_BSD.txt deleted file mode 100644 index 32d66cb..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/LICENSE_BSD.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright 2011-2015 Qianqian Fang . All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are -permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, this list - of conditions and the following disclaimer in the documentation and/or other materials - provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND -FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS -OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -The views and conclusions contained in the software and documentation are those of the -authors and should not be interpreted as representing official policies, either expressed -or implied, of the copyright holders. diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/README.txt b/assignments/machine-learning-ex2/ex2/lib/jsonlab/README.txt deleted file mode 100644 index 7b4f732..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/README.txt +++ /dev/null @@ -1,394 +0,0 @@ -=============================================================================== -= JSONLab = -= An open-source MATLAB/Octave JSON encoder and decoder = -=============================================================================== - -*Copyright (C) 2011-2015 Qianqian Fang -*License: BSD License, see License_BSD.txt for details -*Version: 1.0 (Optimus - Final) - -------------------------------------------------------------------------------- - -Table of Content: - -I. Introduction -II. Installation -III.Using JSONLab -IV. Known Issues and TODOs -V. Contribution and feedback - -------------------------------------------------------------------------------- - -I. Introduction - -JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, -human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format -to represent complex and hierarchical data. It is as powerful as -[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely -used for data-exchange in applications, and is essential for the wild success -of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and -[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. - -UBJSON (Universal Binary JSON) is a binary JSON format, specifically -optimized for compact file size and better performance while keeping -the semantics as simple as the text-based JSON format. Using the UBJSON -format allows to wrap complex binary data in a flexible and extensible -structure, making it possible to process complex and large dataset -without accuracy loss due to text conversions. - -We envision that both JSON and its binary version will serve as part of -the mainstream data-exchange formats for scientific research in the future. -It will provide the flexibility and generality achieved by other popular -general-purpose file specifications, such as -[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly -reduced complexity and enhanced performance. - -JSONLab is a free and open-source implementation of a JSON/UBJSON encoder -and a decoder in the native MATLAB language. It can be used to convert a MATLAB -data structure (array, struct, cell, struct array and cell array) into -JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB -data structure. JSONLab supports both MATLAB and -[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone). - -------------------------------------------------------------------------------- - -II. Installation - -The installation of JSONLab is no different than any other simple -MATLAB toolbox. You only need to download/unzip the JSONLab package -to a folder, and add the folder's path to MATLAB/Octave's path list -by using the following command: - - addpath('/path/to/jsonlab'); - -If you want to add this path permanently, you need to type "pathtool", -browse to the jsonlab root folder and add to the list, then click "Save". -Then, run "rehash" in MATLAB, and type "which loadjson", if you see an -output, that means JSONLab is installed for MATLAB/Octave. - -------------------------------------------------------------------------------- - -III.Using JSONLab - -JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, -and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and -two equivallent functions -- loadubjson and saveubjson for the binary -JSON. The detailed help info for the four functions can be found below: - -=== loadjson.m === -
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          Franois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-
- -=== savejson.m === - -
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-
- -=== loadubjson.m === - -
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-
- -=== saveubjson.m === - -
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-
- - -=== examples === - -Under the "examples" folder, you can find several scripts to demonstrate the -basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you -will see the conversions from MATLAB data structure to JSON text and backward. -In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet -and validate the loadjson/savejson functions for regression testing purposes. -Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson -and loadubjson pairs for various matlab data structures. - -Please run these examples and understand how JSONLab works before you use -it to process your data. - -------------------------------------------------------------------------------- - -IV. Known Issues and TODOs - -JSONLab has several known limitations. We are striving to make it more general -and robust. Hopefully in a few future releases, the limitations become less. - -Here are the known issues: - -# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays; -# When processing names containing multi-byte characters, Octave and MATLAB \ -can give different field-names; you can use feature('DefaultCharacterSet','latin1') \ -in MATLAB to get consistant results -# savejson can not handle class and dataset. -# saveubjson converts a logical array into a uint8 ([U]) array -# an unofficial N-D array count syntax is implemented in saveubjson. We are \ -actively communicating with the UBJSON spec maintainer to investigate the \ -possibility of making it upstream -# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \ -files, however, it can parse all UBJSON files produced by saveubjson. - -------------------------------------------------------------------------------- - -V. Contribution and feedback - -JSONLab is an open-source project. This means you can not only use it and modify -it as you wish, but also you can contribute your changes back to JSONLab so -that everyone else can enjoy the improvement. For anyone who want to contribute, -please download JSONLab source code from it's subversion repository by using the -following command: - - svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab - -You can make changes to the files as needed. Once you are satisfied with your -changes, and ready to share it with others, please cd the root directory of -JSONLab, and type - - svn diff > yourname_featurename.patch - -You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at -the email address shown in the beginning of this file. Qianqian will review -the changes and commit it to the subversion if they are satisfactory. - -We appreciate any suggestions and feedbacks from you. Please use iso2mesh's -mailing list to report any questions you may have with JSONLab: - -http://groups.google.com/group/iso2mesh-users?hl=en&pli=1 - -(Subscription to the mailing list is needed in order to post messages). diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/jsonopt.m b/assignments/machine-learning-ex2/ex2/lib/jsonlab/jsonopt.m deleted file mode 100644 index 0bebd8d..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/jsonopt.m +++ /dev/null @@ -1,32 +0,0 @@ -function val=jsonopt(key,default,varargin) -% -% val=jsonopt(key,default,optstruct) -% -% setting options based on a struct. The struct can be produced -% by varargin2struct from a list of 'param','value' pairs -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% -% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $ -% -% input: -% key: a string with which one look up a value from a struct -% default: if the key does not exist, return default -% optstruct: a struct where each sub-field is a key -% -% output: -% val: if key exists, val=optstruct.key; otherwise val=default -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -val=default; -if(nargin<=2) return; end -opt=varargin{1}; -if(isstruct(opt) && isfield(opt,key)) - val=getfield(opt,key); -end - diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m b/assignments/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m deleted file mode 100644 index 42798c0..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m +++ /dev/null @@ -1,566 +0,0 @@ -function data = loadjson(fname,varargin) -% -% data=loadjson(fname,opt) -% or -% data=loadjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09, including previous works from -% -% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 -% created on 2009/11/02 -% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 -% created on 2009/03/22 -% Joel Feenstra: -% http://www.mathworks.com/matlabcentral/fileexchange/20565 -% created on 2008/07/03 -% -% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a JSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.FastArrayParser [1|0 or integer]: if set to 1, use a -% speed-optimized array parser when loading an -% array object. The fast array parser may -% collapse block arrays into a single large -% array similar to rules defined in cell2mat; 0 to -% use a legacy parser; if set to a larger-than-1 -% value, this option will specify the minimum -% dimension to enable the fast array parser. For -% example, if the input is a 3D array, setting -% FastArrayParser to 1 will return a 3D array; -% setting to 2 will return a cell array of 2D -% arrays; setting to 3 will return to a 2D cell -% array of 1D vectors; setting to 4 will return a -% 3D cell array. -% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}') -% dat=loadjson(['examples' filesep 'example1.json']) -% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); - -if(jsonopt('ShowProgress',0,opt)==1) - opt.progressbar_=waitbar(0,'loading ...'); -end -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end -if(isfield(opt,'progressbar_')) - close(opt.progressbar_); -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=data(j).x0x5F_ArraySize_; - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - if next_char ~= '}' - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - parse_char(':'); - val = parse_value(varargin{:}); - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' - break; - end - parse_char(','); - end - end - parse_char('}'); - -%%------------------------------------------------------------------------- - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim2=[]; - arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:}); - pbar=jsonopt('progressbar_',-1,varargin{:}); - - if next_char ~= ']' - if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:})) - [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos); - arraystr=['[' inStr(pos:endpos)]; - arraystr=regexprep(arraystr,'"_NaN_"','NaN'); - arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf'); - arraystr(arraystr==sprintf('\n'))=[]; - arraystr(arraystr==sprintf('\r'))=[]; - %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed - if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D - astr=inStr((e1l+1):(e1r-1)); - astr=regexprep(astr,'"_NaN_"','NaN'); - astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf'); - astr(astr==sprintf('\n'))=[]; - astr(astr==sprintf('\r'))=[]; - astr(astr==' ')=''; - if(isempty(find(astr=='[', 1))) % array is 2D - dim2=length(sscanf(astr,'%f,',[1 inf])); - end - else % array is 1D - astr=arraystr(2:end-1); - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]); - if(nextidx>=length(astr)-1) - object=obj; - pos=endpos; - parse_char(']'); - return; - end - end - if(~isempty(dim2)) - astr=arraystr; - astr(astr=='[')=''; - astr(astr==']')=''; - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf); - if(nextidx>=length(astr)-1) - object=reshape(obj,dim2,numel(obj)/dim2)'; - pos=endpos; - parse_char(']'); - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end - return; - end - end - arraystr=regexprep(arraystr,'\]\s*,','];'); - else - arraystr='['; - end - try - if(isoct && regexp(arraystr,'"','once')) - error('Octave eval can produce empty cells for JSON-like input'); - end - object=eval(arraystr); - pos=endpos; - catch - while 1 - newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1); - val = parse_value(newopt); - object{end+1} = val; - if next_char == ']' - break; - end - parse_char(','); - end - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - parse_char(']'); - - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr len esc index_esc len_esc - % len, ns = length(inStr), keyboard - if inStr(pos) ~= '"' - error_pos('String starting with " expected at position %d'); - else - pos = pos + 1; - end - str = ''; - while pos <= len - while index_esc <= len_esc && esc(index_esc) < pos - index_esc = index_esc + 1; - end - if index_esc > len_esc - str = [str inStr(pos:len)]; - pos = len + 1; - break; - else - str = [str inStr(pos:esc(index_esc)-1)]; - pos = esc(index_esc); - end - nstr = length(str); switch inStr(pos) - case '"' - pos = pos + 1; - if(~isempty(str)) - if(strcmp(str,'_Inf_')) - str=Inf; - elseif(strcmp(str,'-_Inf_')) - str=-Inf; - elseif(strcmp(str,'_NaN_')) - str=NaN; - end - end - return; - case '\' - if pos+1 > len - error_pos('End of file reached right after escape character'); - end - pos = pos + 1; - switch inStr(pos) - case {'"' '\' '/'} - str(nstr+1) = inStr(pos); - pos = pos + 1; - case {'b' 'f' 'n' 'r' 't'} - str(nstr+1) = sprintf(['\' inStr(pos)]); - pos = pos + 1; - case 'u' - if pos+4 > len - error_pos('End of file reached in escaped unicode character'); - end - str(nstr+(1:6)) = inStr(pos-1:pos+4); - pos = pos + 5; - end - otherwise % should never happen - str(nstr+1) = inStr(pos), keyboard - pos = pos + 1; - end - end - error_pos('End of file while expecting end of inStr'); - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct - currstr=inStr(pos:end); - numstr=0; - if(isoct~=0) - numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end'); - [num, one] = sscanf(currstr, '%f', 1); - delta=numstr+1; - else - [num, one, err, delta] = sscanf(currstr, '%f', 1); - if ~isempty(err) - error_pos('Error reading number at position %d'); - end - end - pos = pos + delta-1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - pbar=jsonopt('progressbar_',-1,varargin{:}); - if(pbar>0) - waitbar(pos/len,pbar,'loading ...'); - end - - switch(inStr(pos)) - case '"' - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'-','0','1','2','3','4','5','6','7','8','9'} - val = parse_number(varargin{:}); - return; - case 't' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true') - val = true; - pos = pos + 4; - return; - end - case 'f' - if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false') - val = false; - pos = pos + 5; - return; - end - case 'n' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null') - val = []; - pos = pos + 4; - return; - end - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m b/assignments/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m deleted file mode 100644 index 0155115..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m +++ /dev/null @@ -1,528 +0,0 @@ -function data = loadubjson(fname,varargin) -% -% data=loadubjson(fname,opt) -% or -% data=loadubjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/01 -% -% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a UBJSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.IntEndian [B|L]: specify the endianness of the integer fields -% in the UBJSON input data. B - Big-Endian format for -% integers (as required in the UBJSON specification); -% L - input integer fields are in Little-Endian order. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% obj=struct('string','value','array',[1 2 3]); -% ubjdata=saveubjson('obj',obj); -% dat=loadubjson(ubjdata) -% dat=loadubjson(['examples' filesep 'example1.ubj']) -% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); -fileendian=upper(jsonopt('IntEndian','B',opt)); -[os,maxelem,systemendian]=computer; - -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end - - -%% -function newdata=parse_collection(id,data,obj) - -if(jsoncount>0 && exist('data','var')) - if(~iscell(data)) - newdata=cell(1); - newdata{1}=data; - data=newdata; - end -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=double(data(j).x0x5F_ArraySize_); - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); % TODO - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - count=double(parse_number()); - end - if next_char ~= '}' - num=0; - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - %parse_char(':'); - val = parse_value(varargin{:}); - num=num+1; - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' || (count>=0 && num>=count) - break; - end - %parse_char(','); - end - end - if(count==-1) - parse_char('}'); - end - -%%------------------------------------------------------------------------- -function [cid,len]=elem_info(type) -id=strfind('iUIlLdD',type); -dataclass={'int8','uint8','int16','int32','int64','single','double'}; -bytelen=[1,1,2,4,8,4,8]; -if(id>0) - cid=dataclass{id}; - len=bytelen(id); -else - error_pos('unsupported type at position %d'); -end -%%------------------------------------------------------------------------- - - -function [data adv]=parse_block(type,count,varargin) -global pos inStr isoct fileendian systemendian -[cid,len]=elem_info(type); -datastr=inStr(pos:pos+len*count-1); -if(isoct) - newdata=int8(datastr); -else - newdata=uint8(datastr); -end -id=strfind('iUIlLdD',type); -if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,cid)); -end -data=typecast(newdata,cid); -adv=double(len*count); - -%%------------------------------------------------------------------------- - - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim=[]; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - if(next_char=='[') - dim=parse_array(varargin{:}); - count=prod(double(dim)); - else - count=double(parse_number()); - end - end - if(~isempty(type)) - if(count>=0) - [object adv]=parse_block(type,count,varargin{:}); - if(~isempty(dim)) - object=reshape(object,dim); - end - pos=pos+adv; - return; - else - endpos=matching_bracket(inStr,pos); - [cid,len]=elem_info(type); - count=(endpos-pos)/len; - [object adv]=parse_block(type,count,varargin{:}); - pos=pos+adv; - parse_char(']'); - return; - end - end - if next_char ~= ']' - while 1 - val = parse_value(varargin{:}); - object{end+1} = val; - if next_char == ']' - break; - end - %parse_char(','); - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - if(count==-1) - parse_char(']'); - end - -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr esc index_esc len_esc - % len, ns = length(inStr), keyboard - type=inStr(pos); - if type ~= 'S' && type ~= 'C' && type ~= 'H' - error_pos('String starting with S expected at position %d'); - else - pos = pos + 1; - end - if(type == 'C') - str=inStr(pos); - pos=pos+1; - return; - end - bytelen=double(parse_number()); - if(length(inStr)>=pos+bytelen-1) - str=inStr(pos:pos+bytelen-1); - pos=pos+bytelen; - else - error_pos('End of file while expecting end of inStr'); - end - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct fileendian systemendian - id=strfind('iUIlLdD',inStr(pos)); - if(isempty(id)) - error_pos('expecting a number at position %d'); - end - type={'int8','uint8','int16','int32','int64','single','double'}; - bytelen=[1,1,2,4,8,4,8]; - datastr=inStr(pos+1:pos+bytelen(id)); - if(isoct) - newdata=int8(datastr); - else - newdata=uint8(datastr); - end - if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,type{id})); - end - num=typecast(newdata,type{id}); - pos = pos + bytelen(id)+1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - switch(inStr(pos)) - case {'S','C','H'} - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'i','U','I','l','L','d','D'} - val = parse_number(varargin{:}); - return; - case 'T' - val = true; - pos = pos + 1; - return; - case 'F' - val = false; - pos = pos + 1; - return; - case {'Z','N'} - val = []; - pos = pos + 1; - return; - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos e1l e1r maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/mergestruct.m b/assignments/machine-learning-ex2/ex2/lib/jsonlab/mergestruct.m deleted file mode 100644 index 6de6100..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/mergestruct.m +++ /dev/null @@ -1,33 +0,0 @@ -function s=mergestruct(s1,s2) -% -% s=mergestruct(s1,s2) -% -% merge two struct objects into one -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% s1,s2: a struct object, s1 and s2 can not be arrays -% -% output: -% s: the merged struct object. fields in s1 and s2 will be combined in s. -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(~isstruct(s1) || ~isstruct(s2)) - error('input parameters contain non-struct'); -end -if(length(s1)>1 || length(s2)>1) - error('can not merge struct arrays'); -end -fn=fieldnames(s2); -s=s1; -for i=1:length(fn) - s=setfield(s,fn{i},getfield(s2,fn{i})); -end - diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/savejson.m b/assignments/machine-learning-ex2/ex2/lib/jsonlab/savejson.m deleted file mode 100644 index 7e84076..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/savejson.m +++ /dev/null @@ -1,475 +0,0 @@ -function json=savejson(rootname,obj,varargin) -% -% json=savejson(rootname,obj,filename) -% or -% json=savejson(rootname,obj,opt) -% json=savejson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript -% Object Notation) string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09 -% -% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array). -% filename: a string for the file name to save the output JSON data. -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.FloatFormat ['%.10g'|string]: format to show each numeric element -% of a 1D/2D array; -% opt.ArrayIndent [1|0]: if 1, output explicit data array with -% precedent indentation; if 0, no indentation -% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [0|1]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern -% to represent +/-Inf. The matched pattern is '([-+]*)Inf' -% and $1 represents the sign. For those who want to use -% 1e999 to represent Inf, they can set opt.Inf to '$11e999' -% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern -% to represent NaN -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSONP='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode. -% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs) -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a string in the JSON format (see http://json.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% savejson('jmesh',jsonmesh) -% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end - -whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -if(jsonopt('Compact',0,opt)==1) - whitespaces=struct('tab','','newline','','sep',','); -end -if(~isfield(opt,'whitespaces_')) - opt.whitespaces_=whitespaces; -end - -nl=whitespaces.newline; - -json=obj2json(rootname,obj,rootlevel,opt); -if(rootisarray) - json=sprintf('%s%s',json,nl); -else - json=sprintf('{%s%s%s}\n',nl,json,nl); -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=sprintf('%s(%s);%s',jsonp,json,nl); -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - if(jsonopt('SaveBinary',0,opt)==1) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - else - fid = fopen(opt.FileName, 'wt'); - fwrite(fid,json,'char'); - end - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2json(name,item,level,varargin) - -if(iscell(item)) - txt=cell2json(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2json(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2json(name,item,level,varargin{:}); -else - txt=mat2json(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2json(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -nl=ws.newline; -if(len>1) - if(~isempty(name)) - txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; - else - txt=sprintf('%s[%s',padding0,nl); - end -elseif(len==0) - if(~isempty(name)) - txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; - else - txt=sprintf('%s[]',padding0); - end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:})); - if(i1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=struct2json(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1)); -nl=ws.newline; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding0,nl); end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); - else - txt=sprintf('%s%s{%s',txt,padding1,nl); - end - if(~isempty(names)) - for e=1:length(names) - txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})); - if(e1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=str2json(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding1,nl); end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - if(isoct) - val=regexprep(item(e,:),'\\','\\'); - val=regexprep(val,'"','\"'); - val=regexprep(val,'^"','\"'); - else - val=regexprep(item(e,:),'\\','\\\\'); - val=regexprep(val,'"','\\"'); - val=regexprep(val,'^"','\\"'); - end - val=escapejsonstring(val); - if(len==1) - obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"']; - if(isempty(name)) obj=['"',val,'"']; end - txt=sprintf('%s%s%s%s',txt,padding1,obj); - else - txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']); - end - if(e==len) sep=''; end - txt=sprintf('%s%s',txt,sep); -end -if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end - -%%------------------------------------------------------------------------- -function txt=mat2json(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:})) - if(isempty(name)) - txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - else - txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - end -else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0) - numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']',''); - else - numtxt=matdata2json(item,level+1,varargin{:}); - end - if(isempty(name)) - txt=sprintf('%s%s',padding1,numtxt); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - else - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - end - end - return; -end -dataformat='%s%s%s%s%s'; - -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep); - if(size(item,1)==1) - % Row vector, store only column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([iy(:),data'],level+2,varargin{:}), nl); - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,data],level+2,varargin{:}), nl); - else - % General case, store row and column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,iy,data],level+2,varargin{:}), nl); - end -else - if(isreal(item)) - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json(item(:)',level+2,varargin{:}), nl); - else - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl); - end -end -txt=sprintf('%s%s%s',txt,padding1,'}'); - -%%------------------------------------------------------------------------- -function txt=matdata2json(mat,level,varargin) - -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -tab=ws.tab; -nl=ws.newline; - -if(size(mat,1)==1) - pre=''; - post=''; - level=level-1; -else - pre=sprintf('[%s',nl); - post=sprintf('%s%s]',nl,repmat(tab,1,level-1)); -end - -if(isempty(mat)) - txt='null'; - return; -end -floatformat=jsonopt('FloatFormat','%.10g',varargin{:}); -%if(numel(mat)>1) - formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]]; -%else -% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]]; -%end - -if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1) - formatstr=[repmat(tab,1,level) formatstr]; -end - -txt=sprintf(formatstr,mat'); -txt(end-length(nl):end)=[]; -if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1) - txt=regexprep(txt,'1','true'); - txt=regexprep(txt,'0','false'); -end -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],\n[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -txt=[pre txt post]; -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end - -%%------------------------------------------------------------------------- -function newstr=escapejsonstring(str) -newstr=str; -isoct=exist('OCTAVE_VERSION','builtin'); -if(isoct) - vv=sscanf(OCTAVE_VERSION,'%f'); - if(vv(1)>=3.8) isoct=0; end -end -if(isoct) - escapechars={'\a','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},escapechars{i}); - end -else - escapechars={'\a','\b','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\')); - end -end diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m b/assignments/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m deleted file mode 100644 index eaec433..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m +++ /dev/null @@ -1,504 +0,0 @@ -function json=saveubjson(rootname,obj,varargin) -% -% json=saveubjson(rootname,obj,filename) -% or -% json=saveubjson(rootname,obj,opt) -% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a Universal -% Binary JSON (UBJSON) binary string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/17 -% -% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array) -% filename: a string for the file name to save the output UBJSON data -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [1|0]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSON='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a binary string in the UBJSON format (see http://ubjson.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% saveubjson('jsonmesh',jsonmesh) -% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end -json=obj2ubjson(rootname,obj,rootlevel,opt); -if(~rootisarray) - json=['{' json '}']; -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=[jsonp '(' json ')']; -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2ubjson(name,item,level,varargin) - -if(iscell(item)) - txt=cell2ubjson(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2ubjson(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2ubjson(name,item,level,varargin{:}); -else - txt=mat2ubjson(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2ubjson(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); % let's handle 1D cell first -if(len>1) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) '[']; name=''; - else - txt='['; - end -elseif(len==0) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; - else - txt='Z'; - end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})]; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=struct2ubjson(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=[txt S_(checkname(name,varargin{:})) '{']; - else - txt=[txt '{']; - end - if(~isempty(names)) - for e=1:length(names) - txt=[txt obj2ubjson(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})]; - end - end - txt=[txt '}']; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=str2ubjson(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - val=item(e,:); - if(len==1) - obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),'']; - if(isempty(name)) obj=['',S_(val),'']; end - txt=[txt,'',obj]; - else - txt=[txt,'',['',S_(val),'']]; - end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=mat2ubjson(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) || jsonopt('ArrayToStruct',0,varargin{:})) - cid=I_(uint32(max(size(item)))); - if(isempty(name)) - txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ]; - else - if(isempty(item)) - txt=[S_(checkname(name,varargin{:})),'Z']; - return; - else - txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))]; - end - end -else - if(isempty(name)) - txt=matdata2ubjson(item,level+1,varargin{:}); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']',''); - txt=[S_(checkname(name,varargin{:})) numtxt]; - else - txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})]; - end - end - return; -end -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=[txt,S_('_ArrayIsComplex_'),'T']; - end - txt=[txt,S_('_ArrayIsSparse_'),'T']; - if(size(item,1)==1) - % Row vector, store only column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([iy(:),data'],level+2,varargin{:})]; - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,data],level+2,varargin{:})]; - else - % General case, store row and column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,iy,data],level+2,varargin{:})]; - end -else - if(isreal(item)) - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson(item(:)',level+2,varargin{:})]; - else - txt=[txt,S_('_ArrayIsComplex_'),'T']; - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})]; - end -end -txt=[txt,'}']; - -%%------------------------------------------------------------------------- -function txt=matdata2ubjson(mat,level,varargin) -if(isempty(mat)) - txt='Z'; - return; -end -if(size(mat,1)==1) - level=level-1; -end -type=''; -hasnegtive=(mat<0); -if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0))) - if(isempty(hasnegtive)) - if(max(mat(:))<=2^8) - type='U'; - end - end - if(isempty(type)) - % todo - need to consider negative ones separately - id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]); - if(isempty(find(id))) - error('high-precision data is not yet supported'); - end - key='iIlL'; - type=key(find(id)); - end - txt=[I_a(mat(:),type,size(mat))]; -elseif(islogical(mat)) - logicalval='FT'; - if(numel(mat)==1) - txt=logicalval(mat+1); - else - txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')]; - end -else - if(numel(mat)==1) - txt=['[' D_(mat) ']']; - else - txt=D_a(mat(:),'D',size(mat)); - end -end - -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end -%%------------------------------------------------------------------------- -function val=S_(str) -if(length(str)==1) - val=['C' str]; -else - val=['S' I_(int32(length(str))) str]; -end -%%------------------------------------------------------------------------- -function val=I_(num) -if(~isinteger(num)) - error('input is not an integer'); -end -if(num>=0 && num<255) - val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')]; - return; -end -key='iIlL'; -cid={'int8','int16','int32','int64'}; -for i=1:4 - if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1))) - val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')]; - return; - end -end -error('unsupported integer'); - -%%------------------------------------------------------------------------- -function val=D_(num) -if(~isfloat(num)) - error('input is not a float'); -end - -if(isa(num,'single')) - val=['d' data2byte(num,'uint8')]; -else - val=['D' data2byte(num,'uint8')]; -end -%%------------------------------------------------------------------------- -function data=I_a(num,type,dim,format) -id=find(ismember('iUIlL',type)); - -if(id==0) - error('unsupported integer array'); -end - -% based on UBJSON specs, all integer types are stored in big endian format - -if(id==1) - data=data2byte(swapbytes(int8(num)),'uint8'); - blen=1; -elseif(id==2) - data=data2byte(swapbytes(uint8(num)),'uint8'); - blen=1; -elseif(id==3) - data=data2byte(swapbytes(int16(num)),'uint8'); - blen=2; -elseif(id==4) - data=data2byte(swapbytes(int32(num)),'uint8'); - blen=4; -elseif(id==5) - data=data2byte(swapbytes(int64(num)),'uint8'); - blen=8; -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/blen)) data(:)']; - end - data=['[' data(:)']; -else - data=reshape(data,blen,numel(data)/blen); - data(2:blen+1,:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function data=D_a(num,type,dim,format) -id=find(ismember('dD',type)); - -if(id==0) - error('unsupported float array'); -end - -if(id==1) - data=data2byte(single(num),'uint8'); -elseif(id==2) - data=data2byte(double(num),'uint8'); -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)']; - end - data=['[' data]; -else - data=reshape(data,(id*4),length(data)/(id*4)); - data(2:(id*4+1),:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function bytes=data2byte(varargin) -bytes=typecast(varargin{:}); -bytes=bytes(:)'; diff --git a/assignments/machine-learning-ex2/ex2/lib/jsonlab/varargin2struct.m b/assignments/machine-learning-ex2/ex2/lib/jsonlab/varargin2struct.m deleted file mode 100644 index 9a5c2b6..0000000 --- a/assignments/machine-learning-ex2/ex2/lib/jsonlab/varargin2struct.m +++ /dev/null @@ -1,40 +0,0 @@ -function opt=varargin2struct(varargin) -% -% opt=varargin2struct('param1',value1,'param2',value2,...) -% or -% opt=varargin2struct(...,optstruct,...) -% -% convert a series of input parameters into a structure -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% 'param', value: the input parameters should be pairs of a string and a value -% optstruct: if a parameter is a struct, the fields will be merged to the output struct -% -% output: -% opt: a struct where opt.param1=value1, opt.param2=value2 ... -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -len=length(varargin); -opt=struct; -if(len==0) return; end -i=1; -while(i<=len) - if(isstruct(varargin{i})) - opt=mergestruct(opt,varargin{i}); - elseif(ischar(varargin{i}) && i3 matrix, where the first column is all-ones - -% Plot Data -plotData(X(:,2:3), y); -hold on - -if size(X, 2) <= 3 - % Only need 2 points to define a line, so choose two endpoints - plot_x = [min(X(:,2))-2, max(X(:,2))+2]; - - % Calculate the decision boundary line - plot_y = (-1./theta(3)).*(theta(2).*plot_x + theta(1)); - - % Plot, and adjust axes for better viewing - plot(plot_x, plot_y) - - % Legend, specific for the exercise - legend('Admitted', 'Not admitted', 'Decision Boundary') - axis([30, 100, 30, 100]) -else - % Here is the grid range - u = linspace(-1, 1.5, 50); - v = linspace(-1, 1.5, 50); - - z = zeros(length(u), length(v)); - % Evaluate z = theta*x over the grid - for i = 1:length(u) - for j = 1:length(v) - z(i,j) = mapFeature(u(i), v(j))*theta; - end - end - z = z'; % important to transpose z before calling contour - - % Plot z = 0 - % Notice you need to specify the range [0, 0] - contour(u, v, z, [0, 0], 'LineWidth', 2) -end -hold off - -end diff --git a/assignments/machine-learning-ex2/ex2/predict.m b/assignments/machine-learning-ex2/ex2/predict.m deleted file mode 100644 index 9432fba..0000000 --- a/assignments/machine-learning-ex2/ex2/predict.m +++ /dev/null @@ -1,27 +0,0 @@ -function p = predict(theta, X) -%PREDICT Predict whether the label is 0 or 1 using learned logistic -%regression parameters theta -% p = PREDICT(theta, X) computes the predictions for X using a -% threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1) - -m = size(X, 1); % Number of training examples - -% You need to return the following variables correctly -p = zeros(m, 1); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Complete the following code to make predictions using -% your learned logistic regression parameters. -% You should set p to a vector of 0's and 1's -% - -prob = sigmoid(X * theta); -pos = find(prob >= 0.5); -p(pos,1) = 1; - - - -% ========================================================================= - - -end diff --git a/assignments/machine-learning-ex2/ex2/sigmoid.m b/assignments/machine-learning-ex2/ex2/sigmoid.m deleted file mode 100644 index b36dbb8..0000000 --- a/assignments/machine-learning-ex2/ex2/sigmoid.m +++ /dev/null @@ -1,19 +0,0 @@ -function g = sigmoid(z) -%SIGMOID Compute sigmoid functoon -% J = SIGMOID(z) computes the sigmoid of z. - -% You need to return the following variables correctly -g = zeros(size(z)); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the sigmoid of each value of z (z can be a matrix, -% vector or scalar). - -%g = 1 ./ (1 .+ e .^ -z); -g = 1.0 ./ (1.0 + exp(-z));%exp(n)-->e的n次方 - - - -% ============================================================= - -end diff --git a/assignments/machine-learning-ex2/ex2/submit.m b/assignments/machine-learning-ex2/ex2/submit.m deleted file mode 100644 index 8ae80d6..0000000 --- a/assignments/machine-learning-ex2/ex2/submit.m +++ /dev/null @@ -1,62 +0,0 @@ -function submit() - addpath('./lib'); - - conf.assignmentSlug = 'logistic-regression'; - conf.itemName = 'Logistic Regression'; - conf.partArrays = { ... - { ... - '1', ... - { 'sigmoid.m' }, ... - 'Sigmoid Function', ... - }, ... - { ... - '2', ... - { 'costFunction.m' }, ... - 'Logistic Regression Cost', ... - }, ... - { ... - '3', ... - { 'costFunction.m' }, ... - 'Logistic Regression Gradient', ... - }, ... - { ... - '4', ... - { 'predict.m' }, ... - 'Predict', ... - }, ... - { ... - '5', ... - { 'costFunctionReg.m' }, ... - 'Regularized Logistic Regression Cost', ... - }, ... - { ... - '6', ... - { 'costFunctionReg.m' }, ... - 'Regularized Logistic Regression Gradient', ... - }, ... - }; - conf.output = @output; - - submitWithConfiguration(conf); -end - -function out = output(partId, auxstring) - % Random Test Cases - X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))']; - y = sin(X(:,1) + X(:,2)) > 0; - if partId == '1' - out = sprintf('%0.5f ', sigmoid(X)); - elseif partId == '2' - out = sprintf('%0.5f ', costFunction([0.25 0.5 -0.5]', X, y)); - elseif partId == '3' - [cost, grad] = costFunction([0.25 0.5 -0.5]', X, y); - out = sprintf('%0.5f ', grad); - elseif partId == '4' - out = sprintf('%0.5f ', predict([0.25 0.5 -0.5]', X)); - elseif partId == '5' - out = sprintf('%0.5f ', costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1)); - elseif partId == '6' - [cost, grad] = costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1); - out = sprintf('%0.5f ', grad); - end -end diff --git a/assignments/machine-learning-ex2/ex2/token.mat b/assignments/machine-learning-ex2/ex2/token.mat deleted file mode 100644 index 8532c88..0000000 --- a/assignments/machine-learning-ex2/ex2/token.mat +++ /dev/null @@ -1,15 +0,0 @@ -# Created by Octave 4.2.0, Sat Nov 19 20:17:18 2016 GMT -# name: email -# type: sq_string -# elements: 1 -# length: 17 -scruel@vip.qq.com - - -# name: token -# type: sq_string -# elements: 1 -# length: 16 -A3Wc4pI1qYjCUaRB - - diff --git a/assignments/machine-learning-ex3/ex3.pdf b/assignments/machine-learning-ex3/ex3.pdf deleted file mode 100644 index 35b0ff6..0000000 Binary files a/assignments/machine-learning-ex3/ex3.pdf and /dev/null differ diff --git a/assignments/machine-learning-ex3/ex3/displayData.m b/assignments/machine-learning-ex3/ex3/displayData.m deleted file mode 100644 index 160697e..0000000 --- a/assignments/machine-learning-ex3/ex3/displayData.m +++ /dev/null @@ -1,59 +0,0 @@ -function [h, display_array] = displayData(X, example_width) -%DISPLAYDATA Display 2D data in a nice grid -% [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data -% stored in X in a nice grid. It returns the figure handle h and the -% displayed array if requested. - -% Set example_width automatically if not passed in -if ~exist('example_width', 'var') || isempty(example_width) - example_width = round(sqrt(size(X, 2))); -end - -% Gray Image -colormap(gray); - -% Compute rows, cols -[m n] = size(X); -example_height = (n / example_width); - -% Compute number of items to display -display_rows = floor(sqrt(m)); -display_cols = ceil(m / display_rows); - -% Between images padding -pad = 1; - -% Setup blank display -display_array = - ones(pad + display_rows * (example_height + pad), ... - pad + display_cols * (example_width + pad)); - -% Copy each example into a patch on the display array -curr_ex = 1; -for j = 1:display_rows - for i = 1:display_cols - if curr_ex > m, - break; - end - % Copy the patch - - % Get the max value of the patch - max_val = max(abs(X(curr_ex, :))); - display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ... - pad + (i - 1) * (example_width + pad) + (1:example_width)) = ... - reshape(X(curr_ex, :), example_height, example_width) / max_val; - curr_ex = curr_ex + 1; - end - if curr_ex > m, - break; - end -end - -% Display Image -h = imagesc(display_array, [-1 1]); - -% Do not show axis -axis image off - -drawnow; - -end diff --git a/assignments/machine-learning-ex3/ex3/ex3.m b/assignments/machine-learning-ex3/ex3/ex3.m deleted file mode 100644 index e0f6c0c..0000000 --- a/assignments/machine-learning-ex3/ex3/ex3.m +++ /dev/null @@ -1,69 +0,0 @@ -%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all - -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% linear exercise. You will need to complete the following functions -% in this exericse: -% -% lrCostFunction.m (logistic regression cost function) -% oneVsAll.m -% predictOneVsAll.m -% predict.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% Setup the parameters you will use for this part of the exercise -input_layer_size = 400; % 20x20 Input Images of Digits -num_labels = 10; % 10 labels, from 1 to 10 ~k - % (note that we have mapped "0" to label 10) - -%% =========== Part 1: Loading and Visualizing Data ============= -% We start the exercise by first loading and visualizing the dataset. -% You will be working with a dataset that contains handwritten digits. -% - -% Load Training Data -fprintf('Loading and Visualizing Data ...\n') - -load('ex3data1.mat'); % training data stored in arrays X, y -m = size(X, 1); - -% Randomly select 100 data points to display -%rand_indices = randperm(m); -%sel = X(rand_indices(1:100), :); - -%displayData(sel); - -%fprintf('Program paused. Press enter to continue.\n'); -%pause; - -%% ============ Part 2: Vectorize Logistic Regression ============ -% In this part of the exercise, you will reuse your logistic regression -% code from the last exercise. You task here is to make sure that your -% regularized logistic regression implementation is vectorized. After -% that, you will implement one-vs-all classification for the handwritten -% digit dataset. -% - -fprintf('\nTraining One-vs-All Logistic Regression...\n') - -lambda = 0.1; -[all_theta] = oneVsAll(X, y, num_labels, lambda); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% ================ Part 3: Predict for One-Vs-All ================ -% After ... -pred = predictOneVsAll(all_theta, X); - -fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100); - diff --git a/assignments/machine-learning-ex3/ex3/ex3_nn.m b/assignments/machine-learning-ex3/ex3/ex3_nn.m deleted file mode 100644 index cadd042..0000000 --- a/assignments/machine-learning-ex3/ex3/ex3_nn.m +++ /dev/null @@ -1,88 +0,0 @@ -%% Machine Learning Online Class - Exercise 3 | Part 2: Neural Networks - -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% linear exercise. You will need to complete the following functions -% in this exericse: -% -% lrCostFunction.m (logistic regression cost function) -% oneVsAll.m -% predictOneVsAll.m -% predict.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% Setup the parameters you will use for this exercise -input_layer_size = 400; % 20x20 Input Images of Digits -hidden_layer_size = 25; % 25 hidden units -num_labels = 10; % 10 labels, from 1 to 10 - % (note that we have mapped "0" to label 10) - -%% =========== Part 1: Loading and Visualizing Data ============= -% We start the exercise by first loading and visualizing the dataset. -% You will be working with a dataset that contains handwritten digits. -% - -% Load Training Data -fprintf('Loading and Visualizing Data ...\n') - -load('ex3data1.mat'); -m = size(X, 1); - -% Randomly select 100 data points to display -sel = randperm(size(X, 1)); -sel = sel(1:100); - -displayData(X(sel, :)); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ================ Part 2: Loading Pameters ================ -% In this part of the exercise, we load some pre-initialized -% neural network parameters. - -fprintf('\nLoading Saved Neural Network Parameters ...\n') - -% Load the weights into variables Theta1 and Theta2 -load('ex3weights.mat'); - -%% ================= Part 3: Implement Predict ================= -% After training the neural network, we would like to use it to predict -% the labels. You will now implement the "predict" function to use the -% neural network to predict the labels of the training set. This lets -% you compute the training set accuracy. -%Theta1,Theta2 = fmincg(@CostFunction, initial_theta, option) -pred = predict(Theta1, Theta2, X); - -fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -% To give you an idea of the network's output, you can also run -% through the examples one at the a time to see what it is predicting. - -% Randomly permute examples -rp = randperm(m); - -for i = 1:m - % Display - fprintf('\nDisplaying Example Image\n'); - displayData(X(rp(i), :)); - - pred = predict(Theta1, Theta2, X(rp(i),:)); - fprintf('\nNeural Network Prediction: %d (digit %d)\n', pred, mod(pred, 10)); - - % Pause - fprintf('Program paused. Press enter to continue.\n'); - pause; -end - diff --git a/assignments/machine-learning-ex3/ex3/ex3data1.mat b/assignments/machine-learning-ex3/ex3/ex3data1.mat deleted file mode 100644 index 371bd0c..0000000 Binary files a/assignments/machine-learning-ex3/ex3/ex3data1.mat and /dev/null differ diff --git a/assignments/machine-learning-ex3/ex3/ex3weights.mat b/assignments/machine-learning-ex3/ex3/ex3weights.mat deleted file mode 100644 index ace2a09..0000000 Binary files a/assignments/machine-learning-ex3/ex3/ex3weights.mat and /dev/null differ diff --git a/assignments/machine-learning-ex3/ex3/fmincg.m b/assignments/machine-learning-ex3/ex3/fmincg.m deleted file mode 100644 index 47a8816..0000000 --- a/assignments/machine-learning-ex3/ex3/fmincg.m +++ /dev/null @@ -1,175 +0,0 @@ -function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) -% Minimize a continuous differentialble multivariate function. Starting point -% is given by "X" (D by 1), and the function named in the string "f", must -% return a function value and a vector of partial derivatives. The Polack- -% Ribiere flavour of conjugate gradients is used to compute search directions, -% and a line search using quadratic and cubic polynomial approximations and the -% Wolfe-Powell stopping criteria is used together with the slope ratio method -% for guessing initial step sizes. Additionally a bunch of checks are made to -% make sure that exploration is taking place and that extrapolation will not -% be unboundedly large. The "length" gives the length of the run: if it is -% positive, it gives the maximum number of line searches, if negative its -% absolute gives the maximum allowed number of function evaluations. You can -% (optionally) give "length" a second component, which will indicate the -% reduction in function value to be expected in the first line-search (defaults -% to 1.0). The function returns when either its length is up, or if no further -% progress can be made (ie, we are at a minimum, or so close that due to -% numerical problems, we cannot get any closer). If the function terminates -% within a few iterations, it could be an indication that the function value -% and derivatives are not consistent (ie, there may be a bug in the -% implementation of your "f" function). The function returns the found -% solution "X", a vector of function values "fX" indicating the progress made -% and "i" the number of iterations (line searches or function evaluations, -% depending on the sign of "length") used. -% -% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) -% -% See also: checkgrad -% -% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13 -% -% -% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen -% -% Permission is granted for anyone to copy, use, or modify these -% programs and accompanying documents for purposes of research or -% education, provided this copyright notice is retained, and note is -% made of any changes that have been made. -% -% These programs and documents are distributed without any warranty, -% express or implied. As the programs were written for research -% purposes only, they have not been tested to the degree that would be -% advisable in any important application. All use of these programs is -% entirely at the user's own risk. -% -% [ml-class] Changes Made: -% 1) Function name and argument specifications -% 2) Output display -% - -% Read options -if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter') - length = options.MaxIter; -else - length = 100; -end - - -RHO = 0.01; % a bunch of constants for line searches -SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions -INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket -EXT = 3.0; % extrapolate maximum 3 times the current bracket -MAX = 20; % max 20 function evaluations per line search -RATIO = 100; % maximum allowed slope ratio - -argstr = ['feval(f, X']; % compose string used to call function -for i = 1:(nargin - 3) - argstr = [argstr, ',P', int2str(i)]; -end -argstr = [argstr, ')']; - -if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end -S=['Iteration ']; - -i = 0; % zero the run length counter -ls_failed = 0; % no previous line search has failed -fX = []; -[f1 df1] = eval(argstr); % get function value and gradient -i = i + (length<0); % count epochs?! -s = -df1; % search direction is steepest -d1 = -s'*s; % this is the slope -z1 = red/(1-d1); % initial step is red/(|s|+1) - -while i < abs(length) % while not finished - i = i + (length>0); % count iterations?! - - X0 = X; f0 = f1; df0 = df1; % make a copy of current values - X = X + z1*s; % begin line search - [f2 df2] = eval(argstr); - i = i + (length<0); % count epochs?! - d2 = df2'*s; - f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1 - if length>0, M = MAX; else M = min(MAX, -length-i); end - success = 0; limit = -1; % initialize quanteties - while 1 - while ((f2 > f1+z1*RHO*d1) || (d2 > -SIG*d1)) && (M > 0) - limit = z1; % tighten the bracket - if f2 > f1 - z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit - else - A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit - B = 3*(f3-f2)-z3*(d3+2*d2); - z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok! - end - if isnan(z2) || isinf(z2) - z2 = z3/2; % if we had a numerical problem then bisect - end - z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits - z1 = z1 + z2; % update the step - X = X + z2*s; - [f2 df2] = eval(argstr); - M = M - 1; i = i + (length<0); % count epochs?! - d2 = df2'*s; - z3 = z3-z2; % z3 is now relative to the location of z2 - end - if f2 > f1+z1*RHO*d1 || d2 > -SIG*d1 - break; % this is a failure - elseif d2 > SIG*d1 - success = 1; break; % success - elseif M == 0 - break; % failure - end - A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation - B = 3*(f3-f2)-z3*(d3+2*d2); - z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok! - if ~isreal(z2) || isnan(z2) || isinf(z2) || z2 < 0 % num prob or wrong sign? - if limit < -0.5 % if we have no upper limit - z2 = z1 * (EXT-1); % the extrapolate the maximum amount - else - z2 = (limit-z1)/2; % otherwise bisect - end - elseif (limit > -0.5) && (z2+z1 > limit) % extraplation beyond max? - z2 = (limit-z1)/2; % bisect - elseif (limit < -0.5) && (z2+z1 > z1*EXT) % extrapolation beyond limit - z2 = z1*(EXT-1.0); % set to extrapolation limit - elseif z2 < -z3*INT - z2 = -z3*INT; - elseif (limit > -0.5) && (z2 < (limit-z1)*(1.0-INT)) % too close to limit? - z2 = (limit-z1)*(1.0-INT); - end - f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2 - z1 = z1 + z2; X = X + z2*s; % update current estimates - [f2 df2] = eval(argstr); - M = M - 1; i = i + (length<0); % count epochs?! - d2 = df2'*s; - end % end of line search - - if success % if line search succeeded - f1 = f2; fX = [fX' f1]'; - fprintf('%s %4i | Cost: %4.6e\r', S, i, f1); - s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction - tmp = df1; df1 = df2; df2 = tmp; % swap derivatives - d2 = df1'*s; - if d2 > 0 % new slope must be negative - s = -df1; % otherwise use steepest direction - d2 = -s'*s; - end - z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO - d1 = d2; - ls_failed = 0; % this line search did not fail - else - X = X0; f1 = f0; df1 = df0; % restore point from before failed line search - if ls_failed || i > abs(length) % line search failed twice in a row - break; % or we ran out of time, so we give up - end - tmp = df1; df1 = df2; df2 = tmp; % swap derivatives - s = -df1; % try steepest - d1 = -s'*s; - z1 = 1/(1-d1); - ls_failed = 1; % this line search failed - end - if exist('OCTAVE_VERSION') - fflush(stdout); - end -end -fprintf('\n'); diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/AUTHORS.txt b/assignments/machine-learning-ex3/ex3/lib/jsonlab/AUTHORS.txt deleted file mode 100644 index 9dd3fc7..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/AUTHORS.txt +++ /dev/null @@ -1,41 +0,0 @@ -The author of "jsonlab" toolbox is Qianqian Fang. Qianqian -is currently an Assistant Professor at Massachusetts General Hospital, -Harvard Medical School. - -Address: Martinos Center for Biomedical Imaging, - Massachusetts General Hospital, - Harvard Medical School - Bldg 149, 13th St, Charlestown, MA 02129, USA -URL: http://nmr.mgh.harvard.edu/~fangq/ -Email: or - - -The script loadjson.m was built upon previous works by - -- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 - date: 2009/11/02 -- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 - date: 2009/03/22 -- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565 - date: 2008/07/03 - - -This toolbox contains patches submitted by the following contributors: - -- Blake Johnson - part of revision 341 - -- Niclas Borlin - various fixes in revision 394, including - - loadjson crashes for all-zero sparse matrix. - - loadjson crashes for empty sparse matrix. - - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson. - - loadjson crashes for sparse real column vector. - - loadjson crashes for sparse complex column vector. - - Data is corrupted by savejson for sparse real row vector. - - savejson crashes for sparse complex row vector. - -- Yul Kang - patches for svn revision 415. - - savejson saves an empty cell array as [] instead of null - - loadjson differentiates an empty struct from an empty array diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/ChangeLog.txt b/assignments/machine-learning-ex3/ex3/lib/jsonlab/ChangeLog.txt deleted file mode 100644 index 07824f5..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/ChangeLog.txt +++ /dev/null @@ -1,74 +0,0 @@ -============================================================================ - - JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave - ----------------------------------------------------------------------------- - -JSONlab ChangeLog (key features marked by *): - -== JSONlab 1.0 (codename: Optimus - Final), FangQ == - - 2015/01/02 polish help info for all major functions, update examples, finalize 1.0 - 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson - -== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ == - - 2014/11/22 show progress bar in loadjson ('ShowProgress') - 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact') - 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels - 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab - -== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ == - - 2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8 - 2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson - 2014/08/04 escape special characters in a JSON string - 2014/02/16 fix a bug when saving ubjson files - -== JSONlab 0.9.9 (codename: Optimus - beta), FangQ == - - 2014/01/22 use binary read and write in saveubjson and loadubjson - -== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ == - - 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang) - -== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ == - 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson - -== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ == - 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin) - -== JSONlab 0.9.0 (codename: Rodimus), FangQ == - - 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson - 2012/06/01 support JSONP in savejson - 2012/05/25 fix the empty cell bug (reported by Cyril Davin) - 2012/04/05 savejson can save to a file (suggested by Patrick Rapin) - -== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ == - - 2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS - 2012/01/25 patch to handle root-less objects, contributed by Blake Johnson - -== JSONlab 0.8.0 (codename: Sentiel), FangQ == - - 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab - 2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer - 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson - 2011/11/18 fix struct array bug reported by Mykel Kochenderfer - -== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ == - - 2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration - 2011/10/20 loadjson supports JSON collections - concatenated JSON objects - -== JSONlab 0.5.0 (codename: Nexus), FangQ == - - 2011/10/16 package and release jsonlab 0.5.0 - 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug - 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level - 2011/10/10 create jsonlab project, start jsonlab website, add online documentation - 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support - 2011/10/06 *savejson works for structs, cells and arrays - 2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/LICENSE_BSD.txt b/assignments/machine-learning-ex3/ex3/lib/jsonlab/LICENSE_BSD.txt deleted file mode 100644 index 32d66cb..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/LICENSE_BSD.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright 2011-2015 Qianqian Fang . All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are -permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, this list - of conditions and the following disclaimer in the documentation and/or other materials - provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND -FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS -OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -The views and conclusions contained in the software and documentation are those of the -authors and should not be interpreted as representing official policies, either expressed -or implied, of the copyright holders. diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/README.txt b/assignments/machine-learning-ex3/ex3/lib/jsonlab/README.txt deleted file mode 100644 index 7b4f732..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/README.txt +++ /dev/null @@ -1,394 +0,0 @@ -=============================================================================== -= JSONLab = -= An open-source MATLAB/Octave JSON encoder and decoder = -=============================================================================== - -*Copyright (C) 2011-2015 Qianqian Fang -*License: BSD License, see License_BSD.txt for details -*Version: 1.0 (Optimus - Final) - -------------------------------------------------------------------------------- - -Table of Content: - -I. Introduction -II. Installation -III.Using JSONLab -IV. Known Issues and TODOs -V. Contribution and feedback - -------------------------------------------------------------------------------- - -I. Introduction - -JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, -human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format -to represent complex and hierarchical data. It is as powerful as -[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely -used for data-exchange in applications, and is essential for the wild success -of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and -[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. - -UBJSON (Universal Binary JSON) is a binary JSON format, specifically -optimized for compact file size and better performance while keeping -the semantics as simple as the text-based JSON format. Using the UBJSON -format allows to wrap complex binary data in a flexible and extensible -structure, making it possible to process complex and large dataset -without accuracy loss due to text conversions. - -We envision that both JSON and its binary version will serve as part of -the mainstream data-exchange formats for scientific research in the future. -It will provide the flexibility and generality achieved by other popular -general-purpose file specifications, such as -[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly -reduced complexity and enhanced performance. - -JSONLab is a free and open-source implementation of a JSON/UBJSON encoder -and a decoder in the native MATLAB language. It can be used to convert a MATLAB -data structure (array, struct, cell, struct array and cell array) into -JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB -data structure. JSONLab supports both MATLAB and -[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone). - -------------------------------------------------------------------------------- - -II. Installation - -The installation of JSONLab is no different than any other simple -MATLAB toolbox. You only need to download/unzip the JSONLab package -to a folder, and add the folder's path to MATLAB/Octave's path list -by using the following command: - - addpath('/path/to/jsonlab'); - -If you want to add this path permanently, you need to type "pathtool", -browse to the jsonlab root folder and add to the list, then click "Save". -Then, run "rehash" in MATLAB, and type "which loadjson", if you see an -output, that means JSONLab is installed for MATLAB/Octave. - -------------------------------------------------------------------------------- - -III.Using JSONLab - -JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, -and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and -two equivallent functions -- loadubjson and saveubjson for the binary -JSON. The detailed help info for the four functions can be found below: - -=== loadjson.m === -
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          Franois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-
- -=== savejson.m === - -
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-
- -=== loadubjson.m === - -
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-
- -=== saveubjson.m === - -
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-
- - -=== examples === - -Under the "examples" folder, you can find several scripts to demonstrate the -basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you -will see the conversions from MATLAB data structure to JSON text and backward. -In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet -and validate the loadjson/savejson functions for regression testing purposes. -Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson -and loadubjson pairs for various matlab data structures. - -Please run these examples and understand how JSONLab works before you use -it to process your data. - -------------------------------------------------------------------------------- - -IV. Known Issues and TODOs - -JSONLab has several known limitations. We are striving to make it more general -and robust. Hopefully in a few future releases, the limitations become less. - -Here are the known issues: - -# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays; -# When processing names containing multi-byte characters, Octave and MATLAB \ -can give different field-names; you can use feature('DefaultCharacterSet','latin1') \ -in MATLAB to get consistant results -# savejson can not handle class and dataset. -# saveubjson converts a logical array into a uint8 ([U]) array -# an unofficial N-D array count syntax is implemented in saveubjson. We are \ -actively communicating with the UBJSON spec maintainer to investigate the \ -possibility of making it upstream -# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \ -files, however, it can parse all UBJSON files produced by saveubjson. - -------------------------------------------------------------------------------- - -V. Contribution and feedback - -JSONLab is an open-source project. This means you can not only use it and modify -it as you wish, but also you can contribute your changes back to JSONLab so -that everyone else can enjoy the improvement. For anyone who want to contribute, -please download JSONLab source code from it's subversion repository by using the -following command: - - svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab - -You can make changes to the files as needed. Once you are satisfied with your -changes, and ready to share it with others, please cd the root directory of -JSONLab, and type - - svn diff > yourname_featurename.patch - -You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at -the email address shown in the beginning of this file. Qianqian will review -the changes and commit it to the subversion if they are satisfactory. - -We appreciate any suggestions and feedbacks from you. Please use iso2mesh's -mailing list to report any questions you may have with JSONLab: - -http://groups.google.com/group/iso2mesh-users?hl=en&pli=1 - -(Subscription to the mailing list is needed in order to post messages). diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/jsonopt.m b/assignments/machine-learning-ex3/ex3/lib/jsonlab/jsonopt.m deleted file mode 100644 index 0bebd8d..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/jsonopt.m +++ /dev/null @@ -1,32 +0,0 @@ -function val=jsonopt(key,default,varargin) -% -% val=jsonopt(key,default,optstruct) -% -% setting options based on a struct. The struct can be produced -% by varargin2struct from a list of 'param','value' pairs -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% -% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $ -% -% input: -% key: a string with which one look up a value from a struct -% default: if the key does not exist, return default -% optstruct: a struct where each sub-field is a key -% -% output: -% val: if key exists, val=optstruct.key; otherwise val=default -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -val=default; -if(nargin<=2) return; end -opt=varargin{1}; -if(isstruct(opt) && isfield(opt,key)) - val=getfield(opt,key); -end - diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m b/assignments/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m deleted file mode 100644 index 42798c0..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m +++ /dev/null @@ -1,566 +0,0 @@ -function data = loadjson(fname,varargin) -% -% data=loadjson(fname,opt) -% or -% data=loadjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09, including previous works from -% -% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 -% created on 2009/11/02 -% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 -% created on 2009/03/22 -% Joel Feenstra: -% http://www.mathworks.com/matlabcentral/fileexchange/20565 -% created on 2008/07/03 -% -% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a JSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.FastArrayParser [1|0 or integer]: if set to 1, use a -% speed-optimized array parser when loading an -% array object. The fast array parser may -% collapse block arrays into a single large -% array similar to rules defined in cell2mat; 0 to -% use a legacy parser; if set to a larger-than-1 -% value, this option will specify the minimum -% dimension to enable the fast array parser. For -% example, if the input is a 3D array, setting -% FastArrayParser to 1 will return a 3D array; -% setting to 2 will return a cell array of 2D -% arrays; setting to 3 will return to a 2D cell -% array of 1D vectors; setting to 4 will return a -% 3D cell array. -% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}') -% dat=loadjson(['examples' filesep 'example1.json']) -% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); - -if(jsonopt('ShowProgress',0,opt)==1) - opt.progressbar_=waitbar(0,'loading ...'); -end -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end -if(isfield(opt,'progressbar_')) - close(opt.progressbar_); -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=data(j).x0x5F_ArraySize_; - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - if next_char ~= '}' - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - parse_char(':'); - val = parse_value(varargin{:}); - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' - break; - end - parse_char(','); - end - end - parse_char('}'); - -%%------------------------------------------------------------------------- - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim2=[]; - arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:}); - pbar=jsonopt('progressbar_',-1,varargin{:}); - - if next_char ~= ']' - if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:})) - [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos); - arraystr=['[' inStr(pos:endpos)]; - arraystr=regexprep(arraystr,'"_NaN_"','NaN'); - arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf'); - arraystr(arraystr==sprintf('\n'))=[]; - arraystr(arraystr==sprintf('\r'))=[]; - %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed - if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D - astr=inStr((e1l+1):(e1r-1)); - astr=regexprep(astr,'"_NaN_"','NaN'); - astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf'); - astr(astr==sprintf('\n'))=[]; - astr(astr==sprintf('\r'))=[]; - astr(astr==' ')=''; - if(isempty(find(astr=='[', 1))) % array is 2D - dim2=length(sscanf(astr,'%f,',[1 inf])); - end - else % array is 1D - astr=arraystr(2:end-1); - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]); - if(nextidx>=length(astr)-1) - object=obj; - pos=endpos; - parse_char(']'); - return; - end - end - if(~isempty(dim2)) - astr=arraystr; - astr(astr=='[')=''; - astr(astr==']')=''; - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf); - if(nextidx>=length(astr)-1) - object=reshape(obj,dim2,numel(obj)/dim2)'; - pos=endpos; - parse_char(']'); - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end - return; - end - end - arraystr=regexprep(arraystr,'\]\s*,','];'); - else - arraystr='['; - end - try - if(isoct && regexp(arraystr,'"','once')) - error('Octave eval can produce empty cells for JSON-like input'); - end - object=eval(arraystr); - pos=endpos; - catch - while 1 - newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1); - val = parse_value(newopt); - object{end+1} = val; - if next_char == ']' - break; - end - parse_char(','); - end - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - parse_char(']'); - - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr len esc index_esc len_esc - % len, ns = length(inStr), keyboard - if inStr(pos) ~= '"' - error_pos('String starting with " expected at position %d'); - else - pos = pos + 1; - end - str = ''; - while pos <= len - while index_esc <= len_esc && esc(index_esc) < pos - index_esc = index_esc + 1; - end - if index_esc > len_esc - str = [str inStr(pos:len)]; - pos = len + 1; - break; - else - str = [str inStr(pos:esc(index_esc)-1)]; - pos = esc(index_esc); - end - nstr = length(str); switch inStr(pos) - case '"' - pos = pos + 1; - if(~isempty(str)) - if(strcmp(str,'_Inf_')) - str=Inf; - elseif(strcmp(str,'-_Inf_')) - str=-Inf; - elseif(strcmp(str,'_NaN_')) - str=NaN; - end - end - return; - case '\' - if pos+1 > len - error_pos('End of file reached right after escape character'); - end - pos = pos + 1; - switch inStr(pos) - case {'"' '\' '/'} - str(nstr+1) = inStr(pos); - pos = pos + 1; - case {'b' 'f' 'n' 'r' 't'} - str(nstr+1) = sprintf(['\' inStr(pos)]); - pos = pos + 1; - case 'u' - if pos+4 > len - error_pos('End of file reached in escaped unicode character'); - end - str(nstr+(1:6)) = inStr(pos-1:pos+4); - pos = pos + 5; - end - otherwise % should never happen - str(nstr+1) = inStr(pos), keyboard - pos = pos + 1; - end - end - error_pos('End of file while expecting end of inStr'); - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct - currstr=inStr(pos:end); - numstr=0; - if(isoct~=0) - numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end'); - [num, one] = sscanf(currstr, '%f', 1); - delta=numstr+1; - else - [num, one, err, delta] = sscanf(currstr, '%f', 1); - if ~isempty(err) - error_pos('Error reading number at position %d'); - end - end - pos = pos + delta-1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - pbar=jsonopt('progressbar_',-1,varargin{:}); - if(pbar>0) - waitbar(pos/len,pbar,'loading ...'); - end - - switch(inStr(pos)) - case '"' - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'-','0','1','2','3','4','5','6','7','8','9'} - val = parse_number(varargin{:}); - return; - case 't' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true') - val = true; - pos = pos + 4; - return; - end - case 'f' - if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false') - val = false; - pos = pos + 5; - return; - end - case 'n' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null') - val = []; - pos = pos + 4; - return; - end - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m b/assignments/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m deleted file mode 100644 index 0155115..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m +++ /dev/null @@ -1,528 +0,0 @@ -function data = loadubjson(fname,varargin) -% -% data=loadubjson(fname,opt) -% or -% data=loadubjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/01 -% -% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a UBJSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.IntEndian [B|L]: specify the endianness of the integer fields -% in the UBJSON input data. B - Big-Endian format for -% integers (as required in the UBJSON specification); -% L - input integer fields are in Little-Endian order. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% obj=struct('string','value','array',[1 2 3]); -% ubjdata=saveubjson('obj',obj); -% dat=loadubjson(ubjdata) -% dat=loadubjson(['examples' filesep 'example1.ubj']) -% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); -fileendian=upper(jsonopt('IntEndian','B',opt)); -[os,maxelem,systemendian]=computer; - -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end - - -%% -function newdata=parse_collection(id,data,obj) - -if(jsoncount>0 && exist('data','var')) - if(~iscell(data)) - newdata=cell(1); - newdata{1}=data; - data=newdata; - end -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=double(data(j).x0x5F_ArraySize_); - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); % TODO - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - count=double(parse_number()); - end - if next_char ~= '}' - num=0; - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - %parse_char(':'); - val = parse_value(varargin{:}); - num=num+1; - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' || (count>=0 && num>=count) - break; - end - %parse_char(','); - end - end - if(count==-1) - parse_char('}'); - end - -%%------------------------------------------------------------------------- -function [cid,len]=elem_info(type) -id=strfind('iUIlLdD',type); -dataclass={'int8','uint8','int16','int32','int64','single','double'}; -bytelen=[1,1,2,4,8,4,8]; -if(id>0) - cid=dataclass{id}; - len=bytelen(id); -else - error_pos('unsupported type at position %d'); -end -%%------------------------------------------------------------------------- - - -function [data adv]=parse_block(type,count,varargin) -global pos inStr isoct fileendian systemendian -[cid,len]=elem_info(type); -datastr=inStr(pos:pos+len*count-1); -if(isoct) - newdata=int8(datastr); -else - newdata=uint8(datastr); -end -id=strfind('iUIlLdD',type); -if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,cid)); -end -data=typecast(newdata,cid); -adv=double(len*count); - -%%------------------------------------------------------------------------- - - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim=[]; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - if(next_char=='[') - dim=parse_array(varargin{:}); - count=prod(double(dim)); - else - count=double(parse_number()); - end - end - if(~isempty(type)) - if(count>=0) - [object adv]=parse_block(type,count,varargin{:}); - if(~isempty(dim)) - object=reshape(object,dim); - end - pos=pos+adv; - return; - else - endpos=matching_bracket(inStr,pos); - [cid,len]=elem_info(type); - count=(endpos-pos)/len; - [object adv]=parse_block(type,count,varargin{:}); - pos=pos+adv; - parse_char(']'); - return; - end - end - if next_char ~= ']' - while 1 - val = parse_value(varargin{:}); - object{end+1} = val; - if next_char == ']' - break; - end - %parse_char(','); - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - if(count==-1) - parse_char(']'); - end - -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr esc index_esc len_esc - % len, ns = length(inStr), keyboard - type=inStr(pos); - if type ~= 'S' && type ~= 'C' && type ~= 'H' - error_pos('String starting with S expected at position %d'); - else - pos = pos + 1; - end - if(type == 'C') - str=inStr(pos); - pos=pos+1; - return; - end - bytelen=double(parse_number()); - if(length(inStr)>=pos+bytelen-1) - str=inStr(pos:pos+bytelen-1); - pos=pos+bytelen; - else - error_pos('End of file while expecting end of inStr'); - end - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct fileendian systemendian - id=strfind('iUIlLdD',inStr(pos)); - if(isempty(id)) - error_pos('expecting a number at position %d'); - end - type={'int8','uint8','int16','int32','int64','single','double'}; - bytelen=[1,1,2,4,8,4,8]; - datastr=inStr(pos+1:pos+bytelen(id)); - if(isoct) - newdata=int8(datastr); - else - newdata=uint8(datastr); - end - if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,type{id})); - end - num=typecast(newdata,type{id}); - pos = pos + bytelen(id)+1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - switch(inStr(pos)) - case {'S','C','H'} - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'i','U','I','l','L','d','D'} - val = parse_number(varargin{:}); - return; - case 'T' - val = true; - pos = pos + 1; - return; - case 'F' - val = false; - pos = pos + 1; - return; - case {'Z','N'} - val = []; - pos = pos + 1; - return; - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos e1l e1r maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/mergestruct.m b/assignments/machine-learning-ex3/ex3/lib/jsonlab/mergestruct.m deleted file mode 100644 index 6de6100..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/mergestruct.m +++ /dev/null @@ -1,33 +0,0 @@ -function s=mergestruct(s1,s2) -% -% s=mergestruct(s1,s2) -% -% merge two struct objects into one -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% s1,s2: a struct object, s1 and s2 can not be arrays -% -% output: -% s: the merged struct object. fields in s1 and s2 will be combined in s. -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(~isstruct(s1) || ~isstruct(s2)) - error('input parameters contain non-struct'); -end -if(length(s1)>1 || length(s2)>1) - error('can not merge struct arrays'); -end -fn=fieldnames(s2); -s=s1; -for i=1:length(fn) - s=setfield(s,fn{i},getfield(s2,fn{i})); -end - diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/savejson.m b/assignments/machine-learning-ex3/ex3/lib/jsonlab/savejson.m deleted file mode 100644 index 7e84076..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/savejson.m +++ /dev/null @@ -1,475 +0,0 @@ -function json=savejson(rootname,obj,varargin) -% -% json=savejson(rootname,obj,filename) -% or -% json=savejson(rootname,obj,opt) -% json=savejson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript -% Object Notation) string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09 -% -% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array). -% filename: a string for the file name to save the output JSON data. -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.FloatFormat ['%.10g'|string]: format to show each numeric element -% of a 1D/2D array; -% opt.ArrayIndent [1|0]: if 1, output explicit data array with -% precedent indentation; if 0, no indentation -% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [0|1]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern -% to represent +/-Inf. The matched pattern is '([-+]*)Inf' -% and $1 represents the sign. For those who want to use -% 1e999 to represent Inf, they can set opt.Inf to '$11e999' -% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern -% to represent NaN -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSONP='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode. -% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs) -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a string in the JSON format (see http://json.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% savejson('jmesh',jsonmesh) -% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end - -whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -if(jsonopt('Compact',0,opt)==1) - whitespaces=struct('tab','','newline','','sep',','); -end -if(~isfield(opt,'whitespaces_')) - opt.whitespaces_=whitespaces; -end - -nl=whitespaces.newline; - -json=obj2json(rootname,obj,rootlevel,opt); -if(rootisarray) - json=sprintf('%s%s',json,nl); -else - json=sprintf('{%s%s%s}\n',nl,json,nl); -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=sprintf('%s(%s);%s',jsonp,json,nl); -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - if(jsonopt('SaveBinary',0,opt)==1) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - else - fid = fopen(opt.FileName, 'wt'); - fwrite(fid,json,'char'); - end - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2json(name,item,level,varargin) - -if(iscell(item)) - txt=cell2json(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2json(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2json(name,item,level,varargin{:}); -else - txt=mat2json(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2json(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -nl=ws.newline; -if(len>1) - if(~isempty(name)) - txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; - else - txt=sprintf('%s[%s',padding0,nl); - end -elseif(len==0) - if(~isempty(name)) - txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; - else - txt=sprintf('%s[]',padding0); - end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:})); - if(i1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=struct2json(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1)); -nl=ws.newline; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding0,nl); end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); - else - txt=sprintf('%s%s{%s',txt,padding1,nl); - end - if(~isempty(names)) - for e=1:length(names) - txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})); - if(e1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=str2json(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding1,nl); end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - if(isoct) - val=regexprep(item(e,:),'\\','\\'); - val=regexprep(val,'"','\"'); - val=regexprep(val,'^"','\"'); - else - val=regexprep(item(e,:),'\\','\\\\'); - val=regexprep(val,'"','\\"'); - val=regexprep(val,'^"','\\"'); - end - val=escapejsonstring(val); - if(len==1) - obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"']; - if(isempty(name)) obj=['"',val,'"']; end - txt=sprintf('%s%s%s%s',txt,padding1,obj); - else - txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']); - end - if(e==len) sep=''; end - txt=sprintf('%s%s',txt,sep); -end -if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end - -%%------------------------------------------------------------------------- -function txt=mat2json(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:})) - if(isempty(name)) - txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - else - txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - end -else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0) - numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']',''); - else - numtxt=matdata2json(item,level+1,varargin{:}); - end - if(isempty(name)) - txt=sprintf('%s%s',padding1,numtxt); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - else - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - end - end - return; -end -dataformat='%s%s%s%s%s'; - -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep); - if(size(item,1)==1) - % Row vector, store only column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([iy(:),data'],level+2,varargin{:}), nl); - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,data],level+2,varargin{:}), nl); - else - % General case, store row and column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,iy,data],level+2,varargin{:}), nl); - end -else - if(isreal(item)) - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json(item(:)',level+2,varargin{:}), nl); - else - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl); - end -end -txt=sprintf('%s%s%s',txt,padding1,'}'); - -%%------------------------------------------------------------------------- -function txt=matdata2json(mat,level,varargin) - -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -tab=ws.tab; -nl=ws.newline; - -if(size(mat,1)==1) - pre=''; - post=''; - level=level-1; -else - pre=sprintf('[%s',nl); - post=sprintf('%s%s]',nl,repmat(tab,1,level-1)); -end - -if(isempty(mat)) - txt='null'; - return; -end -floatformat=jsonopt('FloatFormat','%.10g',varargin{:}); -%if(numel(mat)>1) - formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]]; -%else -% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]]; -%end - -if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1) - formatstr=[repmat(tab,1,level) formatstr]; -end - -txt=sprintf(formatstr,mat'); -txt(end-length(nl):end)=[]; -if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1) - txt=regexprep(txt,'1','true'); - txt=regexprep(txt,'0','false'); -end -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],\n[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -txt=[pre txt post]; -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end - -%%------------------------------------------------------------------------- -function newstr=escapejsonstring(str) -newstr=str; -isoct=exist('OCTAVE_VERSION','builtin'); -if(isoct) - vv=sscanf(OCTAVE_VERSION,'%f'); - if(vv(1)>=3.8) isoct=0; end -end -if(isoct) - escapechars={'\a','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},escapechars{i}); - end -else - escapechars={'\a','\b','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\')); - end -end diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m b/assignments/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m deleted file mode 100644 index eaec433..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m +++ /dev/null @@ -1,504 +0,0 @@ -function json=saveubjson(rootname,obj,varargin) -% -% json=saveubjson(rootname,obj,filename) -% or -% json=saveubjson(rootname,obj,opt) -% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a Universal -% Binary JSON (UBJSON) binary string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/17 -% -% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array) -% filename: a string for the file name to save the output UBJSON data -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [1|0]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSON='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a binary string in the UBJSON format (see http://ubjson.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% saveubjson('jsonmesh',jsonmesh) -% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end -json=obj2ubjson(rootname,obj,rootlevel,opt); -if(~rootisarray) - json=['{' json '}']; -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=[jsonp '(' json ')']; -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2ubjson(name,item,level,varargin) - -if(iscell(item)) - txt=cell2ubjson(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2ubjson(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2ubjson(name,item,level,varargin{:}); -else - txt=mat2ubjson(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2ubjson(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); % let's handle 1D cell first -if(len>1) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) '[']; name=''; - else - txt='['; - end -elseif(len==0) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; - else - txt='Z'; - end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})]; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=struct2ubjson(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=[txt S_(checkname(name,varargin{:})) '{']; - else - txt=[txt '{']; - end - if(~isempty(names)) - for e=1:length(names) - txt=[txt obj2ubjson(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})]; - end - end - txt=[txt '}']; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=str2ubjson(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - val=item(e,:); - if(len==1) - obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),'']; - if(isempty(name)) obj=['',S_(val),'']; end - txt=[txt,'',obj]; - else - txt=[txt,'',['',S_(val),'']]; - end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=mat2ubjson(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) || jsonopt('ArrayToStruct',0,varargin{:})) - cid=I_(uint32(max(size(item)))); - if(isempty(name)) - txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ]; - else - if(isempty(item)) - txt=[S_(checkname(name,varargin{:})),'Z']; - return; - else - txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))]; - end - end -else - if(isempty(name)) - txt=matdata2ubjson(item,level+1,varargin{:}); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']',''); - txt=[S_(checkname(name,varargin{:})) numtxt]; - else - txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})]; - end - end - return; -end -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=[txt,S_('_ArrayIsComplex_'),'T']; - end - txt=[txt,S_('_ArrayIsSparse_'),'T']; - if(size(item,1)==1) - % Row vector, store only column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([iy(:),data'],level+2,varargin{:})]; - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,data],level+2,varargin{:})]; - else - % General case, store row and column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,iy,data],level+2,varargin{:})]; - end -else - if(isreal(item)) - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson(item(:)',level+2,varargin{:})]; - else - txt=[txt,S_('_ArrayIsComplex_'),'T']; - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})]; - end -end -txt=[txt,'}']; - -%%------------------------------------------------------------------------- -function txt=matdata2ubjson(mat,level,varargin) -if(isempty(mat)) - txt='Z'; - return; -end -if(size(mat,1)==1) - level=level-1; -end -type=''; -hasnegtive=(mat<0); -if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0))) - if(isempty(hasnegtive)) - if(max(mat(:))<=2^8) - type='U'; - end - end - if(isempty(type)) - % todo - need to consider negative ones separately - id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]); - if(isempty(find(id))) - error('high-precision data is not yet supported'); - end - key='iIlL'; - type=key(find(id)); - end - txt=[I_a(mat(:),type,size(mat))]; -elseif(islogical(mat)) - logicalval='FT'; - if(numel(mat)==1) - txt=logicalval(mat+1); - else - txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')]; - end -else - if(numel(mat)==1) - txt=['[' D_(mat) ']']; - else - txt=D_a(mat(:),'D',size(mat)); - end -end - -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end -%%------------------------------------------------------------------------- -function val=S_(str) -if(length(str)==1) - val=['C' str]; -else - val=['S' I_(int32(length(str))) str]; -end -%%------------------------------------------------------------------------- -function val=I_(num) -if(~isinteger(num)) - error('input is not an integer'); -end -if(num>=0 && num<255) - val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')]; - return; -end -key='iIlL'; -cid={'int8','int16','int32','int64'}; -for i=1:4 - if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1))) - val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')]; - return; - end -end -error('unsupported integer'); - -%%------------------------------------------------------------------------- -function val=D_(num) -if(~isfloat(num)) - error('input is not a float'); -end - -if(isa(num,'single')) - val=['d' data2byte(num,'uint8')]; -else - val=['D' data2byte(num,'uint8')]; -end -%%------------------------------------------------------------------------- -function data=I_a(num,type,dim,format) -id=find(ismember('iUIlL',type)); - -if(id==0) - error('unsupported integer array'); -end - -% based on UBJSON specs, all integer types are stored in big endian format - -if(id==1) - data=data2byte(swapbytes(int8(num)),'uint8'); - blen=1; -elseif(id==2) - data=data2byte(swapbytes(uint8(num)),'uint8'); - blen=1; -elseif(id==3) - data=data2byte(swapbytes(int16(num)),'uint8'); - blen=2; -elseif(id==4) - data=data2byte(swapbytes(int32(num)),'uint8'); - blen=4; -elseif(id==5) - data=data2byte(swapbytes(int64(num)),'uint8'); - blen=8; -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/blen)) data(:)']; - end - data=['[' data(:)']; -else - data=reshape(data,blen,numel(data)/blen); - data(2:blen+1,:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function data=D_a(num,type,dim,format) -id=find(ismember('dD',type)); - -if(id==0) - error('unsupported float array'); -end - -if(id==1) - data=data2byte(single(num),'uint8'); -elseif(id==2) - data=data2byte(double(num),'uint8'); -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)']; - end - data=['[' data]; -else - data=reshape(data,(id*4),length(data)/(id*4)); - data(2:(id*4+1),:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function bytes=data2byte(varargin) -bytes=typecast(varargin{:}); -bytes=bytes(:)'; diff --git a/assignments/machine-learning-ex3/ex3/lib/jsonlab/varargin2struct.m b/assignments/machine-learning-ex3/ex3/lib/jsonlab/varargin2struct.m deleted file mode 100644 index 9a5c2b6..0000000 --- a/assignments/machine-learning-ex3/ex3/lib/jsonlab/varargin2struct.m +++ /dev/null @@ -1,40 +0,0 @@ -function opt=varargin2struct(varargin) -% -% opt=varargin2struct('param1',value1,'param2',value2,...) -% or -% opt=varargin2struct(...,optstruct,...) -% -% convert a series of input parameters into a structure -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% 'param', value: the input parameters should be pairs of a string and a value -% optstruct: if a parameter is a struct, the fields will be merged to the output struct -% -% output: -% opt: a struct where opt.param1=value1, opt.param2=value2 ... -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -len=length(varargin); -opt=struct; -if(len==0) return; end -i=1; -while(i<=len) - if(isstruct(varargin{i})) - opt=mergestruct(opt,varargin{i}); - elseif(ischar(varargin{i}) && i k - -% Some useful variables -m = size(X, 1); -n = size(X, 2); - -% You need to return the following variables correctly -all_theta = zeros(num_labels, n + 1); - -% Add ones to the X data matrix -X = [ones(m, 1) X]; - -% ====================== YOUR CODE HERE ====================== -% Instructions: You should complete the following code to train num_labels -% logistic regression classifiers with regularization -% parameter lambda. -% -% Hint: theta(:) will return a column vector. -% -% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use -% whether the ground truth is true/false for this class. -% -% Note: For this assignment, we recommend using fmincg to optimize the cost -% function. It is okay to use a for-loop (for c = 1:num_labels) to -% loop over the different classes. -% -% fmincg works similarly to fminunc, but is more efficient when we -% are dealing with large number of parameters. -% -% Example Code for fmincg: -% -% % Set Initial theta -% initial_theta = zeros(n + 1, 1); -% -% % Set options for fminunc -% options = optimset('GradObj', 'on', 'MaxIter', 50); -% -% % Run fmincg to obtain the optimal theta -% % This function will return theta and the cost -% [theta] = ... -% fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ... -% initial_theta, options); -% - -options = optimset('GradObj', 'on', 'MaxIter', 50); -for c = 1 : num_labels -initial_theta = zeros(n + 1, 1); -all_theta(c,:)= fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ... - initial_theta, options); -size(all_theta(c,:)) -end -%size(all_theta) - -%initial_theta = zeros(n + 1, num_labels); -%all_theta= fmincg (@(t)(lrCostFunction(t, X, y, lambda)), ... - % initial_theta, options); - - - - - - -% ========================================================================= - - -end diff --git a/assignments/machine-learning-ex3/ex3/predict.m b/assignments/machine-learning-ex3/ex3/predict.m deleted file mode 100644 index 70e2f29..0000000 --- a/assignments/machine-learning-ex3/ex3/predict.m +++ /dev/null @@ -1,37 +0,0 @@ -function p = predict(Theta1, Theta2, X) -%PREDICT Predict the label of an input given a trained neural network -% p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the -% trained weights of a neural network (Theta1, Theta2) - -% Useful values -m = size(X, 1); -num_labels = size(Theta2, 1); - -% You need to return the following variables correctly -p = zeros(size(X, 1), 1); -X = [ones(m,1), X]; -% ====================== YOUR CODE HERE ====================== -% Instructions: Complete the following code to make predictions using -% your learned neural network. You should set p to a -% vector containing labels between 1 to num_labels. -% -% Hint: The max function might come in useful. In particular, the max -% function can also return the index of the max element, for more -% information see 'help max'. If your examples are in rows, then, you -% can use max(A, [], 2) to obtain the max for each row. -% - - - -a2 = sigmoid(X * Theta1'); -a2 = [ones(size(a2,1), 1), a2]; -p = sigmoid(a2 *Theta2'); -[temp_p,p] = max(p, [], 2); - - - - -% ========================================================================= - - -end diff --git a/assignments/machine-learning-ex3/ex3/predictOneVsAll.m b/assignments/machine-learning-ex3/ex3/predictOneVsAll.m deleted file mode 100644 index 2d6ca5f..0000000 --- a/assignments/machine-learning-ex3/ex3/predictOneVsAll.m +++ /dev/null @@ -1,44 +0,0 @@ -function p = predictOneVsAll(all_theta, X) -%PREDICT Predict the label for a trained one-vs-all classifier. The labels -%are in the range 1..K, where K = size(all_theta, 1). -% p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions -% for each example in the matrix X. Note that X contains the examples in -% rows. all_theta is a matrix where the i-th row is a trained logistic -% regression theta vector for the i-th class. You should set p to a vector -% of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2 -% for 4 examples) - -m = size(X, 1); -num_labels = size(all_theta, 1); - -% You need to return the following variables correctly -p = zeros(size(X, 1), 1); - -% Add ones to the X data matrix -X = [ones(m, 1) X]; - -% ====================== YOUR CODE HERE ====================== -% Instructions: Complete the following code to make predictions using -% your learned logistic regression parameters (one-vs-all). -% You should set p to a vector of predictions (from 1 to -% num_labels). -% -% Hint: This code can be done all vectorized using the max function. -% In particular, the max function can also return the index of the -% max element, for more information see 'help max'. If your examples -% are in rows, then, you can use max(A, [], 2) to obtain the max -% for each row. -% -p = X * all_theta'; -[temp_p,p] = max(p, [], 2); -%p(find(p == 10)) = 0; - - - - - - -% ========================================================================= - - -end diff --git a/assignments/machine-learning-ex3/ex3/sigmoid.m b/assignments/machine-learning-ex3/ex3/sigmoid.m deleted file mode 100644 index 6deca13..0000000 --- a/assignments/machine-learning-ex3/ex3/sigmoid.m +++ /dev/null @@ -1,6 +0,0 @@ -function g = sigmoid(z) -%SIGMOID Compute sigmoid functoon -% J = SIGMOID(z) computes the sigmoid of z. - -g = 1.0 ./ (1.0 + exp(-z)); -end diff --git a/assignments/machine-learning-ex3/ex3/submit.m b/assignments/machine-learning-ex3/ex3/submit.m deleted file mode 100644 index 11822f4..0000000 --- a/assignments/machine-learning-ex3/ex3/submit.m +++ /dev/null @@ -1,56 +0,0 @@ -function submit() - addpath('./lib'); - - conf.assignmentSlug = 'multi-class-classification-and-neural-networks'; - conf.itemName = 'Multi-class Classification and Neural Networks'; - conf.partArrays = { ... - { ... - '1', ... - { 'lrCostFunction.m' }, ... - 'Regularized Logistic Regression', ... - }, ... - { ... - '2', ... - { 'oneVsAll.m' }, ... - 'One-vs-All Classifier Training', ... - }, ... - { ... - '3', ... - { 'predictOneVsAll.m' }, ... - 'One-vs-All Classifier Prediction', ... - }, ... - { ... - '4', ... - { 'predict.m' }, ... - 'Neural Network Prediction Function' ... - }, ... - }; - conf.output = @output; - - submitWithConfiguration(conf); -end - -function out = output(partId, auxdata) - % Random Test Cases - X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))']; - y = sin(X(:,1) + X(:,2)) > 0; - Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ... - 1 1 ; 1 2 ; 2 1 ; 2 2 ; ... - -1 1 ; -1 2 ; -2 1 ; -2 2 ; ... - 1 -1 ; 1 -2 ; -2 -1 ; -2 -2 ]; - ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]'; - t1 = sin(reshape(1:2:24, 4, 3)); - t2 = cos(reshape(1:2:40, 4, 5)); - - if partId == '1' - [J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1); - out = sprintf('%0.5f ', J); - out = [out sprintf('%0.5f ', grad)]; - elseif partId == '2' - out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1)); - elseif partId == '3' - out = sprintf('%0.5f ', predictOneVsAll(t1, Xm)); - elseif partId == '4' - out = sprintf('%0.5f ', predict(t1, t2, Xm)); - end -end diff --git a/assignments/machine-learning-ex3/ex3/token.mat b/assignments/machine-learning-ex3/ex3/token.mat deleted file mode 100644 index 7763a7f..0000000 --- a/assignments/machine-learning-ex3/ex3/token.mat +++ /dev/null @@ -1,15 +0,0 @@ -# Created by Octave 4.2.0, Sun Nov 27 12:41:13 2016 GMT -# name: email -# type: sq_string -# elements: 1 -# length: 17 -scruel@vip.qq.com - - -# name: token -# type: sq_string -# elements: 1 -# length: 16 -CPfDADNFz0Hd0Qgk - - diff --git a/assignments/machine-learning-ex4/ex4.pdf b/assignments/machine-learning-ex4/ex4.pdf deleted file mode 100644 index f05b27a..0000000 Binary files a/assignments/machine-learning-ex4/ex4.pdf and /dev/null differ diff --git a/assignments/machine-learning-ex4/ex4/checkNNGradients.m b/assignments/machine-learning-ex4/ex4/checkNNGradients.m deleted file mode 100644 index 5d6e301..0000000 --- a/assignments/machine-learning-ex4/ex4/checkNNGradients.m +++ /dev/null @@ -1,50 +0,0 @@ -function checkNNGradients(lambda) -%CHECKNNGRADIENTS Creates a small neural network to check the -%backpropagation gradients -% CHECKNNGRADIENTS(lambda) Creates a small neural network to check the -% backpropagation gradients, it will output the analytical gradients -% produced by your backprop code and the numerical gradients (computed -% using computeNumericalGradient). These two gradient computations should -% result in very similar values. -% - -if ~exist('lambda', 'var') || isempty(lambda) - lambda = 0; -end - -input_layer_size = 3; -hidden_layer_size = 5; -num_labels = 3; -m = 5; - -% We generate some 'random' test data -Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size); -Theta2 = debugInitializeWeights(num_labels, hidden_layer_size); -% Reusing debugInitializeWeights to generate X -X = debugInitializeWeights(m, input_layer_size - 1); -y = 1 + mod(1:m, num_labels)'; - -% Unroll parameters -nn_params = [Theta1(:) ; Theta2(:)]; -% Short hand for cost function -costFunc = @(p) nnCostFunction(p, input_layer_size, hidden_layer_size, ... - num_labels, X, y, lambda); -[cost, grad] = costFunc(nn_params); -numgrad = computeNumericalGradient(costFunc, nn_params); - -% Visually examine the two gradient computations. The two columns -% you get should be very similar. -disp([numgrad grad]); -fprintf(['The above two columns you get should be very similar.\n' ... - '(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']); - -% Evaluate the norm of the difference between two solutions. -% If you have a correct implementation, and assuming you used EPSILON = 0.0001 -% in computeNumericalGradient.m, then diff below should be less than 1e-9 -diff = norm(numgrad-grad)/norm(numgrad+grad); - -fprintf(['If your backpropagation implementation is correct, then \n' ... - 'the relative difference will be small (less than 1e-9). \n' ... - '\nRelative Difference: %g\n'], diff); - -end diff --git a/assignments/machine-learning-ex4/ex4/computeNumericalGradient.m b/assignments/machine-learning-ex4/ex4/computeNumericalGradient.m deleted file mode 100644 index c3abeac..0000000 --- a/assignments/machine-learning-ex4/ex4/computeNumericalGradient.m +++ /dev/null @@ -1,29 +0,0 @@ -function numgrad = computeNumericalGradient(J, theta) -%COMPUTENUMERICALGRADIENT Computes the gradient using "finite differences" -%and gives us a numerical estimate of the gradient. -% numgrad = COMPUTENUMERICALGRADIENT(J, theta) computes the numerical -% gradient of the function J around theta. Calling y = J(theta) should -% return the function value at theta. - -% Notes: The following code implements numerical gradient checking, and -% returns the numerical gradient.It sets numgrad(i) to (a numerical -% approximation of) the partial derivative of J with respect to the -% i-th input argument, evaluated at theta. (i.e., numgrad(i) should -% be the (approximately) the partial derivative of J with respect -% to theta(i).) -% - -numgrad = zeros(size(theta)); -perturb = zeros(size(theta)); -e = 1e-4; -for p = 1:numel(theta) - % Set perturbation vector - perturb(p) = e; - loss1 = J(theta - perturb); - loss2 = J(theta + perturb); - % Compute Numerical Gradient - numgrad(p) = (loss2 - loss1) / (2*e); - perturb(p) = 0; -end - -end diff --git a/assignments/machine-learning-ex4/ex4/debugInitializeWeights.m b/assignments/machine-learning-ex4/ex4/debugInitializeWeights.m deleted file mode 100644 index a71b5ab..0000000 --- a/assignments/machine-learning-ex4/ex4/debugInitializeWeights.m +++ /dev/null @@ -1,22 +0,0 @@ -function W = debugInitializeWeights(fan_out, fan_in) -%DEBUGINITIALIZEWEIGHTS Initialize the weights of a layer with fan_in -%incoming connections and fan_out outgoing connections using a fixed -%strategy, this will help you later in debugging -% W = DEBUGINITIALIZEWEIGHTS(fan_in, fan_out) initializes the weights -% of a layer with fan_in incoming connections and fan_out outgoing -% connections using a fix set of values -% -% Note that W should be set to a matrix of size(1 + fan_in, fan_out) as -% the first row of W handles the "bias" terms -% - -% Set W to zeros -W = zeros(fan_out, 1 + fan_in); - -% Initialize W using "sin", this ensures that W is always of the same -% values and will be useful for debugging -W = reshape(sin(1:numel(W)), size(W)) / 10; - -% ========================================================================= - -end diff --git a/assignments/machine-learning-ex4/ex4/displayData.m b/assignments/machine-learning-ex4/ex4/displayData.m deleted file mode 100644 index 160697e..0000000 --- a/assignments/machine-learning-ex4/ex4/displayData.m +++ /dev/null @@ -1,59 +0,0 @@ -function [h, display_array] = displayData(X, example_width) -%DISPLAYDATA Display 2D data in a nice grid -% [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data -% stored in X in a nice grid. It returns the figure handle h and the -% displayed array if requested. - -% Set example_width automatically if not passed in -if ~exist('example_width', 'var') || isempty(example_width) - example_width = round(sqrt(size(X, 2))); -end - -% Gray Image -colormap(gray); - -% Compute rows, cols -[m n] = size(X); -example_height = (n / example_width); - -% Compute number of items to display -display_rows = floor(sqrt(m)); -display_cols = ceil(m / display_rows); - -% Between images padding -pad = 1; - -% Setup blank display -display_array = - ones(pad + display_rows * (example_height + pad), ... - pad + display_cols * (example_width + pad)); - -% Copy each example into a patch on the display array -curr_ex = 1; -for j = 1:display_rows - for i = 1:display_cols - if curr_ex > m, - break; - end - % Copy the patch - - % Get the max value of the patch - max_val = max(abs(X(curr_ex, :))); - display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ... - pad + (i - 1) * (example_width + pad) + (1:example_width)) = ... - reshape(X(curr_ex, :), example_height, example_width) / max_val; - curr_ex = curr_ex + 1; - end - if curr_ex > m, - break; - end -end - -% Display Image -h = imagesc(display_array, [-1 1]); - -% Do not show axis -axis image off - -drawnow; - -end diff --git a/assignments/machine-learning-ex4/ex4/ex4.m b/assignments/machine-learning-ex4/ex4/ex4.m deleted file mode 100644 index 5d77540..0000000 --- a/assignments/machine-learning-ex4/ex4/ex4.m +++ /dev/null @@ -1,250 +0,0 @@ -%% Machine Learning Online Class - Exercise 4 Neural Network Learning - -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% linear exercise. You will need to complete the following functions -% in this exericse: -% -% sigmoidGradient.m -% randInitializeWeights.m -% nnCostFunction.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% Setup the parameters you will use for this exercise -input_layer_size = 400; % 20x20 Input Images of Digits -hidden_layer_size = 25; % 25 hidden units -num_labels = 10; % 10 labels, from 1 to 10 - % (note that we have mapped "0" to label 10) - -%% =========== Part 1: Loading and Visualizing Data ============= -% We start the exercise by first loading and visualizing the dataset. -% You will be working with a dataset that contains handwritten digits. -% - -% Load Training Data -fprintf('Loading and Visualizing Data ...\n') - -load('ex4data1.mat'); -m = size(X, 1); - -% Randomly select 100 data points to display -sel = randperm(size(X, 1)); -sel = sel(1:100); - -displayData(X(sel, :)); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% ================ Part 2: Loading Parameters ================ -% In this part of the exercise, we load some pre-initialized -% neural network parameters. - -fprintf('\nLoading Saved Neural Network Parameters ...\n') - -% Load the weights into variables Theta1 and Theta2 -load('ex4weights.mat'); - -% Unroll parameters -nn_params = [Theta1(:) ; Theta2(:)]; - -%% ================ Part 3: Compute Cost (Feedforward) ================ -% To the neural network, you should first start by implementing the -% feedforward part of the neural network that returns the cost only. You -% should complete the code in nnCostFunction.m to return cost. After -% implementing the feedforward to compute the cost, you can verify that -% your implementation is correct by verifying that you get the same cost -% as us for the fixed debugging parameters. -% -% We suggest implementing the feedforward cost *without* regularization -% first so that it will be easier for you to debug. Later, in part 4, you -% will get to implement the regularized cost. -% -fprintf('\nFeedforward Using Neural Network ...\n') - -% Weight regularization parameter (we set this to 0 here). -lambda = 0; - -J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ... - num_labels, X, y, lambda); - -fprintf(['Cost at parameters (loaded from ex4weights): %f '... - '\n(this value should be about 0.287629)\n'], J); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - -%% =============== Part 4: Implement Regularization =============== -% Once your cost function implementation is correct, you should now -% continue to implement the regularization with the cost. -% - -fprintf('\nChecking Cost Function (w/ Regularization) ... \n') - -% Weight regularization parameter (we set this to 1 here). -lambda = 1; - -J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ... - num_labels, X, y, lambda); - -fprintf(['Cost at parameters (loaded from ex4weights): %f '... - '\n(this value should be about 0.383770)\n'], J); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% ================ Part 5: Sigmoid Gradient ================ -% Before you start implementing the neural network, you will first -% implement the gradient for the sigmoid function. You should complete the -% code in the sigmoidGradient.m file. -% - -fprintf('\nEvaluating sigmoid gradient...\n') - -g = sigmoidGradient([1 -0.5 0 0.5 1]); -fprintf('Sigmoid gradient evaluated at [1 -0.5 0 0.5 1]:\n '); -fprintf('%f ', g); -fprintf('\n\n'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% ================ Part 6: Initializing Pameters ================ -% In this part of the exercise, you will be starting to implment a two -% layer neural network that classifies digits. You will start by -% implementing a function to initialize the weights of the neural network -% (randInitializeWeights.m) - -fprintf('\nInitializing Neural Network Parameters ...\n') - -initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size); -initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels); - -% Unroll parameters -initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)]; - - -%% =============== Part 7: Implement Backpropagation =============== -% Once your cost matches up with ours, you should proceed to implement the -% backpropagation algorithm for the neural network. You should add to the -% code you've written in nnCostFunction.m to return the partial -% derivatives of the parameters. -% -fprintf('\nChecking Backpropagation... \n'); - -% Check gradients by running checkNNGradients -checkNNGradients; - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - -%% =============== Part 8: Implement Regularization =============== -% Once your backpropagation implementation is correct, you should now -% continue to implement the regularization with the cost and gradient. -% - -fprintf('\nChecking Backpropagation (w/ Regularization) ... \n') - -% Check gradients by running checkNNGradients -lambda = 3; -checkNNGradients(lambda); - -% Also output the costFunction debugging values -debug_J = nnCostFunction(nn_params, input_layer_size, ... - hidden_layer_size, num_labels, X, y, lambda); - -fprintf(['\n\nCost at (fixed) debugging parameters (w/ lambda = 10): %f ' ... - '\n(this value should be about 0.576051)\n\n'], debug_J); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% =================== Part 8: Training NN =================== -% You have now implemented all the code necessary to train a neural -% network. To train your neural network, we will now use "fmincg", which -% is a function which works similarly to "fminunc". Recall that these -% advanced optimizers are able to train our cost functions efficiently as -% long as we provide them with the gradient computations. -% -fprintf('\nTraining Neural Network... \n') - -% After you have completed the assignment, change the MaxIter to a larger -% value to see how more training helps. -options = optimset('MaxIter', 400); - -% You should also try different values of lambda -lambda = 1; - -% Create "short hand" for the cost function to be minimized -costFunction = @(p) nnCostFunction(p, ... - input_layer_size, ... - hidden_layer_size, ... - num_labels, X, y, lambda); - -% Now, costFunction is a function that takes in only one argument (the -% neural network parameters) - -[nn_params, cost] = fmincg(costFunction, initial_nn_params, options); - -% Obtain Theta1 and Theta2 back from nn_params -disp("test") -Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ... - hidden_layer_size, (input_layer_size + 1)); - -Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ... - num_labels, (hidden_layer_size + 1)); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% ================= Part 9: Visualize Weights ================= -% You can now "visualize" what the neural network is learning by -% displaying the hidden units to see what features they are capturing in -% the data. - -fprintf('\nVisualizing Neural Network... \n') - -displayData(Theta1(:, 2:end)); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - -%% ================= Part 10: Implement Predict ================= -% After training the neural network, we would like to use it to predict -% the labels. You will now implement the "predict" function to use the -% neural network to predict the labels of the training set. This lets -% you compute the training set accuracy. - -pred = predict(Theta1, Theta2, X); - -fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100); - -% Randomly permute examples -rp = randperm(m); - -for i = 1:m - % Display - fprintf('\nDisplaying Example Image\n'); - displayData(X(rp(i), :)); - - pred = predict(Theta1, Theta2, X(rp(i),:)); - fprintf('\nNeural Network Prediction: %d (digit %d)\n', pred, mod(pred, 10)); - - % Pause - fprintf('Program paused. Press enter to continue.\n'); - pause; -end diff --git a/assignments/machine-learning-ex4/ex4/ex4data1.mat b/assignments/machine-learning-ex4/ex4/ex4data1.mat deleted file mode 100644 index 371bd0c..0000000 Binary files a/assignments/machine-learning-ex4/ex4/ex4data1.mat and /dev/null differ diff --git a/assignments/machine-learning-ex4/ex4/ex4weights.mat b/assignments/machine-learning-ex4/ex4/ex4weights.mat deleted file mode 100644 index ace2a09..0000000 Binary files a/assignments/machine-learning-ex4/ex4/ex4weights.mat and /dev/null differ diff --git a/assignments/machine-learning-ex4/ex4/fmincg.m b/assignments/machine-learning-ex4/ex4/fmincg.m deleted file mode 100644 index 47a8816..0000000 --- a/assignments/machine-learning-ex4/ex4/fmincg.m +++ /dev/null @@ -1,175 +0,0 @@ -function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) -% Minimize a continuous differentialble multivariate function. Starting point -% is given by "X" (D by 1), and the function named in the string "f", must -% return a function value and a vector of partial derivatives. The Polack- -% Ribiere flavour of conjugate gradients is used to compute search directions, -% and a line search using quadratic and cubic polynomial approximations and the -% Wolfe-Powell stopping criteria is used together with the slope ratio method -% for guessing initial step sizes. Additionally a bunch of checks are made to -% make sure that exploration is taking place and that extrapolation will not -% be unboundedly large. The "length" gives the length of the run: if it is -% positive, it gives the maximum number of line searches, if negative its -% absolute gives the maximum allowed number of function evaluations. You can -% (optionally) give "length" a second component, which will indicate the -% reduction in function value to be expected in the first line-search (defaults -% to 1.0). The function returns when either its length is up, or if no further -% progress can be made (ie, we are at a minimum, or so close that due to -% numerical problems, we cannot get any closer). If the function terminates -% within a few iterations, it could be an indication that the function value -% and derivatives are not consistent (ie, there may be a bug in the -% implementation of your "f" function). The function returns the found -% solution "X", a vector of function values "fX" indicating the progress made -% and "i" the number of iterations (line searches or function evaluations, -% depending on the sign of "length") used. -% -% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) -% -% See also: checkgrad -% -% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13 -% -% -% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen -% -% Permission is granted for anyone to copy, use, or modify these -% programs and accompanying documents for purposes of research or -% education, provided this copyright notice is retained, and note is -% made of any changes that have been made. -% -% These programs and documents are distributed without any warranty, -% express or implied. As the programs were written for research -% purposes only, they have not been tested to the degree that would be -% advisable in any important application. All use of these programs is -% entirely at the user's own risk. -% -% [ml-class] Changes Made: -% 1) Function name and argument specifications -% 2) Output display -% - -% Read options -if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter') - length = options.MaxIter; -else - length = 100; -end - - -RHO = 0.01; % a bunch of constants for line searches -SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions -INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket -EXT = 3.0; % extrapolate maximum 3 times the current bracket -MAX = 20; % max 20 function evaluations per line search -RATIO = 100; % maximum allowed slope ratio - -argstr = ['feval(f, X']; % compose string used to call function -for i = 1:(nargin - 3) - argstr = [argstr, ',P', int2str(i)]; -end -argstr = [argstr, ')']; - -if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end -S=['Iteration ']; - -i = 0; % zero the run length counter -ls_failed = 0; % no previous line search has failed -fX = []; -[f1 df1] = eval(argstr); % get function value and gradient -i = i + (length<0); % count epochs?! -s = -df1; % search direction is steepest -d1 = -s'*s; % this is the slope -z1 = red/(1-d1); % initial step is red/(|s|+1) - -while i < abs(length) % while not finished - i = i + (length>0); % count iterations?! - - X0 = X; f0 = f1; df0 = df1; % make a copy of current values - X = X + z1*s; % begin line search - [f2 df2] = eval(argstr); - i = i + (length<0); % count epochs?! - d2 = df2'*s; - f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1 - if length>0, M = MAX; else M = min(MAX, -length-i); end - success = 0; limit = -1; % initialize quanteties - while 1 - while ((f2 > f1+z1*RHO*d1) || (d2 > -SIG*d1)) && (M > 0) - limit = z1; % tighten the bracket - if f2 > f1 - z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit - else - A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit - B = 3*(f3-f2)-z3*(d3+2*d2); - z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok! - end - if isnan(z2) || isinf(z2) - z2 = z3/2; % if we had a numerical problem then bisect - end - z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits - z1 = z1 + z2; % update the step - X = X + z2*s; - [f2 df2] = eval(argstr); - M = M - 1; i = i + (length<0); % count epochs?! - d2 = df2'*s; - z3 = z3-z2; % z3 is now relative to the location of z2 - end - if f2 > f1+z1*RHO*d1 || d2 > -SIG*d1 - break; % this is a failure - elseif d2 > SIG*d1 - success = 1; break; % success - elseif M == 0 - break; % failure - end - A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation - B = 3*(f3-f2)-z3*(d3+2*d2); - z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok! - if ~isreal(z2) || isnan(z2) || isinf(z2) || z2 < 0 % num prob or wrong sign? - if limit < -0.5 % if we have no upper limit - z2 = z1 * (EXT-1); % the extrapolate the maximum amount - else - z2 = (limit-z1)/2; % otherwise bisect - end - elseif (limit > -0.5) && (z2+z1 > limit) % extraplation beyond max? - z2 = (limit-z1)/2; % bisect - elseif (limit < -0.5) && (z2+z1 > z1*EXT) % extrapolation beyond limit - z2 = z1*(EXT-1.0); % set to extrapolation limit - elseif z2 < -z3*INT - z2 = -z3*INT; - elseif (limit > -0.5) && (z2 < (limit-z1)*(1.0-INT)) % too close to limit? - z2 = (limit-z1)*(1.0-INT); - end - f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2 - z1 = z1 + z2; X = X + z2*s; % update current estimates - [f2 df2] = eval(argstr); - M = M - 1; i = i + (length<0); % count epochs?! - d2 = df2'*s; - end % end of line search - - if success % if line search succeeded - f1 = f2; fX = [fX' f1]'; - fprintf('%s %4i | Cost: %4.6e\r', S, i, f1); - s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction - tmp = df1; df1 = df2; df2 = tmp; % swap derivatives - d2 = df1'*s; - if d2 > 0 % new slope must be negative - s = -df1; % otherwise use steepest direction - d2 = -s'*s; - end - z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO - d1 = d2; - ls_failed = 0; % this line search did not fail - else - X = X0; f1 = f0; df1 = df0; % restore point from before failed line search - if ls_failed || i > abs(length) % line search failed twice in a row - break; % or we ran out of time, so we give up - end - tmp = df1; df1 = df2; df2 = tmp; % swap derivatives - s = -df1; % try steepest - d1 = -s'*s; - z1 = 1/(1-d1); - ls_failed = 1; % this line search failed - end - if exist('OCTAVE_VERSION') - fflush(stdout); - end -end -fprintf('\n'); diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/AUTHORS.txt b/assignments/machine-learning-ex4/ex4/lib/jsonlab/AUTHORS.txt deleted file mode 100644 index 9dd3fc7..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/AUTHORS.txt +++ /dev/null @@ -1,41 +0,0 @@ -The author of "jsonlab" toolbox is Qianqian Fang. Qianqian -is currently an Assistant Professor at Massachusetts General Hospital, -Harvard Medical School. - -Address: Martinos Center for Biomedical Imaging, - Massachusetts General Hospital, - Harvard Medical School - Bldg 149, 13th St, Charlestown, MA 02129, USA -URL: http://nmr.mgh.harvard.edu/~fangq/ -Email: or - - -The script loadjson.m was built upon previous works by - -- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 - date: 2009/11/02 -- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 - date: 2009/03/22 -- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565 - date: 2008/07/03 - - -This toolbox contains patches submitted by the following contributors: - -- Blake Johnson - part of revision 341 - -- Niclas Borlin - various fixes in revision 394, including - - loadjson crashes for all-zero sparse matrix. - - loadjson crashes for empty sparse matrix. - - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson. - - loadjson crashes for sparse real column vector. - - loadjson crashes for sparse complex column vector. - - Data is corrupted by savejson for sparse real row vector. - - savejson crashes for sparse complex row vector. - -- Yul Kang - patches for svn revision 415. - - savejson saves an empty cell array as [] instead of null - - loadjson differentiates an empty struct from an empty array diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/ChangeLog.txt b/assignments/machine-learning-ex4/ex4/lib/jsonlab/ChangeLog.txt deleted file mode 100644 index 07824f5..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/ChangeLog.txt +++ /dev/null @@ -1,74 +0,0 @@ -============================================================================ - - JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave - ----------------------------------------------------------------------------- - -JSONlab ChangeLog (key features marked by *): - -== JSONlab 1.0 (codename: Optimus - Final), FangQ == - - 2015/01/02 polish help info for all major functions, update examples, finalize 1.0 - 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson - -== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ == - - 2014/11/22 show progress bar in loadjson ('ShowProgress') - 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact') - 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels - 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab - -== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ == - - 2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8 - 2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson - 2014/08/04 escape special characters in a JSON string - 2014/02/16 fix a bug when saving ubjson files - -== JSONlab 0.9.9 (codename: Optimus - beta), FangQ == - - 2014/01/22 use binary read and write in saveubjson and loadubjson - -== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ == - - 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang) - -== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ == - 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson - -== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ == - 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin) - -== JSONlab 0.9.0 (codename: Rodimus), FangQ == - - 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson - 2012/06/01 support JSONP in savejson - 2012/05/25 fix the empty cell bug (reported by Cyril Davin) - 2012/04/05 savejson can save to a file (suggested by Patrick Rapin) - -== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ == - - 2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS - 2012/01/25 patch to handle root-less objects, contributed by Blake Johnson - -== JSONlab 0.8.0 (codename: Sentiel), FangQ == - - 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab - 2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer - 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson - 2011/11/18 fix struct array bug reported by Mykel Kochenderfer - -== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ == - - 2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration - 2011/10/20 loadjson supports JSON collections - concatenated JSON objects - -== JSONlab 0.5.0 (codename: Nexus), FangQ == - - 2011/10/16 package and release jsonlab 0.5.0 - 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug - 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level - 2011/10/10 create jsonlab project, start jsonlab website, add online documentation - 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support - 2011/10/06 *savejson works for structs, cells and arrays - 2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/LICENSE_BSD.txt b/assignments/machine-learning-ex4/ex4/lib/jsonlab/LICENSE_BSD.txt deleted file mode 100644 index 32d66cb..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/LICENSE_BSD.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright 2011-2015 Qianqian Fang . All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are -permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, this list - of conditions and the following disclaimer in the documentation and/or other materials - provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND -FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS -OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -The views and conclusions contained in the software and documentation are those of the -authors and should not be interpreted as representing official policies, either expressed -or implied, of the copyright holders. diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/README.txt b/assignments/machine-learning-ex4/ex4/lib/jsonlab/README.txt deleted file mode 100644 index 7b4f732..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/README.txt +++ /dev/null @@ -1,394 +0,0 @@ -=============================================================================== -= JSONLab = -= An open-source MATLAB/Octave JSON encoder and decoder = -=============================================================================== - -*Copyright (C) 2011-2015 Qianqian Fang -*License: BSD License, see License_BSD.txt for details -*Version: 1.0 (Optimus - Final) - -------------------------------------------------------------------------------- - -Table of Content: - -I. Introduction -II. Installation -III.Using JSONLab -IV. Known Issues and TODOs -V. Contribution and feedback - -------------------------------------------------------------------------------- - -I. Introduction - -JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, -human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format -to represent complex and hierarchical data. It is as powerful as -[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely -used for data-exchange in applications, and is essential for the wild success -of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and -[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. - -UBJSON (Universal Binary JSON) is a binary JSON format, specifically -optimized for compact file size and better performance while keeping -the semantics as simple as the text-based JSON format. Using the UBJSON -format allows to wrap complex binary data in a flexible and extensible -structure, making it possible to process complex and large dataset -without accuracy loss due to text conversions. - -We envision that both JSON and its binary version will serve as part of -the mainstream data-exchange formats for scientific research in the future. -It will provide the flexibility and generality achieved by other popular -general-purpose file specifications, such as -[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly -reduced complexity and enhanced performance. - -JSONLab is a free and open-source implementation of a JSON/UBJSON encoder -and a decoder in the native MATLAB language. It can be used to convert a MATLAB -data structure (array, struct, cell, struct array and cell array) into -JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB -data structure. JSONLab supports both MATLAB and -[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone). - -------------------------------------------------------------------------------- - -II. Installation - -The installation of JSONLab is no different than any other simple -MATLAB toolbox. You only need to download/unzip the JSONLab package -to a folder, and add the folder's path to MATLAB/Octave's path list -by using the following command: - - addpath('/path/to/jsonlab'); - -If you want to add this path permanently, you need to type "pathtool", -browse to the jsonlab root folder and add to the list, then click "Save". -Then, run "rehash" in MATLAB, and type "which loadjson", if you see an -output, that means JSONLab is installed for MATLAB/Octave. - -------------------------------------------------------------------------------- - -III.Using JSONLab - -JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, -and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and -two equivallent functions -- loadubjson and saveubjson for the binary -JSON. The detailed help info for the four functions can be found below: - -=== loadjson.m === -
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          Franois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-
- -=== savejson.m === - -
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-
- -=== loadubjson.m === - -
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-
- -=== saveubjson.m === - -
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-
- - -=== examples === - -Under the "examples" folder, you can find several scripts to demonstrate the -basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you -will see the conversions from MATLAB data structure to JSON text and backward. -In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet -and validate the loadjson/savejson functions for regression testing purposes. -Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson -and loadubjson pairs for various matlab data structures. - -Please run these examples and understand how JSONLab works before you use -it to process your data. - -------------------------------------------------------------------------------- - -IV. Known Issues and TODOs - -JSONLab has several known limitations. We are striving to make it more general -and robust. Hopefully in a few future releases, the limitations become less. - -Here are the known issues: - -# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays; -# When processing names containing multi-byte characters, Octave and MATLAB \ -can give different field-names; you can use feature('DefaultCharacterSet','latin1') \ -in MATLAB to get consistant results -# savejson can not handle class and dataset. -# saveubjson converts a logical array into a uint8 ([U]) array -# an unofficial N-D array count syntax is implemented in saveubjson. We are \ -actively communicating with the UBJSON spec maintainer to investigate the \ -possibility of making it upstream -# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \ -files, however, it can parse all UBJSON files produced by saveubjson. - -------------------------------------------------------------------------------- - -V. Contribution and feedback - -JSONLab is an open-source project. This means you can not only use it and modify -it as you wish, but also you can contribute your changes back to JSONLab so -that everyone else can enjoy the improvement. For anyone who want to contribute, -please download JSONLab source code from it's subversion repository by using the -following command: - - svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab - -You can make changes to the files as needed. Once you are satisfied with your -changes, and ready to share it with others, please cd the root directory of -JSONLab, and type - - svn diff > yourname_featurename.patch - -You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at -the email address shown in the beginning of this file. Qianqian will review -the changes and commit it to the subversion if they are satisfactory. - -We appreciate any suggestions and feedbacks from you. Please use iso2mesh's -mailing list to report any questions you may have with JSONLab: - -http://groups.google.com/group/iso2mesh-users?hl=en&pli=1 - -(Subscription to the mailing list is needed in order to post messages). diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/jsonopt.m b/assignments/machine-learning-ex4/ex4/lib/jsonlab/jsonopt.m deleted file mode 100644 index 0bebd8d..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/jsonopt.m +++ /dev/null @@ -1,32 +0,0 @@ -function val=jsonopt(key,default,varargin) -% -% val=jsonopt(key,default,optstruct) -% -% setting options based on a struct. The struct can be produced -% by varargin2struct from a list of 'param','value' pairs -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% -% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $ -% -% input: -% key: a string with which one look up a value from a struct -% default: if the key does not exist, return default -% optstruct: a struct where each sub-field is a key -% -% output: -% val: if key exists, val=optstruct.key; otherwise val=default -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -val=default; -if(nargin<=2) return; end -opt=varargin{1}; -if(isstruct(opt) && isfield(opt,key)) - val=getfield(opt,key); -end - diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m b/assignments/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m deleted file mode 100644 index 42798c0..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m +++ /dev/null @@ -1,566 +0,0 @@ -function data = loadjson(fname,varargin) -% -% data=loadjson(fname,opt) -% or -% data=loadjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09, including previous works from -% -% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 -% created on 2009/11/02 -% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 -% created on 2009/03/22 -% Joel Feenstra: -% http://www.mathworks.com/matlabcentral/fileexchange/20565 -% created on 2008/07/03 -% -% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a JSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.FastArrayParser [1|0 or integer]: if set to 1, use a -% speed-optimized array parser when loading an -% array object. The fast array parser may -% collapse block arrays into a single large -% array similar to rules defined in cell2mat; 0 to -% use a legacy parser; if set to a larger-than-1 -% value, this option will specify the minimum -% dimension to enable the fast array parser. For -% example, if the input is a 3D array, setting -% FastArrayParser to 1 will return a 3D array; -% setting to 2 will return a cell array of 2D -% arrays; setting to 3 will return to a 2D cell -% array of 1D vectors; setting to 4 will return a -% 3D cell array. -% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}') -% dat=loadjson(['examples' filesep 'example1.json']) -% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); - -if(jsonopt('ShowProgress',0,opt)==1) - opt.progressbar_=waitbar(0,'loading ...'); -end -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end -if(isfield(opt,'progressbar_')) - close(opt.progressbar_); -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=data(j).x0x5F_ArraySize_; - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - if next_char ~= '}' - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - parse_char(':'); - val = parse_value(varargin{:}); - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' - break; - end - parse_char(','); - end - end - parse_char('}'); - -%%------------------------------------------------------------------------- - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim2=[]; - arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:}); - pbar=jsonopt('progressbar_',-1,varargin{:}); - - if next_char ~= ']' - if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:})) - [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos); - arraystr=['[' inStr(pos:endpos)]; - arraystr=regexprep(arraystr,'"_NaN_"','NaN'); - arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf'); - arraystr(arraystr==sprintf('\n'))=[]; - arraystr(arraystr==sprintf('\r'))=[]; - %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed - if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D - astr=inStr((e1l+1):(e1r-1)); - astr=regexprep(astr,'"_NaN_"','NaN'); - astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf'); - astr(astr==sprintf('\n'))=[]; - astr(astr==sprintf('\r'))=[]; - astr(astr==' ')=''; - if(isempty(find(astr=='[', 1))) % array is 2D - dim2=length(sscanf(astr,'%f,',[1 inf])); - end - else % array is 1D - astr=arraystr(2:end-1); - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]); - if(nextidx>=length(astr)-1) - object=obj; - pos=endpos; - parse_char(']'); - return; - end - end - if(~isempty(dim2)) - astr=arraystr; - astr(astr=='[')=''; - astr(astr==']')=''; - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf); - if(nextidx>=length(astr)-1) - object=reshape(obj,dim2,numel(obj)/dim2)'; - pos=endpos; - parse_char(']'); - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end - return; - end - end - arraystr=regexprep(arraystr,'\]\s*,','];'); - else - arraystr='['; - end - try - if(isoct && regexp(arraystr,'"','once')) - error('Octave eval can produce empty cells for JSON-like input'); - end - object=eval(arraystr); - pos=endpos; - catch - while 1 - newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1); - val = parse_value(newopt); - object{end+1} = val; - if next_char == ']' - break; - end - parse_char(','); - end - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - parse_char(']'); - - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr len esc index_esc len_esc - % len, ns = length(inStr), keyboard - if inStr(pos) ~= '"' - error_pos('String starting with " expected at position %d'); - else - pos = pos + 1; - end - str = ''; - while pos <= len - while index_esc <= len_esc && esc(index_esc) < pos - index_esc = index_esc + 1; - end - if index_esc > len_esc - str = [str inStr(pos:len)]; - pos = len + 1; - break; - else - str = [str inStr(pos:esc(index_esc)-1)]; - pos = esc(index_esc); - end - nstr = length(str); switch inStr(pos) - case '"' - pos = pos + 1; - if(~isempty(str)) - if(strcmp(str,'_Inf_')) - str=Inf; - elseif(strcmp(str,'-_Inf_')) - str=-Inf; - elseif(strcmp(str,'_NaN_')) - str=NaN; - end - end - return; - case '\' - if pos+1 > len - error_pos('End of file reached right after escape character'); - end - pos = pos + 1; - switch inStr(pos) - case {'"' '\' '/'} - str(nstr+1) = inStr(pos); - pos = pos + 1; - case {'b' 'f' 'n' 'r' 't'} - str(nstr+1) = sprintf(['\' inStr(pos)]); - pos = pos + 1; - case 'u' - if pos+4 > len - error_pos('End of file reached in escaped unicode character'); - end - str(nstr+(1:6)) = inStr(pos-1:pos+4); - pos = pos + 5; - end - otherwise % should never happen - str(nstr+1) = inStr(pos), keyboard - pos = pos + 1; - end - end - error_pos('End of file while expecting end of inStr'); - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct - currstr=inStr(pos:end); - numstr=0; - if(isoct~=0) - numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end'); - [num, one] = sscanf(currstr, '%f', 1); - delta=numstr+1; - else - [num, one, err, delta] = sscanf(currstr, '%f', 1); - if ~isempty(err) - error_pos('Error reading number at position %d'); - end - end - pos = pos + delta-1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - pbar=jsonopt('progressbar_',-1,varargin{:}); - if(pbar>0) - waitbar(pos/len,pbar,'loading ...'); - end - - switch(inStr(pos)) - case '"' - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'-','0','1','2','3','4','5','6','7','8','9'} - val = parse_number(varargin{:}); - return; - case 't' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true') - val = true; - pos = pos + 4; - return; - end - case 'f' - if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false') - val = false; - pos = pos + 5; - return; - end - case 'n' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null') - val = []; - pos = pos + 4; - return; - end - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m b/assignments/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m deleted file mode 100644 index 0155115..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m +++ /dev/null @@ -1,528 +0,0 @@ -function data = loadubjson(fname,varargin) -% -% data=loadubjson(fname,opt) -% or -% data=loadubjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/01 -% -% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a UBJSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.IntEndian [B|L]: specify the endianness of the integer fields -% in the UBJSON input data. B - Big-Endian format for -% integers (as required in the UBJSON specification); -% L - input integer fields are in Little-Endian order. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% obj=struct('string','value','array',[1 2 3]); -% ubjdata=saveubjson('obj',obj); -% dat=loadubjson(ubjdata) -% dat=loadubjson(['examples' filesep 'example1.ubj']) -% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); -fileendian=upper(jsonopt('IntEndian','B',opt)); -[os,maxelem,systemendian]=computer; - -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end - - -%% -function newdata=parse_collection(id,data,obj) - -if(jsoncount>0 && exist('data','var')) - if(~iscell(data)) - newdata=cell(1); - newdata{1}=data; - data=newdata; - end -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=double(data(j).x0x5F_ArraySize_); - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); % TODO - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - count=double(parse_number()); - end - if next_char ~= '}' - num=0; - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - %parse_char(':'); - val = parse_value(varargin{:}); - num=num+1; - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' || (count>=0 && num>=count) - break; - end - %parse_char(','); - end - end - if(count==-1) - parse_char('}'); - end - -%%------------------------------------------------------------------------- -function [cid,len]=elem_info(type) -id=strfind('iUIlLdD',type); -dataclass={'int8','uint8','int16','int32','int64','single','double'}; -bytelen=[1,1,2,4,8,4,8]; -if(id>0) - cid=dataclass{id}; - len=bytelen(id); -else - error_pos('unsupported type at position %d'); -end -%%------------------------------------------------------------------------- - - -function [data adv]=parse_block(type,count,varargin) -global pos inStr isoct fileendian systemendian -[cid,len]=elem_info(type); -datastr=inStr(pos:pos+len*count-1); -if(isoct) - newdata=int8(datastr); -else - newdata=uint8(datastr); -end -id=strfind('iUIlLdD',type); -if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,cid)); -end -data=typecast(newdata,cid); -adv=double(len*count); - -%%------------------------------------------------------------------------- - - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim=[]; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - if(next_char=='[') - dim=parse_array(varargin{:}); - count=prod(double(dim)); - else - count=double(parse_number()); - end - end - if(~isempty(type)) - if(count>=0) - [object adv]=parse_block(type,count,varargin{:}); - if(~isempty(dim)) - object=reshape(object,dim); - end - pos=pos+adv; - return; - else - endpos=matching_bracket(inStr,pos); - [cid,len]=elem_info(type); - count=(endpos-pos)/len; - [object adv]=parse_block(type,count,varargin{:}); - pos=pos+adv; - parse_char(']'); - return; - end - end - if next_char ~= ']' - while 1 - val = parse_value(varargin{:}); - object{end+1} = val; - if next_char == ']' - break; - end - %parse_char(','); - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - if(count==-1) - parse_char(']'); - end - -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr esc index_esc len_esc - % len, ns = length(inStr), keyboard - type=inStr(pos); - if type ~= 'S' && type ~= 'C' && type ~= 'H' - error_pos('String starting with S expected at position %d'); - else - pos = pos + 1; - end - if(type == 'C') - str=inStr(pos); - pos=pos+1; - return; - end - bytelen=double(parse_number()); - if(length(inStr)>=pos+bytelen-1) - str=inStr(pos:pos+bytelen-1); - pos=pos+bytelen; - else - error_pos('End of file while expecting end of inStr'); - end - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct fileendian systemendian - id=strfind('iUIlLdD',inStr(pos)); - if(isempty(id)) - error_pos('expecting a number at position %d'); - end - type={'int8','uint8','int16','int32','int64','single','double'}; - bytelen=[1,1,2,4,8,4,8]; - datastr=inStr(pos+1:pos+bytelen(id)); - if(isoct) - newdata=int8(datastr); - else - newdata=uint8(datastr); - end - if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,type{id})); - end - num=typecast(newdata,type{id}); - pos = pos + bytelen(id)+1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - switch(inStr(pos)) - case {'S','C','H'} - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'i','U','I','l','L','d','D'} - val = parse_number(varargin{:}); - return; - case 'T' - val = true; - pos = pos + 1; - return; - case 'F' - val = false; - pos = pos + 1; - return; - case {'Z','N'} - val = []; - pos = pos + 1; - return; - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos e1l e1r maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/mergestruct.m b/assignments/machine-learning-ex4/ex4/lib/jsonlab/mergestruct.m deleted file mode 100644 index 6de6100..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/mergestruct.m +++ /dev/null @@ -1,33 +0,0 @@ -function s=mergestruct(s1,s2) -% -% s=mergestruct(s1,s2) -% -% merge two struct objects into one -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% s1,s2: a struct object, s1 and s2 can not be arrays -% -% output: -% s: the merged struct object. fields in s1 and s2 will be combined in s. -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(~isstruct(s1) || ~isstruct(s2)) - error('input parameters contain non-struct'); -end -if(length(s1)>1 || length(s2)>1) - error('can not merge struct arrays'); -end -fn=fieldnames(s2); -s=s1; -for i=1:length(fn) - s=setfield(s,fn{i},getfield(s2,fn{i})); -end - diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/savejson.m b/assignments/machine-learning-ex4/ex4/lib/jsonlab/savejson.m deleted file mode 100644 index 7e84076..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/savejson.m +++ /dev/null @@ -1,475 +0,0 @@ -function json=savejson(rootname,obj,varargin) -% -% json=savejson(rootname,obj,filename) -% or -% json=savejson(rootname,obj,opt) -% json=savejson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript -% Object Notation) string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09 -% -% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array). -% filename: a string for the file name to save the output JSON data. -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.FloatFormat ['%.10g'|string]: format to show each numeric element -% of a 1D/2D array; -% opt.ArrayIndent [1|0]: if 1, output explicit data array with -% precedent indentation; if 0, no indentation -% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [0|1]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern -% to represent +/-Inf. The matched pattern is '([-+]*)Inf' -% and $1 represents the sign. For those who want to use -% 1e999 to represent Inf, they can set opt.Inf to '$11e999' -% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern -% to represent NaN -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSONP='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode. -% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs) -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a string in the JSON format (see http://json.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% savejson('jmesh',jsonmesh) -% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end - -whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -if(jsonopt('Compact',0,opt)==1) - whitespaces=struct('tab','','newline','','sep',','); -end -if(~isfield(opt,'whitespaces_')) - opt.whitespaces_=whitespaces; -end - -nl=whitespaces.newline; - -json=obj2json(rootname,obj,rootlevel,opt); -if(rootisarray) - json=sprintf('%s%s',json,nl); -else - json=sprintf('{%s%s%s}\n',nl,json,nl); -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=sprintf('%s(%s);%s',jsonp,json,nl); -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - if(jsonopt('SaveBinary',0,opt)==1) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - else - fid = fopen(opt.FileName, 'wt'); - fwrite(fid,json,'char'); - end - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2json(name,item,level,varargin) - -if(iscell(item)) - txt=cell2json(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2json(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2json(name,item,level,varargin{:}); -else - txt=mat2json(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2json(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -nl=ws.newline; -if(len>1) - if(~isempty(name)) - txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; - else - txt=sprintf('%s[%s',padding0,nl); - end -elseif(len==0) - if(~isempty(name)) - txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; - else - txt=sprintf('%s[]',padding0); - end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:})); - if(i1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=struct2json(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1)); -nl=ws.newline; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding0,nl); end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); - else - txt=sprintf('%s%s{%s',txt,padding1,nl); - end - if(~isempty(names)) - for e=1:length(names) - txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})); - if(e1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=str2json(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding1,nl); end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - if(isoct) - val=regexprep(item(e,:),'\\','\\'); - val=regexprep(val,'"','\"'); - val=regexprep(val,'^"','\"'); - else - val=regexprep(item(e,:),'\\','\\\\'); - val=regexprep(val,'"','\\"'); - val=regexprep(val,'^"','\\"'); - end - val=escapejsonstring(val); - if(len==1) - obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"']; - if(isempty(name)) obj=['"',val,'"']; end - txt=sprintf('%s%s%s%s',txt,padding1,obj); - else - txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']); - end - if(e==len) sep=''; end - txt=sprintf('%s%s',txt,sep); -end -if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end - -%%------------------------------------------------------------------------- -function txt=mat2json(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:})) - if(isempty(name)) - txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - else - txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - end -else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0) - numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']',''); - else - numtxt=matdata2json(item,level+1,varargin{:}); - end - if(isempty(name)) - txt=sprintf('%s%s',padding1,numtxt); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - else - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - end - end - return; -end -dataformat='%s%s%s%s%s'; - -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep); - if(size(item,1)==1) - % Row vector, store only column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([iy(:),data'],level+2,varargin{:}), nl); - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,data],level+2,varargin{:}), nl); - else - % General case, store row and column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,iy,data],level+2,varargin{:}), nl); - end -else - if(isreal(item)) - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json(item(:)',level+2,varargin{:}), nl); - else - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl); - end -end -txt=sprintf('%s%s%s',txt,padding1,'}'); - -%%------------------------------------------------------------------------- -function txt=matdata2json(mat,level,varargin) - -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -tab=ws.tab; -nl=ws.newline; - -if(size(mat,1)==1) - pre=''; - post=''; - level=level-1; -else - pre=sprintf('[%s',nl); - post=sprintf('%s%s]',nl,repmat(tab,1,level-1)); -end - -if(isempty(mat)) - txt='null'; - return; -end -floatformat=jsonopt('FloatFormat','%.10g',varargin{:}); -%if(numel(mat)>1) - formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]]; -%else -% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]]; -%end - -if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1) - formatstr=[repmat(tab,1,level) formatstr]; -end - -txt=sprintf(formatstr,mat'); -txt(end-length(nl):end)=[]; -if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1) - txt=regexprep(txt,'1','true'); - txt=regexprep(txt,'0','false'); -end -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],\n[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -txt=[pre txt post]; -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end - -%%------------------------------------------------------------------------- -function newstr=escapejsonstring(str) -newstr=str; -isoct=exist('OCTAVE_VERSION','builtin'); -if(isoct) - vv=sscanf(OCTAVE_VERSION,'%f'); - if(vv(1)>=3.8) isoct=0; end -end -if(isoct) - escapechars={'\a','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},escapechars{i}); - end -else - escapechars={'\a','\b','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\')); - end -end diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m b/assignments/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m deleted file mode 100644 index eaec433..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m +++ /dev/null @@ -1,504 +0,0 @@ -function json=saveubjson(rootname,obj,varargin) -% -% json=saveubjson(rootname,obj,filename) -% or -% json=saveubjson(rootname,obj,opt) -% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a Universal -% Binary JSON (UBJSON) binary string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/17 -% -% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array) -% filename: a string for the file name to save the output UBJSON data -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [1|0]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSON='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a binary string in the UBJSON format (see http://ubjson.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% saveubjson('jsonmesh',jsonmesh) -% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end -json=obj2ubjson(rootname,obj,rootlevel,opt); -if(~rootisarray) - json=['{' json '}']; -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=[jsonp '(' json ')']; -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2ubjson(name,item,level,varargin) - -if(iscell(item)) - txt=cell2ubjson(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2ubjson(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2ubjson(name,item,level,varargin{:}); -else - txt=mat2ubjson(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2ubjson(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); % let's handle 1D cell first -if(len>1) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) '[']; name=''; - else - txt='['; - end -elseif(len==0) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; - else - txt='Z'; - end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})]; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=struct2ubjson(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=[txt S_(checkname(name,varargin{:})) '{']; - else - txt=[txt '{']; - end - if(~isempty(names)) - for e=1:length(names) - txt=[txt obj2ubjson(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})]; - end - end - txt=[txt '}']; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=str2ubjson(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - val=item(e,:); - if(len==1) - obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),'']; - if(isempty(name)) obj=['',S_(val),'']; end - txt=[txt,'',obj]; - else - txt=[txt,'',['',S_(val),'']]; - end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=mat2ubjson(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) || jsonopt('ArrayToStruct',0,varargin{:})) - cid=I_(uint32(max(size(item)))); - if(isempty(name)) - txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ]; - else - if(isempty(item)) - txt=[S_(checkname(name,varargin{:})),'Z']; - return; - else - txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))]; - end - end -else - if(isempty(name)) - txt=matdata2ubjson(item,level+1,varargin{:}); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']',''); - txt=[S_(checkname(name,varargin{:})) numtxt]; - else - txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})]; - end - end - return; -end -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=[txt,S_('_ArrayIsComplex_'),'T']; - end - txt=[txt,S_('_ArrayIsSparse_'),'T']; - if(size(item,1)==1) - % Row vector, store only column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([iy(:),data'],level+2,varargin{:})]; - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,data],level+2,varargin{:})]; - else - % General case, store row and column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,iy,data],level+2,varargin{:})]; - end -else - if(isreal(item)) - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson(item(:)',level+2,varargin{:})]; - else - txt=[txt,S_('_ArrayIsComplex_'),'T']; - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})]; - end -end -txt=[txt,'}']; - -%%------------------------------------------------------------------------- -function txt=matdata2ubjson(mat,level,varargin) -if(isempty(mat)) - txt='Z'; - return; -end -if(size(mat,1)==1) - level=level-1; -end -type=''; -hasnegtive=(mat<0); -if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0))) - if(isempty(hasnegtive)) - if(max(mat(:))<=2^8) - type='U'; - end - end - if(isempty(type)) - % todo - need to consider negative ones separately - id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]); - if(isempty(find(id))) - error('high-precision data is not yet supported'); - end - key='iIlL'; - type=key(find(id)); - end - txt=[I_a(mat(:),type,size(mat))]; -elseif(islogical(mat)) - logicalval='FT'; - if(numel(mat)==1) - txt=logicalval(mat+1); - else - txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')]; - end -else - if(numel(mat)==1) - txt=['[' D_(mat) ']']; - else - txt=D_a(mat(:),'D',size(mat)); - end -end - -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end -%%------------------------------------------------------------------------- -function val=S_(str) -if(length(str)==1) - val=['C' str]; -else - val=['S' I_(int32(length(str))) str]; -end -%%------------------------------------------------------------------------- -function val=I_(num) -if(~isinteger(num)) - error('input is not an integer'); -end -if(num>=0 && num<255) - val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')]; - return; -end -key='iIlL'; -cid={'int8','int16','int32','int64'}; -for i=1:4 - if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1))) - val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')]; - return; - end -end -error('unsupported integer'); - -%%------------------------------------------------------------------------- -function val=D_(num) -if(~isfloat(num)) - error('input is not a float'); -end - -if(isa(num,'single')) - val=['d' data2byte(num,'uint8')]; -else - val=['D' data2byte(num,'uint8')]; -end -%%------------------------------------------------------------------------- -function data=I_a(num,type,dim,format) -id=find(ismember('iUIlL',type)); - -if(id==0) - error('unsupported integer array'); -end - -% based on UBJSON specs, all integer types are stored in big endian format - -if(id==1) - data=data2byte(swapbytes(int8(num)),'uint8'); - blen=1; -elseif(id==2) - data=data2byte(swapbytes(uint8(num)),'uint8'); - blen=1; -elseif(id==3) - data=data2byte(swapbytes(int16(num)),'uint8'); - blen=2; -elseif(id==4) - data=data2byte(swapbytes(int32(num)),'uint8'); - blen=4; -elseif(id==5) - data=data2byte(swapbytes(int64(num)),'uint8'); - blen=8; -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/blen)) data(:)']; - end - data=['[' data(:)']; -else - data=reshape(data,blen,numel(data)/blen); - data(2:blen+1,:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function data=D_a(num,type,dim,format) -id=find(ismember('dD',type)); - -if(id==0) - error('unsupported float array'); -end - -if(id==1) - data=data2byte(single(num),'uint8'); -elseif(id==2) - data=data2byte(double(num),'uint8'); -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)']; - end - data=['[' data]; -else - data=reshape(data,(id*4),length(data)/(id*4)); - data(2:(id*4+1),:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function bytes=data2byte(varargin) -bytes=typecast(varargin{:}); -bytes=bytes(:)'; diff --git a/assignments/machine-learning-ex4/ex4/lib/jsonlab/varargin2struct.m b/assignments/machine-learning-ex4/ex4/lib/jsonlab/varargin2struct.m deleted file mode 100644 index 9a5c2b6..0000000 --- a/assignments/machine-learning-ex4/ex4/lib/jsonlab/varargin2struct.m +++ /dev/null @@ -1,40 +0,0 @@ -function opt=varargin2struct(varargin) -% -% opt=varargin2struct('param1',value1,'param2',value2,...) -% or -% opt=varargin2struct(...,optstruct,...) -% -% convert a series of input parameters into a structure -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% 'param', value: the input parameters should be pairs of a string and a value -% optstruct: if a parameter is a struct, the fields will be merged to the output struct -% -% output: -% opt: a struct where opt.param1=value1, opt.param2=value2 ... -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -len=length(varargin); -opt=struct; -if(len==0) return; end -i=1; -while(i<=len) - if(isstruct(varargin{i})) - opt=mergestruct(opt,varargin{i}); - elseif(ischar(varargin{i}) && i -# name: email -# type: sq_string -# elements: 1 -# length: 17 -scruel@vip.qq.com - - -# name: token -# type: sq_string -# elements: 1 -# length: 16 -UQOWJsUpzaxYOnXk - - diff --git a/assignments/machine-learning-ex5/ex5.pdf b/assignments/machine-learning-ex5/ex5.pdf deleted file mode 100644 index f1226e8..0000000 Binary files a/assignments/machine-learning-ex5/ex5.pdf and /dev/null differ diff --git a/assignments/machine-learning-ex5/ex5/ex5.m b/assignments/machine-learning-ex5/ex5/ex5.m deleted file mode 100644 index 961ee74..0000000 --- a/assignments/machine-learning-ex5/ex5/ex5.m +++ /dev/null @@ -1,220 +0,0 @@ -%% Machine Learning Online Class -% Exercise 5 | Regularized Linear Regression and Bias-Variance -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% exercise. You will need to complete the following functions: -% -% linearRegCostFunction.m -% learningCurve.m -% validationCurve.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% =========== Part 1: Loading and Visualizing Data ============= -% We start the exercise by first loading and visualizing the dataset. -% The following code will load the dataset into your environment and plot -% the data. -% - -% Load Training Data -fprintf('Loading and Visualizing Data ...\n') - -% Load from ex5data1: -% You will have X, y, Xval, yval, Xtest, ytest in your environment -load ('ex5data1.mat'); - -% m = Number of examples -m = size(X, 1); - -% Plot training data -plot(X, y, 'rx', 'MarkerSize', 10, 'LineWidth', 1.5); -xlabel('Change in water level (x)'); -ylabel('Water flowing out of the dam (y)'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =========== Part 2: Regularized Linear Regression Cost ============= -% You should now implement the cost function for regularized linear -% regression. -% - -theta = [1 ; 1]; -J = linearRegCostFunction([ones(m, 1) X], y, theta, 1); - -fprintf(['Cost at theta = [1 ; 1]: %f '... - '\n(this value should be about 303.993192)\n'], J); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =========== Part 3: Regularized Linear Regression Gradient ============= -% You should now implement the gradient for regularized linear -% regression. -% - -theta = [1 ; 1]; -[J, grad] = linearRegCostFunction([ones(m, 1) X], y, theta, 1); - -fprintf(['Gradient at theta = [1 ; 1]: [%f; %f] '... - '\n(this value should be about [-15.303016; 598.250744])\n'], ... - grad(1), grad(2)); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% =========== Part 4: Train Linear Regression ============= -% Once you have implemented the cost and gradient correctly, the -% trainLinearReg function will use your cost function to train -% regularized linear regression. -% -% Write Up Note: The data is non-linear, so this will not give a great -% fit. -% - -% Train linear regression with lambda = 0 -lambda = 0; -[theta] = trainLinearReg([ones(m, 1) X], y, lambda); - -% Plot fit over the data -plot(X, y, 'rx', 'MarkerSize', 10, 'LineWidth', 1.5); -xlabel('Change in water level (x)'); -ylabel('Water flowing out of the dam (y)'); -hold on; -plot(X, [ones(m, 1) X]*theta, '--', 'LineWidth', 2) -hold off; - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% =========== Part 5: Learning Curve for Linear Regression ============= -% Next, you should implement the learningCurve function. -% -% Write Up Note: Since the model is underfitting the data, we expect to -% see a graph with "high bias" -- slide 8 in ML-advice.pdf -% - -lambda = 10000; -[error_train, error_val] = ... - learningCurve([ones(m, 1) X], y, ... - [ones(size(Xval, 1), 1) Xval], yval, ... - lambda); - -plot(1:m, error_train, 1:m, error_val); -title('Learning curve for linear regression') -legend('Train', 'Cross Validation') -xlabel('Number of training examples') -ylabel('Error') -axis([0 13 0 150]) - -fprintf('# Training Examples Train Error Cross Validation Error\n'); -for i = 1:m - fprintf(' \t%d %f %f\n', i, error_train(i), error_val(i)); -end - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =========== Part 6: Feature Mapping for Polynomial Regression ============= -% One solution to this is to use polynomial regression. You should now -% complete polyFeatures to map each example into its powers -% - -p = 8; - -% Map X onto Polynomial Features and Normalize -X_poly = polyFeatures(X, p); -[X_poly, mu, sigma] = featureNormalize(X_poly); % Normalize -X_poly = [ones(m, 1), X_poly]; % Add Ones - -% Map X_poly_test and normalize (using mu and sigma) -X_poly_test = polyFeatures(Xtest, p); -X_poly_test = bsxfun(@minus, X_poly_test, mu); -X_poly_test = bsxfun(@rdivide, X_poly_test, sigma); -X_poly_test = [ones(size(X_poly_test, 1), 1), X_poly_test]; % Add Ones - -% Map X_poly_val and normalize (using mu and sigma) -X_poly_val = polyFeatures(Xval, p); -X_poly_val = bsxfun(@minus, X_poly_val, mu); -X_poly_val = bsxfun(@rdivide, X_poly_val, sigma); -X_poly_val = [ones(size(X_poly_val, 1), 1), X_poly_val]; % Add Ones - -fprintf('Normalized Training Example 1:\n'); -fprintf(' %f \n', X_poly(1, :)); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - - -%% =========== Part 7: Learning Curve for Polynomial Regression ============= -% Now, you will get to experiment with polynomial regression with multiple -% values of lambda. The code below runs polynomial regression with -% lambda = 0. You should try running the code with different values of -% lambda to see how the fit and learning curve change. -% - -lambda = 100; -[theta] = trainLinearReg(X_poly, y, lambda); - -% Plot training data and fit -figure(1); -plot(X, y, 'rx', 'MarkerSize', 10, 'LineWidth', 1.5); -plotFit(min(X), max(X), mu, sigma, theta, p); -xlabel('Change in water level (x)'); -ylabel('Water flowing out of the dam (y)'); -title (sprintf('Polynomial Regression Fit (lambda = %f)', lambda)); - -figure(2); -[error_train, error_val] = ... - learningCurve(X_poly, y, X_poly_val, yval, lambda); -plot(1:m, error_train, 1:m, error_val); - -title(sprintf('Polynomial Regression Learning Curve (lambda = %f)', lambda)); -xlabel('Number of training examples') -ylabel('Error') -axis([0 13 0 100]) -legend('Train', 'Cross Validation') - -fprintf('Polynomial Regression (lambda = %f)\n\n', lambda); -fprintf('# Training Examples\tTrain Error\tCross Validation Error\n'); -for i = 1:m - fprintf(' \t%d\t\t%f\t%f\n', i, error_train(i), error_val(i)); -end - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =========== Part 8: Validation for Selecting Lambda ============= -% You will now implement validationCurve to test various values of -% lambda on a validation set. You will then use this to select the -% "best" lambda value. -% - -[lambda_vec, error_train, error_val] = ... - validationCurve(X_poly, y, X_poly_val, yval); - -close all; -plot(lambda_vec, error_train, lambda_vec, error_val); -legend('Train', 'Cross Validation'); -xlabel('lambda'); -ylabel('Error'); - -fprintf('lambda Train Error Validation Error\n'); -for i = 1:length(lambda_vec) - fprintf(' %f %f %f\n', ... - lambda_vec(i), error_train(i), error_val(i)); -end - -fprintf('Program paused. Press enter to continue.\n'); -pause; diff --git a/assignments/machine-learning-ex5/ex5/ex5data1.mat b/assignments/machine-learning-ex5/ex5/ex5data1.mat deleted file mode 100644 index 5a17abd..0000000 Binary files a/assignments/machine-learning-ex5/ex5/ex5data1.mat and /dev/null differ diff --git a/assignments/machine-learning-ex5/ex5/featureNormalize.m b/assignments/machine-learning-ex5/ex5/featureNormalize.m deleted file mode 100644 index 8901ffb..0000000 --- a/assignments/machine-learning-ex5/ex5/featureNormalize.m +++ /dev/null @@ -1,17 +0,0 @@ -function [X_norm, mu, sigma] = featureNormalize(X) -%FEATURENORMALIZE Normalizes the features in X -% FEATURENORMALIZE(X) returns a normalized version of X where -% the mean value of each feature is 0 and the standard deviation -% is 1. This is often a good preprocessing step to do when -% working with learning algorithms. - - mu = mean(X); - X_norm = bsxfun(@minus, X, mu); - - sigma = std(X_norm); - X_norm = bsxfun(@rdivide, X_norm, sigma); - - -% ============================================================ - -end diff --git a/assignments/machine-learning-ex5/ex5/fmincg.m b/assignments/machine-learning-ex5/ex5/fmincg.m deleted file mode 100644 index 47a8816..0000000 --- a/assignments/machine-learning-ex5/ex5/fmincg.m +++ /dev/null @@ -1,175 +0,0 @@ -function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) -% Minimize a continuous differentialble multivariate function. Starting point -% is given by "X" (D by 1), and the function named in the string "f", must -% return a function value and a vector of partial derivatives. The Polack- -% Ribiere flavour of conjugate gradients is used to compute search directions, -% and a line search using quadratic and cubic polynomial approximations and the -% Wolfe-Powell stopping criteria is used together with the slope ratio method -% for guessing initial step sizes. Additionally a bunch of checks are made to -% make sure that exploration is taking place and that extrapolation will not -% be unboundedly large. The "length" gives the length of the run: if it is -% positive, it gives the maximum number of line searches, if negative its -% absolute gives the maximum allowed number of function evaluations. You can -% (optionally) give "length" a second component, which will indicate the -% reduction in function value to be expected in the first line-search (defaults -% to 1.0). The function returns when either its length is up, or if no further -% progress can be made (ie, we are at a minimum, or so close that due to -% numerical problems, we cannot get any closer). If the function terminates -% within a few iterations, it could be an indication that the function value -% and derivatives are not consistent (ie, there may be a bug in the -% implementation of your "f" function). The function returns the found -% solution "X", a vector of function values "fX" indicating the progress made -% and "i" the number of iterations (line searches or function evaluations, -% depending on the sign of "length") used. -% -% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) -% -% See also: checkgrad -% -% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13 -% -% -% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen -% -% Permission is granted for anyone to copy, use, or modify these -% programs and accompanying documents for purposes of research or -% education, provided this copyright notice is retained, and note is -% made of any changes that have been made. -% -% These programs and documents are distributed without any warranty, -% express or implied. As the programs were written for research -% purposes only, they have not been tested to the degree that would be -% advisable in any important application. All use of these programs is -% entirely at the user's own risk. -% -% [ml-class] Changes Made: -% 1) Function name and argument specifications -% 2) Output display -% - -% Read options -if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter') - length = options.MaxIter; -else - length = 100; -end - - -RHO = 0.01; % a bunch of constants for line searches -SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions -INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket -EXT = 3.0; % extrapolate maximum 3 times the current bracket -MAX = 20; % max 20 function evaluations per line search -RATIO = 100; % maximum allowed slope ratio - -argstr = ['feval(f, X']; % compose string used to call function -for i = 1:(nargin - 3) - argstr = [argstr, ',P', int2str(i)]; -end -argstr = [argstr, ')']; - -if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end -S=['Iteration ']; - -i = 0; % zero the run length counter -ls_failed = 0; % no previous line search has failed -fX = []; -[f1 df1] = eval(argstr); % get function value and gradient -i = i + (length<0); % count epochs?! -s = -df1; % search direction is steepest -d1 = -s'*s; % this is the slope -z1 = red/(1-d1); % initial step is red/(|s|+1) - -while i < abs(length) % while not finished - i = i + (length>0); % count iterations?! - - X0 = X; f0 = f1; df0 = df1; % make a copy of current values - X = X + z1*s; % begin line search - [f2 df2] = eval(argstr); - i = i + (length<0); % count epochs?! - d2 = df2'*s; - f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1 - if length>0, M = MAX; else M = min(MAX, -length-i); end - success = 0; limit = -1; % initialize quanteties - while 1 - while ((f2 > f1+z1*RHO*d1) || (d2 > -SIG*d1)) && (M > 0) - limit = z1; % tighten the bracket - if f2 > f1 - z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit - else - A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit - B = 3*(f3-f2)-z3*(d3+2*d2); - z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok! - end - if isnan(z2) || isinf(z2) - z2 = z3/2; % if we had a numerical problem then bisect - end - z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits - z1 = z1 + z2; % update the step - X = X + z2*s; - [f2 df2] = eval(argstr); - M = M - 1; i = i + (length<0); % count epochs?! - d2 = df2'*s; - z3 = z3-z2; % z3 is now relative to the location of z2 - end - if f2 > f1+z1*RHO*d1 || d2 > -SIG*d1 - break; % this is a failure - elseif d2 > SIG*d1 - success = 1; break; % success - elseif M == 0 - break; % failure - end - A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation - B = 3*(f3-f2)-z3*(d3+2*d2); - z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok! - if ~isreal(z2) || isnan(z2) || isinf(z2) || z2 < 0 % num prob or wrong sign? - if limit < -0.5 % if we have no upper limit - z2 = z1 * (EXT-1); % the extrapolate the maximum amount - else - z2 = (limit-z1)/2; % otherwise bisect - end - elseif (limit > -0.5) && (z2+z1 > limit) % extraplation beyond max? - z2 = (limit-z1)/2; % bisect - elseif (limit < -0.5) && (z2+z1 > z1*EXT) % extrapolation beyond limit - z2 = z1*(EXT-1.0); % set to extrapolation limit - elseif z2 < -z3*INT - z2 = -z3*INT; - elseif (limit > -0.5) && (z2 < (limit-z1)*(1.0-INT)) % too close to limit? - z2 = (limit-z1)*(1.0-INT); - end - f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2 - z1 = z1 + z2; X = X + z2*s; % update current estimates - [f2 df2] = eval(argstr); - M = M - 1; i = i + (length<0); % count epochs?! - d2 = df2'*s; - end % end of line search - - if success % if line search succeeded - f1 = f2; fX = [fX' f1]'; - fprintf('%s %4i | Cost: %4.6e\r', S, i, f1); - s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction - tmp = df1; df1 = df2; df2 = tmp; % swap derivatives - d2 = df1'*s; - if d2 > 0 % new slope must be negative - s = -df1; % otherwise use steepest direction - d2 = -s'*s; - end - z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO - d1 = d2; - ls_failed = 0; % this line search did not fail - else - X = X0; f1 = f0; df1 = df0; % restore point from before failed line search - if ls_failed || i > abs(length) % line search failed twice in a row - break; % or we ran out of time, so we give up - end - tmp = df1; df1 = df2; df2 = tmp; % swap derivatives - s = -df1; % try steepest - d1 = -s'*s; - z1 = 1/(1-d1); - ls_failed = 1; % this line search failed - end - if exist('OCTAVE_VERSION') - fflush(stdout); - end -end -fprintf('\n'); diff --git a/assignments/machine-learning-ex5/ex5/learningCurve.m b/assignments/machine-learning-ex5/ex5/learningCurve.m deleted file mode 100644 index edba712..0000000 --- a/assignments/machine-learning-ex5/ex5/learningCurve.m +++ /dev/null @@ -1,75 +0,0 @@ -function [error_train, error_val] = ... - learningCurve(X, y, Xval, yval, lambda) -%LEARNINGCURVE Generates the train and cross validation set errors needed -%to plot a learning curve -% [error_train, error_val] = ... -% LEARNINGCURVE(X, y, Xval, yval, lambda) returns the train and -% cross validation set errors for a learning curve. In particular, -% it returns two vectors of the same length - error_train and -% error_val. Then, error_train(i) contains the training error for -% i examples (and similarly for error_val(i)). -% -% In this function, you will compute the train and test errors for -% dataset sizes from 1 up to m. In practice, when working with larger -% datasets, you might want to do this in larger intervals. -% - -% Number of training examples -m = size(X, 1); - -% You need to return these values correctly -error_train = zeros(m, 1); -error_val = zeros(m, 1); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Fill in this function to return training errors in -% error_train and the cross validation errors in error_val. -% i.e., error_train(i) and -% error_val(i) should give you the errors -% obtained after training on i examples. -% -% Note: You should evaluate the training error on the first i training -% examples (i.e., X(1:i, :) and y(1:i)). -% -% For the cross-validation error, you should instead evaluate on -% the _entire_ cross validation set (Xval and yval). -% -% Note: If you are using your cost function (linearRegCostFunction) -% to compute the training and cross validation error, you should -% call the function with the lambda argument set to 0. -% Do note that you will still need to use lambda when running -% the training to obtain the theta parameters. -% -% Hint: You can loop over the examples with the following: -% -% for i = 1:m -% % Compute train/cross validation errors using training examples -% % X(1:i, :) and y(1:i), storing the result in -% % error_train(i) and error_val(i) -% .... -% -% end -% - -% ---------------------- Sample Solution ---------------------- -for i = 1:m - [theta] = trainLinearReg(X(1:i, :), y(1:i), lambda); - error_train(i) = linearRegCostFunction(X(1:i, :), y(1:i), theta, 0); - error_val(i) = linearRegCostFunction(Xval, yval, theta, 0); -% % Compute train/cross validation errors using training examples -% % X(1:i, :) and y(1:i), storing the result in -% % error_train(i) and error_val(i) -% .... -% -end - - - - - - -% ------------------------------------------------------------- - -% ========================================================================= - -end diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/AUTHORS.txt b/assignments/machine-learning-ex5/ex5/lib/jsonlab/AUTHORS.txt deleted file mode 100644 index 9dd3fc7..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/AUTHORS.txt +++ /dev/null @@ -1,41 +0,0 @@ -The author of "jsonlab" toolbox is Qianqian Fang. Qianqian -is currently an Assistant Professor at Massachusetts General Hospital, -Harvard Medical School. - -Address: Martinos Center for Biomedical Imaging, - Massachusetts General Hospital, - Harvard Medical School - Bldg 149, 13th St, Charlestown, MA 02129, USA -URL: http://nmr.mgh.harvard.edu/~fangq/ -Email: or - - -The script loadjson.m was built upon previous works by - -- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 - date: 2009/11/02 -- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 - date: 2009/03/22 -- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565 - date: 2008/07/03 - - -This toolbox contains patches submitted by the following contributors: - -- Blake Johnson - part of revision 341 - -- Niclas Borlin - various fixes in revision 394, including - - loadjson crashes for all-zero sparse matrix. - - loadjson crashes for empty sparse matrix. - - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson. - - loadjson crashes for sparse real column vector. - - loadjson crashes for sparse complex column vector. - - Data is corrupted by savejson for sparse real row vector. - - savejson crashes for sparse complex row vector. - -- Yul Kang - patches for svn revision 415. - - savejson saves an empty cell array as [] instead of null - - loadjson differentiates an empty struct from an empty array diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/ChangeLog.txt b/assignments/machine-learning-ex5/ex5/lib/jsonlab/ChangeLog.txt deleted file mode 100644 index 07824f5..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/ChangeLog.txt +++ /dev/null @@ -1,74 +0,0 @@ -============================================================================ - - JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave - ----------------------------------------------------------------------------- - -JSONlab ChangeLog (key features marked by *): - -== JSONlab 1.0 (codename: Optimus - Final), FangQ == - - 2015/01/02 polish help info for all major functions, update examples, finalize 1.0 - 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson - -== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ == - - 2014/11/22 show progress bar in loadjson ('ShowProgress') - 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact') - 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels - 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab - -== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ == - - 2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8 - 2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson - 2014/08/04 escape special characters in a JSON string - 2014/02/16 fix a bug when saving ubjson files - -== JSONlab 0.9.9 (codename: Optimus - beta), FangQ == - - 2014/01/22 use binary read and write in saveubjson and loadubjson - -== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ == - - 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang) - -== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ == - 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson - -== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ == - 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin) - -== JSONlab 0.9.0 (codename: Rodimus), FangQ == - - 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson - 2012/06/01 support JSONP in savejson - 2012/05/25 fix the empty cell bug (reported by Cyril Davin) - 2012/04/05 savejson can save to a file (suggested by Patrick Rapin) - -== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ == - - 2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS - 2012/01/25 patch to handle root-less objects, contributed by Blake Johnson - -== JSONlab 0.8.0 (codename: Sentiel), FangQ == - - 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab - 2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer - 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson - 2011/11/18 fix struct array bug reported by Mykel Kochenderfer - -== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ == - - 2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration - 2011/10/20 loadjson supports JSON collections - concatenated JSON objects - -== JSONlab 0.5.0 (codename: Nexus), FangQ == - - 2011/10/16 package and release jsonlab 0.5.0 - 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug - 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level - 2011/10/10 create jsonlab project, start jsonlab website, add online documentation - 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support - 2011/10/06 *savejson works for structs, cells and arrays - 2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/LICENSE_BSD.txt b/assignments/machine-learning-ex5/ex5/lib/jsonlab/LICENSE_BSD.txt deleted file mode 100644 index 32d66cb..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/LICENSE_BSD.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright 2011-2015 Qianqian Fang . All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are -permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, this list - of conditions and the following disclaimer in the documentation and/or other materials - provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND -FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS -OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -The views and conclusions contained in the software and documentation are those of the -authors and should not be interpreted as representing official policies, either expressed -or implied, of the copyright holders. diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/README.txt b/assignments/machine-learning-ex5/ex5/lib/jsonlab/README.txt deleted file mode 100644 index 7b4f732..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/README.txt +++ /dev/null @@ -1,394 +0,0 @@ -=============================================================================== -= JSONLab = -= An open-source MATLAB/Octave JSON encoder and decoder = -=============================================================================== - -*Copyright (C) 2011-2015 Qianqian Fang -*License: BSD License, see License_BSD.txt for details -*Version: 1.0 (Optimus - Final) - -------------------------------------------------------------------------------- - -Table of Content: - -I. Introduction -II. Installation -III.Using JSONLab -IV. Known Issues and TODOs -V. Contribution and feedback - -------------------------------------------------------------------------------- - -I. Introduction - -JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, -human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format -to represent complex and hierarchical data. It is as powerful as -[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely -used for data-exchange in applications, and is essential for the wild success -of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and -[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. - -UBJSON (Universal Binary JSON) is a binary JSON format, specifically -optimized for compact file size and better performance while keeping -the semantics as simple as the text-based JSON format. Using the UBJSON -format allows to wrap complex binary data in a flexible and extensible -structure, making it possible to process complex and large dataset -without accuracy loss due to text conversions. - -We envision that both JSON and its binary version will serve as part of -the mainstream data-exchange formats for scientific research in the future. -It will provide the flexibility and generality achieved by other popular -general-purpose file specifications, such as -[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly -reduced complexity and enhanced performance. - -JSONLab is a free and open-source implementation of a JSON/UBJSON encoder -and a decoder in the native MATLAB language. It can be used to convert a MATLAB -data structure (array, struct, cell, struct array and cell array) into -JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB -data structure. JSONLab supports both MATLAB and -[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone). - -------------------------------------------------------------------------------- - -II. Installation - -The installation of JSONLab is no different than any other simple -MATLAB toolbox. You only need to download/unzip the JSONLab package -to a folder, and add the folder's path to MATLAB/Octave's path list -by using the following command: - - addpath('/path/to/jsonlab'); - -If you want to add this path permanently, you need to type "pathtool", -browse to the jsonlab root folder and add to the list, then click "Save". -Then, run "rehash" in MATLAB, and type "which loadjson", if you see an -output, that means JSONLab is installed for MATLAB/Octave. - -------------------------------------------------------------------------------- - -III.Using JSONLab - -JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, -and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and -two equivallent functions -- loadubjson and saveubjson for the binary -JSON. The detailed help info for the four functions can be found below: - -=== loadjson.m === -
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          Franois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-
- -=== savejson.m === - -
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-
- -=== loadubjson.m === - -
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-
- -=== saveubjson.m === - -
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-
- - -=== examples === - -Under the "examples" folder, you can find several scripts to demonstrate the -basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you -will see the conversions from MATLAB data structure to JSON text and backward. -In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet -and validate the loadjson/savejson functions for regression testing purposes. -Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson -and loadubjson pairs for various matlab data structures. - -Please run these examples and understand how JSONLab works before you use -it to process your data. - -------------------------------------------------------------------------------- - -IV. Known Issues and TODOs - -JSONLab has several known limitations. We are striving to make it more general -and robust. Hopefully in a few future releases, the limitations become less. - -Here are the known issues: - -# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays; -# When processing names containing multi-byte characters, Octave and MATLAB \ -can give different field-names; you can use feature('DefaultCharacterSet','latin1') \ -in MATLAB to get consistant results -# savejson can not handle class and dataset. -# saveubjson converts a logical array into a uint8 ([U]) array -# an unofficial N-D array count syntax is implemented in saveubjson. We are \ -actively communicating with the UBJSON spec maintainer to investigate the \ -possibility of making it upstream -# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \ -files, however, it can parse all UBJSON files produced by saveubjson. - -------------------------------------------------------------------------------- - -V. Contribution and feedback - -JSONLab is an open-source project. This means you can not only use it and modify -it as you wish, but also you can contribute your changes back to JSONLab so -that everyone else can enjoy the improvement. For anyone who want to contribute, -please download JSONLab source code from it's subversion repository by using the -following command: - - svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab - -You can make changes to the files as needed. Once you are satisfied with your -changes, and ready to share it with others, please cd the root directory of -JSONLab, and type - - svn diff > yourname_featurename.patch - -You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at -the email address shown in the beginning of this file. Qianqian will review -the changes and commit it to the subversion if they are satisfactory. - -We appreciate any suggestions and feedbacks from you. Please use iso2mesh's -mailing list to report any questions you may have with JSONLab: - -http://groups.google.com/group/iso2mesh-users?hl=en&pli=1 - -(Subscription to the mailing list is needed in order to post messages). diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/jsonopt.m b/assignments/machine-learning-ex5/ex5/lib/jsonlab/jsonopt.m deleted file mode 100644 index 0bebd8d..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/jsonopt.m +++ /dev/null @@ -1,32 +0,0 @@ -function val=jsonopt(key,default,varargin) -% -% val=jsonopt(key,default,optstruct) -% -% setting options based on a struct. The struct can be produced -% by varargin2struct from a list of 'param','value' pairs -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% -% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $ -% -% input: -% key: a string with which one look up a value from a struct -% default: if the key does not exist, return default -% optstruct: a struct where each sub-field is a key -% -% output: -% val: if key exists, val=optstruct.key; otherwise val=default -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -val=default; -if(nargin<=2) return; end -opt=varargin{1}; -if(isstruct(opt) && isfield(opt,key)) - val=getfield(opt,key); -end - diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m b/assignments/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m deleted file mode 100644 index 42798c0..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m +++ /dev/null @@ -1,566 +0,0 @@ -function data = loadjson(fname,varargin) -% -% data=loadjson(fname,opt) -% or -% data=loadjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09, including previous works from -% -% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 -% created on 2009/11/02 -% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 -% created on 2009/03/22 -% Joel Feenstra: -% http://www.mathworks.com/matlabcentral/fileexchange/20565 -% created on 2008/07/03 -% -% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a JSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.FastArrayParser [1|0 or integer]: if set to 1, use a -% speed-optimized array parser when loading an -% array object. The fast array parser may -% collapse block arrays into a single large -% array similar to rules defined in cell2mat; 0 to -% use a legacy parser; if set to a larger-than-1 -% value, this option will specify the minimum -% dimension to enable the fast array parser. For -% example, if the input is a 3D array, setting -% FastArrayParser to 1 will return a 3D array; -% setting to 2 will return a cell array of 2D -% arrays; setting to 3 will return to a 2D cell -% array of 1D vectors; setting to 4 will return a -% 3D cell array. -% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}') -% dat=loadjson(['examples' filesep 'example1.json']) -% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); - -if(jsonopt('ShowProgress',0,opt)==1) - opt.progressbar_=waitbar(0,'loading ...'); -end -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end -if(isfield(opt,'progressbar_')) - close(opt.progressbar_); -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=data(j).x0x5F_ArraySize_; - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - if next_char ~= '}' - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - parse_char(':'); - val = parse_value(varargin{:}); - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' - break; - end - parse_char(','); - end - end - parse_char('}'); - -%%------------------------------------------------------------------------- - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim2=[]; - arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:}); - pbar=jsonopt('progressbar_',-1,varargin{:}); - - if next_char ~= ']' - if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:})) - [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos); - arraystr=['[' inStr(pos:endpos)]; - arraystr=regexprep(arraystr,'"_NaN_"','NaN'); - arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf'); - arraystr(arraystr==sprintf('\n'))=[]; - arraystr(arraystr==sprintf('\r'))=[]; - %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed - if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D - astr=inStr((e1l+1):(e1r-1)); - astr=regexprep(astr,'"_NaN_"','NaN'); - astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf'); - astr(astr==sprintf('\n'))=[]; - astr(astr==sprintf('\r'))=[]; - astr(astr==' ')=''; - if(isempty(find(astr=='[', 1))) % array is 2D - dim2=length(sscanf(astr,'%f,',[1 inf])); - end - else % array is 1D - astr=arraystr(2:end-1); - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]); - if(nextidx>=length(astr)-1) - object=obj; - pos=endpos; - parse_char(']'); - return; - end - end - if(~isempty(dim2)) - astr=arraystr; - astr(astr=='[')=''; - astr(astr==']')=''; - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf); - if(nextidx>=length(astr)-1) - object=reshape(obj,dim2,numel(obj)/dim2)'; - pos=endpos; - parse_char(']'); - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end - return; - end - end - arraystr=regexprep(arraystr,'\]\s*,','];'); - else - arraystr='['; - end - try - if(isoct && regexp(arraystr,'"','once')) - error('Octave eval can produce empty cells for JSON-like input'); - end - object=eval(arraystr); - pos=endpos; - catch - while 1 - newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1); - val = parse_value(newopt); - object{end+1} = val; - if next_char == ']' - break; - end - parse_char(','); - end - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - parse_char(']'); - - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr len esc index_esc len_esc - % len, ns = length(inStr), keyboard - if inStr(pos) ~= '"' - error_pos('String starting with " expected at position %d'); - else - pos = pos + 1; - end - str = ''; - while pos <= len - while index_esc <= len_esc && esc(index_esc) < pos - index_esc = index_esc + 1; - end - if index_esc > len_esc - str = [str inStr(pos:len)]; - pos = len + 1; - break; - else - str = [str inStr(pos:esc(index_esc)-1)]; - pos = esc(index_esc); - end - nstr = length(str); switch inStr(pos) - case '"' - pos = pos + 1; - if(~isempty(str)) - if(strcmp(str,'_Inf_')) - str=Inf; - elseif(strcmp(str,'-_Inf_')) - str=-Inf; - elseif(strcmp(str,'_NaN_')) - str=NaN; - end - end - return; - case '\' - if pos+1 > len - error_pos('End of file reached right after escape character'); - end - pos = pos + 1; - switch inStr(pos) - case {'"' '\' '/'} - str(nstr+1) = inStr(pos); - pos = pos + 1; - case {'b' 'f' 'n' 'r' 't'} - str(nstr+1) = sprintf(['\' inStr(pos)]); - pos = pos + 1; - case 'u' - if pos+4 > len - error_pos('End of file reached in escaped unicode character'); - end - str(nstr+(1:6)) = inStr(pos-1:pos+4); - pos = pos + 5; - end - otherwise % should never happen - str(nstr+1) = inStr(pos), keyboard - pos = pos + 1; - end - end - error_pos('End of file while expecting end of inStr'); - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct - currstr=inStr(pos:end); - numstr=0; - if(isoct~=0) - numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end'); - [num, one] = sscanf(currstr, '%f', 1); - delta=numstr+1; - else - [num, one, err, delta] = sscanf(currstr, '%f', 1); - if ~isempty(err) - error_pos('Error reading number at position %d'); - end - end - pos = pos + delta-1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - pbar=jsonopt('progressbar_',-1,varargin{:}); - if(pbar>0) - waitbar(pos/len,pbar,'loading ...'); - end - - switch(inStr(pos)) - case '"' - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'-','0','1','2','3','4','5','6','7','8','9'} - val = parse_number(varargin{:}); - return; - case 't' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true') - val = true; - pos = pos + 4; - return; - end - case 'f' - if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false') - val = false; - pos = pos + 5; - return; - end - case 'n' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null') - val = []; - pos = pos + 4; - return; - end - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m b/assignments/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m deleted file mode 100644 index 0155115..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m +++ /dev/null @@ -1,528 +0,0 @@ -function data = loadubjson(fname,varargin) -% -% data=loadubjson(fname,opt) -% or -% data=loadubjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/01 -% -% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a UBJSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.IntEndian [B|L]: specify the endianness of the integer fields -% in the UBJSON input data. B - Big-Endian format for -% integers (as required in the UBJSON specification); -% L - input integer fields are in Little-Endian order. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% obj=struct('string','value','array',[1 2 3]); -% ubjdata=saveubjson('obj',obj); -% dat=loadubjson(ubjdata) -% dat=loadubjson(['examples' filesep 'example1.ubj']) -% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); -fileendian=upper(jsonopt('IntEndian','B',opt)); -[os,maxelem,systemendian]=computer; - -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end - - -%% -function newdata=parse_collection(id,data,obj) - -if(jsoncount>0 && exist('data','var')) - if(~iscell(data)) - newdata=cell(1); - newdata{1}=data; - data=newdata; - end -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=double(data(j).x0x5F_ArraySize_); - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); % TODO - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - count=double(parse_number()); - end - if next_char ~= '}' - num=0; - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - %parse_char(':'); - val = parse_value(varargin{:}); - num=num+1; - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' || (count>=0 && num>=count) - break; - end - %parse_char(','); - end - end - if(count==-1) - parse_char('}'); - end - -%%------------------------------------------------------------------------- -function [cid,len]=elem_info(type) -id=strfind('iUIlLdD',type); -dataclass={'int8','uint8','int16','int32','int64','single','double'}; -bytelen=[1,1,2,4,8,4,8]; -if(id>0) - cid=dataclass{id}; - len=bytelen(id); -else - error_pos('unsupported type at position %d'); -end -%%------------------------------------------------------------------------- - - -function [data adv]=parse_block(type,count,varargin) -global pos inStr isoct fileendian systemendian -[cid,len]=elem_info(type); -datastr=inStr(pos:pos+len*count-1); -if(isoct) - newdata=int8(datastr); -else - newdata=uint8(datastr); -end -id=strfind('iUIlLdD',type); -if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,cid)); -end -data=typecast(newdata,cid); -adv=double(len*count); - -%%------------------------------------------------------------------------- - - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim=[]; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - if(next_char=='[') - dim=parse_array(varargin{:}); - count=prod(double(dim)); - else - count=double(parse_number()); - end - end - if(~isempty(type)) - if(count>=0) - [object adv]=parse_block(type,count,varargin{:}); - if(~isempty(dim)) - object=reshape(object,dim); - end - pos=pos+adv; - return; - else - endpos=matching_bracket(inStr,pos); - [cid,len]=elem_info(type); - count=(endpos-pos)/len; - [object adv]=parse_block(type,count,varargin{:}); - pos=pos+adv; - parse_char(']'); - return; - end - end - if next_char ~= ']' - while 1 - val = parse_value(varargin{:}); - object{end+1} = val; - if next_char == ']' - break; - end - %parse_char(','); - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - if(count==-1) - parse_char(']'); - end - -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr esc index_esc len_esc - % len, ns = length(inStr), keyboard - type=inStr(pos); - if type ~= 'S' && type ~= 'C' && type ~= 'H' - error_pos('String starting with S expected at position %d'); - else - pos = pos + 1; - end - if(type == 'C') - str=inStr(pos); - pos=pos+1; - return; - end - bytelen=double(parse_number()); - if(length(inStr)>=pos+bytelen-1) - str=inStr(pos:pos+bytelen-1); - pos=pos+bytelen; - else - error_pos('End of file while expecting end of inStr'); - end - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct fileendian systemendian - id=strfind('iUIlLdD',inStr(pos)); - if(isempty(id)) - error_pos('expecting a number at position %d'); - end - type={'int8','uint8','int16','int32','int64','single','double'}; - bytelen=[1,1,2,4,8,4,8]; - datastr=inStr(pos+1:pos+bytelen(id)); - if(isoct) - newdata=int8(datastr); - else - newdata=uint8(datastr); - end - if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,type{id})); - end - num=typecast(newdata,type{id}); - pos = pos + bytelen(id)+1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - switch(inStr(pos)) - case {'S','C','H'} - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'i','U','I','l','L','d','D'} - val = parse_number(varargin{:}); - return; - case 'T' - val = true; - pos = pos + 1; - return; - case 'F' - val = false; - pos = pos + 1; - return; - case {'Z','N'} - val = []; - pos = pos + 1; - return; - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos e1l e1r maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/mergestruct.m b/assignments/machine-learning-ex5/ex5/lib/jsonlab/mergestruct.m deleted file mode 100644 index 6de6100..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/mergestruct.m +++ /dev/null @@ -1,33 +0,0 @@ -function s=mergestruct(s1,s2) -% -% s=mergestruct(s1,s2) -% -% merge two struct objects into one -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% s1,s2: a struct object, s1 and s2 can not be arrays -% -% output: -% s: the merged struct object. fields in s1 and s2 will be combined in s. -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(~isstruct(s1) || ~isstruct(s2)) - error('input parameters contain non-struct'); -end -if(length(s1)>1 || length(s2)>1) - error('can not merge struct arrays'); -end -fn=fieldnames(s2); -s=s1; -for i=1:length(fn) - s=setfield(s,fn{i},getfield(s2,fn{i})); -end - diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/savejson.m b/assignments/machine-learning-ex5/ex5/lib/jsonlab/savejson.m deleted file mode 100644 index 7e84076..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/savejson.m +++ /dev/null @@ -1,475 +0,0 @@ -function json=savejson(rootname,obj,varargin) -% -% json=savejson(rootname,obj,filename) -% or -% json=savejson(rootname,obj,opt) -% json=savejson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript -% Object Notation) string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09 -% -% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array). -% filename: a string for the file name to save the output JSON data. -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.FloatFormat ['%.10g'|string]: format to show each numeric element -% of a 1D/2D array; -% opt.ArrayIndent [1|0]: if 1, output explicit data array with -% precedent indentation; if 0, no indentation -% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [0|1]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern -% to represent +/-Inf. The matched pattern is '([-+]*)Inf' -% and $1 represents the sign. For those who want to use -% 1e999 to represent Inf, they can set opt.Inf to '$11e999' -% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern -% to represent NaN -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSONP='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode. -% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs) -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a string in the JSON format (see http://json.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% savejson('jmesh',jsonmesh) -% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end - -whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -if(jsonopt('Compact',0,opt)==1) - whitespaces=struct('tab','','newline','','sep',','); -end -if(~isfield(opt,'whitespaces_')) - opt.whitespaces_=whitespaces; -end - -nl=whitespaces.newline; - -json=obj2json(rootname,obj,rootlevel,opt); -if(rootisarray) - json=sprintf('%s%s',json,nl); -else - json=sprintf('{%s%s%s}\n',nl,json,nl); -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=sprintf('%s(%s);%s',jsonp,json,nl); -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - if(jsonopt('SaveBinary',0,opt)==1) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - else - fid = fopen(opt.FileName, 'wt'); - fwrite(fid,json,'char'); - end - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2json(name,item,level,varargin) - -if(iscell(item)) - txt=cell2json(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2json(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2json(name,item,level,varargin{:}); -else - txt=mat2json(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2json(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -nl=ws.newline; -if(len>1) - if(~isempty(name)) - txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; - else - txt=sprintf('%s[%s',padding0,nl); - end -elseif(len==0) - if(~isempty(name)) - txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; - else - txt=sprintf('%s[]',padding0); - end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:})); - if(i1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=struct2json(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1)); -nl=ws.newline; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding0,nl); end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); - else - txt=sprintf('%s%s{%s',txt,padding1,nl); - end - if(~isempty(names)) - for e=1:length(names) - txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})); - if(e1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=str2json(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding1,nl); end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - if(isoct) - val=regexprep(item(e,:),'\\','\\'); - val=regexprep(val,'"','\"'); - val=regexprep(val,'^"','\"'); - else - val=regexprep(item(e,:),'\\','\\\\'); - val=regexprep(val,'"','\\"'); - val=regexprep(val,'^"','\\"'); - end - val=escapejsonstring(val); - if(len==1) - obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"']; - if(isempty(name)) obj=['"',val,'"']; end - txt=sprintf('%s%s%s%s',txt,padding1,obj); - else - txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']); - end - if(e==len) sep=''; end - txt=sprintf('%s%s',txt,sep); -end -if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end - -%%------------------------------------------------------------------------- -function txt=mat2json(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:})) - if(isempty(name)) - txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - else - txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - end -else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0) - numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']',''); - else - numtxt=matdata2json(item,level+1,varargin{:}); - end - if(isempty(name)) - txt=sprintf('%s%s',padding1,numtxt); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - else - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - end - end - return; -end -dataformat='%s%s%s%s%s'; - -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep); - if(size(item,1)==1) - % Row vector, store only column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([iy(:),data'],level+2,varargin{:}), nl); - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,data],level+2,varargin{:}), nl); - else - % General case, store row and column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,iy,data],level+2,varargin{:}), nl); - end -else - if(isreal(item)) - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json(item(:)',level+2,varargin{:}), nl); - else - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl); - end -end -txt=sprintf('%s%s%s',txt,padding1,'}'); - -%%------------------------------------------------------------------------- -function txt=matdata2json(mat,level,varargin) - -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -tab=ws.tab; -nl=ws.newline; - -if(size(mat,1)==1) - pre=''; - post=''; - level=level-1; -else - pre=sprintf('[%s',nl); - post=sprintf('%s%s]',nl,repmat(tab,1,level-1)); -end - -if(isempty(mat)) - txt='null'; - return; -end -floatformat=jsonopt('FloatFormat','%.10g',varargin{:}); -%if(numel(mat)>1) - formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]]; -%else -% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]]; -%end - -if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1) - formatstr=[repmat(tab,1,level) formatstr]; -end - -txt=sprintf(formatstr,mat'); -txt(end-length(nl):end)=[]; -if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1) - txt=regexprep(txt,'1','true'); - txt=regexprep(txt,'0','false'); -end -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],\n[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -txt=[pre txt post]; -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end - -%%------------------------------------------------------------------------- -function newstr=escapejsonstring(str) -newstr=str; -isoct=exist('OCTAVE_VERSION','builtin'); -if(isoct) - vv=sscanf(OCTAVE_VERSION,'%f'); - if(vv(1)>=3.8) isoct=0; end -end -if(isoct) - escapechars={'\a','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},escapechars{i}); - end -else - escapechars={'\a','\b','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\')); - end -end diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m b/assignments/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m deleted file mode 100644 index eaec433..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m +++ /dev/null @@ -1,504 +0,0 @@ -function json=saveubjson(rootname,obj,varargin) -% -% json=saveubjson(rootname,obj,filename) -% or -% json=saveubjson(rootname,obj,opt) -% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a Universal -% Binary JSON (UBJSON) binary string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/17 -% -% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array) -% filename: a string for the file name to save the output UBJSON data -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [1|0]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSON='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a binary string in the UBJSON format (see http://ubjson.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% saveubjson('jsonmesh',jsonmesh) -% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end -json=obj2ubjson(rootname,obj,rootlevel,opt); -if(~rootisarray) - json=['{' json '}']; -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=[jsonp '(' json ')']; -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2ubjson(name,item,level,varargin) - -if(iscell(item)) - txt=cell2ubjson(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2ubjson(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2ubjson(name,item,level,varargin{:}); -else - txt=mat2ubjson(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2ubjson(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); % let's handle 1D cell first -if(len>1) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) '[']; name=''; - else - txt='['; - end -elseif(len==0) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; - else - txt='Z'; - end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})]; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=struct2ubjson(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=[txt S_(checkname(name,varargin{:})) '{']; - else - txt=[txt '{']; - end - if(~isempty(names)) - for e=1:length(names) - txt=[txt obj2ubjson(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})]; - end - end - txt=[txt '}']; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=str2ubjson(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - val=item(e,:); - if(len==1) - obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),'']; - if(isempty(name)) obj=['',S_(val),'']; end - txt=[txt,'',obj]; - else - txt=[txt,'',['',S_(val),'']]; - end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=mat2ubjson(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) || jsonopt('ArrayToStruct',0,varargin{:})) - cid=I_(uint32(max(size(item)))); - if(isempty(name)) - txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ]; - else - if(isempty(item)) - txt=[S_(checkname(name,varargin{:})),'Z']; - return; - else - txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))]; - end - end -else - if(isempty(name)) - txt=matdata2ubjson(item,level+1,varargin{:}); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']',''); - txt=[S_(checkname(name,varargin{:})) numtxt]; - else - txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})]; - end - end - return; -end -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=[txt,S_('_ArrayIsComplex_'),'T']; - end - txt=[txt,S_('_ArrayIsSparse_'),'T']; - if(size(item,1)==1) - % Row vector, store only column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([iy(:),data'],level+2,varargin{:})]; - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,data],level+2,varargin{:})]; - else - % General case, store row and column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,iy,data],level+2,varargin{:})]; - end -else - if(isreal(item)) - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson(item(:)',level+2,varargin{:})]; - else - txt=[txt,S_('_ArrayIsComplex_'),'T']; - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})]; - end -end -txt=[txt,'}']; - -%%------------------------------------------------------------------------- -function txt=matdata2ubjson(mat,level,varargin) -if(isempty(mat)) - txt='Z'; - return; -end -if(size(mat,1)==1) - level=level-1; -end -type=''; -hasnegtive=(mat<0); -if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0))) - if(isempty(hasnegtive)) - if(max(mat(:))<=2^8) - type='U'; - end - end - if(isempty(type)) - % todo - need to consider negative ones separately - id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]); - if(isempty(find(id))) - error('high-precision data is not yet supported'); - end - key='iIlL'; - type=key(find(id)); - end - txt=[I_a(mat(:),type,size(mat))]; -elseif(islogical(mat)) - logicalval='FT'; - if(numel(mat)==1) - txt=logicalval(mat+1); - else - txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')]; - end -else - if(numel(mat)==1) - txt=['[' D_(mat) ']']; - else - txt=D_a(mat(:),'D',size(mat)); - end -end - -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end -%%------------------------------------------------------------------------- -function val=S_(str) -if(length(str)==1) - val=['C' str]; -else - val=['S' I_(int32(length(str))) str]; -end -%%------------------------------------------------------------------------- -function val=I_(num) -if(~isinteger(num)) - error('input is not an integer'); -end -if(num>=0 && num<255) - val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')]; - return; -end -key='iIlL'; -cid={'int8','int16','int32','int64'}; -for i=1:4 - if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1))) - val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')]; - return; - end -end -error('unsupported integer'); - -%%------------------------------------------------------------------------- -function val=D_(num) -if(~isfloat(num)) - error('input is not a float'); -end - -if(isa(num,'single')) - val=['d' data2byte(num,'uint8')]; -else - val=['D' data2byte(num,'uint8')]; -end -%%------------------------------------------------------------------------- -function data=I_a(num,type,dim,format) -id=find(ismember('iUIlL',type)); - -if(id==0) - error('unsupported integer array'); -end - -% based on UBJSON specs, all integer types are stored in big endian format - -if(id==1) - data=data2byte(swapbytes(int8(num)),'uint8'); - blen=1; -elseif(id==2) - data=data2byte(swapbytes(uint8(num)),'uint8'); - blen=1; -elseif(id==3) - data=data2byte(swapbytes(int16(num)),'uint8'); - blen=2; -elseif(id==4) - data=data2byte(swapbytes(int32(num)),'uint8'); - blen=4; -elseif(id==5) - data=data2byte(swapbytes(int64(num)),'uint8'); - blen=8; -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/blen)) data(:)']; - end - data=['[' data(:)']; -else - data=reshape(data,blen,numel(data)/blen); - data(2:blen+1,:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function data=D_a(num,type,dim,format) -id=find(ismember('dD',type)); - -if(id==0) - error('unsupported float array'); -end - -if(id==1) - data=data2byte(single(num),'uint8'); -elseif(id==2) - data=data2byte(double(num),'uint8'); -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)']; - end - data=['[' data]; -else - data=reshape(data,(id*4),length(data)/(id*4)); - data(2:(id*4+1),:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function bytes=data2byte(varargin) -bytes=typecast(varargin{:}); -bytes=bytes(:)'; diff --git a/assignments/machine-learning-ex5/ex5/lib/jsonlab/varargin2struct.m b/assignments/machine-learning-ex5/ex5/lib/jsonlab/varargin2struct.m deleted file mode 100644 index 9a5c2b6..0000000 --- a/assignments/machine-learning-ex5/ex5/lib/jsonlab/varargin2struct.m +++ /dev/null @@ -1,40 +0,0 @@ -function opt=varargin2struct(varargin) -% -% opt=varargin2struct('param1',value1,'param2',value2,...) -% or -% opt=varargin2struct(...,optstruct,...) -% -% convert a series of input parameters into a structure -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% 'param', value: the input parameters should be pairs of a string and a value -% optstruct: if a parameter is a struct, the fields will be merged to the output struct -% -% output: -% opt: a struct where opt.param1=value1, opt.param2=value2 ... -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -len=length(varargin); -opt=struct; -if(len==0) return; end -i=1; -while(i<=len) - if(isstruct(varargin{i})) - opt=mergestruct(opt,varargin{i}); - elseif(ischar(varargin{i}) && i -# name: email -# type: sq_string -# elements: 1 -# length: 17 -scruel@vip.qq.com - - -# name: token -# type: sq_string -# elements: 1 -# length: 16 -rzlLbulJXcLtmnlY - - diff --git a/assignments/machine-learning-ex5/ex5/trainLinearReg.m b/assignments/machine-learning-ex5/ex5/trainLinearReg.m deleted file mode 100644 index eb89860..0000000 --- a/assignments/machine-learning-ex5/ex5/trainLinearReg.m +++ /dev/null @@ -1,21 +0,0 @@ -function [theta] = trainLinearReg(X, y, lambda) -%TRAINLINEARREG Trains linear regression given a dataset (X, y) and a -%regularization parameter lambda -% [theta] = TRAINLINEARREG (X, y, lambda) trains linear regression using -% the dataset (X, y) and regularization parameter lambda. Returns the -% trained parameters theta. -% - -% Initialize Theta -initial_theta = zeros(size(X, 2), 1); - -% Create "short hand" for the cost function to be minimized -costFunction = @(t) linearRegCostFunction(X, y, t, lambda); - -% Now, costFunction is a function that takes in only one argument -options = optimset('MaxIter', 200, 'GradObj', 'on'); - -% Minimize using fmincg -theta = fmincg(costFunction, initial_theta, options); - -end diff --git a/assignments/machine-learning-ex5/ex5/validationCurve.m b/assignments/machine-learning-ex5/ex5/validationCurve.m deleted file mode 100644 index 5d2135e..0000000 --- a/assignments/machine-learning-ex5/ex5/validationCurve.m +++ /dev/null @@ -1,58 +0,0 @@ -function [lambda_vec, error_train, error_val] = ... - validationCurve(X, y, Xval, yval) -%VALIDATIONCURVE Generate the train and validation errors needed to -%plot a validation curve that we can use to select lambda -% [lambda_vec, error_train, error_val] = ... -% VALIDATIONCURVE(X, y, Xval, yval) returns the train -% and validation errors (in error_train, error_val) -% for different values of lambda. You are given the training set (X, -% y) and validation set (Xval, yval). -% - -% Selected values of lambda (you should not change this) -lambda_vec = [10000]'; - -% You need to return these variables correctly. -error_train = zeros(length(lambda_vec), 1); -error_val = zeros(length(lambda_vec), 1); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Fill in this function to return training errors in -% error_train and the validation errors in error_val. The -% vector lambda_vec contains the different lambda parameters -% to use for each calculation of the errors, i.e, -% error_train(i), and error_val(i) should give -% you the errors obtained after training with -% lambda = lambda_vec(i) -% -% Note: You can loop over lambda_vec with the following: -% -% for i = 1:length(lambda_vec) -% lambda = lambda_vec(i); -% % Compute train / val errors when training linear -% % regression with regularization parameter lambda -% % You should store the result in error_train(i) -% % and error_val(i) -% .... -% -% end -% -% -for i = 1:length(lambda_vec) - lambda = lambda_vec(i) - [theta] = trainLinearReg(X, y, lambda); - error_train(i) = linearRegCostFunction(X, y, theta, 0); - error_val(i) = linearRegCostFunction(Xval, yval, theta, 0); -end - - - - - - - - - -% ========================================================================= - -end diff --git a/assignments/machine-learning-ex6/ex6.pdf b/assignments/machine-learning-ex6/ex6.pdf deleted file mode 100644 index da15504..0000000 Binary files a/assignments/machine-learning-ex6/ex6.pdf and /dev/null differ diff --git a/assignments/machine-learning-ex6/ex6/dataset3Params.m b/assignments/machine-learning-ex6/ex6/dataset3Params.m deleted file mode 100644 index 47e5bb0..0000000 --- a/assignments/machine-learning-ex6/ex6/dataset3Params.m +++ /dev/null @@ -1,46 +0,0 @@ -function [C, sigma] = dataset3Params(X, y, Xval, yval) -%DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise -%where you select the optimal (C, sigma) learning parameters to use for SVM -%with RBF kernel -% [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and -% sigma. You should complete this function to return the optimal C and -% sigma based on a cross-validation set. -% - -% You need to return the following variables correctly. -C = 1; -sigma = 0.3; - -% ====================== YOUR CODE HERE ====================== -% Instructions: Fill in this function to return the optimal C and sigma -% learning parameters found using the cross validation set. -% You can use svmPredict to predict the labels on the cross -% validation set. For example, -% predictions = svmPredict(model, Xval); -% will return the predictions on the cross validation set. -% -% Note: You can compute the prediction error using -% mean(double(predictions ~= yval)) -% -min = 999; -CA = [0.01; 0.03; 0.1; 0.3; 1; 3; 10; 30]; -sigmaA = [0.01; 0.03; 0.1; 0.3; 1; 3; 10; 30]; - - - -for i = 1 : length(CA) - for j = 1 : length(sigmaA) - model = svmTrain(X, y, CA(i), @(x1, x2) gaussianKernel(x1, x2, sigmaA(j))); - predictions = svmPredict(model, Xval); - tempMin = mean(double(predictions ~= yval)); - if tempMin < min - min = tempMin; - C = CA(i); - sigma = sigmaA(j); - end - end -end - -% ========================================================================= - -end diff --git a/assignments/machine-learning-ex6/ex6/emailFeatures.m b/assignments/machine-learning-ex6/ex6/emailFeatures.m deleted file mode 100644 index 9c60353..0000000 --- a/assignments/machine-learning-ex6/ex6/emailFeatures.m +++ /dev/null @@ -1,63 +0,0 @@ -function x = emailFeatures(word_indices) -%EMAILFEATURES takes in a word_indices vector and produces a feature vector -%from the word indices -% x = EMAILFEATURES(word_indices) takes in a word_indices vector and -% produces a feature vector from the word indices. - -% Total number of words in the dictionary -n = 1899; - -% You need to return the following variables correctly. -x = zeros(n, 1); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Fill in this function to return a feature vector for the -% given email (word_indices). To help make it easier to -% process the emails, we have have already pre-processed each -% email and converted each word in the email into an index in -% a fixed dictionary (of 1899 words). The variable -% word_indices contains the list of indices of the words -% which occur in one email. -% -% Concretely, if an email has the text: -% -% The quick brown fox jumped over the lazy dog. -% -% Then, the word_indices vector for this text might look -% like: -% -% 60 100 33 44 10 53 60 58 5 -% -% where, we have mapped each word onto a number, for example: -% -% the -- 60 -% quick -- 100 -% ... -% -% (note: the above numbers are just an example and are not the -% actual mappings). -% -% Your task is take one such word_indices vector and construct -% a binary feature vector that indicates whether a particular -% word occurs in the email. That is, x(i) = 1 when word i -% is present in the email. Concretely, if the word 'the' (say, -% index 60) appears in the email, then x(60) = 1. The feature -% vector should look like: -% -% x = [ 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 ... 0 0 0 1 0 ..]; -% -% - -for i = 1 : length(word_indices) - x(word_indices(i)) = 1; -end - - - - - - -% ========================================================================= - - -end diff --git a/assignments/machine-learning-ex6/ex6/emailSample1.txt b/assignments/machine-learning-ex6/ex6/emailSample1.txt deleted file mode 100644 index eac52a3..0000000 --- a/assignments/machine-learning-ex6/ex6/emailSample1.txt +++ /dev/null @@ -1,10 +0,0 @@ -> Anyone knows how much it costs to host a web portal ? -> -Well, it depends on how many visitors you're expecting. -This can be anywhere from less than 10 bucks a month to a couple of $100. -You should checkout http://www.rackspace.com/ or perhaps Amazon EC2 -if youre running something big.. - -To unsubscribe yourself from this mailing list, send an email to: -groupname-unsubscribe@egroups.com - diff --git a/assignments/machine-learning-ex6/ex6/emailSample2.txt b/assignments/machine-learning-ex6/ex6/emailSample2.txt deleted file mode 100644 index e47acda..0000000 --- a/assignments/machine-learning-ex6/ex6/emailSample2.txt +++ /dev/null @@ -1,34 +0,0 @@ -Folks, - -my first time posting - have a bit of Unix experience, but am new to Linux. - - -Just got a new PC at home - Dell box with Windows XP. Added a second hard disk -for Linux. Partitioned the disk and have installed Suse 7.2 from CD, which went -fine except it didn't pick up my monitor. - -I have a Dell branded E151FPp 15" LCD flat panel monitor and a nVidia GeForce4 -Ti4200 video card, both of which are probably too new to feature in Suse's default -set. I downloaded a driver from the nVidia website and installed it using RPM. -Then I ran Sax2 (as was recommended in some postings I found on the net), but -it still doesn't feature my video card in the available list. What next? - -Another problem. I have a Dell branded keyboard and if I hit Caps-Lock twice, -the whole machine crashes (in Linux, not Windows) - even the on/off switch is -inactive, leaving me to reach for the power cable instead. - -If anyone can help me in any way with these probs., I'd be really grateful - -I've searched the 'net but have run out of ideas. - -Or should I be going for a different version of Linux such as RedHat? Opinions -welcome. - -Thanks a lot, -Peter - --- -Irish Linux Users' Group: ilug@linux.ie -http://www.linux.ie/mailman/listinfo/ilug for (un)subscription information. -List maintainer: listmaster@linux.ie - - diff --git a/assignments/machine-learning-ex6/ex6/ex6.m b/assignments/machine-learning-ex6/ex6/ex6.m deleted file mode 100644 index 89d81b0..0000000 --- a/assignments/machine-learning-ex6/ex6/ex6.m +++ /dev/null @@ -1,150 +0,0 @@ -%% Machine Learning Online Class -% Exercise 6 | Support Vector Machines -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% exercise. You will need to complete the following functions: -% -% gaussianKernel.m -% dataset3Params.m -% processEmail.m -% emailFeatures.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% =============== Part 1: Loading and Visualizing Data ================ -% We start the exercise by first loading and visualizing the dataset. -% The following code will load the dataset into your environment and plot -% the data. -% - -fprintf('Loading and Visualizing Data ...\n') - -% Load from ex6data1: -% You will have X, y in your environment -load('ex6data1.mat'); - -% Plot training data -plotData(X, y); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ==================== Part 2: Training Linear SVM ==================== -% The following code will train a linear SVM on the dataset and plot the -% decision boundary learned. -% - -% Load from ex6data1: -% You will have X, y in your environment -load('ex6data1.mat'); - -fprintf('\nTraining Linear SVM ...\n') - -% You should try to change the C value below and see how the decision -% boundary varies (e.g., try C = 1000) -C = 1; -model = svmTrain(X, y, C, @linearKernel, 1e-3, 20); -visualizeBoundaryLinear(X, y, model); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =============== Part 3: Implementing Gaussian Kernel =============== -% You will now implement the Gaussian kernel to use -% with the SVM. You should complete the code in gaussianKernel.m -% -fprintf('\nEvaluating the Gaussian Kernel ...\n') - -x1 = [1 2 1]; x2 = [0 4 -1]; sigma = 2; -sim = gaussianKernel(x1, x2, sigma); - -fprintf(['Gaussian Kernel between x1 = [1; 2; 1], x2 = [0; 4; -1], sigma = %f :' ... - '\n\t%f\n(for sigma = 2, this value should be about 0.324652)\n'], sigma, sim); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =============== Part 4: Visualizing Dataset 2 ================ -% The following code will load the next dataset into your environment and -% plot the data. -% - -fprintf('Loading and Visualizing Data ...\n') - -% Load from ex6data2: -% You will have X, y in your environment -load('ex6data2.mat'); - -% Plot training data -plotData(X, y); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ========== Part 5: Training SVM with RBF Kernel (Dataset 2) ========== -% After you have implemented the kernel, we can now use it to train the -% SVM classifier. -% -fprintf('\nTraining SVM with RBF Kernel (this may take 1 to 2 minutes) ...\n'); - -% Load from ex6data2: -% You will have X, y in your environment -load('ex6data2.mat'); - -% SVM Parameters -C = 1; sigma = 0.1; - -% We set the tolerance and max_passes lower here so that the code will run -% faster. However, in practice, you will want to run the training to -% convergence. -model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma)); -visualizeBoundary(X, y, model); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =============== Part 6: Visualizing Dataset 3 ================ -% The following code will load the next dataset into your environment and -% plot the data. -% - -fprintf('Loading and Visualizing Data ...\n') - -% Load from ex6data3: -% You will have X, y in your environment -load('ex6data3.mat'); - -% Plot training data -plotData(X, y); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ========== Part 7: Training SVM with RBF Kernel (Dataset 3) ========== - -% This is a different dataset that you can use to experiment with. Try -% different values of C and sigma here. -% - -% Load from ex6data3: -% You will have X, y in your environment -load('ex6data3.mat'); - -% Try different SVM Parameters here -[C, sigma] = dataset3Params(X, y, Xval, yval); - -% Train the SVM -model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma)); -visualizeBoundary(X, y, model); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - diff --git a/assignments/machine-learning-ex6/ex6/ex6_spam.m b/assignments/machine-learning-ex6/ex6/ex6_spam.m deleted file mode 100644 index 479848e..0000000 --- a/assignments/machine-learning-ex6/ex6/ex6_spam.m +++ /dev/null @@ -1,138 +0,0 @@ -%% Machine Learning Online Class -% Exercise 6 | Spam Classification with SVMs -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% exercise. You will need to complete the following functions: -% -% gaussianKernel.m -% dataset3Params.m -% processEmail.m -% emailFeatures.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% ==================== Part 1: Email Preprocessing ==================== -% To use an SVM to classify emails into Spam v.s. Non-Spam, you first need -% to convert each email into a vector of features. In this part, you will -% implement the preprocessing steps for each email. You should -% complete the code in processEmail.m to produce a word indices vector -% for a given email. - -fprintf('\nPreprocessing sample email (emailSample1.txt)\n'); - -% Extract Features -file_contents = readFile('emailSample1.txt'); -word_indices = processEmail(file_contents); - -% Print Stats -fprintf('Word Indices: \n'); -fprintf(' %d', word_indices); -fprintf('\n\n'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ==================== Part 2: Feature Extraction ==================== -% Now, you will convert each email into a vector of features in R^n. -% You should complete the code in emailFeatures.m to produce a feature -% vector for a given email. - -fprintf('\nExtracting features from sample email (emailSample1.txt)\n'); - -% Extract Features -file_contents = readFile('emailSample1.txt'); -word_indices = processEmail(file_contents); -features = emailFeatures(word_indices); - -% Print Stats -fprintf('Length of feature vector: %d\n', length(features)); -fprintf('Number of non-zero entries: %d\n', sum(features > 0)); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =========== Part 3: Train Linear SVM for Spam Classification ======== -% In this section, you will train a linear classifier to determine if an -% email is Spam or Not-Spam. - -% Load the Spam Email dataset -% You will have X, y in your environment -load('spamTrain.mat'); - -fprintf('\nTraining Linear SVM (Spam Classification)\n') -fprintf('(this may take 1 to 2 minutes) ...\n') - -C = 0.1; -model = svmTrain(X, y, C, @linearKernel); - -p = svmPredict(model, X); - -fprintf('Training Accuracy: %f\n', mean(double(p == y)) * 100); - -%% =================== Part 4: Test Spam Classification ================ -% After training the classifier, we can evaluate it on a test set. We have -% included a test set in spamTest.mat - -% Load the test dataset -% You will have Xtest, ytest in your environment -load('spamTest.mat'); - -fprintf('\nEvaluating the trained Linear SVM on a test set ...\n') - -p = svmPredict(model, Xtest); - -fprintf('Test Accuracy: %f\n', mean(double(p == ytest)) * 100); -pause; - - -%% ================= Part 5: Top Predictors of Spam ==================== -% Since the model we are training is a linear SVM, we can inspect the -% weights learned by the model to understand better how it is determining -% whether an email is spam or not. The following code finds the words with -% the highest weights in the classifier. Informally, the classifier -% 'thinks' that these words are the most likely indicators of spam. -% - -% Sort the weights and obtin the vocabulary list -[weight, idx] = sort(model.w, 'descend'); -vocabList = getVocabList(); - -fprintf('\nTop predictors of spam: \n'); -for i = 1:15 - fprintf(' %-15s (%f) \n', vocabList{idx(i)}, weight(i)); -end - -fprintf('\n\n'); -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - -%% =================== Part 6: Try Your Own Emails ===================== -% Now that you've trained the spam classifier, you can use it on your own -% emails! In the starter code, we have included spamSample1.txt, -% spamSample2.txt, emailSample1.txt and emailSample2.txt as examples. -% The following code reads in one of these emails and then uses your -% learned SVM classifier to determine whether the email is Spam or -% Not Spam - -% Set the file to be read in (change this to spamSample2.txt, -% emailSample1.txt or emailSample2.txt to see different predictions on -% different emails types). Try your own emails as well! -filename = 'spamSample1.txt'; - -% Read and predict -file_contents = readFile(filename); -word_indices = processEmail(file_contents); -x = emailFeatures(word_indices); -p = svmPredict(model, x); - -fprintf('\nProcessed %s\n\nSpam Classification: %d\n', filename, p); -fprintf('(1 indicates spam, 0 indicates not spam)\n\n'); - diff --git a/assignments/machine-learning-ex6/ex6/ex6data1.mat b/assignments/machine-learning-ex6/ex6/ex6data1.mat deleted file mode 100644 index ae0d2aa..0000000 Binary files a/assignments/machine-learning-ex6/ex6/ex6data1.mat and /dev/null differ diff --git a/assignments/machine-learning-ex6/ex6/ex6data2.mat b/assignments/machine-learning-ex6/ex6/ex6data2.mat deleted file mode 100644 index c6ad661..0000000 Binary files a/assignments/machine-learning-ex6/ex6/ex6data2.mat and /dev/null differ diff --git a/assignments/machine-learning-ex6/ex6/ex6data3.mat b/assignments/machine-learning-ex6/ex6/ex6data3.mat deleted file mode 100644 index a0441ac..0000000 Binary files a/assignments/machine-learning-ex6/ex6/ex6data3.mat and /dev/null differ diff --git a/assignments/machine-learning-ex6/ex6/gaussianKernel.m b/assignments/machine-learning-ex6/ex6/gaussianKernel.m deleted file mode 100644 index 9efc57e..0000000 --- a/assignments/machine-learning-ex6/ex6/gaussianKernel.m +++ /dev/null @@ -1,26 +0,0 @@ -function sim = gaussianKernel(x1, x2, sigma) -%RBFKERNEL returns a radial basis function kernel between x1 and x2 -% sim = gaussianKernel(x1, x2) returns a gaussian kernel between x1 and x2 -% and returns the value in sim - -% Ensure that x1 and x2 are column vectors -x1 = x1(:); x2 = x2(:); - -% You need to return the following variables correctly. -sim = 0; - -% ====================== YOUR CODE HERE ====================== -% Instructions: Fill in this function to return the similarity between x1 -% and x2 computed using a Gaussian kernel with bandwidth -% sigma -% -% - -sim = exp(-sum((x1 .- x2) .^ 2) / (2 * sigma ^ 2)); - - - - -% ============================================================= - -end diff --git a/assignments/machine-learning-ex6/ex6/getVocabList.m b/assignments/machine-learning-ex6/ex6/getVocabList.m deleted file mode 100644 index 0b5f427..0000000 --- a/assignments/machine-learning-ex6/ex6/getVocabList.m +++ /dev/null @@ -1,25 +0,0 @@ -function vocabList = getVocabList() -%GETVOCABLIST reads the fixed vocabulary list in vocab.txt and returns a -%cell array of the words -% vocabList = GETVOCABLIST() reads the fixed vocabulary list in vocab.txt -% and returns a cell array of the words in vocabList. - - -%% Read the fixed vocabulary list -fid = fopen('vocab.txt'); - -% Store all dictionary words in cell array vocab{} -n = 1899; % Total number of words in the dictionary - -% For ease of implementation, we use a struct to map the strings => integers -% In practice, you'll want to use some form of hashmap -vocabList = cell(n, 1); -for i = 1:n - % Word Index (can ignore since it will be = i) - fscanf(fid, '%d', 1); - % Actual Word - vocabList{i} = fscanf(fid, '%s', 1); -end -fclose(fid); - -end diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/AUTHORS.txt b/assignments/machine-learning-ex6/ex6/lib/jsonlab/AUTHORS.txt deleted file mode 100644 index 9dd3fc7..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/AUTHORS.txt +++ /dev/null @@ -1,41 +0,0 @@ -The author of "jsonlab" toolbox is Qianqian Fang. Qianqian -is currently an Assistant Professor at Massachusetts General Hospital, -Harvard Medical School. - -Address: Martinos Center for Biomedical Imaging, - Massachusetts General Hospital, - Harvard Medical School - Bldg 149, 13th St, Charlestown, MA 02129, USA -URL: http://nmr.mgh.harvard.edu/~fangq/ -Email: or - - -The script loadjson.m was built upon previous works by - -- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 - date: 2009/11/02 -- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 - date: 2009/03/22 -- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565 - date: 2008/07/03 - - -This toolbox contains patches submitted by the following contributors: - -- Blake Johnson - part of revision 341 - -- Niclas Borlin - various fixes in revision 394, including - - loadjson crashes for all-zero sparse matrix. - - loadjson crashes for empty sparse matrix. - - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson. - - loadjson crashes for sparse real column vector. - - loadjson crashes for sparse complex column vector. - - Data is corrupted by savejson for sparse real row vector. - - savejson crashes for sparse complex row vector. - -- Yul Kang - patches for svn revision 415. - - savejson saves an empty cell array as [] instead of null - - loadjson differentiates an empty struct from an empty array diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/ChangeLog.txt b/assignments/machine-learning-ex6/ex6/lib/jsonlab/ChangeLog.txt deleted file mode 100644 index 07824f5..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/ChangeLog.txt +++ /dev/null @@ -1,74 +0,0 @@ -============================================================================ - - JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave - ----------------------------------------------------------------------------- - -JSONlab ChangeLog (key features marked by *): - -== JSONlab 1.0 (codename: Optimus - Final), FangQ == - - 2015/01/02 polish help info for all major functions, update examples, finalize 1.0 - 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson - -== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ == - - 2014/11/22 show progress bar in loadjson ('ShowProgress') - 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact') - 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels - 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab - -== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ == - - 2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8 - 2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson - 2014/08/04 escape special characters in a JSON string - 2014/02/16 fix a bug when saving ubjson files - -== JSONlab 0.9.9 (codename: Optimus - beta), FangQ == - - 2014/01/22 use binary read and write in saveubjson and loadubjson - -== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ == - - 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang) - -== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ == - 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson - -== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ == - 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin) - -== JSONlab 0.9.0 (codename: Rodimus), FangQ == - - 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson - 2012/06/01 support JSONP in savejson - 2012/05/25 fix the empty cell bug (reported by Cyril Davin) - 2012/04/05 savejson can save to a file (suggested by Patrick Rapin) - -== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ == - - 2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS - 2012/01/25 patch to handle root-less objects, contributed by Blake Johnson - -== JSONlab 0.8.0 (codename: Sentiel), FangQ == - - 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab - 2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer - 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson - 2011/11/18 fix struct array bug reported by Mykel Kochenderfer - -== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ == - - 2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration - 2011/10/20 loadjson supports JSON collections - concatenated JSON objects - -== JSONlab 0.5.0 (codename: Nexus), FangQ == - - 2011/10/16 package and release jsonlab 0.5.0 - 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug - 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level - 2011/10/10 create jsonlab project, start jsonlab website, add online documentation - 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support - 2011/10/06 *savejson works for structs, cells and arrays - 2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/LICENSE_BSD.txt b/assignments/machine-learning-ex6/ex6/lib/jsonlab/LICENSE_BSD.txt deleted file mode 100644 index 32d66cb..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/LICENSE_BSD.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright 2011-2015 Qianqian Fang . All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are -permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, this list - of conditions and the following disclaimer in the documentation and/or other materials - provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND -FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS -OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -The views and conclusions contained in the software and documentation are those of the -authors and should not be interpreted as representing official policies, either expressed -or implied, of the copyright holders. diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/README.txt b/assignments/machine-learning-ex6/ex6/lib/jsonlab/README.txt deleted file mode 100644 index 7b4f732..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/README.txt +++ /dev/null @@ -1,394 +0,0 @@ -=============================================================================== -= JSONLab = -= An open-source MATLAB/Octave JSON encoder and decoder = -=============================================================================== - -*Copyright (C) 2011-2015 Qianqian Fang -*License: BSD License, see License_BSD.txt for details -*Version: 1.0 (Optimus - Final) - -------------------------------------------------------------------------------- - -Table of Content: - -I. Introduction -II. Installation -III.Using JSONLab -IV. Known Issues and TODOs -V. Contribution and feedback - -------------------------------------------------------------------------------- - -I. Introduction - -JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, -human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format -to represent complex and hierarchical data. It is as powerful as -[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely -used for data-exchange in applications, and is essential for the wild success -of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and -[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. - -UBJSON (Universal Binary JSON) is a binary JSON format, specifically -optimized for compact file size and better performance while keeping -the semantics as simple as the text-based JSON format. Using the UBJSON -format allows to wrap complex binary data in a flexible and extensible -structure, making it possible to process complex and large dataset -without accuracy loss due to text conversions. - -We envision that both JSON and its binary version will serve as part of -the mainstream data-exchange formats for scientific research in the future. -It will provide the flexibility and generality achieved by other popular -general-purpose file specifications, such as -[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly -reduced complexity and enhanced performance. - -JSONLab is a free and open-source implementation of a JSON/UBJSON encoder -and a decoder in the native MATLAB language. It can be used to convert a MATLAB -data structure (array, struct, cell, struct array and cell array) into -JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB -data structure. JSONLab supports both MATLAB and -[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone). - -------------------------------------------------------------------------------- - -II. Installation - -The installation of JSONLab is no different than any other simple -MATLAB toolbox. You only need to download/unzip the JSONLab package -to a folder, and add the folder's path to MATLAB/Octave's path list -by using the following command: - - addpath('/path/to/jsonlab'); - -If you want to add this path permanently, you need to type "pathtool", -browse to the jsonlab root folder and add to the list, then click "Save". -Then, run "rehash" in MATLAB, and type "which loadjson", if you see an -output, that means JSONLab is installed for MATLAB/Octave. - -------------------------------------------------------------------------------- - -III.Using JSONLab - -JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, -and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and -two equivallent functions -- loadubjson and saveubjson for the binary -JSON. The detailed help info for the four functions can be found below: - -=== loadjson.m === -
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          Franois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-
- -=== savejson.m === - -
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-
- -=== loadubjson.m === - -
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-
- -=== saveubjson.m === - -
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-
- - -=== examples === - -Under the "examples" folder, you can find several scripts to demonstrate the -basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you -will see the conversions from MATLAB data structure to JSON text and backward. -In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet -and validate the loadjson/savejson functions for regression testing purposes. -Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson -and loadubjson pairs for various matlab data structures. - -Please run these examples and understand how JSONLab works before you use -it to process your data. - -------------------------------------------------------------------------------- - -IV. Known Issues and TODOs - -JSONLab has several known limitations. We are striving to make it more general -and robust. Hopefully in a few future releases, the limitations become less. - -Here are the known issues: - -# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays; -# When processing names containing multi-byte characters, Octave and MATLAB \ -can give different field-names; you can use feature('DefaultCharacterSet','latin1') \ -in MATLAB to get consistant results -# savejson can not handle class and dataset. -# saveubjson converts a logical array into a uint8 ([U]) array -# an unofficial N-D array count syntax is implemented in saveubjson. We are \ -actively communicating with the UBJSON spec maintainer to investigate the \ -possibility of making it upstream -# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \ -files, however, it can parse all UBJSON files produced by saveubjson. - -------------------------------------------------------------------------------- - -V. Contribution and feedback - -JSONLab is an open-source project. This means you can not only use it and modify -it as you wish, but also you can contribute your changes back to JSONLab so -that everyone else can enjoy the improvement. For anyone who want to contribute, -please download JSONLab source code from it's subversion repository by using the -following command: - - svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab - -You can make changes to the files as needed. Once you are satisfied with your -changes, and ready to share it with others, please cd the root directory of -JSONLab, and type - - svn diff > yourname_featurename.patch - -You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at -the email address shown in the beginning of this file. Qianqian will review -the changes and commit it to the subversion if they are satisfactory. - -We appreciate any suggestions and feedbacks from you. Please use iso2mesh's -mailing list to report any questions you may have with JSONLab: - -http://groups.google.com/group/iso2mesh-users?hl=en&pli=1 - -(Subscription to the mailing list is needed in order to post messages). diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/jsonopt.m b/assignments/machine-learning-ex6/ex6/lib/jsonlab/jsonopt.m deleted file mode 100644 index 0bebd8d..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/jsonopt.m +++ /dev/null @@ -1,32 +0,0 @@ -function val=jsonopt(key,default,varargin) -% -% val=jsonopt(key,default,optstruct) -% -% setting options based on a struct. The struct can be produced -% by varargin2struct from a list of 'param','value' pairs -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% -% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $ -% -% input: -% key: a string with which one look up a value from a struct -% default: if the key does not exist, return default -% optstruct: a struct where each sub-field is a key -% -% output: -% val: if key exists, val=optstruct.key; otherwise val=default -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -val=default; -if(nargin<=2) return; end -opt=varargin{1}; -if(isstruct(opt) && isfield(opt,key)) - val=getfield(opt,key); -end - diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m b/assignments/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m deleted file mode 100644 index 42798c0..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m +++ /dev/null @@ -1,566 +0,0 @@ -function data = loadjson(fname,varargin) -% -% data=loadjson(fname,opt) -% or -% data=loadjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09, including previous works from -% -% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 -% created on 2009/11/02 -% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 -% created on 2009/03/22 -% Joel Feenstra: -% http://www.mathworks.com/matlabcentral/fileexchange/20565 -% created on 2008/07/03 -% -% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a JSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.FastArrayParser [1|0 or integer]: if set to 1, use a -% speed-optimized array parser when loading an -% array object. The fast array parser may -% collapse block arrays into a single large -% array similar to rules defined in cell2mat; 0 to -% use a legacy parser; if set to a larger-than-1 -% value, this option will specify the minimum -% dimension to enable the fast array parser. For -% example, if the input is a 3D array, setting -% FastArrayParser to 1 will return a 3D array; -% setting to 2 will return a cell array of 2D -% arrays; setting to 3 will return to a 2D cell -% array of 1D vectors; setting to 4 will return a -% 3D cell array. -% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}') -% dat=loadjson(['examples' filesep 'example1.json']) -% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); - -if(jsonopt('ShowProgress',0,opt)==1) - opt.progressbar_=waitbar(0,'loading ...'); -end -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end -if(isfield(opt,'progressbar_')) - close(opt.progressbar_); -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=data(j).x0x5F_ArraySize_; - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - if next_char ~= '}' - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - parse_char(':'); - val = parse_value(varargin{:}); - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' - break; - end - parse_char(','); - end - end - parse_char('}'); - -%%------------------------------------------------------------------------- - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim2=[]; - arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:}); - pbar=jsonopt('progressbar_',-1,varargin{:}); - - if next_char ~= ']' - if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:})) - [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos); - arraystr=['[' inStr(pos:endpos)]; - arraystr=regexprep(arraystr,'"_NaN_"','NaN'); - arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf'); - arraystr(arraystr==sprintf('\n'))=[]; - arraystr(arraystr==sprintf('\r'))=[]; - %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed - if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D - astr=inStr((e1l+1):(e1r-1)); - astr=regexprep(astr,'"_NaN_"','NaN'); - astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf'); - astr(astr==sprintf('\n'))=[]; - astr(astr==sprintf('\r'))=[]; - astr(astr==' ')=''; - if(isempty(find(astr=='[', 1))) % array is 2D - dim2=length(sscanf(astr,'%f,',[1 inf])); - end - else % array is 1D - astr=arraystr(2:end-1); - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]); - if(nextidx>=length(astr)-1) - object=obj; - pos=endpos; - parse_char(']'); - return; - end - end - if(~isempty(dim2)) - astr=arraystr; - astr(astr=='[')=''; - astr(astr==']')=''; - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf); - if(nextidx>=length(astr)-1) - object=reshape(obj,dim2,numel(obj)/dim2)'; - pos=endpos; - parse_char(']'); - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end - return; - end - end - arraystr=regexprep(arraystr,'\]\s*,','];'); - else - arraystr='['; - end - try - if(isoct && regexp(arraystr,'"','once')) - error('Octave eval can produce empty cells for JSON-like input'); - end - object=eval(arraystr); - pos=endpos; - catch - while 1 - newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1); - val = parse_value(newopt); - object{end+1} = val; - if next_char == ']' - break; - end - parse_char(','); - end - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - parse_char(']'); - - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr len esc index_esc len_esc - % len, ns = length(inStr), keyboard - if inStr(pos) ~= '"' - error_pos('String starting with " expected at position %d'); - else - pos = pos + 1; - end - str = ''; - while pos <= len - while index_esc <= len_esc && esc(index_esc) < pos - index_esc = index_esc + 1; - end - if index_esc > len_esc - str = [str inStr(pos:len)]; - pos = len + 1; - break; - else - str = [str inStr(pos:esc(index_esc)-1)]; - pos = esc(index_esc); - end - nstr = length(str); switch inStr(pos) - case '"' - pos = pos + 1; - if(~isempty(str)) - if(strcmp(str,'_Inf_')) - str=Inf; - elseif(strcmp(str,'-_Inf_')) - str=-Inf; - elseif(strcmp(str,'_NaN_')) - str=NaN; - end - end - return; - case '\' - if pos+1 > len - error_pos('End of file reached right after escape character'); - end - pos = pos + 1; - switch inStr(pos) - case {'"' '\' '/'} - str(nstr+1) = inStr(pos); - pos = pos + 1; - case {'b' 'f' 'n' 'r' 't'} - str(nstr+1) = sprintf(['\' inStr(pos)]); - pos = pos + 1; - case 'u' - if pos+4 > len - error_pos('End of file reached in escaped unicode character'); - end - str(nstr+(1:6)) = inStr(pos-1:pos+4); - pos = pos + 5; - end - otherwise % should never happen - str(nstr+1) = inStr(pos), keyboard - pos = pos + 1; - end - end - error_pos('End of file while expecting end of inStr'); - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct - currstr=inStr(pos:end); - numstr=0; - if(isoct~=0) - numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end'); - [num, one] = sscanf(currstr, '%f', 1); - delta=numstr+1; - else - [num, one, err, delta] = sscanf(currstr, '%f', 1); - if ~isempty(err) - error_pos('Error reading number at position %d'); - end - end - pos = pos + delta-1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - pbar=jsonopt('progressbar_',-1,varargin{:}); - if(pbar>0) - waitbar(pos/len,pbar,'loading ...'); - end - - switch(inStr(pos)) - case '"' - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'-','0','1','2','3','4','5','6','7','8','9'} - val = parse_number(varargin{:}); - return; - case 't' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true') - val = true; - pos = pos + 4; - return; - end - case 'f' - if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false') - val = false; - pos = pos + 5; - return; - end - case 'n' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null') - val = []; - pos = pos + 4; - return; - end - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m b/assignments/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m deleted file mode 100644 index 0155115..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m +++ /dev/null @@ -1,528 +0,0 @@ -function data = loadubjson(fname,varargin) -% -% data=loadubjson(fname,opt) -% or -% data=loadubjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/01 -% -% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a UBJSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.IntEndian [B|L]: specify the endianness of the integer fields -% in the UBJSON input data. B - Big-Endian format for -% integers (as required in the UBJSON specification); -% L - input integer fields are in Little-Endian order. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% obj=struct('string','value','array',[1 2 3]); -% ubjdata=saveubjson('obj',obj); -% dat=loadubjson(ubjdata) -% dat=loadubjson(['examples' filesep 'example1.ubj']) -% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); -fileendian=upper(jsonopt('IntEndian','B',opt)); -[os,maxelem,systemendian]=computer; - -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end - - -%% -function newdata=parse_collection(id,data,obj) - -if(jsoncount>0 && exist('data','var')) - if(~iscell(data)) - newdata=cell(1); - newdata{1}=data; - data=newdata; - end -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=double(data(j).x0x5F_ArraySize_); - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); % TODO - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - count=double(parse_number()); - end - if next_char ~= '}' - num=0; - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - %parse_char(':'); - val = parse_value(varargin{:}); - num=num+1; - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' || (count>=0 && num>=count) - break; - end - %parse_char(','); - end - end - if(count==-1) - parse_char('}'); - end - -%%------------------------------------------------------------------------- -function [cid,len]=elem_info(type) -id=strfind('iUIlLdD',type); -dataclass={'int8','uint8','int16','int32','int64','single','double'}; -bytelen=[1,1,2,4,8,4,8]; -if(id>0) - cid=dataclass{id}; - len=bytelen(id); -else - error_pos('unsupported type at position %d'); -end -%%------------------------------------------------------------------------- - - -function [data adv]=parse_block(type,count,varargin) -global pos inStr isoct fileendian systemendian -[cid,len]=elem_info(type); -datastr=inStr(pos:pos+len*count-1); -if(isoct) - newdata=int8(datastr); -else - newdata=uint8(datastr); -end -id=strfind('iUIlLdD',type); -if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,cid)); -end -data=typecast(newdata,cid); -adv=double(len*count); - -%%------------------------------------------------------------------------- - - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim=[]; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - if(next_char=='[') - dim=parse_array(varargin{:}); - count=prod(double(dim)); - else - count=double(parse_number()); - end - end - if(~isempty(type)) - if(count>=0) - [object adv]=parse_block(type,count,varargin{:}); - if(~isempty(dim)) - object=reshape(object,dim); - end - pos=pos+adv; - return; - else - endpos=matching_bracket(inStr,pos); - [cid,len]=elem_info(type); - count=(endpos-pos)/len; - [object adv]=parse_block(type,count,varargin{:}); - pos=pos+adv; - parse_char(']'); - return; - end - end - if next_char ~= ']' - while 1 - val = parse_value(varargin{:}); - object{end+1} = val; - if next_char == ']' - break; - end - %parse_char(','); - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - if(count==-1) - parse_char(']'); - end - -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr esc index_esc len_esc - % len, ns = length(inStr), keyboard - type=inStr(pos); - if type ~= 'S' && type ~= 'C' && type ~= 'H' - error_pos('String starting with S expected at position %d'); - else - pos = pos + 1; - end - if(type == 'C') - str=inStr(pos); - pos=pos+1; - return; - end - bytelen=double(parse_number()); - if(length(inStr)>=pos+bytelen-1) - str=inStr(pos:pos+bytelen-1); - pos=pos+bytelen; - else - error_pos('End of file while expecting end of inStr'); - end - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct fileendian systemendian - id=strfind('iUIlLdD',inStr(pos)); - if(isempty(id)) - error_pos('expecting a number at position %d'); - end - type={'int8','uint8','int16','int32','int64','single','double'}; - bytelen=[1,1,2,4,8,4,8]; - datastr=inStr(pos+1:pos+bytelen(id)); - if(isoct) - newdata=int8(datastr); - else - newdata=uint8(datastr); - end - if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,type{id})); - end - num=typecast(newdata,type{id}); - pos = pos + bytelen(id)+1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - switch(inStr(pos)) - case {'S','C','H'} - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'i','U','I','l','L','d','D'} - val = parse_number(varargin{:}); - return; - case 'T' - val = true; - pos = pos + 1; - return; - case 'F' - val = false; - pos = pos + 1; - return; - case {'Z','N'} - val = []; - pos = pos + 1; - return; - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos e1l e1r maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/mergestruct.m b/assignments/machine-learning-ex6/ex6/lib/jsonlab/mergestruct.m deleted file mode 100644 index 6de6100..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/mergestruct.m +++ /dev/null @@ -1,33 +0,0 @@ -function s=mergestruct(s1,s2) -% -% s=mergestruct(s1,s2) -% -% merge two struct objects into one -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% s1,s2: a struct object, s1 and s2 can not be arrays -% -% output: -% s: the merged struct object. fields in s1 and s2 will be combined in s. -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(~isstruct(s1) || ~isstruct(s2)) - error('input parameters contain non-struct'); -end -if(length(s1)>1 || length(s2)>1) - error('can not merge struct arrays'); -end -fn=fieldnames(s2); -s=s1; -for i=1:length(fn) - s=setfield(s,fn{i},getfield(s2,fn{i})); -end - diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/savejson.m b/assignments/machine-learning-ex6/ex6/lib/jsonlab/savejson.m deleted file mode 100644 index 7e84076..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/savejson.m +++ /dev/null @@ -1,475 +0,0 @@ -function json=savejson(rootname,obj,varargin) -% -% json=savejson(rootname,obj,filename) -% or -% json=savejson(rootname,obj,opt) -% json=savejson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript -% Object Notation) string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09 -% -% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array). -% filename: a string for the file name to save the output JSON data. -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.FloatFormat ['%.10g'|string]: format to show each numeric element -% of a 1D/2D array; -% opt.ArrayIndent [1|0]: if 1, output explicit data array with -% precedent indentation; if 0, no indentation -% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [0|1]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern -% to represent +/-Inf. The matched pattern is '([-+]*)Inf' -% and $1 represents the sign. For those who want to use -% 1e999 to represent Inf, they can set opt.Inf to '$11e999' -% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern -% to represent NaN -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSONP='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode. -% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs) -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a string in the JSON format (see http://json.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% savejson('jmesh',jsonmesh) -% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end - -whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -if(jsonopt('Compact',0,opt)==1) - whitespaces=struct('tab','','newline','','sep',','); -end -if(~isfield(opt,'whitespaces_')) - opt.whitespaces_=whitespaces; -end - -nl=whitespaces.newline; - -json=obj2json(rootname,obj,rootlevel,opt); -if(rootisarray) - json=sprintf('%s%s',json,nl); -else - json=sprintf('{%s%s%s}\n',nl,json,nl); -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=sprintf('%s(%s);%s',jsonp,json,nl); -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - if(jsonopt('SaveBinary',0,opt)==1) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - else - fid = fopen(opt.FileName, 'wt'); - fwrite(fid,json,'char'); - end - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2json(name,item,level,varargin) - -if(iscell(item)) - txt=cell2json(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2json(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2json(name,item,level,varargin{:}); -else - txt=mat2json(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2json(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -nl=ws.newline; -if(len>1) - if(~isempty(name)) - txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; - else - txt=sprintf('%s[%s',padding0,nl); - end -elseif(len==0) - if(~isempty(name)) - txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; - else - txt=sprintf('%s[]',padding0); - end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:})); - if(i1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=struct2json(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1)); -nl=ws.newline; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding0,nl); end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); - else - txt=sprintf('%s%s{%s',txt,padding1,nl); - end - if(~isempty(names)) - for e=1:length(names) - txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})); - if(e1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=str2json(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding1,nl); end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - if(isoct) - val=regexprep(item(e,:),'\\','\\'); - val=regexprep(val,'"','\"'); - val=regexprep(val,'^"','\"'); - else - val=regexprep(item(e,:),'\\','\\\\'); - val=regexprep(val,'"','\\"'); - val=regexprep(val,'^"','\\"'); - end - val=escapejsonstring(val); - if(len==1) - obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"']; - if(isempty(name)) obj=['"',val,'"']; end - txt=sprintf('%s%s%s%s',txt,padding1,obj); - else - txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']); - end - if(e==len) sep=''; end - txt=sprintf('%s%s',txt,sep); -end -if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end - -%%------------------------------------------------------------------------- -function txt=mat2json(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:})) - if(isempty(name)) - txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - else - txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - end -else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0) - numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']',''); - else - numtxt=matdata2json(item,level+1,varargin{:}); - end - if(isempty(name)) - txt=sprintf('%s%s',padding1,numtxt); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - else - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - end - end - return; -end -dataformat='%s%s%s%s%s'; - -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep); - if(size(item,1)==1) - % Row vector, store only column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([iy(:),data'],level+2,varargin{:}), nl); - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,data],level+2,varargin{:}), nl); - else - % General case, store row and column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,iy,data],level+2,varargin{:}), nl); - end -else - if(isreal(item)) - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json(item(:)',level+2,varargin{:}), nl); - else - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl); - end -end -txt=sprintf('%s%s%s',txt,padding1,'}'); - -%%------------------------------------------------------------------------- -function txt=matdata2json(mat,level,varargin) - -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -tab=ws.tab; -nl=ws.newline; - -if(size(mat,1)==1) - pre=''; - post=''; - level=level-1; -else - pre=sprintf('[%s',nl); - post=sprintf('%s%s]',nl,repmat(tab,1,level-1)); -end - -if(isempty(mat)) - txt='null'; - return; -end -floatformat=jsonopt('FloatFormat','%.10g',varargin{:}); -%if(numel(mat)>1) - formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]]; -%else -% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]]; -%end - -if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1) - formatstr=[repmat(tab,1,level) formatstr]; -end - -txt=sprintf(formatstr,mat'); -txt(end-length(nl):end)=[]; -if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1) - txt=regexprep(txt,'1','true'); - txt=regexprep(txt,'0','false'); -end -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],\n[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -txt=[pre txt post]; -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end - -%%------------------------------------------------------------------------- -function newstr=escapejsonstring(str) -newstr=str; -isoct=exist('OCTAVE_VERSION','builtin'); -if(isoct) - vv=sscanf(OCTAVE_VERSION,'%f'); - if(vv(1)>=3.8) isoct=0; end -end -if(isoct) - escapechars={'\a','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},escapechars{i}); - end -else - escapechars={'\a','\b','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\')); - end -end diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m b/assignments/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m deleted file mode 100644 index eaec433..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m +++ /dev/null @@ -1,504 +0,0 @@ -function json=saveubjson(rootname,obj,varargin) -% -% json=saveubjson(rootname,obj,filename) -% or -% json=saveubjson(rootname,obj,opt) -% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a Universal -% Binary JSON (UBJSON) binary string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/17 -% -% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array) -% filename: a string for the file name to save the output UBJSON data -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [1|0]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSON='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a binary string in the UBJSON format (see http://ubjson.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% saveubjson('jsonmesh',jsonmesh) -% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end -json=obj2ubjson(rootname,obj,rootlevel,opt); -if(~rootisarray) - json=['{' json '}']; -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=[jsonp '(' json ')']; -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2ubjson(name,item,level,varargin) - -if(iscell(item)) - txt=cell2ubjson(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2ubjson(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2ubjson(name,item,level,varargin{:}); -else - txt=mat2ubjson(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2ubjson(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); % let's handle 1D cell first -if(len>1) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) '[']; name=''; - else - txt='['; - end -elseif(len==0) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; - else - txt='Z'; - end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})]; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=struct2ubjson(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=[txt S_(checkname(name,varargin{:})) '{']; - else - txt=[txt '{']; - end - if(~isempty(names)) - for e=1:length(names) - txt=[txt obj2ubjson(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})]; - end - end - txt=[txt '}']; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=str2ubjson(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - val=item(e,:); - if(len==1) - obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),'']; - if(isempty(name)) obj=['',S_(val),'']; end - txt=[txt,'',obj]; - else - txt=[txt,'',['',S_(val),'']]; - end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=mat2ubjson(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) || jsonopt('ArrayToStruct',0,varargin{:})) - cid=I_(uint32(max(size(item)))); - if(isempty(name)) - txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ]; - else - if(isempty(item)) - txt=[S_(checkname(name,varargin{:})),'Z']; - return; - else - txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))]; - end - end -else - if(isempty(name)) - txt=matdata2ubjson(item,level+1,varargin{:}); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']',''); - txt=[S_(checkname(name,varargin{:})) numtxt]; - else - txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})]; - end - end - return; -end -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=[txt,S_('_ArrayIsComplex_'),'T']; - end - txt=[txt,S_('_ArrayIsSparse_'),'T']; - if(size(item,1)==1) - % Row vector, store only column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([iy(:),data'],level+2,varargin{:})]; - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,data],level+2,varargin{:})]; - else - % General case, store row and column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,iy,data],level+2,varargin{:})]; - end -else - if(isreal(item)) - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson(item(:)',level+2,varargin{:})]; - else - txt=[txt,S_('_ArrayIsComplex_'),'T']; - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})]; - end -end -txt=[txt,'}']; - -%%------------------------------------------------------------------------- -function txt=matdata2ubjson(mat,level,varargin) -if(isempty(mat)) - txt='Z'; - return; -end -if(size(mat,1)==1) - level=level-1; -end -type=''; -hasnegtive=(mat<0); -if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0))) - if(isempty(hasnegtive)) - if(max(mat(:))<=2^8) - type='U'; - end - end - if(isempty(type)) - % todo - need to consider negative ones separately - id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]); - if(isempty(find(id))) - error('high-precision data is not yet supported'); - end - key='iIlL'; - type=key(find(id)); - end - txt=[I_a(mat(:),type,size(mat))]; -elseif(islogical(mat)) - logicalval='FT'; - if(numel(mat)==1) - txt=logicalval(mat+1); - else - txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')]; - end -else - if(numel(mat)==1) - txt=['[' D_(mat) ']']; - else - txt=D_a(mat(:),'D',size(mat)); - end -end - -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end -%%------------------------------------------------------------------------- -function val=S_(str) -if(length(str)==1) - val=['C' str]; -else - val=['S' I_(int32(length(str))) str]; -end -%%------------------------------------------------------------------------- -function val=I_(num) -if(~isinteger(num)) - error('input is not an integer'); -end -if(num>=0 && num<255) - val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')]; - return; -end -key='iIlL'; -cid={'int8','int16','int32','int64'}; -for i=1:4 - if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1))) - val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')]; - return; - end -end -error('unsupported integer'); - -%%------------------------------------------------------------------------- -function val=D_(num) -if(~isfloat(num)) - error('input is not a float'); -end - -if(isa(num,'single')) - val=['d' data2byte(num,'uint8')]; -else - val=['D' data2byte(num,'uint8')]; -end -%%------------------------------------------------------------------------- -function data=I_a(num,type,dim,format) -id=find(ismember('iUIlL',type)); - -if(id==0) - error('unsupported integer array'); -end - -% based on UBJSON specs, all integer types are stored in big endian format - -if(id==1) - data=data2byte(swapbytes(int8(num)),'uint8'); - blen=1; -elseif(id==2) - data=data2byte(swapbytes(uint8(num)),'uint8'); - blen=1; -elseif(id==3) - data=data2byte(swapbytes(int16(num)),'uint8'); - blen=2; -elseif(id==4) - data=data2byte(swapbytes(int32(num)),'uint8'); - blen=4; -elseif(id==5) - data=data2byte(swapbytes(int64(num)),'uint8'); - blen=8; -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/blen)) data(:)']; - end - data=['[' data(:)']; -else - data=reshape(data,blen,numel(data)/blen); - data(2:blen+1,:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function data=D_a(num,type,dim,format) -id=find(ismember('dD',type)); - -if(id==0) - error('unsupported float array'); -end - -if(id==1) - data=data2byte(single(num),'uint8'); -elseif(id==2) - data=data2byte(double(num),'uint8'); -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)']; - end - data=['[' data]; -else - data=reshape(data,(id*4),length(data)/(id*4)); - data(2:(id*4+1),:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function bytes=data2byte(varargin) -bytes=typecast(varargin{:}); -bytes=bytes(:)'; diff --git a/assignments/machine-learning-ex6/ex6/lib/jsonlab/varargin2struct.m b/assignments/machine-learning-ex6/ex6/lib/jsonlab/varargin2struct.m deleted file mode 100644 index 9a5c2b6..0000000 --- a/assignments/machine-learning-ex6/ex6/lib/jsonlab/varargin2struct.m +++ /dev/null @@ -1,40 +0,0 @@ -function opt=varargin2struct(varargin) -% -% opt=varargin2struct('param1',value1,'param2',value2,...) -% or -% opt=varargin2struct(...,optstruct,...) -% -% convert a series of input parameters into a structure -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% 'param', value: the input parameters should be pairs of a string and a value -% optstruct: if a parameter is a struct, the fields will be merged to the output struct -% -% output: -% opt: a struct where opt.param1=value1, opt.param2=value2 ... -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -len=length(varargin); -opt=struct; -if(len==0) return; end -i=1; -while(i<=len) - if(isstruct(varargin{i})) - opt=mergestruct(opt,varargin{i}); - elseif(ischar(varargin{i}) && i 0 && resp(1) == '{'; - isHtml = findstr(lower(resp), ']+>', ' '); - strippedResponse = regexprep(strippedResponse, '[\t ]+', ' '); - fprintf(strippedResponse); -end - - - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% -% Service configuration -% -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function submissionUrl = submissionUrl() - submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1'; -end diff --git a/assignments/machine-learning-ex6/ex6/linearKernel.m b/assignments/machine-learning-ex6/ex6/linearKernel.m deleted file mode 100644 index 11fd759..0000000 --- a/assignments/machine-learning-ex6/ex6/linearKernel.m +++ /dev/null @@ -1,12 +0,0 @@ -function sim = linearKernel(x1, x2) -%LINEARKERNEL returns a linear kernel between x1 and x2 -% sim = linearKernel(x1, x2) returns a linear kernel between x1 and x2 -% and returns the value in sim - -% Ensure that x1 and x2 are column vectors -x1 = x1(:); x2 = x2(:); - -% Compute the kernel -sim = x1' * x2; % dot product - -end \ No newline at end of file diff --git a/assignments/machine-learning-ex6/ex6/plotData.m b/assignments/machine-learning-ex6/ex6/plotData.m deleted file mode 100644 index 795cc16..0000000 --- a/assignments/machine-learning-ex6/ex6/plotData.m +++ /dev/null @@ -1,17 +0,0 @@ -function plotData(X, y) -%PLOTDATA Plots the data points X and y into a new figure -% PLOTDATA(x,y) plots the data points with + for the positive examples -% and o for the negative examples. X is assumed to be a Mx2 matrix. -% -% Note: This was slightly modified such that it expects y = 1 or y = 0 - -% Find Indices of Positive and Negative Examples -pos = find(y == 1); neg = find(y == 0); - -% Plot Examples -plot(X(pos, 1), X(pos, 2), 'k+','LineWidth', 1, 'MarkerSize', 7) -hold on; -plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7) -hold off; - -end diff --git a/assignments/machine-learning-ex6/ex6/porterStemmer.m b/assignments/machine-learning-ex6/ex6/porterStemmer.m deleted file mode 100644 index 6da6fd1..0000000 --- a/assignments/machine-learning-ex6/ex6/porterStemmer.m +++ /dev/null @@ -1,385 +0,0 @@ -function stem = porterStemmer(inString) -% Applies the Porter Stemming algorithm as presented in the following -% paper: -% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14, -% no. 3, pp 130-137 - -% Original code modeled after the C version provided at: -% http://www.tartarus.org/~martin/PorterStemmer/c.txt - -% The main part of the stemming algorithm starts here. b is an array of -% characters, holding the word to be stemmed. The letters are in b[k0], -% b[k0+1] ending at b[k]. In fact k0 = 1 in this demo program (since -% matlab begins indexing by 1 instead of 0). k is readjusted downwards as -% the stemming progresses. Zero termination is not in fact used in the -% algorithm. - -% To call this function, use the string to be stemmed as the input -% argument. This function returns the stemmed word as a string. - -% Lower-case string -inString = lower(inString); - -global j; -b = inString; -k = length(b); -k0 = 1; -j = k; - - - -% With this if statement, strings of length 1 or 2 don't go through the -% stemming process. Remove this conditional to match the published -% algorithm. -stem = b; -if k > 2 - % Output displays per step are commented out. - %disp(sprintf('Word to stem: %s', b)); - x = step1ab(b, k, k0); - %disp(sprintf('Steps 1A and B yield: %s', x{1})); - x = step1c(x{1}, x{2}, k0); - %disp(sprintf('Step 1C yields: %s', x{1})); - x = step2(x{1}, x{2}, k0); - %disp(sprintf('Step 2 yields: %s', x{1})); - x = step3(x{1}, x{2}, k0); - %disp(sprintf('Step 3 yields: %s', x{1})); - x = step4(x{1}, x{2}, k0); - %disp(sprintf('Step 4 yields: %s', x{1})); - x = step5(x{1}, x{2}, k0); - %disp(sprintf('Step 5 yields: %s', x{1})); - stem = x{1}; -end - -% cons(j) is TRUE <=> b[j] is a consonant. -function c = cons(i, b, k0) -c = true; -switch(b(i)) - case {'a', 'e', 'i', 'o', 'u'} - c = false; - case 'y' - if i == k0 - c = true; - else - c = ~cons(i - 1, b, k0); - end -end - -% mseq() measures the number of consonant sequences between k0 and j. If -% c is a consonant sequence and v a vowel sequence, and <..> indicates -% arbitrary presence, - -% gives 0 -% vc gives 1 -% vcvc gives 2 -% vcvcvc gives 3 -% .... -function n = measure(b, k0) -global j; -n = 0; -i = k0; -while true - if i > j - return - end - if ~cons(i, b, k0) - break; - end - i = i + 1; -end -i = i + 1; -while true - while true - if i > j - return - end - if cons(i, b, k0) - break; - end - i = i + 1; - end - i = i + 1; - n = n + 1; - while true - if i > j - return - end - if ~cons(i, b, k0) - break; - end - i = i + 1; - end - i = i + 1; -end - - -% vowelinstem() is TRUE <=> k0,...j contains a vowel -function vis = vowelinstem(b, k0) -global j; -for i = k0:j, - if ~cons(i, b, k0) - vis = true; - return - end -end -vis = false; - -%doublec(i) is TRUE <=> i,(i-1) contain a double consonant. -function dc = doublec(i, b, k0) -if i < k0+1 - dc = false; - return -end -if b(i) ~= b(i-1) - dc = false; - return -end -dc = cons(i, b, k0); - - -% cvc(j) is TRUE <=> j-2,j-1,j has the form consonant - vowel - consonant -% and also if the second c is not w,x or y. this is used when trying to -% restore an e at the end of a short word. e.g. -% -% cav(e), lov(e), hop(e), crim(e), but -% snow, box, tray. - -function c1 = cvc(i, b, k0) -if ((i < (k0+2)) || ~cons(i, b, k0) || cons(i-1, b, k0) || ~cons(i-2, b, k0)) - c1 = false; -else - if (b(i) == 'w' || b(i) == 'x' || b(i) == 'y') - c1 = false; - return - end - c1 = true; -end - -% ends(s) is TRUE <=> k0,...k ends with the string s. -function s = ends(str, b, k) -global j; -if (str(length(str)) ~= b(k)) - s = false; - return -end % tiny speed-up -if (length(str) > k) - s = false; - return -end -if strcmp(b(k-length(str)+1:k), str) - s = true; - j = k - length(str); - return -else - s = false; -end - -% setto(s) sets (j+1),...k to the characters in the string s, readjusting -% k accordingly. - -function so = setto(s, b, k) -global j; -for i = j+1:(j+length(s)) - b(i) = s(i-j); -end -if k > j+length(s) - b((j+length(s)+1):k) = ''; -end -k = length(b); -so = {b, k}; - -% rs(s) is used further down. -% [Note: possible null/value for r if rs is called] -function r = rs(str, b, k, k0) -r = {b, k}; -if measure(b, k0) > 0 - r = setto(str, b, k); -end - -% step1ab() gets rid of plurals and -ed or -ing. e.g. - -% caresses -> caress -% ponies -> poni -% ties -> ti -% caress -> caress -% cats -> cat - -% feed -> feed -% agreed -> agree -% disabled -> disable - -% matting -> mat -% mating -> mate -% meeting -> meet -% milling -> mill -% messing -> mess - -% meetings -> meet - -function s1ab = step1ab(b, k, k0) -global j; -if b(k) == 's' - if ends('sses', b, k) - k = k-2; - elseif ends('ies', b, k) - retVal = setto('i', b, k); - b = retVal{1}; - k = retVal{2}; - elseif (b(k-1) ~= 's') - k = k-1; - end -end -if ends('eed', b, k) - if measure(b, k0) > 0; - k = k-1; - end -elseif (ends('ed', b, k) || ends('ing', b, k)) && vowelinstem(b, k0) - k = j; - retVal = {b, k}; - if ends('at', b, k) - retVal = setto('ate', b(k0:k), k); - elseif ends('bl', b, k) - retVal = setto('ble', b(k0:k), k); - elseif ends('iz', b, k) - retVal = setto('ize', b(k0:k), k); - elseif doublec(k, b, k0) - retVal = {b, k-1}; - if b(retVal{2}) == 'l' || b(retVal{2}) == 's' || ... - b(retVal{2}) == 'z' - retVal = {retVal{1}, retVal{2}+1}; - end - elseif measure(b, k0) == 1 && cvc(k, b, k0) - retVal = setto('e', b(k0:k), k); - end - k = retVal{2}; - b = retVal{1}(k0:k); -end -j = k; -s1ab = {b(k0:k), k}; - -% step1c() turns terminal y to i when there is another vowel in the stem. -function s1c = step1c(b, k, k0) -global j; -if ends('y', b, k) && vowelinstem(b, k0) - b(k) = 'i'; -end -j = k; -s1c = {b, k}; - -% step2() maps double suffices to single ones. so -ization ( = -ize plus -% -ation) maps to -ize etc. note that the string before the suffix must give -% m() > 0. -function s2 = step2(b, k, k0) -global j; -s2 = {b, k}; -switch b(k-1) - case {'a'} - if ends('ational', b, k) s2 = rs('ate', b, k, k0); - elseif ends('tional', b, k) s2 = rs('tion', b, k, k0); end; - case {'c'} - if ends('enci', b, k) s2 = rs('ence', b, k, k0); - elseif ends('anci', b, k) s2 = rs('ance', b, k, k0); end; - case {'e'} - if ends('izer', b, k) s2 = rs('ize', b, k, k0); end; - case {'l'} - if ends('bli', b, k) s2 = rs('ble', b, k, k0); - elseif ends('alli', b, k) s2 = rs('al', b, k, k0); - elseif ends('entli', b, k) s2 = rs('ent', b, k, k0); - elseif ends('eli', b, k) s2 = rs('e', b, k, k0); - elseif ends('ousli', b, k) s2 = rs('ous', b, k, k0); end; - case {'o'} - if ends('ization', b, k) s2 = rs('ize', b, k, k0); - elseif ends('ation', b, k) s2 = rs('ate', b, k, k0); - elseif ends('ator', b, k) s2 = rs('ate', b, k, k0); end; - case {'s'} - if ends('alism', b, k) s2 = rs('al', b, k, k0); - elseif ends('iveness', b, k) s2 = rs('ive', b, k, k0); - elseif ends('fulness', b, k) s2 = rs('ful', b, k, k0); - elseif ends('ousness', b, k) s2 = rs('ous', b, k, k0); end; - case {'t'} - if ends('aliti', b, k) s2 = rs('al', b, k, k0); - elseif ends('iviti', b, k) s2 = rs('ive', b, k, k0); - elseif ends('biliti', b, k) s2 = rs('ble', b, k, k0); end; - case {'g'} - if ends('logi', b, k) s2 = rs('log', b, k, k0); end; -end -j = s2{2}; - -% step3() deals with -ic-, -full, -ness etc. similar strategy to step2. -function s3 = step3(b, k, k0) -global j; -s3 = {b, k}; -switch b(k) - case {'e'} - if ends('icate', b, k) s3 = rs('ic', b, k, k0); - elseif ends('ative', b, k) s3 = rs('', b, k, k0); - elseif ends('alize', b, k) s3 = rs('al', b, k, k0); end; - case {'i'} - if ends('iciti', b, k) s3 = rs('ic', b, k, k0); end; - case {'l'} - if ends('ical', b, k) s3 = rs('ic', b, k, k0); - elseif ends('ful', b, k) s3 = rs('', b, k, k0); end; - case {'s'} - if ends('ness', b, k) s3 = rs('', b, k, k0); end; -end -j = s3{2}; - -% step4() takes off -ant, -ence etc., in context vcvc. -function s4 = step4(b, k, k0) -global j; -switch b(k-1) - case {'a'} - if ends('al', b, k) end; - case {'c'} - if ends('ance', b, k) - elseif ends('ence', b, k) end; - case {'e'} - if ends('er', b, k) end; - case {'i'} - if ends('ic', b, k) end; - case {'l'} - if ends('able', b, k) - elseif ends('ible', b, k) end; - case {'n'} - if ends('ant', b, k) - elseif ends('ement', b, k) - elseif ends('ment', b, k) - elseif ends('ent', b, k) end; - case {'o'} - if ends('ion', b, k) - if j == 0 - elseif ~(strcmp(b(j),'s') || strcmp(b(j),'t')) - j = k; - end - elseif ends('ou', b, k) end; - case {'s'} - if ends('ism', b, k) end; - case {'t'} - if ends('ate', b, k) - elseif ends('iti', b, k) end; - case {'u'} - if ends('ous', b, k) end; - case {'v'} - if ends('ive', b, k) end; - case {'z'} - if ends('ize', b, k) end; -end -if measure(b, k0) > 1 - s4 = {b(k0:j), j}; -else - s4 = {b(k0:k), k}; -end - -% step5() removes a final -e if m() > 1, and changes -ll to -l if m() > 1. -function s5 = step5(b, k, k0) -global j; -j = k; -if b(k) == 'e' - a = measure(b, k0); - if (a > 1) || ((a == 1) && ~cvc(k-1, b, k0)) - k = k-1; - end -end -if (b(k) == 'l') && doublec(k, b, k0) && (measure(b, k0) > 1) - k = k-1; -end -s5 = {b(k0:k), k}; diff --git a/assignments/machine-learning-ex6/ex6/processEmail.m b/assignments/machine-learning-ex6/ex6/processEmail.m deleted file mode 100644 index 3b7e891..0000000 --- a/assignments/machine-learning-ex6/ex6/processEmail.m +++ /dev/null @@ -1,124 +0,0 @@ -function word_indices = processEmail(email_contents) -%PROCESSEMAIL preprocesses a the body of an email and -%returns a list of word_indices -% word_indices = PROCESSEMAIL(email_contents) preprocesses -% the body of an email and returns a list of indices of the -% words contained in the email. -% - -% Load Vocabulary -vocabList = getVocabList(); - -% Init return value -word_indices = []; - -% ========================== Preprocess Email =========================== - -% Find the Headers ( \n\n and remove ) -% Uncomment the following lines if you are working with raw emails with the -% full headers - -% hdrstart = strfind(email_contents, ([char(10) char(10)])); -% email_contents = email_contents(hdrstart(1):end); - -% Lower case -email_contents = lower(email_contents); - -% Strip all HTML -% Looks for any expression that starts with < and ends with > and replace -% and does not have any < or > in the tag it with a space -email_contents = regexprep(email_contents, '<[^<>]+>', ' '); - -% Handle Numbers -% Look for one or more characters between 0-9 -email_contents = regexprep(email_contents, '[0-9]+', 'number'); - -% Handle URLS -% Look for strings starting with http:// or https:// -email_contents = regexprep(email_contents, ... - '(http|https)://[^\s]*', 'httpaddr'); - -% Handle Email Addresses -% Look for strings with @ in the middle -email_contents = regexprep(email_contents, '[^\s]+@[^\s]+', 'emailaddr'); - -% Handle $ sign -email_contents = regexprep(email_contents, '[$]+', 'dollar'); - - -% ========================== Tokenize Email =========================== - -% Output the email to screen as well -fprintf('\n==== Processed Email ====\n\n'); - -% Process file -l = 0; - -while ~isempty(email_contents) - - % Tokenize and also get rid of any punctuation - [str, email_contents] = ... - strtok(email_contents, ... - [' @$/#.-:&*+=[]?!(){},''">_<;%' char(10) char(13)]); - - % Remove any non alphanumeric characters - str = regexprep(str, '[^a-zA-Z0-9]', ''); - - % Stem the word - % (the porterStemmer sometimes has issues, so we use a try catch block) - try str = porterStemmer(strtrim(str)); - catch str = ''; continue; - end; - - % Skip the word if it is too short - if length(str) < 1 - continue; - end - - % Look up the word in the dictionary and add to word_indices if - % found - % ====================== YOUR CODE HERE ====================== - % Instructions: Fill in this function to add the index of str to - % word_indices if it is in the vocabulary. At this point - % of the code, you have a stemmed word from the email in - % the variable str. You should look up str in the - % vocabulary list (vocabList). If a match exists, you - % should add the index of the word to the word_indices - % vector. Concretely, if str = 'action', then you should - % look up the vocabulary list to find where in vocabList - % 'action' appears. For example, if vocabList{18} = - % 'action', then, you should add 18 to the word_indices - % vector (e.g., word_indices = [word_indices ; 18]; ). - % - % Note: vocabList{idx} returns a the word with index idx in the - % vocabulary list. - % - % Note: You can use strcmp(str1, str2) to compare two strings (str1 and - % str2). It will return 1 only if the two strings are equivalent. - % - -for i = 1 : length(vocabList) - if strcmp(str,vocabList{i}) - word_indices = [word_indices ; i]; - break; - end -end - - - % ============================================================= - - - % Print to screen, ensuring that the output lines are not too long - if (l + length(str) + 1) > 78 - fprintf('\n'); - l = 0; - end - fprintf('%s ', str); - l = l + length(str) + 1; - -end - -% Print footer -fprintf('\n\n=========================\n'); - -end diff --git a/assignments/machine-learning-ex6/ex6/readFile.m b/assignments/machine-learning-ex6/ex6/readFile.m deleted file mode 100644 index 08686d6..0000000 --- a/assignments/machine-learning-ex6/ex6/readFile.m +++ /dev/null @@ -1,18 +0,0 @@ -function file_contents = readFile(filename) -%READFILE reads a file and returns its entire contents -% file_contents = READFILE(filename) reads a file and returns its entire -% contents in file_contents -% - -% Load File -fid = fopen(filename); -if fid - file_contents = fscanf(fid, '%c', inf); - fclose(fid); -else - file_contents = ''; - fprintf('Unable to open %s\n', filename); -end - -end - diff --git a/assignments/machine-learning-ex6/ex6/spamSample1.txt b/assignments/machine-learning-ex6/ex6/spamSample1.txt deleted file mode 100644 index bab0ca2..0000000 --- a/assignments/machine-learning-ex6/ex6/spamSample1.txt +++ /dev/null @@ -1,42 +0,0 @@ -Do You Want To Make $1000 Or More Per Week? - - - -If you are a motivated and qualified individual - I -will personally demonstrate to you a system that will -make you $1,000 per week or more! This is NOT mlm. - - - -Call our 24 hour pre-recorded number to get the -details. - - - -000-456-789 - - - -I need people who want to make serious money. Make -the call and get the facts. - -Invest 2 minutes in yourself now! - - - -000-456-789 - - - -Looking forward to your call and I will introduce you -to people like yourself who -are currently making $10,000 plus per week! - - - -000-456-789 - - - -3484lJGv6-241lEaN9080lRmS6-271WxHo7524qiyT5-438rjUv5615hQcf0-662eiDB9057dMtVl72 - diff --git a/assignments/machine-learning-ex6/ex6/spamSample2.txt b/assignments/machine-learning-ex6/ex6/spamSample2.txt deleted file mode 100644 index f8e8fce..0000000 --- a/assignments/machine-learning-ex6/ex6/spamSample2.txt +++ /dev/null @@ -1,8 +0,0 @@ -Best Buy Viagra Generic Online - -Viagra 100mg x 60 Pills $125, Free Pills & Reorder Discount, Top Selling 100% Quality & Satisfaction guaranteed! - -We accept VISA, Master & E-Check Payments, 90000+ Satisfied Customers! -http://medphysitcstech.ru - - diff --git a/assignments/machine-learning-ex6/ex6/spamTest.mat b/assignments/machine-learning-ex6/ex6/spamTest.mat deleted file mode 100644 index b7bf953..0000000 Binary files a/assignments/machine-learning-ex6/ex6/spamTest.mat and /dev/null differ diff --git a/assignments/machine-learning-ex6/ex6/spamTrain.mat b/assignments/machine-learning-ex6/ex6/spamTrain.mat deleted file mode 100644 index 1b9c81f..0000000 Binary files a/assignments/machine-learning-ex6/ex6/spamTrain.mat and /dev/null differ diff --git a/assignments/machine-learning-ex6/ex6/submit.m b/assignments/machine-learning-ex6/ex6/submit.m deleted file mode 100644 index 61479d2..0000000 --- a/assignments/machine-learning-ex6/ex6/submit.m +++ /dev/null @@ -1,55 +0,0 @@ -function submit() - addpath('./lib'); - - conf.assignmentSlug = 'support-vector-machines'; - conf.itemName = 'Support Vector Machines'; - conf.partArrays = { ... - { ... - '1', ... - { 'gaussianKernel.m' }, ... - 'Gaussian Kernel', ... - }, ... - { ... - '2', ... - { 'dataset3Params.m' }, ... - 'Parameters (C, sigma) for Dataset 3', ... - }, ... - { ... - '3', ... - { 'processEmail.m' }, ... - 'Email Preprocessing', ... - }, ... - { ... - '4', ... - { 'emailFeatures.m' }, ... - 'Email Feature Extraction', ... - }, ... - }; - conf.output = @output; - - submitWithConfiguration(conf); -end - -function out = output(partId, auxstring) - % Random Test Cases - x1 = sin(1:10)'; - x2 = cos(1:10)'; - ec = 'the quick brown fox jumped over the lazy dog'; - wi = 1 + abs(round(x1 * 1863)); - wi = [wi ; wi]; - if partId == '1' - sim = gaussianKernel(x1, x2, 2); - out = sprintf('%0.5f ', sim); - elseif partId == '2' - load('ex6data3.mat'); - [C, sigma] = dataset3Params(X, y, Xval, yval); - out = sprintf('%0.5f ', C); - out = [out sprintf('%0.5f ', sigma)]; - elseif partId == '3' - word_indices = processEmail(ec); - out = sprintf('%d ', word_indices); - elseif partId == '4' - x = emailFeatures(wi); - out = sprintf('%d ', x); - end -end diff --git a/assignments/machine-learning-ex6/ex6/svmPredict.m b/assignments/machine-learning-ex6/ex6/svmPredict.m deleted file mode 100644 index ec8ef77..0000000 --- a/assignments/machine-learning-ex6/ex6/svmPredict.m +++ /dev/null @@ -1,54 +0,0 @@ -function pred = svmPredict(model, X) -%SVMPREDICT returns a vector of predictions using a trained SVM model -%(svmTrain). -% pred = SVMPREDICT(model, X) returns a vector of predictions using a -% trained SVM model (svmTrain). X is a mxn matrix where there each -% example is a row. model is a svm model returned from svmTrain. -% predictions pred is a m x 1 column of predictions of {0, 1} values. -% - -% Check if we are getting a column vector, if so, then assume that we only -% need to do prediction for a single example -if (size(X, 2) == 1) - % Examples should be in rows - X = X'; -end - -% Dataset -m = size(X, 1); -p = zeros(m, 1); -pred = zeros(m, 1); - -if strcmp(func2str(model.kernelFunction), 'linearKernel') - % We can use the weights and bias directly if working with the - % linear kernel - p = X * model.w + model.b; -elseif strfind(func2str(model.kernelFunction), 'gaussianKernel') - % Vectorized RBF Kernel - % This is equivalent to computing the kernel on every pair of examples - X1 = sum(X.^2, 2); - X2 = sum(model.X.^2, 2)'; - K = bsxfun(@plus, X1, bsxfun(@plus, X2, - 2 * X * model.X')); - K = model.kernelFunction(1, 0) .^ K; - K = bsxfun(@times, model.y', K); - K = bsxfun(@times, model.alphas', K); - p = sum(K, 2); -else - % Other Non-linear kernel - for i = 1:m - prediction = 0; - for j = 1:size(model.X, 1) - prediction = prediction + ... - model.alphas(j) * model.y(j) * ... - model.kernelFunction(X(i,:)', model.X(j,:)'); - end - p(i) = prediction + model.b; - end -end - -% Convert predictions into 0 / 1 -pred(p >= 0) = 1; -pred(p < 0) = 0; - -end - diff --git a/assignments/machine-learning-ex6/ex6/svmTrain.m b/assignments/machine-learning-ex6/ex6/svmTrain.m deleted file mode 100644 index 2b2f169..0000000 --- a/assignments/machine-learning-ex6/ex6/svmTrain.m +++ /dev/null @@ -1,192 +0,0 @@ -function [model] = svmTrain(X, Y, C, kernelFunction, ... - tol, max_passes) -%SVMTRAIN Trains an SVM classifier using a simplified version of the SMO -%algorithm. -% [model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an -% SVM classifier and returns trained model. X is the matrix of training -% examples. Each row is a training example, and the jth column holds the -% jth feature. Y is a column matrix containing 1 for positive examples -% and 0 for negative examples. C is the standard SVM regularization -% parameter. tol is a tolerance value used for determining equality of -% floating point numbers. max_passes controls the number of iterations -% over the dataset (without changes to alpha) before the algorithm quits. -% -% Note: This is a simplified version of the SMO algorithm for training -% SVMs. In practice, if you want to train an SVM classifier, we -% recommend using an optimized package such as: -% -% LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) -% SVMLight (http://svmlight.joachims.org/) -% -% - -if ~exist('tol', 'var') || isempty(tol) - tol = 1e-3; -end - -if ~exist('max_passes', 'var') || isempty(max_passes) - max_passes = 5; -end - -% Data parameters -m = size(X, 1); -n = size(X, 2); - -% Map 0 to -1 -Y(Y==0) = -1; - -% Variables -alphas = zeros(m, 1); -b = 0; -E = zeros(m, 1); -passes = 0; -eta = 0; -L = 0; -H = 0; - -% Pre-compute the Kernel Matrix since our dataset is small -% (in practice, optimized SVM packages that handle large datasets -% gracefully will _not_ do this) -% -% We have implemented optimized vectorized version of the Kernels here so -% that the svm training will run faster. -if strcmp(func2str(kernelFunction), 'linearKernel') - % Vectorized computation for the Linear Kernel - % This is equivalent to computing the kernel on every pair of examples - K = X*X'; -elseif strfind(func2str(kernelFunction), 'gaussianKernel') - % Vectorized RBF Kernel - % This is equivalent to computing the kernel on every pair of examples - X2 = sum(X.^2, 2); - K = bsxfun(@plus, X2, bsxfun(@plus, X2', - 2 * (X * X'))); - K = kernelFunction(1, 0) .^ K; -else - % Pre-compute the Kernel Matrix - % The following can be slow due to the lack of vectorization - K = zeros(m); - for i = 1:m - for j = i:m - K(i,j) = kernelFunction(X(i,:)', X(j,:)'); - K(j,i) = K(i,j); %the matrix is symmetric - end - end -end - -% Train -fprintf('\nTraining ...'); -dots = 12; -while passes < max_passes, - - num_changed_alphas = 0; - for i = 1:m, - - % Calculate Ei = f(x(i)) - y(i) using (2). - % E(i) = b + sum (X(i, :) * (repmat(alphas.*Y,1,n).*X)') - Y(i); - E(i) = b + sum (alphas.*Y.*K(:,i)) - Y(i); - - if ((Y(i)*E(i) < -tol && alphas(i) < C) || (Y(i)*E(i) > tol && alphas(i) > 0)), - - % In practice, there are many heuristics one can use to select - % the i and j. In this simplified code, we select them randomly. - j = ceil(m * rand()); - while j == i, % Make sure i \neq j - j = ceil(m * rand()); - end - - % Calculate Ej = f(x(j)) - y(j) using (2). - E(j) = b + sum (alphas.*Y.*K(:,j)) - Y(j); - - % Save old alphas - alpha_i_old = alphas(i); - alpha_j_old = alphas(j); - - % Compute L and H by (10) or (11). - if (Y(i) == Y(j)), - L = max(0, alphas(j) + alphas(i) - C); - H = min(C, alphas(j) + alphas(i)); - else - L = max(0, alphas(j) - alphas(i)); - H = min(C, C + alphas(j) - alphas(i)); - end - - if (L == H), - % continue to next i. - continue; - end - - % Compute eta by (14). - eta = 2 * K(i,j) - K(i,i) - K(j,j); - if (eta >= 0), - % continue to next i. - continue; - end - - % Compute and clip new value for alpha j using (12) and (15). - alphas(j) = alphas(j) - (Y(j) * (E(i) - E(j))) / eta; - - % Clip - alphas(j) = min (H, alphas(j)); - alphas(j) = max (L, alphas(j)); - - % Check if change in alpha is significant - if (abs(alphas(j) - alpha_j_old) < tol), - % continue to next i. - % replace anyway - alphas(j) = alpha_j_old; - continue; - end - - % Determine value for alpha i using (16). - alphas(i) = alphas(i) + Y(i)*Y(j)*(alpha_j_old - alphas(j)); - - % Compute b1 and b2 using (17) and (18) respectively. - b1 = b - E(i) ... - - Y(i) * (alphas(i) - alpha_i_old) * K(i,j)' ... - - Y(j) * (alphas(j) - alpha_j_old) * K(i,j)'; - b2 = b - E(j) ... - - Y(i) * (alphas(i) - alpha_i_old) * K(i,j)' ... - - Y(j) * (alphas(j) - alpha_j_old) * K(j,j)'; - - % Compute b by (19). - if (0 < alphas(i) && alphas(i) < C), - b = b1; - elseif (0 < alphas(j) && alphas(j) < C), - b = b2; - else - b = (b1+b2)/2; - end - - num_changed_alphas = num_changed_alphas + 1; - - end - - end - - if (num_changed_alphas == 0), - passes = passes + 1; - else - passes = 0; - end - - fprintf('.'); - dots = dots + 1; - if dots > 78 - dots = 0; - fprintf('\n'); - end - if exist('OCTAVE_VERSION') - fflush(stdout); - end -end -fprintf(' Done! \n\n'); - -% Save the model -idx = alphas > 0; -model.X= X(idx,:); -model.y= Y(idx); -model.kernelFunction = kernelFunction; -model.b= b; -model.alphas= alphas(idx); -model.w = ((alphas.*Y)'*X)'; - -end diff --git a/assignments/machine-learning-ex6/ex6/token.mat b/assignments/machine-learning-ex6/ex6/token.mat deleted file mode 100644 index 7fbb82f..0000000 --- a/assignments/machine-learning-ex6/ex6/token.mat +++ /dev/null @@ -1,15 +0,0 @@ -# Created by Octave 4.2.0, Sat Feb 04 22:59:59 2017 GMT -# name: email -# type: sq_string -# elements: 1 -# length: 17 -scruel@vip.qq.com - - -# name: token -# type: sq_string -# elements: 1 -# length: 16 -PfO5aoUBtxECmBYv - - diff --git a/assignments/machine-learning-ex6/ex6/visualizeBoundary.m b/assignments/machine-learning-ex6/ex6/visualizeBoundary.m deleted file mode 100644 index a9b8086..0000000 --- a/assignments/machine-learning-ex6/ex6/visualizeBoundary.m +++ /dev/null @@ -1,24 +0,0 @@ -function visualizeBoundary(X, y, model, varargin) -%VISUALIZEBOUNDARY plots a non-linear decision boundary learned by the SVM -% VISUALIZEBOUNDARYLINEAR(X, y, model) plots a non-linear decision -% boundary learned by the SVM and overlays the data on it - -% Plot the training data on top of the boundary -plotData(X, y) - -% Make classification predictions over a grid of values -x1plot = linspace(min(X(:,1)), max(X(:,1)), 100)'; -x2plot = linspace(min(X(:,2)), max(X(:,2)), 100)'; -[X1, X2] = meshgrid(x1plot, x2plot); -vals = zeros(size(X1)); -for i = 1:size(X1, 2) - this_X = [X1(:, i), X2(:, i)]; - vals(:, i) = svmPredict(model, this_X); -end - -% Plot the SVM boundary -hold on -contour(X1, X2, vals, [0.5 0.5], 'b'); -hold off; - -end diff --git a/assignments/machine-learning-ex6/ex6/visualizeBoundaryLinear.m b/assignments/machine-learning-ex6/ex6/visualizeBoundaryLinear.m deleted file mode 100644 index f17f5ca..0000000 --- a/assignments/machine-learning-ex6/ex6/visualizeBoundaryLinear.m +++ /dev/null @@ -1,16 +0,0 @@ -function visualizeBoundaryLinear(X, y, model) -%VISUALIZEBOUNDARYLINEAR plots a linear decision boundary learned by the -%SVM -% VISUALIZEBOUNDARYLINEAR(X, y, model) plots a linear decision boundary -% learned by the SVM and overlays the data on it - -w = model.w; -b = model.b; -xp = linspace(min(X(:,1)), max(X(:,1)), 100); -yp = - (w(1)*xp + b)/w(2); -plotData(X, y); -hold on; -plot(xp, yp, '-b'); -hold off - -end diff --git a/assignments/machine-learning-ex6/ex6/vocab.txt b/assignments/machine-learning-ex6/ex6/vocab.txt deleted file mode 100644 index 27f64a3..0000000 --- a/assignments/machine-learning-ex6/ex6/vocab.txt +++ /dev/null @@ -1,1899 +0,0 @@ -1 aa -2 ab -3 abil -4 abl -5 about -6 abov -7 absolut -8 abus -9 ac -10 accept -11 access -12 accord -13 account -14 achiev -15 acquir -16 across -17 act -18 action -19 activ -20 actual -21 ad -22 adam -23 add -24 addit -25 address -26 administr -27 adult -28 advanc -29 advantag -30 advertis -31 advic -32 advis -33 ae -34 af -35 affect -36 affili -37 afford -38 africa 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/dev/null differ diff --git a/assignments/machine-learning-ex7/ex7/bird_small.mat b/assignments/machine-learning-ex7/ex7/bird_small.mat deleted file mode 100644 index 04c224c..0000000 Binary files a/assignments/machine-learning-ex7/ex7/bird_small.mat and /dev/null differ diff --git a/assignments/machine-learning-ex7/ex7/bird_small.png b/assignments/machine-learning-ex7/ex7/bird_small.png deleted file mode 100644 index a3cd00c..0000000 Binary files a/assignments/machine-learning-ex7/ex7/bird_small.png and /dev/null differ diff --git a/assignments/machine-learning-ex7/ex7/computeCentroids.m b/assignments/machine-learning-ex7/ex7/computeCentroids.m deleted file mode 100644 index a3e992a..0000000 --- a/assignments/machine-learning-ex7/ex7/computeCentroids.m +++ /dev/null @@ -1,41 +0,0 @@ -function centroids = computeCentroids(X, idx, K) -%COMPUTECENTROIDS returns the new centroids by computing the means of the -%data points assigned to each centroid. -% centroids = COMPUTECENTROIDS(X, idx, K) returns the new centroids by -% computing the means of the data points assigned to each centroid. It is -% given a dataset X where each row is a single data point, a vector -% idx of centroid assignments (i.e. each entry in range [1..K]) for each -% example, and K, the number of centroids. You should return a matrix -% centroids, where each row of centroids is the mean of the data points -% assigned to it. -% - -% Useful variables -[m n] = size(X); - -% You need to return the following variables correctly. -centroids = zeros(K, n); - - -% ====================== YOUR CODE HERE ====================== -% Instructions: Go over every centroid and compute mean of all points that -% belong to it. Concretely, the row vector centroids(i, :) -% should contain the mean of the data points assigned to -% centroid i. -% -% Note: You can use a for-loop over the centroids to compute this. -% -centroidsSum = zeros(K,1); -for i = 1 : m - centroids(idx(i), :) = centroids(idx(i), :) .+ X(i, :); - centroidsSum(idx(i)) = centroidsSum(idx(i)) + 1; -end -for i = 1 : K - centroids(i,:) = centroids(i,:) ./ centroidsSum(i) ; -end - -% ============================================================= - - -end - diff --git a/assignments/machine-learning-ex7/ex7/displayData.m b/assignments/machine-learning-ex7/ex7/displayData.m deleted file mode 100644 index 160697e..0000000 --- a/assignments/machine-learning-ex7/ex7/displayData.m +++ /dev/null @@ -1,59 +0,0 @@ -function [h, display_array] = displayData(X, example_width) -%DISPLAYDATA Display 2D data in a nice grid -% [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data -% stored in X in a nice grid. It returns the figure handle h and the -% displayed array if requested. - -% Set example_width automatically if not passed in -if ~exist('example_width', 'var') || isempty(example_width) - example_width = round(sqrt(size(X, 2))); -end - -% Gray Image -colormap(gray); - -% Compute rows, cols -[m n] = size(X); -example_height = (n / example_width); - -% Compute number of items to display -display_rows = floor(sqrt(m)); -display_cols = ceil(m / display_rows); - -% Between images padding -pad = 1; - -% Setup blank display -display_array = - ones(pad + display_rows * (example_height + pad), ... - pad + display_cols * (example_width + pad)); - -% Copy each example into a patch on the display array -curr_ex = 1; -for j = 1:display_rows - for i = 1:display_cols - if curr_ex > m, - break; - end - % Copy the patch - - % Get the max value of the patch - max_val = max(abs(X(curr_ex, :))); - display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ... - pad + (i - 1) * (example_width + pad) + (1:example_width)) = ... - reshape(X(curr_ex, :), example_height, example_width) / max_val; - curr_ex = curr_ex + 1; - end - if curr_ex > m, - break; - end -end - -% Display Image -h = imagesc(display_array, [-1 1]); - -% Do not show axis -axis image off - -drawnow; - -end diff --git a/assignments/machine-learning-ex7/ex7/drawLine.m b/assignments/machine-learning-ex7/ex7/drawLine.m deleted file mode 100644 index 85e6c41..0000000 --- a/assignments/machine-learning-ex7/ex7/drawLine.m +++ /dev/null @@ -1,8 +0,0 @@ -function drawLine(p1, p2, varargin) -%DRAWLINE Draws a line from point p1 to point p2 -% DRAWLINE(p1, p2) Draws a line from point p1 to point p2 and holds the -% current figure - -plot([p1(1) p2(1)], [p1(2) p2(2)], varargin{:}); - -end \ No newline at end of file diff --git a/assignments/machine-learning-ex7/ex7/ex7.m b/assignments/machine-learning-ex7/ex7/ex7.m deleted file mode 100644 index 07a5660..0000000 --- a/assignments/machine-learning-ex7/ex7/ex7.m +++ /dev/null @@ -1,174 +0,0 @@ -%% Machine Learning Online Class -% Exercise 7 | Principle Component Analysis and K-Means Clustering -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% exercise. You will need to complete the following functions: -% -% pca.m -% projectData.m -% recoverData.m -% computeCentroids.m -% findClosestCentroids.m -% kMeansInitCentroids.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% ================= Part 1: Find Closest Centroids ==================== -% To help you implement K-Means, we have divided the learning algorithm -% into two functions -- findClosestCentroids and computeCentroids. In this -% part, you should complete the code in the findClosestCentroids function. -% -fprintf('Finding closest centroids.\n\n'); - -% Load an example dataset that we will be using -load('ex7data2.mat'); - -% Select an initial set of centroids -K = 3; % 3 Centroids -initial_centroids = [3 3; 6 2; 8 5]; - -% Find the closest centroids for the examples using the -% initial_centroids -idx = findClosestCentroids(X, initial_centroids); - -fprintf('Closest centroids for the first 3 examples: \n') -fprintf(' %d', idx(1:3)); -fprintf('\n(the closest centroids should be 1, 3, 2 respectively)\n'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ===================== Part 2: Compute Means ========================= -% After implementing the closest centroids function, you should now -% complete the computeCentroids function. -% -fprintf('\nComputing centroids means.\n\n'); - -% Compute means based on the closest centroids found in the previous part. -centroids = computeCentroids(X, idx, K); - -fprintf('Centroids computed after initial finding of closest centroids: \n') -fprintf(' %f %f \n' , centroids'); -fprintf('\n(the centroids should be\n'); -fprintf(' [ 2.428301 3.157924 ]\n'); -fprintf(' [ 5.813503 2.633656 ]\n'); -fprintf(' [ 7.119387 3.616684 ]\n\n'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% =================== Part 3: K-Means Clustering ====================== -% After you have completed the two functions computeCentroids and -% findClosestCentroids, you have all the necessary pieces to run the -% kMeans algorithm. In this part, you will run the K-Means algorithm on -% the example dataset we have provided. -% -fprintf('\nRunning K-Means clustering on example dataset.\n\n'); - -% Load an example dataset -load('ex7data2.mat'); - -% Settings for running K-Means -K = 3; -max_iters = 10; - -% For consistency, here we set centroids to specific values -% but in practice you want to generate them automatically, such as by -% settings them to be random examples (as can be seen in -% kMeansInitCentroids). -initial_centroids = [3 3; 6 2; 8 5]; - -% Run K-Means algorithm. The 'true' at the end tells our function to plot -% the progress of K-Means -[centroids, idx] = runkMeans(X, initial_centroids, max_iters, true); -fprintf('\nK-Means Done.\n\n'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ============= Part 4: K-Means Clustering on Pixels =============== -% In this exercise, you will use K-Means to compress an image. To do this, -% you will first run K-Means on the colors of the pixels in the image and -% then you will map each pixel onto its closest centroid. -% -% You should now complete the code in kMeansInitCentroids.m -% - -fprintf('\nRunning K-Means clustering on pixels from an image.\n\n'); - -% Load an image of a bird -A = double(imread('bird_small.png')); - -% If imread does not work for you, you can try instead -% load ('bird_small.mat'); - -A = A / 255; % Divide by 255 so that all values are in the range 0 - 1 - -% Size of the image -img_size = size(A); - -% Reshape the image into an Nx3 matrix where N = number of pixels. -% Each row will contain the Red, Green and Blue pixel values -% This gives us our dataset matrix X that we will use K-Means on. -X = reshape(A, img_size(1) * img_size(2), 3); - -% Run your K-Means algorithm on this data -% You should try different values of K and max_iters here -K = 16; -max_iters = 10; - -% When using K-Means, it is important the initialize the centroids -% randomly. -% You should complete the code in kMeansInitCentroids.m before proceeding -initial_centroids = kMeansInitCentroids(X, K); - -% Run K-Means -[centroids, idx] = runkMeans(X, initial_centroids, max_iters); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% ================= Part 5: Image Compression ====================== -% In this part of the exercise, you will use the clusters of K-Means to -% compress an image. To do this, we first find the closest clusters for -% each example. After that, we - -fprintf('\nApplying K-Means to compress an image.\n\n'); - -% Find closest cluster members -idx = findClosestCentroids(X, centroids); - -% Essentially, now we have represented the image X as in terms of the -% indices in idx. - -% We can now recover the image from the indices (idx) by mapping each pixel -% (specified by its index in idx) to the centroid value -X_recovered = centroids(idx,:); - -% Reshape the recovered image into proper dimensions -X_recovered = reshape(X_recovered, img_size(1), img_size(2), 3); - -% Display the original image -subplot(1, 2, 1); -imagesc(A); -title('Original'); - -% Display compressed image side by side -subplot(1, 2, 2); -imagesc(X_recovered) -title(sprintf('Compressed, with %d colors.', K)); - - -fprintf('Program paused. Press enter to continue.\n'); -pause; - diff --git a/assignments/machine-learning-ex7/ex7/ex7_pca.m b/assignments/machine-learning-ex7/ex7/ex7_pca.m deleted file mode 100644 index 701cd52..0000000 --- a/assignments/machine-learning-ex7/ex7/ex7_pca.m +++ /dev/null @@ -1,235 +0,0 @@ -%% Machine Learning Online Class -% Exercise 7 | Principle Component Analysis and K-Means Clustering -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% exercise. You will need to complete the following functions: -% -% pca.m -% projectData.m -% recoverData.m -% computeCentroids.m -% findClosestCentroids.m -% kMeansInitCentroids.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% ================== Part 1: Load Example Dataset =================== -% We start this exercise by using a small dataset that is easily to -% visualize -% -fprintf('Visualizing example dataset for PCA.\n\n'); - -% The following command loads the dataset. You should now have the -% variable X in your environment -load ('ex7data1.mat'); - -% Visualize the example dataset -plot(X(:, 1), X(:, 2), 'bo'); -axis([0.5 6.5 2 8]); axis square; - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% =============== Part 2: Principal Component Analysis =============== -% You should now implement PCA, a dimension reduction technique. You -% should complete the code in pca.m -% -fprintf('\nRunning PCA on example dataset.\n\n'); - -% Before running PCA, it is important to first normalize X -[X_norm, mu, sigma] = featureNormalize(X); - -% Run PCA -[U, S] = pca(X_norm); - -% Compute mu, the mean of the each feature - -% Draw the eigenvectors centered at mean of data. These lines show the -% directions of maximum variations in the dataset. -hold on; -drawLine(mu, mu + 1.5 * S(1,1) * U(:,1)', '-k', 'LineWidth', 2); -drawLine(mu, mu + 1.5 * S(2,2) * U(:,2)', '-k', 'LineWidth', 2); -hold off; - -fprintf('Top eigenvector: \n'); -fprintf(' U(:,1) = %f %f \n', U(1,1), U(2,1)); -fprintf('\n(you should expect to see -0.707107 -0.707107)\n'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% =================== Part 3: Dimension Reduction =================== -% You should now implement the projection step to map the data onto the -% first k eigenvectors. The code will then plot the data in this reduced -% dimensional space. This will show you what the data looks like when -% using only the corresponding eigenvectors to reconstruct it. -% -% You should complete the code in projectData.m -% -fprintf('\nDimension reduction on example dataset.\n\n'); - -% Plot the normalized dataset (returned from pca) -plot(X_norm(:, 1), X_norm(:, 2), 'bo'); -axis([-4 3 -4 3]); axis square - -% Project the data onto K = 1 dimension -K = 1; -Z = projectData(X_norm, U, K); -fprintf('Projection of the first example: %f\n', Z(1)); -fprintf('\n(this value should be about 1.481274)\n\n'); - -X_rec = recoverData(Z, U, K); -fprintf('Approximation of the first example: %f %f\n', X_rec(1, 1), X_rec(1, 2)); -fprintf('\n(this value should be about -1.047419 -1.047419)\n\n'); - -% Draw lines connecting the projected points to the original points -hold on; -plot(X_rec(:, 1), X_rec(:, 2), 'ro'); -for i = 1:size(X_norm, 1) - drawLine(X_norm(i,:), X_rec(i,:), '--k', 'LineWidth', 1); -end -hold off - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =============== Part 4: Loading and Visualizing Face Data ============= -% We start the exercise by first loading and visualizing the dataset. -% The following code will load the dataset into your environment -% -fprintf('\nLoading face dataset.\n\n'); - -% Load Face dataset -load ('ex7faces.mat') - -% Display the first 100 faces in the dataset -displayData(X(1:100, :)); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% =========== Part 5: PCA on Face Data: Eigenfaces =================== -% Run PCA and visualize the eigenvectors which are in this case eigenfaces -% We display the first 36 eigenfaces. -% -fprintf(['\nRunning PCA on face dataset.\n' ... - '(this might take a minute or two ...)\n\n']); - -% Before running PCA, it is important to first normalize X by subtracting -% the mean value from each feature -[X_norm, mu, sigma] = featureNormalize(X); - -% Run PCA -[U, S] = pca(X_norm); - -% Visualize the top 36 eigenvectors found -displayData(U(:, 1:36)'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% ============= Part 6: Dimension Reduction for Faces ================= -% Project images to the eigen space using the top k eigenvectors -% If you are applying a machine learning algorithm -fprintf('\nDimension reduction for face dataset.\n\n'); - -K = 100; -Z = projectData(X_norm, U, K); - -fprintf('The projected data Z has a size of: ') -fprintf('%d ', size(Z)); - -fprintf('\n\nProgram paused. Press enter to continue.\n'); -pause; - -%% ==== Part 7: Visualization of Faces after PCA Dimension Reduction ==== -% Project images to the eigen space using the top K eigen vectors and -% visualize only using those K dimensions -% Compare to the original input, which is also displayed - -fprintf('\nVisualizing the projected (reduced dimension) faces.\n\n'); - -K = 100; -X_rec = recoverData(Z, U, K); - -% Display normalized data -subplot(1, 2, 1); -displayData(X_norm(1:100,:)); -title('Original faces'); -axis square; - -% Display reconstructed data from only k eigenfaces -subplot(1, 2, 2); -displayData(X_rec(1:100,:)); -title('Recovered faces'); -axis square; - -fprintf('Program paused. Press enter to continue.\n'); -pause; - - -%% === Part 8(a): Optional (ungraded) Exercise: PCA for Visualization === -% One useful application of PCA is to use it to visualize high-dimensional -% data. In the last K-Means exercise you ran K-Means on 3-dimensional -% pixel colors of an image. We first visualize this output in 3D, and then -% apply PCA to obtain a visualization in 2D. - -close all; close all; clc - -% Reload the image from the previous exercise and run K-Means on it -% For this to work, you need to complete the K-Means assignment first -A = double(imread('bird_small.png')); - -% If imread does not work for you, you can try instead -% load ('bird_small.mat'); - -A = A / 255; -img_size = size(A); -X = reshape(A, img_size(1) * img_size(2), 3); -K = 16; -max_iters = 10; -initial_centroids = kMeansInitCentroids(X, K); -[centroids, idx] = runkMeans(X, initial_centroids, max_iters); - -% Sample 1000 random indexes (since working with all the data is -% too expensive. If you have a fast computer, you may increase this. -sel = floor(rand(1000, 1) * size(X, 1)) + 1; - -% Setup Color Palette -palette = hsv(K); -colors = palette(idx(sel), :); - -% Visualize the data and centroid memberships in 3D -figure; -scatter3(X(sel, 1), X(sel, 2), X(sel, 3), 10, colors); -title('Pixel dataset plotted in 3D. Color shows centroid memberships'); -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% === Part 8(b): Optional (ungraded) Exercise: PCA for Visualization === -% Use PCA to project this cloud to 2D for visualization - -% Subtract the mean to use PCA -[X_norm, mu, sigma] = featureNormalize(X); - -% PCA and project the data to 2D -[U, S] = pca(X_norm); -Z = projectData(X_norm, U, 2); - -% Plot in 2D -figure; -plotDataPoints(Z(sel, :), idx(sel), K); -title('Pixel dataset plotted in 2D, using PCA for dimensionality reduction'); -fprintf('Program paused. Press enter to continue.\n'); -pause; diff --git a/assignments/machine-learning-ex7/ex7/ex7data1.mat b/assignments/machine-learning-ex7/ex7/ex7data1.mat deleted file mode 100644 index f9c3961..0000000 Binary files a/assignments/machine-learning-ex7/ex7/ex7data1.mat and /dev/null differ diff --git a/assignments/machine-learning-ex7/ex7/ex7data2.mat b/assignments/machine-learning-ex7/ex7/ex7data2.mat deleted file mode 100644 index de3f5b9..0000000 Binary files a/assignments/machine-learning-ex7/ex7/ex7data2.mat and /dev/null differ diff --git a/assignments/machine-learning-ex7/ex7/ex7faces.mat b/assignments/machine-learning-ex7/ex7/ex7faces.mat deleted file mode 100644 index 3965bd1..0000000 Binary files a/assignments/machine-learning-ex7/ex7/ex7faces.mat and /dev/null differ diff --git a/assignments/machine-learning-ex7/ex7/featureNormalize.m b/assignments/machine-learning-ex7/ex7/featureNormalize.m deleted file mode 100644 index da03bee..0000000 --- a/assignments/machine-learning-ex7/ex7/featureNormalize.m +++ /dev/null @@ -1,17 +0,0 @@ -function [X_norm, mu, sigma] = featureNormalize(X) -%FEATURENORMALIZE Normalizes the features in X -% FEATURENORMALIZE(X) returns a normalized version of X where -% the mean value of each feature is 0 and the standard deviation -% is 1. This is often a good preprocessing step to do when -% working with learning algorithms. - -mu = mean(X); -X_norm = bsxfun(@minus, X, mu); - -sigma = std(X_norm); -X_norm = bsxfun(@rdivide, X_norm, sigma); - - -% ============================================================ - -end diff --git a/assignments/machine-learning-ex7/ex7/findClosestCentroids.m b/assignments/machine-learning-ex7/ex7/findClosestCentroids.m deleted file mode 100644 index 0f6e16d..0000000 --- a/assignments/machine-learning-ex7/ex7/findClosestCentroids.m +++ /dev/null @@ -1,42 +0,0 @@ -function idx = findClosestCentroids(X, centroids) -%FINDCLOSESTCENTROIDS computes the centroid memberships for every example -% idx = FINDCLOSESTCENTROIDS (X, centroids) returns the closest centroids -% in idx for a dataset X where each row is a single example. idx = m x 1 -% vector of centroid assignments (i.e. each entry in range [1..K]) -% - -% Set K -K = size(centroids, 1); - -% You need to return the following variables correctly. -idx = zeros(size(X,1), 1); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Go over every example, find its closest centroid, and store -% the index inside idx at the appropriate location. -% Concretely, idx(i) should contain the index of the centroid -% closest to example i. Hence, it should be a value in the -% range 1..K -% -% Note: You can use a for-loop over the examples to compute this. -% -for i = 1 : size(X,1) - [temp,idx(i)] = min(sum((X(i,:) .- centroids) .^ 2, 2)); -end - -% for i = 1 : size(X,1) - % minN = sum((X(i,:) - centroids(1,:)) .^ 2); - % idx(i) = 1; - % for k = 2 : K - % tempMinN = sum((X(i,:) - centroids(k,:)) .^ 2); - % if tempMinN < minN - % minN = tempMinN; - % idx(i) = k; - % end - % end -% end - -% ============================================================= - -end - diff --git a/assignments/machine-learning-ex7/ex7/kMeansInitCentroids.m b/assignments/machine-learning-ex7/ex7/kMeansInitCentroids.m deleted file mode 100644 index 5956db7..0000000 --- a/assignments/machine-learning-ex7/ex7/kMeansInitCentroids.m +++ /dev/null @@ -1,25 +0,0 @@ -function centroids = kMeansInitCentroids(X, K) -%KMEANSINITCENTROIDS This function initializes K centroids that are to be -%used in K-Means on the dataset X -% centroids = KMEANSINITCENTROIDS(X, K) returns K initial centroids to be -% used with the K-Means on the dataset X -% - -% You should return this values correctly -centroids = zeros(K, size(X, 2)); - -% ====================== YOUR CODE HERE ====================== -% Instructions: You should set centroids to randomly chosen examples from -% the dataset X -% -% for i = 1 : K - % centroids(i,:) = X(i,:); -% end -randidx = randperm(size(X, 1)); -% Take the first K examples as centroids -centroids = X(randidx(1:K), :); - -% ============================================================= - -end - diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/AUTHORS.txt b/assignments/machine-learning-ex7/ex7/lib/jsonlab/AUTHORS.txt deleted file mode 100644 index 9dd3fc7..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/AUTHORS.txt +++ /dev/null @@ -1,41 +0,0 @@ -The author of "jsonlab" toolbox is Qianqian Fang. Qianqian -is currently an Assistant Professor at Massachusetts General Hospital, -Harvard Medical School. - -Address: Martinos Center for Biomedical Imaging, - Massachusetts General Hospital, - Harvard Medical School - Bldg 149, 13th St, Charlestown, MA 02129, USA -URL: http://nmr.mgh.harvard.edu/~fangq/ -Email: or - - -The script loadjson.m was built upon previous works by - -- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 - date: 2009/11/02 -- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 - date: 2009/03/22 -- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565 - date: 2008/07/03 - - -This toolbox contains patches submitted by the following contributors: - -- Blake Johnson - part of revision 341 - -- Niclas Borlin - various fixes in revision 394, including - - loadjson crashes for all-zero sparse matrix. - - loadjson crashes for empty sparse matrix. - - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson. - - loadjson crashes for sparse real column vector. - - loadjson crashes for sparse complex column vector. - - Data is corrupted by savejson for sparse real row vector. - - savejson crashes for sparse complex row vector. - -- Yul Kang - patches for svn revision 415. - - savejson saves an empty cell array as [] instead of null - - loadjson differentiates an empty struct from an empty array diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/ChangeLog.txt b/assignments/machine-learning-ex7/ex7/lib/jsonlab/ChangeLog.txt deleted file mode 100644 index 07824f5..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/ChangeLog.txt +++ /dev/null @@ -1,74 +0,0 @@ -============================================================================ - - JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave - ----------------------------------------------------------------------------- - -JSONlab ChangeLog (key features marked by *): - -== JSONlab 1.0 (codename: Optimus - Final), FangQ == - - 2015/01/02 polish help info for all major functions, update examples, finalize 1.0 - 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson - -== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ == - - 2014/11/22 show progress bar in loadjson ('ShowProgress') - 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact') - 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels - 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab - -== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ == - - 2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8 - 2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson - 2014/08/04 escape special characters in a JSON string - 2014/02/16 fix a bug when saving ubjson files - -== JSONlab 0.9.9 (codename: Optimus - beta), FangQ == - - 2014/01/22 use binary read and write in saveubjson and loadubjson - -== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ == - - 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang) - -== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ == - 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson - -== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ == - 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin) - -== JSONlab 0.9.0 (codename: Rodimus), FangQ == - - 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson - 2012/06/01 support JSONP in savejson - 2012/05/25 fix the empty cell bug (reported by Cyril Davin) - 2012/04/05 savejson can save to a file (suggested by Patrick Rapin) - -== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ == - - 2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS - 2012/01/25 patch to handle root-less objects, contributed by Blake Johnson - -== JSONlab 0.8.0 (codename: Sentiel), FangQ == - - 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab - 2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer - 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson - 2011/11/18 fix struct array bug reported by Mykel Kochenderfer - -== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ == - - 2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration - 2011/10/20 loadjson supports JSON collections - concatenated JSON objects - -== JSONlab 0.5.0 (codename: Nexus), FangQ == - - 2011/10/16 package and release jsonlab 0.5.0 - 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug - 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level - 2011/10/10 create jsonlab project, start jsonlab website, add online documentation - 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support - 2011/10/06 *savejson works for structs, cells and arrays - 2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/LICENSE_BSD.txt b/assignments/machine-learning-ex7/ex7/lib/jsonlab/LICENSE_BSD.txt deleted file mode 100644 index 32d66cb..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/LICENSE_BSD.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright 2011-2015 Qianqian Fang . All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are -permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, this list - of conditions and the following disclaimer in the documentation and/or other materials - provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND -FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS -OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -The views and conclusions contained in the software and documentation are those of the -authors and should not be interpreted as representing official policies, either expressed -or implied, of the copyright holders. diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/README.txt b/assignments/machine-learning-ex7/ex7/lib/jsonlab/README.txt deleted file mode 100644 index 7b4f732..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/README.txt +++ /dev/null @@ -1,394 +0,0 @@ -=============================================================================== -= JSONLab = -= An open-source MATLAB/Octave JSON encoder and decoder = -=============================================================================== - -*Copyright (C) 2011-2015 Qianqian Fang -*License: BSD License, see License_BSD.txt for details -*Version: 1.0 (Optimus - Final) - -------------------------------------------------------------------------------- - -Table of Content: - -I. Introduction -II. Installation -III.Using JSONLab -IV. Known Issues and TODOs -V. Contribution and feedback - -------------------------------------------------------------------------------- - -I. Introduction - -JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, -human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format -to represent complex and hierarchical data. It is as powerful as -[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely -used for data-exchange in applications, and is essential for the wild success -of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and -[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. - -UBJSON (Universal Binary JSON) is a binary JSON format, specifically -optimized for compact file size and better performance while keeping -the semantics as simple as the text-based JSON format. Using the UBJSON -format allows to wrap complex binary data in a flexible and extensible -structure, making it possible to process complex and large dataset -without accuracy loss due to text conversions. - -We envision that both JSON and its binary version will serve as part of -the mainstream data-exchange formats for scientific research in the future. -It will provide the flexibility and generality achieved by other popular -general-purpose file specifications, such as -[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly -reduced complexity and enhanced performance. - -JSONLab is a free and open-source implementation of a JSON/UBJSON encoder -and a decoder in the native MATLAB language. It can be used to convert a MATLAB -data structure (array, struct, cell, struct array and cell array) into -JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB -data structure. JSONLab supports both MATLAB and -[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone). - -------------------------------------------------------------------------------- - -II. Installation - -The installation of JSONLab is no different than any other simple -MATLAB toolbox. You only need to download/unzip the JSONLab package -to a folder, and add the folder's path to MATLAB/Octave's path list -by using the following command: - - addpath('/path/to/jsonlab'); - -If you want to add this path permanently, you need to type "pathtool", -browse to the jsonlab root folder and add to the list, then click "Save". -Then, run "rehash" in MATLAB, and type "which loadjson", if you see an -output, that means JSONLab is installed for MATLAB/Octave. - -------------------------------------------------------------------------------- - -III.Using JSONLab - -JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, -and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and -two equivallent functions -- loadubjson and saveubjson for the binary -JSON. The detailed help info for the four functions can be found below: - -=== loadjson.m === -
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          Franois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-
- -=== savejson.m === - -
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-
- -=== loadubjson.m === - -
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-
- -=== saveubjson.m === - -
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-
- - -=== examples === - -Under the "examples" folder, you can find several scripts to demonstrate the -basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you -will see the conversions from MATLAB data structure to JSON text and backward. -In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet -and validate the loadjson/savejson functions for regression testing purposes. -Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson -and loadubjson pairs for various matlab data structures. - -Please run these examples and understand how JSONLab works before you use -it to process your data. - -------------------------------------------------------------------------------- - -IV. Known Issues and TODOs - -JSONLab has several known limitations. We are striving to make it more general -and robust. Hopefully in a few future releases, the limitations become less. - -Here are the known issues: - -# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays; -# When processing names containing multi-byte characters, Octave and MATLAB \ -can give different field-names; you can use feature('DefaultCharacterSet','latin1') \ -in MATLAB to get consistant results -# savejson can not handle class and dataset. -# saveubjson converts a logical array into a uint8 ([U]) array -# an unofficial N-D array count syntax is implemented in saveubjson. We are \ -actively communicating with the UBJSON spec maintainer to investigate the \ -possibility of making it upstream -# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \ -files, however, it can parse all UBJSON files produced by saveubjson. - -------------------------------------------------------------------------------- - -V. Contribution and feedback - -JSONLab is an open-source project. This means you can not only use it and modify -it as you wish, but also you can contribute your changes back to JSONLab so -that everyone else can enjoy the improvement. For anyone who want to contribute, -please download JSONLab source code from it's subversion repository by using the -following command: - - svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab - -You can make changes to the files as needed. Once you are satisfied with your -changes, and ready to share it with others, please cd the root directory of -JSONLab, and type - - svn diff > yourname_featurename.patch - -You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at -the email address shown in the beginning of this file. Qianqian will review -the changes and commit it to the subversion if they are satisfactory. - -We appreciate any suggestions and feedbacks from you. Please use iso2mesh's -mailing list to report any questions you may have with JSONLab: - -http://groups.google.com/group/iso2mesh-users?hl=en&pli=1 - -(Subscription to the mailing list is needed in order to post messages). diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/jsonopt.m b/assignments/machine-learning-ex7/ex7/lib/jsonlab/jsonopt.m deleted file mode 100644 index 0bebd8d..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/jsonopt.m +++ /dev/null @@ -1,32 +0,0 @@ -function val=jsonopt(key,default,varargin) -% -% val=jsonopt(key,default,optstruct) -% -% setting options based on a struct. The struct can be produced -% by varargin2struct from a list of 'param','value' pairs -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% -% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $ -% -% input: -% key: a string with which one look up a value from a struct -% default: if the key does not exist, return default -% optstruct: a struct where each sub-field is a key -% -% output: -% val: if key exists, val=optstruct.key; otherwise val=default -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -val=default; -if(nargin<=2) return; end -opt=varargin{1}; -if(isstruct(opt) && isfield(opt,key)) - val=getfield(opt,key); -end - diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m b/assignments/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m deleted file mode 100644 index 42798c0..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m +++ /dev/null @@ -1,566 +0,0 @@ -function data = loadjson(fname,varargin) -% -% data=loadjson(fname,opt) -% or -% data=loadjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09, including previous works from -% -% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 -% created on 2009/11/02 -% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 -% created on 2009/03/22 -% Joel Feenstra: -% http://www.mathworks.com/matlabcentral/fileexchange/20565 -% created on 2008/07/03 -% -% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a JSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.FastArrayParser [1|0 or integer]: if set to 1, use a -% speed-optimized array parser when loading an -% array object. The fast array parser may -% collapse block arrays into a single large -% array similar to rules defined in cell2mat; 0 to -% use a legacy parser; if set to a larger-than-1 -% value, this option will specify the minimum -% dimension to enable the fast array parser. For -% example, if the input is a 3D array, setting -% FastArrayParser to 1 will return a 3D array; -% setting to 2 will return a cell array of 2D -% arrays; setting to 3 will return to a 2D cell -% array of 1D vectors; setting to 4 will return a -% 3D cell array. -% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}') -% dat=loadjson(['examples' filesep 'example1.json']) -% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); - -if(jsonopt('ShowProgress',0,opt)==1) - opt.progressbar_=waitbar(0,'loading ...'); -end -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end -if(isfield(opt,'progressbar_')) - close(opt.progressbar_); -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=data(j).x0x5F_ArraySize_; - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - if next_char ~= '}' - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - parse_char(':'); - val = parse_value(varargin{:}); - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' - break; - end - parse_char(','); - end - end - parse_char('}'); - -%%------------------------------------------------------------------------- - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim2=[]; - arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:}); - pbar=jsonopt('progressbar_',-1,varargin{:}); - - if next_char ~= ']' - if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:})) - [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos); - arraystr=['[' inStr(pos:endpos)]; - arraystr=regexprep(arraystr,'"_NaN_"','NaN'); - arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf'); - arraystr(arraystr==sprintf('\n'))=[]; - arraystr(arraystr==sprintf('\r'))=[]; - %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed - if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D - astr=inStr((e1l+1):(e1r-1)); - astr=regexprep(astr,'"_NaN_"','NaN'); - astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf'); - astr(astr==sprintf('\n'))=[]; - astr(astr==sprintf('\r'))=[]; - astr(astr==' ')=''; - if(isempty(find(astr=='[', 1))) % array is 2D - dim2=length(sscanf(astr,'%f,',[1 inf])); - end - else % array is 1D - astr=arraystr(2:end-1); - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]); - if(nextidx>=length(astr)-1) - object=obj; - pos=endpos; - parse_char(']'); - return; - end - end - if(~isempty(dim2)) - astr=arraystr; - astr(astr=='[')=''; - astr(astr==']')=''; - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf); - if(nextidx>=length(astr)-1) - object=reshape(obj,dim2,numel(obj)/dim2)'; - pos=endpos; - parse_char(']'); - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end - return; - end - end - arraystr=regexprep(arraystr,'\]\s*,','];'); - else - arraystr='['; - end - try - if(isoct && regexp(arraystr,'"','once')) - error('Octave eval can produce empty cells for JSON-like input'); - end - object=eval(arraystr); - pos=endpos; - catch - while 1 - newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1); - val = parse_value(newopt); - object{end+1} = val; - if next_char == ']' - break; - end - parse_char(','); - end - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - parse_char(']'); - - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr len esc index_esc len_esc - % len, ns = length(inStr), keyboard - if inStr(pos) ~= '"' - error_pos('String starting with " expected at position %d'); - else - pos = pos + 1; - end - str = ''; - while pos <= len - while index_esc <= len_esc && esc(index_esc) < pos - index_esc = index_esc + 1; - end - if index_esc > len_esc - str = [str inStr(pos:len)]; - pos = len + 1; - break; - else - str = [str inStr(pos:esc(index_esc)-1)]; - pos = esc(index_esc); - end - nstr = length(str); switch inStr(pos) - case '"' - pos = pos + 1; - if(~isempty(str)) - if(strcmp(str,'_Inf_')) - str=Inf; - elseif(strcmp(str,'-_Inf_')) - str=-Inf; - elseif(strcmp(str,'_NaN_')) - str=NaN; - end - end - return; - case '\' - if pos+1 > len - error_pos('End of file reached right after escape character'); - end - pos = pos + 1; - switch inStr(pos) - case {'"' '\' '/'} - str(nstr+1) = inStr(pos); - pos = pos + 1; - case {'b' 'f' 'n' 'r' 't'} - str(nstr+1) = sprintf(['\' inStr(pos)]); - pos = pos + 1; - case 'u' - if pos+4 > len - error_pos('End of file reached in escaped unicode character'); - end - str(nstr+(1:6)) = inStr(pos-1:pos+4); - pos = pos + 5; - end - otherwise % should never happen - str(nstr+1) = inStr(pos), keyboard - pos = pos + 1; - end - end - error_pos('End of file while expecting end of inStr'); - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct - currstr=inStr(pos:end); - numstr=0; - if(isoct~=0) - numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end'); - [num, one] = sscanf(currstr, '%f', 1); - delta=numstr+1; - else - [num, one, err, delta] = sscanf(currstr, '%f', 1); - if ~isempty(err) - error_pos('Error reading number at position %d'); - end - end - pos = pos + delta-1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - pbar=jsonopt('progressbar_',-1,varargin{:}); - if(pbar>0) - waitbar(pos/len,pbar,'loading ...'); - end - - switch(inStr(pos)) - case '"' - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'-','0','1','2','3','4','5','6','7','8','9'} - val = parse_number(varargin{:}); - return; - case 't' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true') - val = true; - pos = pos + 4; - return; - end - case 'f' - if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false') - val = false; - pos = pos + 5; - return; - end - case 'n' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null') - val = []; - pos = pos + 4; - return; - end - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m b/assignments/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m deleted file mode 100644 index 0155115..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m +++ /dev/null @@ -1,528 +0,0 @@ -function data = loadubjson(fname,varargin) -% -% data=loadubjson(fname,opt) -% or -% data=loadubjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/01 -% -% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a UBJSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.IntEndian [B|L]: specify the endianness of the integer fields -% in the UBJSON input data. B - Big-Endian format for -% integers (as required in the UBJSON specification); -% L - input integer fields are in Little-Endian order. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% obj=struct('string','value','array',[1 2 3]); -% ubjdata=saveubjson('obj',obj); -% dat=loadubjson(ubjdata) -% dat=loadubjson(['examples' filesep 'example1.ubj']) -% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); -fileendian=upper(jsonopt('IntEndian','B',opt)); -[os,maxelem,systemendian]=computer; - -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end - - -%% -function newdata=parse_collection(id,data,obj) - -if(jsoncount>0 && exist('data','var')) - if(~iscell(data)) - newdata=cell(1); - newdata{1}=data; - data=newdata; - end -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=double(data(j).x0x5F_ArraySize_); - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); % TODO - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - count=double(parse_number()); - end - if next_char ~= '}' - num=0; - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - %parse_char(':'); - val = parse_value(varargin{:}); - num=num+1; - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' || (count>=0 && num>=count) - break; - end - %parse_char(','); - end - end - if(count==-1) - parse_char('}'); - end - -%%------------------------------------------------------------------------- -function [cid,len]=elem_info(type) -id=strfind('iUIlLdD',type); -dataclass={'int8','uint8','int16','int32','int64','single','double'}; -bytelen=[1,1,2,4,8,4,8]; -if(id>0) - cid=dataclass{id}; - len=bytelen(id); -else - error_pos('unsupported type at position %d'); -end -%%------------------------------------------------------------------------- - - -function [data adv]=parse_block(type,count,varargin) -global pos inStr isoct fileendian systemendian -[cid,len]=elem_info(type); -datastr=inStr(pos:pos+len*count-1); -if(isoct) - newdata=int8(datastr); -else - newdata=uint8(datastr); -end -id=strfind('iUIlLdD',type); -if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,cid)); -end -data=typecast(newdata,cid); -adv=double(len*count); - -%%------------------------------------------------------------------------- - - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim=[]; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - if(next_char=='[') - dim=parse_array(varargin{:}); - count=prod(double(dim)); - else - count=double(parse_number()); - end - end - if(~isempty(type)) - if(count>=0) - [object adv]=parse_block(type,count,varargin{:}); - if(~isempty(dim)) - object=reshape(object,dim); - end - pos=pos+adv; - return; - else - endpos=matching_bracket(inStr,pos); - [cid,len]=elem_info(type); - count=(endpos-pos)/len; - [object adv]=parse_block(type,count,varargin{:}); - pos=pos+adv; - parse_char(']'); - return; - end - end - if next_char ~= ']' - while 1 - val = parse_value(varargin{:}); - object{end+1} = val; - if next_char == ']' - break; - end - %parse_char(','); - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - if(count==-1) - parse_char(']'); - end - -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr esc index_esc len_esc - % len, ns = length(inStr), keyboard - type=inStr(pos); - if type ~= 'S' && type ~= 'C' && type ~= 'H' - error_pos('String starting with S expected at position %d'); - else - pos = pos + 1; - end - if(type == 'C') - str=inStr(pos); - pos=pos+1; - return; - end - bytelen=double(parse_number()); - if(length(inStr)>=pos+bytelen-1) - str=inStr(pos:pos+bytelen-1); - pos=pos+bytelen; - else - error_pos('End of file while expecting end of inStr'); - end - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct fileendian systemendian - id=strfind('iUIlLdD',inStr(pos)); - if(isempty(id)) - error_pos('expecting a number at position %d'); - end - type={'int8','uint8','int16','int32','int64','single','double'}; - bytelen=[1,1,2,4,8,4,8]; - datastr=inStr(pos+1:pos+bytelen(id)); - if(isoct) - newdata=int8(datastr); - else - newdata=uint8(datastr); - end - if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,type{id})); - end - num=typecast(newdata,type{id}); - pos = pos + bytelen(id)+1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - switch(inStr(pos)) - case {'S','C','H'} - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'i','U','I','l','L','d','D'} - val = parse_number(varargin{:}); - return; - case 'T' - val = true; - pos = pos + 1; - return; - case 'F' - val = false; - pos = pos + 1; - return; - case {'Z','N'} - val = []; - pos = pos + 1; - return; - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos e1l e1r maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/mergestruct.m b/assignments/machine-learning-ex7/ex7/lib/jsonlab/mergestruct.m deleted file mode 100644 index 6de6100..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/mergestruct.m +++ /dev/null @@ -1,33 +0,0 @@ -function s=mergestruct(s1,s2) -% -% s=mergestruct(s1,s2) -% -% merge two struct objects into one -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% s1,s2: a struct object, s1 and s2 can not be arrays -% -% output: -% s: the merged struct object. fields in s1 and s2 will be combined in s. -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(~isstruct(s1) || ~isstruct(s2)) - error('input parameters contain non-struct'); -end -if(length(s1)>1 || length(s2)>1) - error('can not merge struct arrays'); -end -fn=fieldnames(s2); -s=s1; -for i=1:length(fn) - s=setfield(s,fn{i},getfield(s2,fn{i})); -end - diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/savejson.m b/assignments/machine-learning-ex7/ex7/lib/jsonlab/savejson.m deleted file mode 100644 index 7e84076..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/savejson.m +++ /dev/null @@ -1,475 +0,0 @@ -function json=savejson(rootname,obj,varargin) -% -% json=savejson(rootname,obj,filename) -% or -% json=savejson(rootname,obj,opt) -% json=savejson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript -% Object Notation) string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09 -% -% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array). -% filename: a string for the file name to save the output JSON data. -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.FloatFormat ['%.10g'|string]: format to show each numeric element -% of a 1D/2D array; -% opt.ArrayIndent [1|0]: if 1, output explicit data array with -% precedent indentation; if 0, no indentation -% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [0|1]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern -% to represent +/-Inf. The matched pattern is '([-+]*)Inf' -% and $1 represents the sign. For those who want to use -% 1e999 to represent Inf, they can set opt.Inf to '$11e999' -% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern -% to represent NaN -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSONP='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode. -% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs) -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a string in the JSON format (see http://json.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% savejson('jmesh',jsonmesh) -% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end - -whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -if(jsonopt('Compact',0,opt)==1) - whitespaces=struct('tab','','newline','','sep',','); -end -if(~isfield(opt,'whitespaces_')) - opt.whitespaces_=whitespaces; -end - -nl=whitespaces.newline; - -json=obj2json(rootname,obj,rootlevel,opt); -if(rootisarray) - json=sprintf('%s%s',json,nl); -else - json=sprintf('{%s%s%s}\n',nl,json,nl); -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=sprintf('%s(%s);%s',jsonp,json,nl); -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - if(jsonopt('SaveBinary',0,opt)==1) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - else - fid = fopen(opt.FileName, 'wt'); - fwrite(fid,json,'char'); - end - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2json(name,item,level,varargin) - -if(iscell(item)) - txt=cell2json(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2json(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2json(name,item,level,varargin{:}); -else - txt=mat2json(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2json(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -nl=ws.newline; -if(len>1) - if(~isempty(name)) - txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; - else - txt=sprintf('%s[%s',padding0,nl); - end -elseif(len==0) - if(~isempty(name)) - txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; - else - txt=sprintf('%s[]',padding0); - end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:})); - if(i1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=struct2json(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1)); -nl=ws.newline; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding0,nl); end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); - else - txt=sprintf('%s%s{%s',txt,padding1,nl); - end - if(~isempty(names)) - for e=1:length(names) - txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})); - if(e1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=str2json(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding1,nl); end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - if(isoct) - val=regexprep(item(e,:),'\\','\\'); - val=regexprep(val,'"','\"'); - val=regexprep(val,'^"','\"'); - else - val=regexprep(item(e,:),'\\','\\\\'); - val=regexprep(val,'"','\\"'); - val=regexprep(val,'^"','\\"'); - end - val=escapejsonstring(val); - if(len==1) - obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"']; - if(isempty(name)) obj=['"',val,'"']; end - txt=sprintf('%s%s%s%s',txt,padding1,obj); - else - txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']); - end - if(e==len) sep=''; end - txt=sprintf('%s%s',txt,sep); -end -if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end - -%%------------------------------------------------------------------------- -function txt=mat2json(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:})) - if(isempty(name)) - txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - else - txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - end -else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0) - numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']',''); - else - numtxt=matdata2json(item,level+1,varargin{:}); - end - if(isempty(name)) - txt=sprintf('%s%s',padding1,numtxt); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - else - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - end - end - return; -end -dataformat='%s%s%s%s%s'; - -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep); - if(size(item,1)==1) - % Row vector, store only column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([iy(:),data'],level+2,varargin{:}), nl); - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,data],level+2,varargin{:}), nl); - else - % General case, store row and column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,iy,data],level+2,varargin{:}), nl); - end -else - if(isreal(item)) - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json(item(:)',level+2,varargin{:}), nl); - else - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl); - end -end -txt=sprintf('%s%s%s',txt,padding1,'}'); - -%%------------------------------------------------------------------------- -function txt=matdata2json(mat,level,varargin) - -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -tab=ws.tab; -nl=ws.newline; - -if(size(mat,1)==1) - pre=''; - post=''; - level=level-1; -else - pre=sprintf('[%s',nl); - post=sprintf('%s%s]',nl,repmat(tab,1,level-1)); -end - -if(isempty(mat)) - txt='null'; - return; -end -floatformat=jsonopt('FloatFormat','%.10g',varargin{:}); -%if(numel(mat)>1) - formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]]; -%else -% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]]; -%end - -if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1) - formatstr=[repmat(tab,1,level) formatstr]; -end - -txt=sprintf(formatstr,mat'); -txt(end-length(nl):end)=[]; -if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1) - txt=regexprep(txt,'1','true'); - txt=regexprep(txt,'0','false'); -end -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],\n[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -txt=[pre txt post]; -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end - -%%------------------------------------------------------------------------- -function newstr=escapejsonstring(str) -newstr=str; -isoct=exist('OCTAVE_VERSION','builtin'); -if(isoct) - vv=sscanf(OCTAVE_VERSION,'%f'); - if(vv(1)>=3.8) isoct=0; end -end -if(isoct) - escapechars={'\a','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},escapechars{i}); - end -else - escapechars={'\a','\b','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\')); - end -end diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m b/assignments/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m deleted file mode 100644 index eaec433..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m +++ /dev/null @@ -1,504 +0,0 @@ -function json=saveubjson(rootname,obj,varargin) -% -% json=saveubjson(rootname,obj,filename) -% or -% json=saveubjson(rootname,obj,opt) -% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a Universal -% Binary JSON (UBJSON) binary string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/17 -% -% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array) -% filename: a string for the file name to save the output UBJSON data -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [1|0]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSON='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a binary string in the UBJSON format (see http://ubjson.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% saveubjson('jsonmesh',jsonmesh) -% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end -json=obj2ubjson(rootname,obj,rootlevel,opt); -if(~rootisarray) - json=['{' json '}']; -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=[jsonp '(' json ')']; -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2ubjson(name,item,level,varargin) - -if(iscell(item)) - txt=cell2ubjson(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2ubjson(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2ubjson(name,item,level,varargin{:}); -else - txt=mat2ubjson(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2ubjson(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); % let's handle 1D cell first -if(len>1) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) '[']; name=''; - else - txt='['; - end -elseif(len==0) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; - else - txt='Z'; - end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})]; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=struct2ubjson(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=[txt S_(checkname(name,varargin{:})) '{']; - else - txt=[txt '{']; - end - if(~isempty(names)) - for e=1:length(names) - txt=[txt obj2ubjson(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})]; - end - end - txt=[txt '}']; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=str2ubjson(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - val=item(e,:); - if(len==1) - obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),'']; - if(isempty(name)) obj=['',S_(val),'']; end - txt=[txt,'',obj]; - else - txt=[txt,'',['',S_(val),'']]; - end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=mat2ubjson(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) || jsonopt('ArrayToStruct',0,varargin{:})) - cid=I_(uint32(max(size(item)))); - if(isempty(name)) - txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ]; - else - if(isempty(item)) - txt=[S_(checkname(name,varargin{:})),'Z']; - return; - else - txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))]; - end - end -else - if(isempty(name)) - txt=matdata2ubjson(item,level+1,varargin{:}); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']',''); - txt=[S_(checkname(name,varargin{:})) numtxt]; - else - txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})]; - end - end - return; -end -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=[txt,S_('_ArrayIsComplex_'),'T']; - end - txt=[txt,S_('_ArrayIsSparse_'),'T']; - if(size(item,1)==1) - % Row vector, store only column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([iy(:),data'],level+2,varargin{:})]; - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,data],level+2,varargin{:})]; - else - % General case, store row and column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,iy,data],level+2,varargin{:})]; - end -else - if(isreal(item)) - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson(item(:)',level+2,varargin{:})]; - else - txt=[txt,S_('_ArrayIsComplex_'),'T']; - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})]; - end -end -txt=[txt,'}']; - -%%------------------------------------------------------------------------- -function txt=matdata2ubjson(mat,level,varargin) -if(isempty(mat)) - txt='Z'; - return; -end -if(size(mat,1)==1) - level=level-1; -end -type=''; -hasnegtive=(mat<0); -if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0))) - if(isempty(hasnegtive)) - if(max(mat(:))<=2^8) - type='U'; - end - end - if(isempty(type)) - % todo - need to consider negative ones separately - id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]); - if(isempty(find(id))) - error('high-precision data is not yet supported'); - end - key='iIlL'; - type=key(find(id)); - end - txt=[I_a(mat(:),type,size(mat))]; -elseif(islogical(mat)) - logicalval='FT'; - if(numel(mat)==1) - txt=logicalval(mat+1); - else - txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')]; - end -else - if(numel(mat)==1) - txt=['[' D_(mat) ']']; - else - txt=D_a(mat(:),'D',size(mat)); - end -end - -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end -%%------------------------------------------------------------------------- -function val=S_(str) -if(length(str)==1) - val=['C' str]; -else - val=['S' I_(int32(length(str))) str]; -end -%%------------------------------------------------------------------------- -function val=I_(num) -if(~isinteger(num)) - error('input is not an integer'); -end -if(num>=0 && num<255) - val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')]; - return; -end -key='iIlL'; -cid={'int8','int16','int32','int64'}; -for i=1:4 - if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1))) - val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')]; - return; - end -end -error('unsupported integer'); - -%%------------------------------------------------------------------------- -function val=D_(num) -if(~isfloat(num)) - error('input is not a float'); -end - -if(isa(num,'single')) - val=['d' data2byte(num,'uint8')]; -else - val=['D' data2byte(num,'uint8')]; -end -%%------------------------------------------------------------------------- -function data=I_a(num,type,dim,format) -id=find(ismember('iUIlL',type)); - -if(id==0) - error('unsupported integer array'); -end - -% based on UBJSON specs, all integer types are stored in big endian format - -if(id==1) - data=data2byte(swapbytes(int8(num)),'uint8'); - blen=1; -elseif(id==2) - data=data2byte(swapbytes(uint8(num)),'uint8'); - blen=1; -elseif(id==3) - data=data2byte(swapbytes(int16(num)),'uint8'); - blen=2; -elseif(id==4) - data=data2byte(swapbytes(int32(num)),'uint8'); - blen=4; -elseif(id==5) - data=data2byte(swapbytes(int64(num)),'uint8'); - blen=8; -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/blen)) data(:)']; - end - data=['[' data(:)']; -else - data=reshape(data,blen,numel(data)/blen); - data(2:blen+1,:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function data=D_a(num,type,dim,format) -id=find(ismember('dD',type)); - -if(id==0) - error('unsupported float array'); -end - -if(id==1) - data=data2byte(single(num),'uint8'); -elseif(id==2) - data=data2byte(double(num),'uint8'); -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)']; - end - data=['[' data]; -else - data=reshape(data,(id*4),length(data)/(id*4)); - data(2:(id*4+1),:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function bytes=data2byte(varargin) -bytes=typecast(varargin{:}); -bytes=bytes(:)'; diff --git a/assignments/machine-learning-ex7/ex7/lib/jsonlab/varargin2struct.m b/assignments/machine-learning-ex7/ex7/lib/jsonlab/varargin2struct.m deleted file mode 100644 index 9a5c2b6..0000000 --- a/assignments/machine-learning-ex7/ex7/lib/jsonlab/varargin2struct.m +++ /dev/null @@ -1,40 +0,0 @@ -function opt=varargin2struct(varargin) -% -% opt=varargin2struct('param1',value1,'param2',value2,...) -% or -% opt=varargin2struct(...,optstruct,...) -% -% convert a series of input parameters into a structure -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% 'param', value: the input parameters should be pairs of a string and a value -% optstruct: if a parameter is a struct, the fields will be merged to the output struct -% -% output: -% opt: a struct where opt.param1=value1, opt.param2=value2 ... -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -len=length(varargin); -opt=struct; -if(len==0) return; end -i=1; -while(i<=len) - if(isstruct(varargin{i})) - opt=mergestruct(opt,varargin{i}); - elseif(ischar(varargin{i}) && i 0 && resp(1) == '{'; - isHtml = findstr(lower(resp), ']+>', ' '); - strippedResponse = regexprep(strippedResponse, '[\t ]+', ' '); - fprintf(strippedResponse); -end - - - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% -% Service configuration -% -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function submissionUrl = submissionUrl() - submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1'; -end diff --git a/assignments/machine-learning-ex7/ex7/pca.m b/assignments/machine-learning-ex7/ex7/pca.m deleted file mode 100644 index e240d18..0000000 --- a/assignments/machine-learning-ex7/ex7/pca.m +++ /dev/null @@ -1,32 +0,0 @@ -function [U, S] = pca(X) -%PCA Run principal component analysis on the dataset X -% [U, S, X] = pca(X) computes eigenvectors of the covariance matrix of X -% Returns the eigenvectors U, the eigenvalues (on diagonal) in S -% - -% Useful values -[m, n] = size(X); - -% You need to return the following variables correctly. -U = zeros(n); -S = zeros(n); - -% ====================== YOUR CODE HERE ====================== -% Instructions: You should first compute the covariance matrix. Then, you -% should use the "svd" function to compute the eigenvectors -% and eigenvalues of the covariance matrix. -% -% Note: When computing the covariance matrix, remember to divide by m (the -% number of examples). -% - -U = X' * X / m; -% [U,S,V] -[U,S,V] = svd(U); - - - - -% ========================================================================= - -end diff --git a/assignments/machine-learning-ex7/ex7/plotDataPoints.m b/assignments/machine-learning-ex7/ex7/plotDataPoints.m deleted file mode 100644 index 77c4623..0000000 --- a/assignments/machine-learning-ex7/ex7/plotDataPoints.m +++ /dev/null @@ -1,14 +0,0 @@ -function plotDataPoints(X, idx, K) -%PLOTDATAPOINTS plots data points in X, coloring them so that those with the same -%index assignments in idx have the same color -% PLOTDATAPOINTS(X, idx, K) plots data points in X, coloring them so that those -% with the same index assignments in idx have the same color - -% Create palette -palette = hsv(K + 1); -colors = palette(idx, :); - -% Plot the data -scatter(X(:,1), X(:,2), 15, colors); - -end diff --git a/assignments/machine-learning-ex7/ex7/plotProgresskMeans.m b/assignments/machine-learning-ex7/ex7/plotProgresskMeans.m deleted file mode 100644 index f14d1c7..0000000 --- a/assignments/machine-learning-ex7/ex7/plotProgresskMeans.m +++ /dev/null @@ -1,27 +0,0 @@ -function plotProgresskMeans(X, centroids, previous, idx, K, i) -%PLOTPROGRESSKMEANS is a helper function that displays the progress of -%k-Means as it is running. It is intended for use only with 2D data. -% PLOTPROGRESSKMEANS(X, centroids, previous, idx, K, i) plots the data -% points with colors assigned to each centroid. With the previous -% centroids, it also plots a line between the previous locations and -% current locations of the centroids. -% - -% Plot the examples -plotDataPoints(X, idx, K); - -% Plot the centroids as black x's -plot(centroids(:,1), centroids(:,2), 'x', ... - 'MarkerEdgeColor','k', ... - 'MarkerSize', 10, 'LineWidth', 3); - -% Plot the history of the centroids with lines -for j=1:size(centroids,1) - drawLine(centroids(j, :), previous(j, :)); -end - -% Title -title(sprintf('Iteration number %d', i)) - -end - diff --git a/assignments/machine-learning-ex7/ex7/projectData.m b/assignments/machine-learning-ex7/ex7/projectData.m deleted file mode 100644 index cb53038..0000000 --- a/assignments/machine-learning-ex7/ex7/projectData.m +++ /dev/null @@ -1,27 +0,0 @@ -function Z = projectData(X, U, K) -%PROJECTDATA Computes the reduced data representation when projecting only -%on to the top k eigenvectors -% Z = projectData(X, U, K) computes the projection of -% the normalized inputs X into the reduced dimensional space spanned by -% the first K columns of U. It returns the projected examples in Z. -% - -% You need to return the following variables correctly. -Z = zeros(size(X, 1), K); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the projection of the data using only the top K -% eigenvectors in U (first K columns). -% For the i-th example X(i,:), the projection on to the k-th -% eigenvector is given as follows: -% x = X(i, :)'; -% projection_k = x' * U(:, k); -% -U_reduce = U(:, 1:K); -x = X'; -Z = x' * U_reduce; - - -% ============================================================= - -end diff --git a/assignments/machine-learning-ex7/ex7/recoverData.m b/assignments/machine-learning-ex7/ex7/recoverData.m deleted file mode 100644 index 3621d11..0000000 --- a/assignments/machine-learning-ex7/ex7/recoverData.m +++ /dev/null @@ -1,28 +0,0 @@ -function X_rec = recoverData(Z, U, K) -%RECOVERDATA Recovers an approximation of the original data when using the -%projected data -% X_rec = RECOVERDATA(Z, U, K) recovers an approximation the -% original data that has been reduced to K dimensions. It returns the -% approximate reconstruction in X_rec. -% - -% You need to return the following variables correctly. -X_rec = zeros(size(Z, 1), size(U, 1)); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the approximation of the data by projecting back -% onto the original space using the top K eigenvectors in U. -% -% For the i-th example Z(i,:), the (approximate) -% recovered data for dimension j is given as follows: -% v = Z(i, :)'; -% recovered_j = v' * U(j, 1:K)'; -% -% Notice that U(j, 1:K) is a row vector. -% - -X_rec = Z * U(:,1:K)'; - -% ============================================================= - -end diff --git a/assignments/machine-learning-ex7/ex7/runkMeans.m b/assignments/machine-learning-ex7/ex7/runkMeans.m deleted file mode 100644 index fc22c1b..0000000 --- a/assignments/machine-learning-ex7/ex7/runkMeans.m +++ /dev/null @@ -1,64 +0,0 @@ -function [centroids, idx] = runkMeans(X, initial_centroids, ... - max_iters, plot_progress) -%RUNKMEANS runs the K-Means algorithm on data matrix X, where each row of X -%is a single example -% [centroids, idx] = RUNKMEANS(X, initial_centroids, max_iters, ... -% plot_progress) runs the K-Means algorithm on data matrix X, where each -% row of X is a single example. It uses initial_centroids used as the -% initial centroids. max_iters specifies the total number of interactions -% of K-Means to execute. plot_progress is a true/false flag that -% indicates if the function should also plot its progress as the -% learning happens. This is set to false by default. runkMeans returns -% centroids, a Kxn matrix of the computed centroids and idx, a m x 1 -% vector of centroid assignments (i.e. each entry in range [1..K]) -% - -% Set default value for plot progress -if ~exist('plot_progress', 'var') || isempty(plot_progress) - plot_progress = false; -end - -% Plot the data if we are plotting progress -if plot_progress - figure; - hold on; -end - -% Initialize values -[m n] = size(X); -K = size(initial_centroids, 1); -centroids = initial_centroids; -previous_centroids = centroids; -idx = zeros(m, 1); - -% Run K-Means -for i=1:max_iters - - % Output progress - fprintf('K-Means iteration %d/%d...\n', i, max_iters); - if exist('OCTAVE_VERSION') - fflush(stdout); - end - - % For each example in X, assign it to the closest centroid - idx = findClosestCentroids(X, centroids); - - % Optionally, plot progress here - if plot_progress - plotProgresskMeans(X, centroids, previous_centroids, idx, K, i); - previous_centroids = centroids; - fprintf('Press enter to continue.\n'); - pause; - end - - % Given the memberships, compute new centroids - centroids = computeCentroids(X, idx, K); -end - -% Hold off if we are plotting progress -if plot_progress - hold off; -end - -end - diff --git a/assignments/machine-learning-ex7/ex7/submit.m b/assignments/machine-learning-ex7/ex7/submit.m deleted file mode 100644 index 192a387..0000000 --- a/assignments/machine-learning-ex7/ex7/submit.m +++ /dev/null @@ -1,60 +0,0 @@ -function submit() - addpath('./lib'); - - conf.assignmentSlug = 'k-means-clustering-and-pca'; - conf.itemName = 'K-Means Clustering and PCA'; - conf.partArrays = { ... - { ... - '1', ... - { 'findClosestCentroids.m' }, ... - 'Find Closest Centroids (k-Means)', ... - }, ... - { ... - '2', ... - { 'computeCentroids.m' }, ... - 'Compute Centroid Means (k-Means)', ... - }, ... - { ... - '3', ... - { 'pca.m' }, ... - 'PCA', ... - }, ... - { ... - '4', ... - { 'projectData.m' }, ... - 'Project Data (PCA)', ... - }, ... - { ... - '5', ... - { 'recoverData.m' }, ... - 'Recover Data (PCA)', ... - }, ... - }; - conf.output = @output; - - submitWithConfiguration(conf); -end - -function out = output(partId, auxstring) - % Random Test Cases - X = reshape(sin(1:165), 15, 11); - Z = reshape(cos(1:121), 11, 11); - C = Z(1:5, :); - idx = (1 + mod(1:15, 3))'; - if partId == '1' - idx = findClosestCentroids(X, C); - out = sprintf('%0.5f ', idx(:)); - elseif partId == '2' - centroids = computeCentroids(X, idx, 3); - out = sprintf('%0.5f ', centroids(:)); - elseif partId == '3' - [U, S] = pca(X); - out = sprintf('%0.5f ', abs([U(:); S(:)])); - elseif partId == '4' - X_proj = projectData(X, Z, 5); - out = sprintf('%0.5f ', X_proj(:)); - elseif partId == '5' - X_rec = recoverData(X(:,1:5), Z, 5); - out = sprintf('%0.5f ', X_rec(:)); - end -end diff --git a/assignments/machine-learning-ex7/ex7/token.mat b/assignments/machine-learning-ex7/ex7/token.mat deleted file mode 100644 index 03936ab..0000000 --- a/assignments/machine-learning-ex7/ex7/token.mat +++ /dev/null @@ -1,15 +0,0 @@ -# Created by Octave 4.2.0, Wed Feb 15 20:08:56 2017 GMT -# name: email -# type: sq_string -# elements: 1 -# length: 17 -scruel@vip.qq.com - - -# name: token -# type: sq_string -# elements: 1 -# length: 16 -UnueCgTfOcUB5hYb - - diff --git a/assignments/machine-learning-ex8/ex8.pdf b/assignments/machine-learning-ex8/ex8.pdf deleted file mode 100644 index c6811c2..0000000 Binary files a/assignments/machine-learning-ex8/ex8.pdf and /dev/null differ diff --git a/assignments/machine-learning-ex8/ex8/checkCostFunction.m b/assignments/machine-learning-ex8/ex8/checkCostFunction.m deleted file mode 100644 index e72fe32..0000000 --- a/assignments/machine-learning-ex8/ex8/checkCostFunction.m +++ /dev/null @@ -1,48 +0,0 @@ -function checkCostFunction(lambda) -%CHECKCOSTFUNCTION Creates a collaborative filering problem -%to check your cost function and gradients -% CHECKCOSTFUNCTION(lambda) Creates a collaborative filering problem -% to check your cost function and gradients, it will output the -% analytical gradients produced by your code and the numerical gradients -% (computed using computeNumericalGradient). These two gradient -% computations should result in very similar values. - -% Set lambda -if ~exist('lambda', 'var') || isempty(lambda) - lambda = 0; -end - -%% Create small problem -X_t = rand(4, 3); -Theta_t = rand(5, 3); - -% Zap out most entries -Y = X_t * Theta_t'; -Y(rand(size(Y)) > 0.5) = 0; -R = zeros(size(Y)); -R(Y ~= 0) = 1; - -%% Run Gradient Checking -X = randn(size(X_t)); -Theta = randn(size(Theta_t)); -num_users = size(Y, 2); -num_movies = size(Y, 1); -num_features = size(Theta_t, 2); - -numgrad = computeNumericalGradient( ... - @(t) cofiCostFunc(t, Y, R, num_users, num_movies, ... - num_features, lambda), [X(:); Theta(:)]); - -[cost, grad] = cofiCostFunc([X(:); Theta(:)], Y, R, num_users, ... - num_movies, num_features, lambda); - -disp([numgrad grad]); -fprintf(['The above two columns you get should be very similar.\n' ... - '(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']); - -diff = norm(numgrad-grad)/norm(numgrad+grad); -fprintf(['If your backpropagation implementation is correct, then \n' ... - 'the relative difference will be small (less than 1e-9). \n' ... - '\nRelative Difference: %g\n'], diff); - -end \ No newline at end of file diff --git a/assignments/machine-learning-ex8/ex8/cofiCostFunc.m b/assignments/machine-learning-ex8/ex8/cofiCostFunc.m deleted file mode 100644 index 3320f21..0000000 --- a/assignments/machine-learning-ex8/ex8/cofiCostFunc.m +++ /dev/null @@ -1,62 +0,0 @@ -function [J, grad] = cofiCostFunc(params, Y, R, num_users, num_movies, ... - num_features, lambda) -%COFICOSTFUNC Collaborative filtering cost function -% [J, grad] = COFICOSTFUNC(params, Y, R, num_users, num_movies, ... -% num_features, lambda) returns the cost and gradient for the -% collaborative filtering problem. -% - -% Unfold the U and W matrices from params -X = reshape(params(1:num_movies*num_features), num_movies, num_features); -Theta = reshape(params(num_movies*num_features+1:end), ... - num_users, num_features); - - -% You need to return the following values correctly -J = 0; -X_grad = zeros(size(X)); -Theta_grad = zeros(size(Theta)); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the cost function and gradient for collaborative -% filtering. Concretely, you should first implement the cost -% function (without regularization) and make sure it is -% matches our costs. After that, you should implement the -% gradient and use the checkCostFunction routine to check -% that the gradient is correct. Finally, you should implement -% regularization. -% -% Notes: X - num_movies x num_features matrix of movie features -% Theta - num_users x num_features matrix of user features -% Y - num_movies x num_users matrix of user ratings of movies -% R - num_movies x num_users matrix, where R(i, j) = 1 if the -% i-th movie was rated by the j-th user -% -% You should set the following variables correctly: -% -% X_grad - num_movies x num_features matrix, containing the -% partial derivatives w.r.t. to each element of X -% Theta_grad - num_users x num_features matrix, containing the -% partial derivatives w.r.t. to each element of Theta -% - -%sum(R .* (((X * Theta') .- Y) .^ 2)) 1*4 matrix -J = 1 / 2 * sum(sum(R .* (((X * Theta') .- Y) .^ 2))); -J = J + lambda / 2 * sum(sum(X .^ 2)) + lambda / 2 * sum(sum(Theta .^ 2)); - -% (((X * Theta') .- Y) * Theta) - -X_grad = (R .* ((X * Theta') .- Y)) * Theta; -X_grad = X_grad + lambda .* X; -Theta_grad = (R .* ((X * Theta') .- Y))' * X; -Theta_grad = Theta_grad + lambda .* Theta; - - - - - -% ============================================================= - -grad = [X_grad(:); Theta_grad(:)]; - -end diff --git a/assignments/machine-learning-ex8/ex8/computeNumericalGradient.m b/assignments/machine-learning-ex8/ex8/computeNumericalGradient.m deleted file mode 100644 index c3abeac..0000000 --- a/assignments/machine-learning-ex8/ex8/computeNumericalGradient.m +++ /dev/null @@ -1,29 +0,0 @@ -function numgrad = computeNumericalGradient(J, theta) -%COMPUTENUMERICALGRADIENT Computes the gradient using "finite differences" -%and gives us a numerical estimate of the gradient. -% numgrad = COMPUTENUMERICALGRADIENT(J, theta) computes the numerical -% gradient of the function J around theta. Calling y = J(theta) should -% return the function value at theta. - -% Notes: The following code implements numerical gradient checking, and -% returns the numerical gradient.It sets numgrad(i) to (a numerical -% approximation of) the partial derivative of J with respect to the -% i-th input argument, evaluated at theta. (i.e., numgrad(i) should -% be the (approximately) the partial derivative of J with respect -% to theta(i).) -% - -numgrad = zeros(size(theta)); -perturb = zeros(size(theta)); -e = 1e-4; -for p = 1:numel(theta) - % Set perturbation vector - perturb(p) = e; - loss1 = J(theta - perturb); - loss2 = J(theta + perturb); - % Compute Numerical Gradient - numgrad(p) = (loss2 - loss1) / (2*e); - perturb(p) = 0; -end - -end diff --git a/assignments/machine-learning-ex8/ex8/estimateGaussian.m b/assignments/machine-learning-ex8/ex8/estimateGaussian.m deleted file mode 100644 index 713ad35..0000000 --- a/assignments/machine-learning-ex8/ex8/estimateGaussian.m +++ /dev/null @@ -1,34 +0,0 @@ -function [mu sigma2] = estimateGaussian(X) -%ESTIMATEGAUSSIAN This function estimates the parameters of a -%Gaussian distribution using the data in X -% [mu sigma2] = estimateGaussian(X), -% The input X is the dataset with each n-dimensional data point in one row -% The output is an n-dimensional vector mu, the mean of the data set -% and the variances sigma^2, an n x 1 vector -% - -% Useful variables -[m, n] = size(X); - -% You should return these values correctly -mu = zeros(n, 1); -sigma2 = zeros(n, 1); - -% ====================== YOUR CODE HERE ====================== -% Instructions: Compute the mean of the data and the variances -% In particular, mu(i) should contain the mean of -% the data for the i-th feature and sigma2(i) -% should contain variance of the i-th feature. -% - -mu = mean(X)'; -for i = 1 : n - sigma2(i) = sum((X(:, i) - mu(i)) .^2) /m; -end - - - -% ============================================================= - - -end diff --git a/assignments/machine-learning-ex8/ex8/ex8.m b/assignments/machine-learning-ex8/ex8/ex8.m deleted file mode 100644 index 54bcdf2..0000000 --- a/assignments/machine-learning-ex8/ex8/ex8.m +++ /dev/null @@ -1,123 +0,0 @@ -%% Machine Learning Online Class -% Exercise 8 | Anomaly Detection and Collaborative Filtering -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% exercise. You will need to complete the following functions: -% -% estimateGaussian.m -% selectThreshold.m -% cofiCostFunc.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% Initialization -clear ; close all; clc - -%% ================== Part 1: Load Example Dataset =================== -% We start this exercise by using a small dataset that is easy to -% visualize. -% -% Our example case consists of 2 network server statistics across -% several machines: the latency and throughput of each machine. -% This exercise will help us find possibly faulty (or very fast) machines. -% - -fprintf('Visualizing example dataset for outlier detection.\n\n'); - -% The following command loads the dataset. You should now have the -% variables X, Xval, yval in your environment -load('ex8data1.mat'); - -% Visualize the example dataset -plot(X(:, 1), X(:, 2), 'bx'); -axis([0 30 0 30]); -xlabel('Latency (ms)'); -ylabel('Throughput (mb/s)'); - -fprintf('Program paused. Press enter to continue.\n'); -pause - - -%% ================== Part 2: Estimate the dataset statistics =================== -% For this exercise, we assume a Gaussian distribution for the dataset. -% -% We first estimate the parameters of our assumed Gaussian distribution, -% then compute the probabilities for each of the points and then visualize -% both the overall distribution and where each of the points falls in -% terms of that distribution. -% -fprintf('Visualizing Gaussian fit.\n\n'); - -% Estimate my and sigma2 -[mu sigma2] = estimateGaussian(X); - -% Returns the density of the multivariate normal at each data point (row) -% of X -p = multivariateGaussian(X, mu, sigma2); - -% Visualize the fit -visualizeFit(X, mu, sigma2); -xlabel('Latency (ms)'); -ylabel('Throughput (mb/s)'); - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ================== Part 3: Find Outliers =================== -% Now you will find a good epsilon threshold using a cross-validation set -% probabilities given the estimated Gaussian distribution -% - -pval = multivariateGaussian(Xval, mu, sigma2); - -[epsilon F1] = selectThreshold(yval, pval); -fprintf('Best epsilon found using cross-validation: %e\n', epsilon); -fprintf('Best F1 on Cross Validation Set: %f\n', F1); -fprintf(' (you should see a value epsilon of about 8.99e-05)\n\n'); - -% Find the outliers in the training set and plot the -outliers = find(p < epsilon); - -% Draw a red circle around those outliers -hold on -plot(X(outliers, 1), X(outliers, 2), 'ro', 'LineWidth', 2, 'MarkerSize', 10); -hold off - -fprintf('Program paused. Press enter to continue.\n'); -pause; - -%% ================== Part 4: Multidimensional Outliers =================== -% We will now use the code from the previous part and apply it to a -% harder problem in which more features describe each datapoint and only -% some features indicate whether a point is an outlier. -% - -% Loads the second dataset. You should now have the -% variables X, Xval, yval in your environment -load('ex8data2.mat'); - -% Apply the same steps to the larger dataset -[mu sigma2] = estimateGaussian(X); - -% Training set -p = multivariateGaussian(X, mu, sigma2); - -% Cross-validation set -pval = multivariateGaussian(Xval, mu, sigma2); - -% Find the best threshold -[epsilon F1] = selectThreshold(yval, pval); - -fprintf('Best epsilon found using cross-validation: %e\n', epsilon); -fprintf('Best F1 on Cross Validation Set: %f\n', F1); -fprintf('# Outliers found: %d\n', sum(p < epsilon)); -fprintf(' (you should see a value epsilon of about 1.38e-18)\n\n'); -pause - - - diff --git a/assignments/machine-learning-ex8/ex8/ex8_cofi.m b/assignments/machine-learning-ex8/ex8/ex8_cofi.m deleted file mode 100644 index d10ff6f..0000000 --- a/assignments/machine-learning-ex8/ex8/ex8_cofi.m +++ /dev/null @@ -1,237 +0,0 @@ -%% Machine Learning Online Class -% Exercise 8 | Anomaly Detection and Collaborative Filtering -% -% Instructions -% ------------ -% -% This file contains code that helps you get started on the -% exercise. You will need to complete the following functions: -% -% estimateGaussian.m -% selectThreshold.m -% cofiCostFunc.m -% -% For this exercise, you will not need to change any code in this file, -% or any other files other than those mentioned above. -% - -%% =============== Part 1: Loading movie ratings dataset ================ -% You will start by loading the movie ratings dataset to understand the -% structure of the data. -% -fprintf('Loading movie ratings dataset.\n\n'); - -% Load data -load ('ex8_movies.mat'); - -% Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies on -% 943 users -% -% R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a -% rating to movie i - -% From the matrix, we can compute statistics like average rating. -fprintf('Average rating for movie 1 (Toy Story): %f / 5\n\n', ... - mean(Y(1, R(1, :)))); - -% We can "visualize" the ratings matrix by plotting it with imagesc -imagesc(Y); -ylabel('Movies'); -xlabel('Users'); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - -%% ============ Part 2: Collaborative Filtering Cost Function =========== -% You will now implement the cost function for collaborative filtering. -% To help you debug your cost function, we have included set of weights -% that we trained on that. Specifically, you should complete the code in -% cofiCostFunc.m to return J. - -% Load pre-trained weights (X, Theta, num_users, num_movies, num_features) -load ('ex8_movieParams.mat'); - -% Reduce the data set size so that this runs faster -num_users = 4; num_movies = 5; num_features = 3; -X = X(1:num_movies, 1:num_features); -Theta = Theta(1:num_users, 1:num_features); -Y = Y(1:num_movies, 1:num_users); -R = R(1:num_movies, 1:num_users); - -% Evaluate cost function -J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ... - num_features, 0); - -fprintf(['Cost at loaded parameters: %f '... - '\n(this value should be about 22.22)\n'], J); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - -%% ============== Part 3: Collaborative Filtering Gradient ============== -% Once your cost function matches up with ours, you should now implement -% the collaborative filtering gradient function. Specifically, you should -% complete the code in cofiCostFunc.m to return the grad argument. -% -fprintf('\nChecking Gradients (without regularization) ... \n'); - -% Check gradients by running checkNNGradients -checkCostFunction; - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - -%% ========= Part 4: Collaborative Filtering Cost Regularization ======== -% Now, you should implement regularization for the cost function for -% collaborative filtering. You can implement it by adding the cost of -% regularization to the original cost computation. -% - -% Evaluate cost function -J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ... - num_features, 1.5); - -fprintf(['Cost at loaded parameters (lambda = 1.5): %f '... - '\n(this value should be about 31.34)\n'], J); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - -%% ======= Part 5: Collaborative Filtering Gradient Regularization ====== -% Once your cost matches up with ours, you should proceed to implement -% regularization for the gradient. -% - -% -fprintf('\nChecking Gradients (with regularization) ... \n'); - -% Check gradients by running checkNNGradients -checkCostFunction(1.5); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - -%% ============== Part 6: Entering ratings for a new user =============== -% Before we will train the collaborative filtering model, we will first -% add ratings that correspond to a new user that we just observed. This -% part of the code will also allow you to put in your own ratings for the -% movies in our dataset! -% -movieList = loadMovieList(); - -% Initialize my ratings -my_ratings = zeros(1682, 1); - -% Check the file movie_idx.txt for id of each movie in our dataset -% For example, Toy Story (1995) has ID 1, so to rate it "4", you can set -my_ratings(1) = 4; - -% Or suppose did not enjoy Silence of the Lambs (1991), you can set -my_ratings(98) = 2; - -% We have selected a few movies we liked / did not like and the ratings we -% gave are as follows: -my_ratings(7) = 3; -my_ratings(12)= 5; -my_ratings(54) = 4; -my_ratings(64)= 5; -my_ratings(66)= 3; -my_ratings(69) = 5; -my_ratings(183) = 4; -my_ratings(226) = 5; -my_ratings(355)= 5; - -fprintf('\n\nNew user ratings:\n'); -for i = 1:length(my_ratings) - if my_ratings(i) > 0 - fprintf('Rated %d for %s\n', my_ratings(i), ... - movieList{i}); - end -end - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - - -%% ================== Part 7: Learning Movie Ratings ==================== -% Now, you will train the collaborative filtering model on a movie rating -% dataset of 1682 movies and 943 users -% - -fprintf('\nTraining collaborative filtering...\n'); - -% Load data -load('ex8_movies.mat'); - -% Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by -% 943 users -% -% R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a -% rating to movie i - -% Add our own ratings to the data matrix -Y = [my_ratings Y]; -R = [(my_ratings ~= 0) R]; - -% Normalize Ratings -[Ynorm, Ymean] = normalizeRatings(Y, R); - -% Useful Values -num_users = size(Y, 2); -num_movies = size(Y, 1); -num_features = 10; - -% Set Initial Parameters (Theta, X) -X = randn(num_movies, num_features); -Theta = randn(num_users, num_features); - -initial_parameters = [X(:); Theta(:)]; - -% Set options for fmincg -options = optimset('GradObj', 'on', 'MaxIter', 100); - -% Set Regularization -lambda = 10; -theta = fmincg (@(t)(cofiCostFunc(t, Ynorm, R, num_users, num_movies, ... - num_features, lambda)), ... - initial_parameters, options); - -% Unfold the returned theta back into U and W -X = reshape(theta(1:num_movies*num_features), num_movies, num_features); -Theta = reshape(theta(num_movies*num_features+1:end), ... - num_users, num_features); - -fprintf('Recommender system learning completed.\n'); - -fprintf('\nProgram paused. Press enter to continue.\n'); -pause; - -%% ================== Part 8: Recommendation for you ==================== -% After training the model, you can now make recommendations by computing -% the predictions matrix. -% - -p = X * Theta'; -my_predictions = p(:,1) + Ymean; - -movieList = loadMovieList(); - -[r, ix] = sort(my_predictions, 'descend'); -fprintf('\nTop recommendations for you:\n'); -for i=1:10 - j = ix(i); - fprintf('Predicting rating %.1f for movie %s\n', my_predictions(j), ... - movieList{j}); -end - -fprintf('\n\nOriginal ratings provided:\n'); -for i = 1:length(my_ratings) - if my_ratings(i) > 0 - fprintf('Rated %d for %s\n', my_ratings(i), ... - movieList{i}); - end -end diff --git a/assignments/machine-learning-ex8/ex8/ex8_movieParams.mat b/assignments/machine-learning-ex8/ex8/ex8_movieParams.mat deleted file mode 100644 index 2dea689..0000000 Binary files a/assignments/machine-learning-ex8/ex8/ex8_movieParams.mat and /dev/null differ diff --git a/assignments/machine-learning-ex8/ex8/ex8_movies.mat b/assignments/machine-learning-ex8/ex8/ex8_movies.mat deleted file mode 100644 index 31ecd00..0000000 Binary files a/assignments/machine-learning-ex8/ex8/ex8_movies.mat and /dev/null differ diff --git a/assignments/machine-learning-ex8/ex8/ex8data1.mat b/assignments/machine-learning-ex8/ex8/ex8data1.mat deleted file mode 100644 index 1f08123..0000000 Binary files a/assignments/machine-learning-ex8/ex8/ex8data1.mat and /dev/null differ diff --git a/assignments/machine-learning-ex8/ex8/ex8data2.mat b/assignments/machine-learning-ex8/ex8/ex8data2.mat deleted file mode 100644 index fe48db3..0000000 Binary files a/assignments/machine-learning-ex8/ex8/ex8data2.mat and /dev/null differ diff --git a/assignments/machine-learning-ex8/ex8/fmincg.m b/assignments/machine-learning-ex8/ex8/fmincg.m deleted file mode 100644 index 47a8816..0000000 --- a/assignments/machine-learning-ex8/ex8/fmincg.m +++ /dev/null @@ -1,175 +0,0 @@ -function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) -% Minimize a continuous differentialble multivariate function. Starting point -% is given by "X" (D by 1), and the function named in the string "f", must -% return a function value and a vector of partial derivatives. The Polack- -% Ribiere flavour of conjugate gradients is used to compute search directions, -% and a line search using quadratic and cubic polynomial approximations and the -% Wolfe-Powell stopping criteria is used together with the slope ratio method -% for guessing initial step sizes. Additionally a bunch of checks are made to -% make sure that exploration is taking place and that extrapolation will not -% be unboundedly large. The "length" gives the length of the run: if it is -% positive, it gives the maximum number of line searches, if negative its -% absolute gives the maximum allowed number of function evaluations. You can -% (optionally) give "length" a second component, which will indicate the -% reduction in function value to be expected in the first line-search (defaults -% to 1.0). The function returns when either its length is up, or if no further -% progress can be made (ie, we are at a minimum, or so close that due to -% numerical problems, we cannot get any closer). If the function terminates -% within a few iterations, it could be an indication that the function value -% and derivatives are not consistent (ie, there may be a bug in the -% implementation of your "f" function). The function returns the found -% solution "X", a vector of function values "fX" indicating the progress made -% and "i" the number of iterations (line searches or function evaluations, -% depending on the sign of "length") used. -% -% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5) -% -% See also: checkgrad -% -% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13 -% -% -% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen -% -% Permission is granted for anyone to copy, use, or modify these -% programs and accompanying documents for purposes of research or -% education, provided this copyright notice is retained, and note is -% made of any changes that have been made. -% -% These programs and documents are distributed without any warranty, -% express or implied. As the programs were written for research -% purposes only, they have not been tested to the degree that would be -% advisable in any important application. All use of these programs is -% entirely at the user's own risk. -% -% [ml-class] Changes Made: -% 1) Function name and argument specifications -% 2) Output display -% - -% Read options -if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter') - length = options.MaxIter; -else - length = 100; -end - - -RHO = 0.01; % a bunch of constants for line searches -SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions -INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket -EXT = 3.0; % extrapolate maximum 3 times the current bracket -MAX = 20; % max 20 function evaluations per line search -RATIO = 100; % maximum allowed slope ratio - -argstr = ['feval(f, X']; % compose string used to call function -for i = 1:(nargin - 3) - argstr = [argstr, ',P', int2str(i)]; -end -argstr = [argstr, ')']; - -if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end -S=['Iteration ']; - -i = 0; % zero the run length counter -ls_failed = 0; % no previous line search has failed -fX = []; -[f1 df1] = eval(argstr); % get function value and gradient -i = i + (length<0); % count epochs?! -s = -df1; % search direction is steepest -d1 = -s'*s; % this is the slope -z1 = red/(1-d1); % initial step is red/(|s|+1) - -while i < abs(length) % while not finished - i = i + (length>0); % count iterations?! - - X0 = X; f0 = f1; df0 = df1; % make a copy of current values - X = X + z1*s; % begin line search - [f2 df2] = eval(argstr); - i = i + (length<0); % count epochs?! - d2 = df2'*s; - f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1 - if length>0, M = MAX; else M = min(MAX, -length-i); end - success = 0; limit = -1; % initialize quanteties - while 1 - while ((f2 > f1+z1*RHO*d1) || (d2 > -SIG*d1)) && (M > 0) - limit = z1; % tighten the bracket - if f2 > f1 - z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit - else - A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit - B = 3*(f3-f2)-z3*(d3+2*d2); - z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok! - end - if isnan(z2) || isinf(z2) - z2 = z3/2; % if we had a numerical problem then bisect - end - z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits - z1 = z1 + z2; % update the step - X = X + z2*s; - [f2 df2] = eval(argstr); - M = M - 1; i = i + (length<0); % count epochs?! - d2 = df2'*s; - z3 = z3-z2; % z3 is now relative to the location of z2 - end - if f2 > f1+z1*RHO*d1 || d2 > -SIG*d1 - break; % this is a failure - elseif d2 > SIG*d1 - success = 1; break; % success - elseif M == 0 - break; % failure - end - A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation - B = 3*(f3-f2)-z3*(d3+2*d2); - z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok! - if ~isreal(z2) || isnan(z2) || isinf(z2) || z2 < 0 % num prob or wrong sign? - if limit < -0.5 % if we have no upper limit - z2 = z1 * (EXT-1); % the extrapolate the maximum amount - else - z2 = (limit-z1)/2; % otherwise bisect - end - elseif (limit > -0.5) && (z2+z1 > limit) % extraplation beyond max? - z2 = (limit-z1)/2; % bisect - elseif (limit < -0.5) && (z2+z1 > z1*EXT) % extrapolation beyond limit - z2 = z1*(EXT-1.0); % set to extrapolation limit - elseif z2 < -z3*INT - z2 = -z3*INT; - elseif (limit > -0.5) && (z2 < (limit-z1)*(1.0-INT)) % too close to limit? - z2 = (limit-z1)*(1.0-INT); - end - f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2 - z1 = z1 + z2; X = X + z2*s; % update current estimates - [f2 df2] = eval(argstr); - M = M - 1; i = i + (length<0); % count epochs?! - d2 = df2'*s; - end % end of line search - - if success % if line search succeeded - f1 = f2; fX = [fX' f1]'; - fprintf('%s %4i | Cost: %4.6e\r', S, i, f1); - s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction - tmp = df1; df1 = df2; df2 = tmp; % swap derivatives - d2 = df1'*s; - if d2 > 0 % new slope must be negative - s = -df1; % otherwise use steepest direction - d2 = -s'*s; - end - z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO - d1 = d2; - ls_failed = 0; % this line search did not fail - else - X = X0; f1 = f0; df1 = df0; % restore point from before failed line search - if ls_failed || i > abs(length) % line search failed twice in a row - break; % or we ran out of time, so we give up - end - tmp = df1; df1 = df2; df2 = tmp; % swap derivatives - s = -df1; % try steepest - d1 = -s'*s; - z1 = 1/(1-d1); - ls_failed = 1; % this line search failed - end - if exist('OCTAVE_VERSION') - fflush(stdout); - end -end -fprintf('\n'); diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/AUTHORS.txt b/assignments/machine-learning-ex8/ex8/lib/jsonlab/AUTHORS.txt deleted file mode 100644 index 9dd3fc7..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/AUTHORS.txt +++ /dev/null @@ -1,41 +0,0 @@ -The author of "jsonlab" toolbox is Qianqian Fang. Qianqian -is currently an Assistant Professor at Massachusetts General Hospital, -Harvard Medical School. - -Address: Martinos Center for Biomedical Imaging, - Massachusetts General Hospital, - Harvard Medical School - Bldg 149, 13th St, Charlestown, MA 02129, USA -URL: http://nmr.mgh.harvard.edu/~fangq/ -Email: or - - -The script loadjson.m was built upon previous works by - -- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 - date: 2009/11/02 -- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 - date: 2009/03/22 -- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565 - date: 2008/07/03 - - -This toolbox contains patches submitted by the following contributors: - -- Blake Johnson - part of revision 341 - -- Niclas Borlin - various fixes in revision 394, including - - loadjson crashes for all-zero sparse matrix. - - loadjson crashes for empty sparse matrix. - - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson. - - loadjson crashes for sparse real column vector. - - loadjson crashes for sparse complex column vector. - - Data is corrupted by savejson for sparse real row vector. - - savejson crashes for sparse complex row vector. - -- Yul Kang - patches for svn revision 415. - - savejson saves an empty cell array as [] instead of null - - loadjson differentiates an empty struct from an empty array diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/ChangeLog.txt b/assignments/machine-learning-ex8/ex8/lib/jsonlab/ChangeLog.txt deleted file mode 100644 index 07824f5..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/ChangeLog.txt +++ /dev/null @@ -1,74 +0,0 @@ -============================================================================ - - JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave - ----------------------------------------------------------------------------- - -JSONlab ChangeLog (key features marked by *): - -== JSONlab 1.0 (codename: Optimus - Final), FangQ == - - 2015/01/02 polish help info for all major functions, update examples, finalize 1.0 - 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson - -== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ == - - 2014/11/22 show progress bar in loadjson ('ShowProgress') - 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact') - 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels - 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab - -== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ == - - 2014/09/17 fix several compatibility issues when running on octave versions 3.2-3.8 - 2014/09/17 support 2D cell and struct arrays in both savejson and saveubjson - 2014/08/04 escape special characters in a JSON string - 2014/02/16 fix a bug when saving ubjson files - -== JSONlab 0.9.9 (codename: Optimus - beta), FangQ == - - 2014/01/22 use binary read and write in saveubjson and loadubjson - -== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ == - - 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang) - -== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ == - 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson - -== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ == - 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin) - -== JSONlab 0.9.0 (codename: Rodimus), FangQ == - - 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson - 2012/06/01 support JSONP in savejson - 2012/05/25 fix the empty cell bug (reported by Cyril Davin) - 2012/04/05 savejson can save to a file (suggested by Patrick Rapin) - -== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ == - - 2012/02/28 loadjson quotation mark escape bug, see http://bit.ly/yyk1nS - 2012/01/25 patch to handle root-less objects, contributed by Blake Johnson - -== JSONlab 0.8.0 (codename: Sentiel), FangQ == - - 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab - 2012/01/11 remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer - 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson - 2011/11/18 fix struct array bug reported by Mykel Kochenderfer - -== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ == - - 2011/10/21 fix a bug in loadjson, previous code does not use any of the acceleration - 2011/10/20 loadjson supports JSON collections - concatenated JSON objects - -== JSONlab 0.5.0 (codename: Nexus), FangQ == - - 2011/10/16 package and release jsonlab 0.5.0 - 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug - 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level - 2011/10/10 create jsonlab project, start jsonlab website, add online documentation - 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support - 2011/10/06 *savejson works for structs, cells and arrays - 2011/09/09 derive loadjson from JSON parser from MATLAB Central, draft savejson.m diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/LICENSE_BSD.txt b/assignments/machine-learning-ex8/ex8/lib/jsonlab/LICENSE_BSD.txt deleted file mode 100644 index 32d66cb..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/LICENSE_BSD.txt +++ /dev/null @@ -1,25 +0,0 @@ -Copyright 2011-2015 Qianqian Fang . All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are -permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this list of - conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, this list - of conditions and the following disclaimer in the documentation and/or other materials - provided with the distribution. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND -FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS -OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR -CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON -ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING -NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF -ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - -The views and conclusions contained in the software and documentation are those of the -authors and should not be interpreted as representing official policies, either expressed -or implied, of the copyright holders. diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/README.txt b/assignments/machine-learning-ex8/ex8/lib/jsonlab/README.txt deleted file mode 100644 index 7b4f732..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/README.txt +++ /dev/null @@ -1,394 +0,0 @@ -=============================================================================== -= JSONLab = -= An open-source MATLAB/Octave JSON encoder and decoder = -=============================================================================== - -*Copyright (C) 2011-2015 Qianqian Fang -*License: BSD License, see License_BSD.txt for details -*Version: 1.0 (Optimus - Final) - -------------------------------------------------------------------------------- - -Table of Content: - -I. Introduction -II. Installation -III.Using JSONLab -IV. Known Issues and TODOs -V. Contribution and feedback - -------------------------------------------------------------------------------- - -I. Introduction - -JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, -human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format -to represent complex and hierarchical data. It is as powerful as -[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely -used for data-exchange in applications, and is essential for the wild success -of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and -[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. - -UBJSON (Universal Binary JSON) is a binary JSON format, specifically -optimized for compact file size and better performance while keeping -the semantics as simple as the text-based JSON format. Using the UBJSON -format allows to wrap complex binary data in a flexible and extensible -structure, making it possible to process complex and large dataset -without accuracy loss due to text conversions. - -We envision that both JSON and its binary version will serve as part of -the mainstream data-exchange formats for scientific research in the future. -It will provide the flexibility and generality achieved by other popular -general-purpose file specifications, such as -[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly -reduced complexity and enhanced performance. - -JSONLab is a free and open-source implementation of a JSON/UBJSON encoder -and a decoder in the native MATLAB language. It can be used to convert a MATLAB -data structure (array, struct, cell, struct array and cell array) into -JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB -data structure. JSONLab supports both MATLAB and -[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone). - -------------------------------------------------------------------------------- - -II. Installation - -The installation of JSONLab is no different than any other simple -MATLAB toolbox. You only need to download/unzip the JSONLab package -to a folder, and add the folder's path to MATLAB/Octave's path list -by using the following command: - - addpath('/path/to/jsonlab'); - -If you want to add this path permanently, you need to type "pathtool", -browse to the jsonlab root folder and add to the list, then click "Save". -Then, run "rehash" in MATLAB, and type "which loadjson", if you see an -output, that means JSONLab is installed for MATLAB/Octave. - -------------------------------------------------------------------------------- - -III.Using JSONLab - -JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, -and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and -two equivallent functions -- loadubjson and saveubjson for the binary -JSON. The detailed help info for the four functions can be found below: - -=== loadjson.m === -
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          Franois Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-
- -=== savejson.m === - -
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-
- -=== loadubjson.m === - -
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-
- -=== saveubjson.m === - -
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-
- - -=== examples === - -Under the "examples" folder, you can find several scripts to demonstrate the -basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you -will see the conversions from MATLAB data structure to JSON text and backward. -In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet -and validate the loadjson/savejson functions for regression testing purposes. -Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson -and loadubjson pairs for various matlab data structures. - -Please run these examples and understand how JSONLab works before you use -it to process your data. - -------------------------------------------------------------------------------- - -IV. Known Issues and TODOs - -JSONLab has several known limitations. We are striving to make it more general -and robust. Hopefully in a few future releases, the limitations become less. - -Here are the known issues: - -# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays; -# When processing names containing multi-byte characters, Octave and MATLAB \ -can give different field-names; you can use feature('DefaultCharacterSet','latin1') \ -in MATLAB to get consistant results -# savejson can not handle class and dataset. -# saveubjson converts a logical array into a uint8 ([U]) array -# an unofficial N-D array count syntax is implemented in saveubjson. We are \ -actively communicating with the UBJSON spec maintainer to investigate the \ -possibility of making it upstream -# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \ -files, however, it can parse all UBJSON files produced by saveubjson. - -------------------------------------------------------------------------------- - -V. Contribution and feedback - -JSONLab is an open-source project. This means you can not only use it and modify -it as you wish, but also you can contribute your changes back to JSONLab so -that everyone else can enjoy the improvement. For anyone who want to contribute, -please download JSONLab source code from it's subversion repository by using the -following command: - - svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab - -You can make changes to the files as needed. Once you are satisfied with your -changes, and ready to share it with others, please cd the root directory of -JSONLab, and type - - svn diff > yourname_featurename.patch - -You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at -the email address shown in the beginning of this file. Qianqian will review -the changes and commit it to the subversion if they are satisfactory. - -We appreciate any suggestions and feedbacks from you. Please use iso2mesh's -mailing list to report any questions you may have with JSONLab: - -http://groups.google.com/group/iso2mesh-users?hl=en&pli=1 - -(Subscription to the mailing list is needed in order to post messages). diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/jsonopt.m b/assignments/machine-learning-ex8/ex8/lib/jsonlab/jsonopt.m deleted file mode 100644 index 0bebd8d..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/jsonopt.m +++ /dev/null @@ -1,32 +0,0 @@ -function val=jsonopt(key,default,varargin) -% -% val=jsonopt(key,default,optstruct) -% -% setting options based on a struct. The struct can be produced -% by varargin2struct from a list of 'param','value' pairs -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% -% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $ -% -% input: -% key: a string with which one look up a value from a struct -% default: if the key does not exist, return default -% optstruct: a struct where each sub-field is a key -% -% output: -% val: if key exists, val=optstruct.key; otherwise val=default -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -val=default; -if(nargin<=2) return; end -opt=varargin{1}; -if(isstruct(opt) && isfield(opt,key)) - val=getfield(opt,key); -end - diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m b/assignments/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m deleted file mode 100644 index 42798c0..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m +++ /dev/null @@ -1,566 +0,0 @@ -function data = loadjson(fname,varargin) -% -% data=loadjson(fname,opt) -% or -% data=loadjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09, including previous works from -% -% Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713 -% created on 2009/11/02 -% François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393 -% created on 2009/03/22 -% Joel Feenstra: -% http://www.mathworks.com/matlabcentral/fileexchange/20565 -% created on 2008/07/03 -% -% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a JSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.FastArrayParser [1|0 or integer]: if set to 1, use a -% speed-optimized array parser when loading an -% array object. The fast array parser may -% collapse block arrays into a single large -% array similar to rules defined in cell2mat; 0 to -% use a legacy parser; if set to a larger-than-1 -% value, this option will specify the minimum -% dimension to enable the fast array parser. For -% example, if the input is a 3D array, setting -% FastArrayParser to 1 will return a 3D array; -% setting to 2 will return a cell array of 2D -% arrays; setting to 3 will return to a 2D cell -% array of 1D vectors; setting to 4 will return a -% 3D cell array. -% opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}') -% dat=loadjson(['examples' filesep 'example1.json']) -% dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); - -if(jsonopt('ShowProgress',0,opt)==1) - opt.progressbar_=waitbar(0,'loading ...'); -end -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end -if(isfield(opt,'progressbar_')) - close(opt.progressbar_); -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=data(j).x0x5F_ArraySize_; - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - if next_char ~= '}' - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - parse_char(':'); - val = parse_value(varargin{:}); - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' - break; - end - parse_char(','); - end - end - parse_char('}'); - -%%------------------------------------------------------------------------- - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim2=[]; - arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:}); - pbar=jsonopt('progressbar_',-1,varargin{:}); - - if next_char ~= ']' - if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:})) - [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos); - arraystr=['[' inStr(pos:endpos)]; - arraystr=regexprep(arraystr,'"_NaN_"','NaN'); - arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf'); - arraystr(arraystr==sprintf('\n'))=[]; - arraystr(arraystr==sprintf('\r'))=[]; - %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed - if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D - astr=inStr((e1l+1):(e1r-1)); - astr=regexprep(astr,'"_NaN_"','NaN'); - astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf'); - astr(astr==sprintf('\n'))=[]; - astr(astr==sprintf('\r'))=[]; - astr(astr==' ')=''; - if(isempty(find(astr=='[', 1))) % array is 2D - dim2=length(sscanf(astr,'%f,',[1 inf])); - end - else % array is 1D - astr=arraystr(2:end-1); - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]); - if(nextidx>=length(astr)-1) - object=obj; - pos=endpos; - parse_char(']'); - return; - end - end - if(~isempty(dim2)) - astr=arraystr; - astr(astr=='[')=''; - astr(astr==']')=''; - astr(astr==' ')=''; - [obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf); - if(nextidx>=length(astr)-1) - object=reshape(obj,dim2,numel(obj)/dim2)'; - pos=endpos; - parse_char(']'); - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end - return; - end - end - arraystr=regexprep(arraystr,'\]\s*,','];'); - else - arraystr='['; - end - try - if(isoct && regexp(arraystr,'"','once')) - error('Octave eval can produce empty cells for JSON-like input'); - end - object=eval(arraystr); - pos=endpos; - catch - while 1 - newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1); - val = parse_value(newopt); - object{end+1} = val; - if next_char == ']' - break; - end - parse_char(','); - end - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - parse_char(']'); - - if(pbar>0) - waitbar(pos/length(inStr),pbar,'loading ...'); - end -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr len esc index_esc len_esc - % len, ns = length(inStr), keyboard - if inStr(pos) ~= '"' - error_pos('String starting with " expected at position %d'); - else - pos = pos + 1; - end - str = ''; - while pos <= len - while index_esc <= len_esc && esc(index_esc) < pos - index_esc = index_esc + 1; - end - if index_esc > len_esc - str = [str inStr(pos:len)]; - pos = len + 1; - break; - else - str = [str inStr(pos:esc(index_esc)-1)]; - pos = esc(index_esc); - end - nstr = length(str); switch inStr(pos) - case '"' - pos = pos + 1; - if(~isempty(str)) - if(strcmp(str,'_Inf_')) - str=Inf; - elseif(strcmp(str,'-_Inf_')) - str=-Inf; - elseif(strcmp(str,'_NaN_')) - str=NaN; - end - end - return; - case '\' - if pos+1 > len - error_pos('End of file reached right after escape character'); - end - pos = pos + 1; - switch inStr(pos) - case {'"' '\' '/'} - str(nstr+1) = inStr(pos); - pos = pos + 1; - case {'b' 'f' 'n' 'r' 't'} - str(nstr+1) = sprintf(['\' inStr(pos)]); - pos = pos + 1; - case 'u' - if pos+4 > len - error_pos('End of file reached in escaped unicode character'); - end - str(nstr+(1:6)) = inStr(pos-1:pos+4); - pos = pos + 5; - end - otherwise % should never happen - str(nstr+1) = inStr(pos), keyboard - pos = pos + 1; - end - end - error_pos('End of file while expecting end of inStr'); - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct - currstr=inStr(pos:end); - numstr=0; - if(isoct~=0) - numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end'); - [num, one] = sscanf(currstr, '%f', 1); - delta=numstr+1; - else - [num, one, err, delta] = sscanf(currstr, '%f', 1); - if ~isempty(err) - error_pos('Error reading number at position %d'); - end - end - pos = pos + delta-1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - pbar=jsonopt('progressbar_',-1,varargin{:}); - if(pbar>0) - waitbar(pos/len,pbar,'loading ...'); - end - - switch(inStr(pos)) - case '"' - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'-','0','1','2','3','4','5','6','7','8','9'} - val = parse_number(varargin{:}); - return; - case 't' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true') - val = true; - pos = pos + 4; - return; - end - case 'f' - if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false') - val = false; - pos = pos + 5; - return; - end - case 'n' - if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null') - val = []; - pos = pos + 4; - return; - end - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m b/assignments/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m deleted file mode 100644 index 0155115..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m +++ /dev/null @@ -1,528 +0,0 @@ -function data = loadubjson(fname,varargin) -% -% data=loadubjson(fname,opt) -% or -% data=loadubjson(fname,'param1',value1,'param2',value2,...) -% -% parse a JSON (JavaScript Object Notation) file or string -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/01 -% -% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% fname: input file name, if fname contains "{}" or "[]", fname -% will be interpreted as a UBJSON string -% opt: a struct to store parsing options, opt can be replaced by -% a list of ('param',value) pairs - the param string is equivallent -% to a field in opt. opt can have the following -% fields (first in [.|.] is the default) -% -% opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat -% for each element of the JSON data, and group -% arrays based on the cell2mat rules. -% opt.IntEndian [B|L]: specify the endianness of the integer fields -% in the UBJSON input data. B - Big-Endian format for -% integers (as required in the UBJSON specification); -% L - input integer fields are in Little-Endian order. -% -% output: -% dat: a cell array, where {...} blocks are converted into cell arrays, -% and [...] are converted to arrays -% -% examples: -% obj=struct('string','value','array',[1 2 3]); -% ubjdata=saveubjson('obj',obj); -% dat=loadubjson(ubjdata) -% dat=loadubjson(['examples' filesep 'example1.ubj']) -% dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1) -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -global pos inStr len esc index_esc len_esc isoct arraytoken fileendian systemendian - -if(regexp(fname,'[\{\}\]\[]','once')) - string=fname; -elseif(exist(fname,'file')) - fid = fopen(fname,'rb'); - string = fread(fid,inf,'uint8=>char')'; - fclose(fid); -else - error('input file does not exist'); -end - -pos = 1; len = length(string); inStr = string; -isoct=exist('OCTAVE_VERSION','builtin'); -arraytoken=find(inStr=='[' | inStr==']' | inStr=='"'); -jstr=regexprep(inStr,'\\\\',' '); -escquote=regexp(jstr,'\\"'); -arraytoken=sort([arraytoken escquote]); - -% String delimiters and escape chars identified to improve speed: -esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]'); -index_esc = 1; len_esc = length(esc); - -opt=varargin2struct(varargin{:}); -fileendian=upper(jsonopt('IntEndian','B',opt)); -[os,maxelem,systemendian]=computer; - -jsoncount=1; -while pos <= len - switch(next_char) - case '{' - data{jsoncount} = parse_object(opt); - case '[' - data{jsoncount} = parse_array(opt); - otherwise - error_pos('Outer level structure must be an object or an array'); - end - jsoncount=jsoncount+1; -end % while - -jsoncount=length(data); -if(jsoncount==1 && iscell(data)) - data=data{1}; -end - -if(~isempty(data)) - if(isstruct(data)) % data can be a struct array - data=jstruct2array(data); - elseif(iscell(data)) - data=jcell2array(data); - end -end - - -%% -function newdata=parse_collection(id,data,obj) - -if(jsoncount>0 && exist('data','var')) - if(~iscell(data)) - newdata=cell(1); - newdata{1}=data; - data=newdata; - end -end - -%% -function newdata=jcell2array(data) -len=length(data); -newdata=data; -for i=1:len - if(isstruct(data{i})) - newdata{i}=jstruct2array(data{i}); - elseif(iscell(data{i})) - newdata{i}=jcell2array(data{i}); - end -end - -%%------------------------------------------------------------------------- -function newdata=jstruct2array(data) -fn=fieldnames(data); -newdata=data; -len=length(data); -for i=1:length(fn) % depth-first - for j=1:len - if(isstruct(getfield(data(j),fn{i}))) - newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i}))); - end - end -end -if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn))) - newdata=cell(len,1); - for j=1:len - ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_); - iscpx=0; - if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn))) - if(data(j).x0x5F_ArrayIsComplex_) - iscpx=1; - end - end - if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn))) - if(data(j).x0x5F_ArrayIsSparse_) - if(~isempty(strmatch('x0x5F_ArraySize_',fn))) - dim=double(data(j).x0x5F_ArraySize_); - if(iscpx && size(ndata,2)==4-any(dim==1)) - ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end)); - end - if isempty(ndata) - % All-zeros sparse - ndata=sparse(dim(1),prod(dim(2:end))); - elseif dim(1)==1 - % Sparse row vector - ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end))); - elseif dim(2)==1 - % Sparse column vector - ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end))); - else - % Generic sparse array. - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end))); - end - else - if(iscpx && size(ndata,2)==4) - ndata(:,3)=complex(ndata(:,3),ndata(:,4)); - end - ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3)); - end - end - elseif(~isempty(strmatch('x0x5F_ArraySize_',fn))) - if(iscpx && size(ndata,2)==2) - ndata=complex(ndata(:,1),ndata(:,2)); - end - ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_); - end - newdata{j}=ndata; - end - if(len==1) - newdata=newdata{1}; - end -end - -%%------------------------------------------------------------------------- -function object = parse_object(varargin) - parse_char('{'); - object = []; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); % TODO - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - count=double(parse_number()); - end - if next_char ~= '}' - num=0; - while 1 - str = parseStr(varargin{:}); - if isempty(str) - error_pos('Name of value at position %d cannot be empty'); - end - %parse_char(':'); - val = parse_value(varargin{:}); - num=num+1; - eval( sprintf( 'object.%s = val;', valid_field(str) ) ); - if next_char == '}' || (count>=0 && num>=count) - break; - end - %parse_char(','); - end - end - if(count==-1) - parse_char('}'); - end - -%%------------------------------------------------------------------------- -function [cid,len]=elem_info(type) -id=strfind('iUIlLdD',type); -dataclass={'int8','uint8','int16','int32','int64','single','double'}; -bytelen=[1,1,2,4,8,4,8]; -if(id>0) - cid=dataclass{id}; - len=bytelen(id); -else - error_pos('unsupported type at position %d'); -end -%%------------------------------------------------------------------------- - - -function [data adv]=parse_block(type,count,varargin) -global pos inStr isoct fileendian systemendian -[cid,len]=elem_info(type); -datastr=inStr(pos:pos+len*count-1); -if(isoct) - newdata=int8(datastr); -else - newdata=uint8(datastr); -end -id=strfind('iUIlLdD',type); -if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,cid)); -end -data=typecast(newdata,cid); -adv=double(len*count); - -%%------------------------------------------------------------------------- - - -function object = parse_array(varargin) % JSON array is written in row-major order -global pos inStr isoct - parse_char('['); - object = cell(0, 1); - dim=[]; - type=''; - count=-1; - if(next_char == '$') - type=inStr(pos+1); - pos=pos+2; - end - if(next_char == '#') - pos=pos+1; - if(next_char=='[') - dim=parse_array(varargin{:}); - count=prod(double(dim)); - else - count=double(parse_number()); - end - end - if(~isempty(type)) - if(count>=0) - [object adv]=parse_block(type,count,varargin{:}); - if(~isempty(dim)) - object=reshape(object,dim); - end - pos=pos+adv; - return; - else - endpos=matching_bracket(inStr,pos); - [cid,len]=elem_info(type); - count=(endpos-pos)/len; - [object adv]=parse_block(type,count,varargin{:}); - pos=pos+adv; - parse_char(']'); - return; - end - end - if next_char ~= ']' - while 1 - val = parse_value(varargin{:}); - object{end+1} = val; - if next_char == ']' - break; - end - %parse_char(','); - end - end - if(jsonopt('SimplifyCell',0,varargin{:})==1) - try - oldobj=object; - object=cell2mat(object')'; - if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0) - object=oldobj; - elseif(size(object,1)>1 && ndims(object)==2) - object=object'; - end - catch - end - end - if(count==-1) - parse_char(']'); - end - -%%------------------------------------------------------------------------- - -function parse_char(c) - global pos inStr len - skip_whitespace; - if pos > len || inStr(pos) ~= c - error_pos(sprintf('Expected %c at position %%d', c)); - else - pos = pos + 1; - skip_whitespace; - end - -%%------------------------------------------------------------------------- - -function c = next_char - global pos inStr len - skip_whitespace; - if pos > len - c = []; - else - c = inStr(pos); - end - -%%------------------------------------------------------------------------- - -function skip_whitespace - global pos inStr len - while pos <= len && isspace(inStr(pos)) - pos = pos + 1; - end - -%%------------------------------------------------------------------------- -function str = parseStr(varargin) - global pos inStr esc index_esc len_esc - % len, ns = length(inStr), keyboard - type=inStr(pos); - if type ~= 'S' && type ~= 'C' && type ~= 'H' - error_pos('String starting with S expected at position %d'); - else - pos = pos + 1; - end - if(type == 'C') - str=inStr(pos); - pos=pos+1; - return; - end - bytelen=double(parse_number()); - if(length(inStr)>=pos+bytelen-1) - str=inStr(pos:pos+bytelen-1); - pos=pos+bytelen; - else - error_pos('End of file while expecting end of inStr'); - end - -%%------------------------------------------------------------------------- - -function num = parse_number(varargin) - global pos inStr len isoct fileendian systemendian - id=strfind('iUIlLdD',inStr(pos)); - if(isempty(id)) - error_pos('expecting a number at position %d'); - end - type={'int8','uint8','int16','int32','int64','single','double'}; - bytelen=[1,1,2,4,8,4,8]; - datastr=inStr(pos+1:pos+bytelen(id)); - if(isoct) - newdata=int8(datastr); - else - newdata=uint8(datastr); - end - if(id<=5 && fileendian~=systemendian) - newdata=swapbytes(typecast(newdata,type{id})); - end - num=typecast(newdata,type{id}); - pos = pos + bytelen(id)+1; - -%%------------------------------------------------------------------------- - -function val = parse_value(varargin) - global pos inStr len - true = 1; false = 0; - - switch(inStr(pos)) - case {'S','C','H'} - val = parseStr(varargin{:}); - return; - case '[' - val = parse_array(varargin{:}); - return; - case '{' - val = parse_object(varargin{:}); - if isstruct(val) - if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact'))) - val=jstruct2array(val); - end - elseif isempty(val) - val = struct; - end - return; - case {'i','U','I','l','L','d','D'} - val = parse_number(varargin{:}); - return; - case 'T' - val = true; - pos = pos + 1; - return; - case 'F' - val = false; - pos = pos + 1; - return; - case {'Z','N'} - val = []; - pos = pos + 1; - return; - end - error_pos('Value expected at position %d'); -%%------------------------------------------------------------------------- - -function error_pos(msg) - global pos inStr len - poShow = max(min([pos-15 pos-1 pos pos+20],len),1); - if poShow(3) == poShow(2) - poShow(3:4) = poShow(2)+[0 -1]; % display nothing after - end - msg = [sprintf(msg, pos) ': ' ... - inStr(poShow(1):poShow(2)) '' inStr(poShow(3):poShow(4)) ]; - error( ['JSONparser:invalidFormat: ' msg] ); - -%%------------------------------------------------------------------------- - -function str = valid_field(str) -global isoct -% From MATLAB doc: field names must begin with a letter, which may be -% followed by any combination of letters, digits, and underscores. -% Invalid characters will be converted to underscores, and the prefix -% "x0x[Hex code]_" will be added if the first character is not a letter. - pos=regexp(str,'^[^A-Za-z]','once'); - if(~isempty(pos)) - if(~isoct) - str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once'); - else - str=sprintf('x0x%X_%s',char(str(1)),str(2:end)); - end - end - if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return; end - if(~isoct) - str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_'); - else - pos=regexp(str,'[^0-9A-Za-z_]'); - if(isempty(pos)) return; end - str0=str; - pos0=[0 pos(:)' length(str)]; - str=''; - for i=1:length(pos) - str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))]; - end - if(pos(end)~=length(str)) - str=[str str0(pos0(end-1)+1:pos0(end))]; - end - end - %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_'; - -%%------------------------------------------------------------------------- -function endpos = matching_quote(str,pos) -len=length(str); -while(pos1 && str(pos-1)=='\')) - endpos=pos; - return; - end - end - pos=pos+1; -end -error('unmatched quotation mark'); -%%------------------------------------------------------------------------- -function [endpos e1l e1r maxlevel] = matching_bracket(str,pos) -global arraytoken -level=1; -maxlevel=level; -endpos=0; -bpos=arraytoken(arraytoken>=pos); -tokens=str(bpos); -len=length(tokens); -pos=1; -e1l=[]; -e1r=[]; -while(pos<=len) - c=tokens(pos); - if(c==']') - level=level-1; - if(isempty(e1r)) e1r=bpos(pos); end - if(level==0) - endpos=bpos(pos); - return - end - end - if(c=='[') - if(isempty(e1l)) e1l=bpos(pos); end - level=level+1; - maxlevel=max(maxlevel,level); - end - if(c=='"') - pos=matching_quote(tokens,pos+1); - end - pos=pos+1; -end -if(endpos==0) - error('unmatched "]"'); -end - diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/mergestruct.m b/assignments/machine-learning-ex8/ex8/lib/jsonlab/mergestruct.m deleted file mode 100644 index 6de6100..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/mergestruct.m +++ /dev/null @@ -1,33 +0,0 @@ -function s=mergestruct(s1,s2) -% -% s=mergestruct(s1,s2) -% -% merge two struct objects into one -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% s1,s2: a struct object, s1 and s2 can not be arrays -% -% output: -% s: the merged struct object. fields in s1 and s2 will be combined in s. -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(~isstruct(s1) || ~isstruct(s2)) - error('input parameters contain non-struct'); -end -if(length(s1)>1 || length(s2)>1) - error('can not merge struct arrays'); -end -fn=fieldnames(s2); -s=s1; -for i=1:length(fn) - s=setfield(s,fn{i},getfield(s2,fn{i})); -end - diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/savejson.m b/assignments/machine-learning-ex8/ex8/lib/jsonlab/savejson.m deleted file mode 100644 index 7e84076..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/savejson.m +++ /dev/null @@ -1,475 +0,0 @@ -function json=savejson(rootname,obj,varargin) -% -% json=savejson(rootname,obj,filename) -% or -% json=savejson(rootname,obj,opt) -% json=savejson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript -% Object Notation) string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2011/09/09 -% -% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array). -% filename: a string for the file name to save the output JSON data. -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.FloatFormat ['%.10g'|string]: format to show each numeric element -% of a 1D/2D array; -% opt.ArrayIndent [1|0]: if 1, output explicit data array with -% precedent indentation; if 0, no indentation -% opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [0|1]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern -% to represent +/-Inf. The matched pattern is '([-+]*)Inf' -% and $1 represents the sign. For those who want to use -% 1e999 to represent Inf, they can set opt.Inf to '$11e999' -% opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern -% to represent NaN -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSONP='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode. -% opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs) -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a string in the JSON format (see http://json.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% savejson('jmesh',jsonmesh) -% savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end - -whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -if(jsonopt('Compact',0,opt)==1) - whitespaces=struct('tab','','newline','','sep',','); -end -if(~isfield(opt,'whitespaces_')) - opt.whitespaces_=whitespaces; -end - -nl=whitespaces.newline; - -json=obj2json(rootname,obj,rootlevel,opt); -if(rootisarray) - json=sprintf('%s%s',json,nl); -else - json=sprintf('{%s%s%s}\n',nl,json,nl); -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=sprintf('%s(%s);%s',jsonp,json,nl); -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - if(jsonopt('SaveBinary',0,opt)==1) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - else - fid = fopen(opt.FileName, 'wt'); - fwrite(fid,json,'char'); - end - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2json(name,item,level,varargin) - -if(iscell(item)) - txt=cell2json(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2json(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2json(name,item,level,varargin{:}); -else - txt=mat2json(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2json(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -nl=ws.newline; -if(len>1) - if(~isempty(name)) - txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; - else - txt=sprintf('%s[%s',padding0,nl); - end -elseif(len==0) - if(~isempty(name)) - txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; - else - txt=sprintf('%s[]',padding0); - end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:})); - if(i1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=struct2json(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding0=repmat(ws.tab,1,level); -padding2=repmat(ws.tab,1,level+1); -padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1)); -nl=ws.newline; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding0,nl); end -end -for j=1:dim(2) - if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); - else - txt=sprintf('%s%s{%s',txt,padding1,nl); - end - if(~isempty(names)) - for e=1:length(names) - txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})); - if(e1) txt=sprintf('%s%s%s]',txt,nl,padding2); end - if(j1) txt=sprintf('%s%s%s]',txt,nl,padding0); end - -%%------------------------------------------------------------------------- -function txt=str2json(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(~isempty(name)) - if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end -else - if(len>1) txt=sprintf('%s[%s',padding1,nl); end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - if(isoct) - val=regexprep(item(e,:),'\\','\\'); - val=regexprep(val,'"','\"'); - val=regexprep(val,'^"','\"'); - else - val=regexprep(item(e,:),'\\','\\\\'); - val=regexprep(val,'"','\\"'); - val=regexprep(val,'^"','\\"'); - end - val=escapejsonstring(val); - if(len==1) - obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"']; - if(isempty(name)) obj=['"',val,'"']; end - txt=sprintf('%s%s%s%s',txt,padding1,obj); - else - txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']); - end - if(e==len) sep=''; end - txt=sprintf('%s%s',txt,sep); -end -if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end - -%%------------------------------------------------------------------------- -function txt=mat2json(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -padding1=repmat(ws.tab,1,level); -padding0=repmat(ws.tab,1,level+1); -nl=ws.newline; -sep=ws.sep; - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:})) - if(isempty(name)) - txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - else - txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',... - padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl); - end -else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0) - numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']',''); - else - numtxt=matdata2json(item,level+1,varargin{:}); - end - if(isempty(name)) - txt=sprintf('%s%s',padding1,numtxt); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - else - txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt); - end - end - return; -end -dataformat='%s%s%s%s%s'; - -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - end - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep); - if(size(item,1)==1) - % Row vector, store only column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([iy(:),data'],level+2,varargin{:}), nl); - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,data],level+2,varargin{:}), nl); - else - % General case, store row and column indices. - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([ix,iy,data],level+2,varargin{:}), nl); - end -else - if(isreal(item)) - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json(item(:)',level+2,varargin{:}), nl); - else - txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep); - txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',... - matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl); - end -end -txt=sprintf('%s%s%s',txt,padding1,'}'); - -%%------------------------------------------------------------------------- -function txt=matdata2json(mat,level,varargin) - -ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')); -ws=jsonopt('whitespaces_',ws,varargin{:}); -tab=ws.tab; -nl=ws.newline; - -if(size(mat,1)==1) - pre=''; - post=''; - level=level-1; -else - pre=sprintf('[%s',nl); - post=sprintf('%s%s]',nl,repmat(tab,1,level-1)); -end - -if(isempty(mat)) - txt='null'; - return; -end -floatformat=jsonopt('FloatFormat','%.10g',varargin{:}); -%if(numel(mat)>1) - formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]]; -%else -% formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]]; -%end - -if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1) - formatstr=[repmat(tab,1,level) formatstr]; -end - -txt=sprintf(formatstr,mat'); -txt(end-length(nl):end)=[]; -if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1) - txt=regexprep(txt,'1','true'); - txt=regexprep(txt,'0','false'); -end -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],\n[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -txt=[pre txt post]; -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end - -%%------------------------------------------------------------------------- -function newstr=escapejsonstring(str) -newstr=str; -isoct=exist('OCTAVE_VERSION','builtin'); -if(isoct) - vv=sscanf(OCTAVE_VERSION,'%f'); - if(vv(1)>=3.8) isoct=0; end -end -if(isoct) - escapechars={'\a','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},escapechars{i}); - end -else - escapechars={'\a','\b','\f','\n','\r','\t','\v'}; - for i=1:length(escapechars); - newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\')); - end -end diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m b/assignments/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m deleted file mode 100644 index eaec433..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m +++ /dev/null @@ -1,504 +0,0 @@ -function json=saveubjson(rootname,obj,varargin) -% -% json=saveubjson(rootname,obj,filename) -% or -% json=saveubjson(rootname,obj,opt) -% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...) -% -% convert a MATLAB object (cell, struct or array) into a Universal -% Binary JSON (UBJSON) binary string -% -% author: Qianqian Fang (fangq nmr.mgh.harvard.edu) -% created on 2013/08/17 -% -% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $ -% -% input: -% rootname: the name of the root-object, when set to '', the root name -% is ignored, however, when opt.ForceRootName is set to 1 (see below), -% the MATLAB variable name will be used as the root name. -% obj: a MATLAB object (array, cell, cell array, struct, struct array) -% filename: a string for the file name to save the output UBJSON data -% opt: a struct for additional options, ignore to use default values. -% opt can have the following fields (first in [.|.] is the default) -% -% opt.FileName [''|string]: a file name to save the output JSON data -% opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D -% array in JSON array format; if sets to 1, an -% array will be shown as a struct with fields -% "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for -% sparse arrays, the non-zero elements will be -% saved to _ArrayData_ field in triplet-format i.e. -% (ix,iy,val) and "_ArrayIsSparse_" will be added -% with a value of 1; for a complex array, the -% _ArrayData_ array will include two columns -% (4 for sparse) to record the real and imaginary -% parts, and also "_ArrayIsComplex_":1 is added. -% opt.ParseLogical [1|0]: if this is set to 1, logical array elem -% will use true/false rather than 1/0. -% opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single -% numerical element will be shown without a square -% bracket, unless it is the root object; if 0, square -% brackets are forced for any numerical arrays. -% opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson -% will use the name of the passed obj variable as the -% root object name; if obj is an expression and -% does not have a name, 'root' will be used; if this -% is set to 0 and rootname is empty, the root level -% will be merged down to the lower level. -% opt.JSONP [''|string]: to generate a JSONP output (JSON with padding), -% for example, if opt.JSON='foo', the JSON data is -% wrapped inside a function call as 'foo(...);' -% opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson -% back to the string form -% -% opt can be replaced by a list of ('param',value) pairs. The param -% string is equivallent to a field in opt and is case sensitive. -% output: -% json: a binary string in the UBJSON format (see http://ubjson.org) -% -% examples: -% jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... -% 'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],... -% 'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;... -% 2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],... -% 'MeshCreator','FangQ','MeshTitle','T6 Cube',... -% 'SpecialData',[nan, inf, -inf]); -% saveubjson('jsonmesh',jsonmesh) -% saveubjson('jsonmesh',jsonmesh,'meshdata.ubj') -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -if(nargin==1) - varname=inputname(1); - obj=rootname; - if(isempty(varname)) - varname='root'; - end - rootname=varname; -else - varname=inputname(2); -end -if(length(varargin)==1 && ischar(varargin{1})) - opt=struct('FileName',varargin{1}); -else - opt=varargin2struct(varargin{:}); -end -opt.IsOctave=exist('OCTAVE_VERSION','builtin'); -rootisarray=0; -rootlevel=1; -forceroot=jsonopt('ForceRootName',0,opt); -if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0) - rootisarray=1; - rootlevel=0; -else - if(isempty(rootname)) - rootname=varname; - end -end -if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot) - rootname='root'; -end -json=obj2ubjson(rootname,obj,rootlevel,opt); -if(~rootisarray) - json=['{' json '}']; -end - -jsonp=jsonopt('JSONP','',opt); -if(~isempty(jsonp)) - json=[jsonp '(' json ')']; -end - -% save to a file if FileName is set, suggested by Patrick Rapin -if(~isempty(jsonopt('FileName','',opt))) - fid = fopen(opt.FileName, 'wb'); - fwrite(fid,json); - fclose(fid); -end - -%%------------------------------------------------------------------------- -function txt=obj2ubjson(name,item,level,varargin) - -if(iscell(item)) - txt=cell2ubjson(name,item,level,varargin{:}); -elseif(isstruct(item)) - txt=struct2ubjson(name,item,level,varargin{:}); -elseif(ischar(item)) - txt=str2ubjson(name,item,level,varargin{:}); -else - txt=mat2ubjson(name,item,level,varargin{:}); -end - -%%------------------------------------------------------------------------- -function txt=cell2ubjson(name,item,level,varargin) -txt=''; -if(~iscell(item)) - error('input is not a cell'); -end - -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); % let's handle 1D cell first -if(len>1) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) '[']; name=''; - else - txt='['; - end -elseif(len==0) - if(~isempty(name)) - txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; - else - txt='Z'; - end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})]; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=struct2ubjson(name,item,level,varargin) -txt=''; -if(~isstruct(item)) - error('input is not a struct'); -end -dim=size(item); -if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now - item=reshape(item,dim(1),numel(item)/dim(1)); - dim=size(item); -end -len=numel(item); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -for j=1:dim(2) - if(dim(1)>1) txt=[txt '[']; end - for i=1:dim(1) - names = fieldnames(item(i,j)); - if(~isempty(name) && len==1) - txt=[txt S_(checkname(name,varargin{:})) '{']; - else - txt=[txt '{']; - end - if(~isempty(names)) - for e=1:length(names) - txt=[txt obj2ubjson(names{e},getfield(item(i,j),... - names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})]; - end - end - txt=[txt '}']; - end - if(dim(1)>1) txt=[txt ']']; end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=str2ubjson(name,item,level,varargin) -txt=''; -if(~ischar(item)) - error('input is not a string'); -end -item=reshape(item, max(size(item),[1 0])); -len=size(item,1); - -if(~isempty(name)) - if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end -else - if(len>1) txt='['; end -end -isoct=jsonopt('IsOctave',0,varargin{:}); -for e=1:len - val=item(e,:); - if(len==1) - obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),'']; - if(isempty(name)) obj=['',S_(val),'']; end - txt=[txt,'',obj]; - else - txt=[txt,'',['',S_(val),'']]; - end -end -if(len>1) txt=[txt ']']; end - -%%------------------------------------------------------------------------- -function txt=mat2ubjson(name,item,level,varargin) -if(~isnumeric(item) && ~islogical(item)) - error('input is not an array'); -end - -if(length(size(item))>2 || issparse(item) || ~isreal(item) || ... - isempty(item) || jsonopt('ArrayToStruct',0,varargin{:})) - cid=I_(uint32(max(size(item)))); - if(isempty(name)) - txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ]; - else - if(isempty(item)) - txt=[S_(checkname(name,varargin{:})),'Z']; - return; - else - txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))]; - end - end -else - if(isempty(name)) - txt=matdata2ubjson(item,level+1,varargin{:}); - else - if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1) - numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']',''); - txt=[S_(checkname(name,varargin{:})) numtxt]; - else - txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})]; - end - end - return; -end -if(issparse(item)) - [ix,iy]=find(item); - data=full(item(find(item))); - if(~isreal(item)) - data=[real(data(:)),imag(data(:))]; - if(size(item,1)==1) - % Kludge to have data's 'transposedness' match item's. - % (Necessary for complex row vector handling below.) - data=data'; - end - txt=[txt,S_('_ArrayIsComplex_'),'T']; - end - txt=[txt,S_('_ArrayIsSparse_'),'T']; - if(size(item,1)==1) - % Row vector, store only column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([iy(:),data'],level+2,varargin{:})]; - elseif(size(item,2)==1) - % Column vector, store only row indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,data],level+2,varargin{:})]; - else - % General case, store row and column indices. - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([ix,iy,data],level+2,varargin{:})]; - end -else - if(isreal(item)) - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson(item(:)',level+2,varargin{:})]; - else - txt=[txt,S_('_ArrayIsComplex_'),'T']; - txt=[txt,S_('_ArrayData_'),... - matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})]; - end -end -txt=[txt,'}']; - -%%------------------------------------------------------------------------- -function txt=matdata2ubjson(mat,level,varargin) -if(isempty(mat)) - txt='Z'; - return; -end -if(size(mat,1)==1) - level=level-1; -end -type=''; -hasnegtive=(mat<0); -if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0))) - if(isempty(hasnegtive)) - if(max(mat(:))<=2^8) - type='U'; - end - end - if(isempty(type)) - % todo - need to consider negative ones separately - id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]); - if(isempty(find(id))) - error('high-precision data is not yet supported'); - end - key='iIlL'; - type=key(find(id)); - end - txt=[I_a(mat(:),type,size(mat))]; -elseif(islogical(mat)) - logicalval='FT'; - if(numel(mat)==1) - txt=logicalval(mat+1); - else - txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')]; - end -else - if(numel(mat)==1) - txt=['[' D_(mat) ']']; - else - txt=D_a(mat(:),'D',size(mat)); - end -end - -%txt=regexprep(mat2str(mat),'\s+',','); -%txt=regexprep(txt,';',sprintf('],[')); -% if(nargin>=2 && size(mat,1)>1) -% txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']); -% end -if(any(isinf(mat(:)))) - txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:})); -end -if(any(isnan(mat(:)))) - txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:})); -end - -%%------------------------------------------------------------------------- -function newname=checkname(name,varargin) -isunpack=jsonopt('UnpackHex',1,varargin{:}); -newname=name; -if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once'))) - return -end -if(isunpack) - isoct=jsonopt('IsOctave',0,varargin{:}); - if(~isoct) - newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}'); - else - pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start'); - pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end'); - if(isempty(pos)) return; end - str0=name; - pos0=[0 pend(:)' length(name)]; - newname=''; - for i=1:length(pos) - newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))]; - end - if(pos(end)~=length(name)) - newname=[newname str0(pos0(end-1)+1:pos0(end))]; - end - end -end -%%------------------------------------------------------------------------- -function val=S_(str) -if(length(str)==1) - val=['C' str]; -else - val=['S' I_(int32(length(str))) str]; -end -%%------------------------------------------------------------------------- -function val=I_(num) -if(~isinteger(num)) - error('input is not an integer'); -end -if(num>=0 && num<255) - val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')]; - return; -end -key='iIlL'; -cid={'int8','int16','int32','int64'}; -for i=1:4 - if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1))) - val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')]; - return; - end -end -error('unsupported integer'); - -%%------------------------------------------------------------------------- -function val=D_(num) -if(~isfloat(num)) - error('input is not a float'); -end - -if(isa(num,'single')) - val=['d' data2byte(num,'uint8')]; -else - val=['D' data2byte(num,'uint8')]; -end -%%------------------------------------------------------------------------- -function data=I_a(num,type,dim,format) -id=find(ismember('iUIlL',type)); - -if(id==0) - error('unsupported integer array'); -end - -% based on UBJSON specs, all integer types are stored in big endian format - -if(id==1) - data=data2byte(swapbytes(int8(num)),'uint8'); - blen=1; -elseif(id==2) - data=data2byte(swapbytes(uint8(num)),'uint8'); - blen=1; -elseif(id==3) - data=data2byte(swapbytes(int16(num)),'uint8'); - blen=2; -elseif(id==4) - data=data2byte(swapbytes(int32(num)),'uint8'); - blen=4; -elseif(id==5) - data=data2byte(swapbytes(int64(num)),'uint8'); - blen=8; -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/blen)) data(:)']; - end - data=['[' data(:)']; -else - data=reshape(data,blen,numel(data)/blen); - data(2:blen+1,:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function data=D_a(num,type,dim,format) -id=find(ismember('dD',type)); - -if(id==0) - error('unsupported float array'); -end - -if(id==1) - data=data2byte(single(num),'uint8'); -elseif(id==2) - data=data2byte(double(num),'uint8'); -end - -if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2)) - format='opt'; -end -if((nargin<4 || strcmp(format,'opt')) && numel(num)>1) - if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2)))) - cid=I_(uint32(max(dim))); - data=['$' type '#' I_a(dim,cid(1)) data(:)']; - else - data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)']; - end - data=['[' data]; -else - data=reshape(data,(id*4),length(data)/(id*4)); - data(2:(id*4+1),:)=data; - data(1,:)=type; - data=data(:)'; - data=['[' data(:)' ']']; -end -%%------------------------------------------------------------------------- -function bytes=data2byte(varargin) -bytes=typecast(varargin{:}); -bytes=bytes(:)'; diff --git a/assignments/machine-learning-ex8/ex8/lib/jsonlab/varargin2struct.m b/assignments/machine-learning-ex8/ex8/lib/jsonlab/varargin2struct.m deleted file mode 100644 index 9a5c2b6..0000000 --- a/assignments/machine-learning-ex8/ex8/lib/jsonlab/varargin2struct.m +++ /dev/null @@ -1,40 +0,0 @@ -function opt=varargin2struct(varargin) -% -% opt=varargin2struct('param1',value1,'param2',value2,...) -% or -% opt=varargin2struct(...,optstruct,...) -% -% convert a series of input parameters into a structure -% -% authors:Qianqian Fang (fangq nmr.mgh.harvard.edu) -% date: 2012/12/22 -% -% input: -% 'param', value: the input parameters should be pairs of a string and a value -% optstruct: if a parameter is a struct, the fields will be merged to the output struct -% -% output: -% opt: a struct where opt.param1=value1, opt.param2=value2 ... -% -% license: -% BSD, see LICENSE_BSD.txt files for details -% -% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab) -% - -len=length(varargin); -opt=struct; -if(len==0) return; end -i=1; -while(i<=len) - if(isstruct(varargin{i})) - opt=mergestruct(opt,varargin{i}); - elseif(ischar(varargin{i}) && i 0 && resp(1) == '{'; - isHtml = findstr(lower(resp), ']+>', ' '); - strippedResponse = regexprep(strippedResponse, '[\t ]+', ' '); - fprintf(strippedResponse); -end - - - - -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -% -% Service configuration -% -%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -function submissionUrl = submissionUrl() - submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1'; -end diff --git a/assignments/machine-learning-ex8/ex8/loadMovieList.m b/assignments/machine-learning-ex8/ex8/loadMovieList.m deleted file mode 100644 index 161321d..0000000 --- a/assignments/machine-learning-ex8/ex8/loadMovieList.m +++ /dev/null @@ -1,25 +0,0 @@ -function movieList = loadMovieList() -%GETMOVIELIST reads the fixed movie list in movie.txt and returns a -%cell array of the words -% movieList = GETMOVIELIST() reads the fixed movie list in movie.txt -% and returns a cell array of the words in movieList. - - -%% Read the fixed movieulary list -fid = fopen('movie_ids.txt'); - -% Store all movies in cell array movie{} -n = 1682; % Total number of movies - -movieList = cell(n, 1); -for i = 1:n - % Read line - line = fgets(fid); - % Word Index (can ignore since it will be = i) - [idx, movieName] = strtok(line, ' '); - % Actual Word - movieList{i} = strtrim(movieName); -end -fclose(fid); - -end diff --git a/assignments/machine-learning-ex8/ex8/movie_ids.txt b/assignments/machine-learning-ex8/ex8/movie_ids.txt deleted file mode 100644 index 392427a..0000000 --- a/assignments/machine-learning-ex8/ex8/movie_ids.txt +++ /dev/null @@ -1,1682 +0,0 @@ -1 Toy Story (1995) -2 GoldenEye (1995) -3 Four Rooms (1995) -4 Get Shorty (1995) -5 Copycat (1995) -6 Shanghai Triad (Yao a yao yao dao waipo qiao) (1995) -7 Twelve Monkeys (1995) -8 Babe (1995) -9 Dead Man Walking (1995) -10 Richard III (1995) -11 Seven (Se7en) (1995) -12 Usual Suspects, The (1995) -13 Mighty Aphrodite (1995) -14 Postino, Il (1994) -15 Mr. Holland's Opus (1995) -16 French Twist (Gazon maudit) (1995) -17 From Dusk Till Dawn (1996) -18 White Balloon, The (1995) -19 Antonia's Line (1995) -20 Angels and Insects (1995) -21 Muppet Treasure Island (1996) -22 Braveheart (1995) -23 Taxi Driver (1976) -24 Rumble in the Bronx (1995) -25 Birdcage, The (1996) -26 Brothers McMullen, The (1995) -27 Bad Boys (1995) -28 Apollo 13 (1995) -29 Batman Forever (1995) -30 Belle de jour (1967) -31 Crimson Tide (1995) -32 Crumb (1994) -33 Desperado (1995) -34 Doom Generation, The (1995) -35 Free Willy 2: The Adventure Home (1995) -36 Mad Love (1995) -37 Nadja (1994) -38 Net, The (1995) -39 Strange Days (1995) -40 To Wong Foo, Thanks for Everything! Julie Newmar (1995) -41 Billy Madison (1995) -42 Clerks (1994) -43 Disclosure (1994) -44 Dolores Claiborne (1994) -45 Eat Drink Man Woman (1994) -46 Exotica (1994) -47 Ed Wood (1994) -48 Hoop Dreams (1994) -49 I.Q. (1994) -50 Star Wars (1977) -51 Legends of the Fall (1994) -52 Madness of King George, The (1994) -53 Natural Born Killers (1994) -54 Outbreak (1995) -55 Professional, The (1994) -56 Pulp Fiction (1994) -57 Priest (1994) -58 Quiz Show (1994) -59 Three Colors: Red (1994) -60 Three Colors: Blue (1993) -61 Three Colors: White (1994) -62 Stargate (1994) -63 Santa Clause, The (1994) -64 Shawshank Redemption, The (1994) -65 What's Eating Gilbert Grape (1993) -66 While You Were Sleeping (1995) -67 Ace Ventura: Pet Detective (1994) -68 Crow, The (1994) -69 Forrest Gump (1994) -70 Four Weddings and a Funeral (1994) -71 Lion King, The (1994) -72 Mask, The (1994) -73 Maverick (1994) -74 Faster Pussycat! Kill! Kill! (1965) -75 Brother Minister: The Assassination of Malcolm X (1994) -76 Carlito's Way (1993) -77 Firm, The (1993) -78 Free Willy (1993) -79 Fugitive, The (1993) -80 Hot Shots! Part Deux (1993) -81 Hudsucker Proxy, The (1994) -82 Jurassic Park (1993) -83 Much Ado About Nothing (1993) -84 Robert A. Heinlein's The Puppet Masters (1994) -85 Ref, The (1994) -86 Remains of the Day, The (1993) -87 Searching for Bobby Fischer (1993) -88 Sleepless in Seattle (1993) -89 Blade Runner (1982) -90 So I Married an Axe Murderer (1993) -91 Nightmare Before Christmas, The (1993) -92 True Romance (1993) -93 Welcome to the Dollhouse (1995) -94 Home Alone (1990) -95 Aladdin (1992) -96 Terminator 2: Judgment Day (1991) -97 Dances with Wolves (1990) -98 Silence of the Lambs, The (1991) -99 Snow White and the Seven Dwarfs (1937) -100 Fargo (1996) -101 Heavy Metal (1981) -102 Aristocats, The (1970) -103 All Dogs Go to Heaven 2 (1996) -104 Theodore Rex (1995) -105 Sgt. Bilko (1996) -106 Diabolique (1996) -107 Moll Flanders (1996) -108 Kids in the Hall: Brain Candy (1996) -109 Mystery Science Theater 3000: The Movie (1996) -110 Operation Dumbo Drop (1995) -111 Truth About Cats & Dogs, The (1996) -112 Flipper (1996) -113 Horseman on the Roof, The (Hussard sur le toit, Le) (1995) -114 Wallace & Gromit: The Best of Aardman Animation (1996) -115 Haunted World of Edward D. Wood Jr., The (1995) -116 Cold Comfort Farm (1995) -117 Rock, The (1996) -118 Twister (1996) -119 Maya Lin: A Strong Clear Vision (1994) -120 Striptease (1996) -121 Independence Day (ID4) (1996) -122 Cable Guy, The (1996) -123 Frighteners, The (1996) -124 Lone Star (1996) -125 Phenomenon (1996) -126 Spitfire Grill, The (1996) -127 Godfather, The (1972) -128 Supercop (1992) -129 Bound (1996) -130 Kansas City (1996) -131 Breakfast at Tiffany's (1961) -132 Wizard of Oz, The (1939) -133 Gone with the Wind (1939) -134 Citizen Kane (1941) -135 2001: A Space Odyssey (1968) -136 Mr. Smith Goes to Washington (1939) -137 Big Night (1996) -138 D3: The Mighty Ducks (1996) -139 Love Bug, The (1969) -140 Homeward Bound: The Incredible Journey (1993) -141 20,000 Leagues Under the Sea (1954) -142 Bedknobs and Broomsticks (1971) -143 Sound of Music, The (1965) -144 Die Hard (1988) -145 Lawnmower Man, The (1992) -146 Unhook the Stars (1996) -147 Long Kiss Goodnight, The (1996) -148 Ghost and the Darkness, The (1996) -149 Jude (1996) -150 Swingers (1996) -151 Willy Wonka and the Chocolate Factory (1971) -152 Sleeper (1973) -153 Fish Called Wanda, A (1988) -154 Monty Python's Life of Brian (1979) -155 Dirty Dancing (1987) -156 Reservoir Dogs (1992) -157 Platoon (1986) -158 Weekend at Bernie's (1989) -159 Basic Instinct (1992) -160 Glengarry Glen Ross (1992) -161 Top Gun (1986) -162 On Golden Pond (1981) -163 Return of the Pink Panther, The (1974) -164 Abyss, The (1989) -165 Jean de Florette (1986) -166 Manon of the Spring (Manon des sources) (1986) -167 Private Benjamin (1980) -168 Monty Python and the Holy Grail (1974) -169 Wrong Trousers, The (1993) -170 Cinema Paradiso (1988) -171 Delicatessen (1991) -172 Empire Strikes Back, The (1980) -173 Princess Bride, The (1987) -174 Raiders of the Lost Ark (1981) -175 Brazil (1985) -176 Aliens (1986) -177 Good, The Bad and The Ugly, The (1966) -178 12 Angry Men (1957) -179 Clockwork Orange, A (1971) -180 Apocalypse Now (1979) -181 Return of the Jedi (1983) -182 GoodFellas (1990) -183 Alien (1979) -184 Army of Darkness (1993) -185 Psycho (1960) -186 Blues Brothers, The (1980) -187 Godfather: Part II, The (1974) -188 Full Metal Jacket (1987) -189 Grand Day Out, A (1992) -190 Henry V (1989) -191 Amadeus (1984) -192 Raging Bull (1980) -193 Right Stuff, The (1983) -194 Sting, The (1973) -195 Terminator, The (1984) -196 Dead Poets Society (1989) -197 Graduate, The (1967) -198 Nikita (La Femme Nikita) (1990) -199 Bridge on the River Kwai, The (1957) -200 Shining, The (1980) -201 Evil Dead II (1987) -202 Groundhog Day (1993) -203 Unforgiven (1992) -204 Back to the Future (1985) -205 Patton (1970) -206 Akira (1988) -207 Cyrano de Bergerac (1990) -208 Young Frankenstein (1974) -209 This Is Spinal Tap (1984) -210 Indiana Jones and the Last Crusade (1989) -211 M*A*S*H (1970) -212 Unbearable Lightness of Being, The (1988) -213 Room with a View, A (1986) -214 Pink Floyd - The Wall (1982) -215 Field of Dreams (1989) -216 When Harry Met Sally... (1989) -217 Bram Stoker's Dracula (1992) -218 Cape Fear (1991) -219 Nightmare on Elm Street, A (1984) -220 Mirror Has Two Faces, The (1996) -221 Breaking the Waves (1996) -222 Star Trek: First Contact (1996) -223 Sling Blade (1996) -224 Ridicule (1996) -225 101 Dalmatians (1996) -226 Die Hard 2 (1990) -227 Star Trek VI: The Undiscovered Country (1991) -228 Star Trek: The Wrath of Khan (1982) -229 Star Trek III: The Search for Spock (1984) -230 Star Trek IV: The Voyage Home (1986) -231 Batman Returns (1992) -232 Young Guns (1988) -233 Under Siege (1992) -234 Jaws (1975) -235 Mars Attacks! (1996) -236 Citizen Ruth (1996) -237 Jerry Maguire (1996) -238 Raising Arizona (1987) -239 Sneakers (1992) -240 Beavis and Butt-head Do America (1996) -241 Last of the Mohicans, The (1992) -242 Kolya (1996) -243 Jungle2Jungle (1997) -244 Smilla's Sense of Snow (1997) -245 Devil's Own, The (1997) -246 Chasing Amy (1997) -247 Turbo: A Power Rangers Movie (1997) -248 Grosse Pointe Blank (1997) -249 Austin Powers: International Man of Mystery (1997) -250 Fifth Element, The (1997) -251 Shall We Dance? (1996) -252 Lost World: Jurassic Park, The (1997) -253 Pillow Book, The (1995) -254 Batman & Robin (1997) -255 My Best Friend's Wedding (1997) -256 When the Cats Away (Chacun cherche son chat) (1996) -257 Men in Black (1997) -258 Contact (1997) -259 George of the Jungle (1997) -260 Event Horizon (1997) -261 Air Bud (1997) -262 In the Company of Men (1997) -263 Steel (1997) -264 Mimic (1997) -265 Hunt for Red October, The (1990) -266 Kull the Conqueror (1997) -267 unknown -268 Chasing Amy (1997) -269 Full Monty, The (1997) -270 Gattaca (1997) -271 Starship Troopers (1997) -272 Good Will Hunting (1997) -273 Heat (1995) -274 Sabrina (1995) -275 Sense and Sensibility (1995) -276 Leaving Las Vegas (1995) -277 Restoration (1995) -278 Bed of Roses (1996) -279 Once Upon a Time... When We Were Colored (1995) -280 Up Close and Personal (1996) -281 River Wild, The (1994) -282 Time to Kill, A (1996) -283 Emma (1996) -284 Tin Cup (1996) -285 Secrets & Lies (1996) -286 English Patient, The (1996) -287 Marvin's Room (1996) -288 Scream (1996) -289 Evita (1996) -290 Fierce Creatures (1997) -291 Absolute Power (1997) -292 Rosewood (1997) -293 Donnie Brasco (1997) -294 Liar Liar (1997) -295 Breakdown (1997) -296 Promesse, La (1996) -297 Ulee's Gold (1997) -298 Face/Off (1997) -299 Hoodlum (1997) -300 Air Force One (1997) -301 In & Out (1997) -302 L.A. Confidential (1997) -303 Ulee's Gold (1997) -304 Fly Away Home (1996) -305 Ice Storm, The (1997) -306 Mrs. Brown (Her Majesty, Mrs. Brown) (1997) -307 Devil's Advocate, The (1997) -308 FairyTale: A True Story (1997) -309 Deceiver (1997) -310 Rainmaker, The (1997) -311 Wings of the Dove, The (1997) -312 Midnight in the Garden of Good and Evil (1997) -313 Titanic (1997) -314 3 Ninjas: High Noon At Mega Mountain (1998) -315 Apt Pupil (1998) -316 As Good As It Gets (1997) -317 In the Name of the Father (1993) -318 Schindler's List (1993) -319 Everyone Says I Love You (1996) -320 Paradise Lost: The Child Murders at Robin Hood Hills (1996) -321 Mother (1996) -322 Murder at 1600 (1997) -323 Dante's Peak (1997) -324 Lost Highway (1997) -325 Crash (1996) -326 G.I. Jane (1997) -327 Cop Land (1997) -328 Conspiracy Theory (1997) -329 Desperate Measures (1998) -330 187 (1997) -331 Edge, The (1997) -332 Kiss the Girls (1997) -333 Game, The (1997) -334 U Turn (1997) -335 How to Be a Player (1997) -336 Playing God (1997) -337 House of Yes, The (1997) -338 Bean (1997) -339 Mad City (1997) -340 Boogie Nights (1997) -341 Critical Care (1997) -342 Man Who Knew Too Little, The (1997) -343 Alien: Resurrection (1997) -344 Apostle, The (1997) -345 Deconstructing Harry (1997) -346 Jackie Brown (1997) -347 Wag the Dog (1997) -348 Desperate Measures (1998) -349 Hard Rain (1998) -350 Fallen (1998) -351 Prophecy II, The (1998) -352 Spice World (1997) -353 Deep Rising (1998) -354 Wedding Singer, The (1998) -355 Sphere (1998) -356 Client, The (1994) -357 One Flew Over the Cuckoo's Nest (1975) -358 Spawn (1997) -359 Assignment, The (1997) -360 Wonderland (1997) -361 Incognito (1997) -362 Blues Brothers 2000 (1998) -363 Sudden Death (1995) -364 Ace Ventura: When Nature Calls (1995) -365 Powder (1995) -366 Dangerous Minds (1995) -367 Clueless (1995) -368 Bio-Dome (1996) -369 Black Sheep (1996) -370 Mary Reilly (1996) -371 Bridges of Madison County, The (1995) -372 Jeffrey (1995) -373 Judge Dredd (1995) -374 Mighty Morphin Power Rangers: The Movie (1995) -375 Showgirls (1995) -376 Houseguest (1994) -377 Heavyweights (1994) -378 Miracle on 34th Street (1994) -379 Tales From the Crypt Presents: Demon Knight (1995) -380 Star Trek: Generations (1994) -381 Muriel's Wedding (1994) -382 Adventures of Priscilla, Queen of the Desert, The (1994) -383 Flintstones, The (1994) -384 Naked Gun 33 1/3: The Final Insult (1994) -385 True Lies (1994) -386 Addams Family Values (1993) -387 Age of Innocence, The (1993) -388 Beverly Hills Cop III (1994) -389 Black Beauty (1994) -390 Fear of a Black Hat (1993) -391 Last Action Hero (1993) -392 Man Without a Face, The (1993) -393 Mrs. Doubtfire (1993) -394 Radioland Murders (1994) -395 Robin Hood: Men in Tights (1993) -396 Serial Mom (1994) -397 Striking Distance (1993) -398 Super Mario Bros. (1993) -399 Three Musketeers, The (1993) -400 Little Rascals, The (1994) -401 Brady Bunch Movie, The (1995) -402 Ghost (1990) -403 Batman (1989) -404 Pinocchio (1940) -405 Mission: Impossible (1996) -406 Thinner (1996) -407 Spy Hard (1996) -408 Close Shave, A (1995) -409 Jack (1996) -410 Kingpin (1996) -411 Nutty Professor, The (1996) -412 Very Brady Sequel, A (1996) -413 Tales from the Crypt Presents: Bordello of Blood (1996) -414 My Favorite Year (1982) -415 Apple Dumpling Gang, The (1975) -416 Old Yeller (1957) -417 Parent Trap, The (1961) -418 Cinderella (1950) -419 Mary Poppins (1964) -420 Alice in Wonderland (1951) -421 William Shakespeare's Romeo and Juliet (1996) -422 Aladdin and the King of Thieves (1996) -423 E.T. the Extra-Terrestrial (1982) -424 Children of the Corn: The Gathering (1996) -425 Bob Roberts (1992) -426 Transformers: The Movie, The (1986) -427 To Kill a Mockingbird (1962) -428 Harold and Maude (1971) -429 Day the Earth Stood Still, The (1951) -430 Duck Soup (1933) -431 Highlander (1986) -432 Fantasia (1940) -433 Heathers (1989) -434 Forbidden Planet (1956) -435 Butch Cassidy and the Sundance Kid (1969) -436 American Werewolf in London, An (1981) -437 Amityville 1992: It's About Time (1992) -438 Amityville 3-D (1983) -439 Amityville: A New Generation (1993) -440 Amityville II: The Possession (1982) -441 Amityville Horror, The (1979) -442 Amityville Curse, The (1990) -443 Birds, The (1963) -444 Blob, The (1958) -445 Body Snatcher, The (1945) -446 Burnt Offerings (1976) -447 Carrie (1976) -448 Omen, The (1976) -449 Star Trek: The Motion Picture (1979) -450 Star Trek V: The Final Frontier (1989) -451 Grease (1978) -452 Jaws 2 (1978) -453 Jaws 3-D (1983) -454 Bastard Out of Carolina (1996) -455 Jackie Chan's First Strike (1996) -456 Beverly Hills Ninja (1997) -457 Free Willy 3: The Rescue (1997) -458 Nixon (1995) -459 Cry, the Beloved Country (1995) -460 Crossing Guard, The (1995) -461 Smoke (1995) -462 Like Water For Chocolate (Como agua para chocolate) (1992) -463 Secret of Roan Inish, The (1994) -464 Vanya on 42nd Street (1994) -465 Jungle Book, The (1994) -466 Red Rock West (1992) -467 Bronx Tale, A (1993) -468 Rudy (1993) -469 Short Cuts (1993) -470 Tombstone (1993) -471 Courage Under Fire (1996) -472 Dragonheart (1996) -473 James and the Giant Peach (1996) -474 Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963) -475 Trainspotting (1996) -476 First Wives Club, The (1996) -477 Matilda (1996) -478 Philadelphia Story, The (1940) -479 Vertigo (1958) -480 North by Northwest (1959) -481 Apartment, The (1960) -482 Some Like It Hot (1959) -483 Casablanca (1942) -484 Maltese Falcon, The (1941) -485 My Fair Lady (1964) -486 Sabrina (1954) -487 Roman Holiday (1953) -488 Sunset Blvd. (1950) -489 Notorious (1946) -490 To Catch a Thief (1955) -491 Adventures of Robin Hood, The (1938) -492 East of Eden (1955) -493 Thin Man, The (1934) -494 His Girl Friday (1940) -495 Around the World in 80 Days (1956) -496 It's a Wonderful Life (1946) -497 Bringing Up Baby (1938) -498 African Queen, The (1951) -499 Cat on a Hot Tin Roof (1958) -500 Fly Away Home (1996) -501 Dumbo (1941) -502 Bananas (1971) -503 Candidate, The (1972) -504 Bonnie and Clyde (1967) -505 Dial M for Murder (1954) -506 Rebel Without a Cause (1955) -507 Streetcar Named Desire, A (1951) -508 People vs. Larry Flynt, The (1996) -509 My Left Foot (1989) -510 Magnificent Seven, The (1954) -511 Lawrence of Arabia (1962) -512 Wings of Desire (1987) -513 Third Man, The (1949) -514 Annie Hall (1977) -515 Boot, Das (1981) -516 Local Hero (1983) -517 Manhattan (1979) -518 Miller's Crossing (1990) -519 Treasure of the Sierra Madre, The (1948) -520 Great Escape, The (1963) -521 Deer Hunter, The (1978) -522 Down by Law (1986) -523 Cool Hand Luke (1967) -524 Great Dictator, The (1940) -525 Big Sleep, The (1946) -526 Ben-Hur (1959) -527 Gandhi (1982) -528 Killing Fields, The (1984) -529 My Life as a Dog (Mitt liv som hund) (1985) -530 Man Who Would Be King, The (1975) -531 Shine (1996) -532 Kama Sutra: A Tale of Love (1996) -533 Daytrippers, The (1996) -534 Traveller (1997) -535 Addicted to Love (1997) -536 Ponette (1996) -537 My Own Private Idaho (1991) -538 Anastasia (1997) -539 Mouse Hunt (1997) -540 Money Train (1995) -541 Mortal Kombat (1995) -542 Pocahontas (1995) -543 Misrables, Les (1995) -544 Things to Do in Denver when You're Dead (1995) -545 Vampire in Brooklyn (1995) -546 Broken Arrow (1996) -547 Young Poisoner's Handbook, The (1995) -548 NeverEnding Story III, The (1994) -549 Rob Roy (1995) -550 Die Hard: With a Vengeance (1995) -551 Lord of Illusions (1995) -552 Species (1995) -553 Walk in the Clouds, A (1995) -554 Waterworld (1995) -555 White Man's Burden (1995) -556 Wild Bill (1995) -557 Farinelli: il castrato (1994) -558 Heavenly Creatures (1994) -559 Interview with the Vampire (1994) -560 Kid in King Arthur's Court, A (1995) -561 Mary Shelley's Frankenstein (1994) -562 Quick and the Dead, The (1995) -563 Stephen King's The Langoliers (1995) -564 Tales from the Hood (1995) -565 Village of the Damned (1995) -566 Clear and Present Danger (1994) -567 Wes Craven's New Nightmare (1994) -568 Speed (1994) -569 Wolf (1994) -570 Wyatt Earp (1994) -571 Another Stakeout (1993) -572 Blown Away (1994) -573 Body Snatchers (1993) -574 Boxing Helena (1993) -575 City Slickers II: The Legend of Curly's Gold (1994) -576 Cliffhanger (1993) -577 Coneheads (1993) -578 Demolition Man (1993) -579 Fatal Instinct (1993) -580 Englishman Who Went Up a Hill, But Came Down a Mountain, The (1995) -581 Kalifornia (1993) -582 Piano, The (1993) -583 Romeo Is Bleeding (1993) -584 Secret Garden, The (1993) -585 Son in Law (1993) -586 Terminal Velocity (1994) -587 Hour of the Pig, The (1993) -588 Beauty and the Beast (1991) -589 Wild Bunch, The (1969) -590 Hellraiser: Bloodline (1996) -591 Primal Fear (1996) -592 True Crime (1995) -593 Stalingrad (1993) -594 Heavy (1995) -595 Fan, The (1996) -596 Hunchback of Notre Dame, The (1996) -597 Eraser (1996) -598 Big Squeeze, The (1996) -599 Police Story 4: Project S (Chao ji ji hua) (1993) -600 Daniel Defoe's Robinson Crusoe (1996) -601 For Whom the Bell Tolls (1943) -602 American in Paris, An (1951) -603 Rear Window (1954) -604 It Happened One Night (1934) -605 Meet Me in St. Louis (1944) -606 All About Eve (1950) -607 Rebecca (1940) -608 Spellbound (1945) -609 Father of the Bride (1950) -610 Gigi (1958) -611 Laura (1944) -612 Lost Horizon (1937) -613 My Man Godfrey (1936) -614 Giant (1956) -615 39 Steps, The (1935) -616 Night of the Living Dead (1968) -617 Blue Angel, The (Blaue Engel, Der) (1930) -618 Picnic (1955) -619 Extreme Measures (1996) -620 Chamber, The (1996) -621 Davy Crockett, King of the Wild Frontier (1955) -622 Swiss Family Robinson (1960) -623 Angels in the Outfield (1994) -624 Three Caballeros, The (1945) -625 Sword in the Stone, The (1963) -626 So Dear to My Heart (1949) -627 Robin Hood: Prince of Thieves (1991) -628 Sleepers (1996) -629 Victor/Victoria (1982) -630 Great Race, The (1965) -631 Crying Game, The (1992) -632 Sophie's Choice (1982) -633 Christmas Carol, A (1938) -634 Microcosmos: Le peuple de l'herbe (1996) -635 Fog, The (1980) -636 Escape from New York (1981) -637 Howling, The (1981) -638 Return of Martin Guerre, The (Retour de Martin Guerre, Le) (1982) -639 Tin Drum, The (Blechtrommel, Die) (1979) -640 Cook the Thief His Wife & Her Lover, The (1989) -641 Paths of Glory (1957) -642 Grifters, The (1990) -643 The Innocent (1994) -644 Thin Blue Line, The (1988) -645 Paris Is Burning (1990) -646 Once Upon a Time in the West (1969) -647 Ran (1985) -648 Quiet Man, The (1952) -649 Once Upon a Time in America (1984) -650 Seventh Seal, The (Sjunde inseglet, Det) (1957) -651 Glory (1989) -652 Rosencrantz and Guildenstern Are Dead (1990) -653 Touch of Evil (1958) -654 Chinatown (1974) -655 Stand by Me (1986) -656 M (1931) -657 Manchurian Candidate, The (1962) -658 Pump Up the Volume (1990) -659 Arsenic and Old Lace (1944) -660 Fried Green Tomatoes (1991) -661 High Noon (1952) -662 Somewhere in Time (1980) -663 Being There (1979) -664 Paris, Texas (1984) -665 Alien 3 (1992) -666 Blood For Dracula (Andy Warhol's Dracula) (1974) -667 Audrey Rose (1977) -668 Blood Beach (1981) -669 Body Parts (1991) -670 Body Snatchers (1993) -671 Bride of Frankenstein (1935) -672 Candyman (1992) -673 Cape Fear (1962) -674 Cat People (1982) -675 Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922) -676 Crucible, The (1996) -677 Fire on the Mountain (1996) -678 Volcano (1997) -679 Conan the Barbarian (1981) -680 Kull the Conqueror (1997) -681 Wishmaster (1997) -682 I Know What You Did Last Summer (1997) -683 Rocket Man (1997) -684 In the Line of Fire (1993) -685 Executive Decision (1996) -686 Perfect World, A (1993) -687 McHale's Navy (1997) -688 Leave It to Beaver (1997) -689 Jackal, The (1997) -690 Seven Years in Tibet (1997) -691 Dark City (1998) -692 American President, The (1995) -693 Casino (1995) -694 Persuasion (1995) -695 Kicking and Screaming (1995) -696 City Hall (1996) -697 Basketball Diaries, The (1995) -698 Browning Version, The (1994) -699 Little Women (1994) -700 Miami Rhapsody (1995) -701 Wonderful, Horrible Life of Leni Riefenstahl, The (1993) -702 Barcelona (1994) -703 Widows' Peak (1994) -704 House of the Spirits, The (1993) -705 Singin' in the Rain (1952) -706 Bad Moon (1996) -707 Enchanted April (1991) -708 Sex, Lies, and Videotape (1989) -709 Strictly Ballroom (1992) -710 Better Off Dead... (1985) -711 Substance of Fire, The (1996) -712 Tin Men (1987) -713 Othello (1995) -714 Carrington (1995) -715 To Die For (1995) -716 Home for the Holidays (1995) -717 Juror, The (1996) -718 In the Bleak Midwinter (1995) -719 Canadian Bacon (1994) -720 First Knight (1995) -721 Mallrats (1995) -722 Nine Months (1995) -723 Boys on the Side (1995) -724 Circle of Friends (1995) -725 Exit to Eden (1994) -726 Fluke (1995) -727 Immortal Beloved (1994) -728 Junior (1994) -729 Nell (1994) -730 Queen Margot (Reine Margot, La) (1994) -731 Corrina, Corrina (1994) -732 Dave (1993) -733 Go Fish (1994) -734 Made in America (1993) -735 Philadelphia (1993) -736 Shadowlands (1993) -737 Sirens (1994) -738 Threesome (1994) -739 Pretty Woman (1990) -740 Jane Eyre (1996) -741 Last Supper, The (1995) -742 Ransom (1996) -743 Crow: City of Angels, The (1996) -744 Michael Collins (1996) -745 Ruling Class, The (1972) -746 Real Genius (1985) -747 Benny & Joon (1993) -748 Saint, The (1997) -749 MatchMaker, The (1997) -750 Amistad (1997) -751 Tomorrow Never Dies (1997) -752 Replacement Killers, The (1998) -753 Burnt By the Sun (1994) -754 Red Corner (1997) -755 Jumanji (1995) -756 Father of the Bride Part II (1995) -757 Across the Sea of Time (1995) -758 Lawnmower Man 2: Beyond Cyberspace (1996) -759 Fair Game (1995) -760 Screamers (1995) -761 Nick of Time (1995) -762 Beautiful Girls (1996) -763 Happy Gilmore (1996) -764 If Lucy Fell (1996) -765 Boomerang (1992) -766 Man of the Year (1995) -767 Addiction, The (1995) -768 Casper (1995) -769 Congo (1995) -770 Devil in a Blue Dress (1995) -771 Johnny Mnemonic (1995) -772 Kids (1995) -773 Mute Witness (1994) -774 Prophecy, The (1995) -775 Something to Talk About (1995) -776 Three Wishes (1995) -777 Castle Freak (1995) -778 Don Juan DeMarco (1995) -779 Drop Zone (1994) -780 Dumb & Dumber (1994) -781 French Kiss (1995) -782 Little Odessa (1994) -783 Milk Money (1994) -784 Beyond Bedlam (1993) -785 Only You (1994) -786 Perez Family, The (1995) -787 Roommates (1995) -788 Relative Fear (1994) -789 Swimming with Sharks (1995) -790 Tommy Boy (1995) -791 Baby-Sitters Club, The (1995) -792 Bullets Over Broadway (1994) -793 Crooklyn (1994) -794 It Could Happen to You (1994) -795 Richie Rich (1994) -796 Speechless (1994) -797 Timecop (1994) -798 Bad Company (1995) -799 Boys Life (1995) -800 In the Mouth of Madness (1995) -801 Air Up There, The (1994) -802 Hard Target (1993) -803 Heaven & Earth (1993) -804 Jimmy Hollywood (1994) -805 Manhattan Murder Mystery (1993) -806 Menace II Society (1993) -807 Poetic Justice (1993) -808 Program, The (1993) -809 Rising Sun (1993) -810 Shadow, The (1994) -811 Thirty-Two Short Films About Glenn Gould (1993) -812 Andre (1994) -813 Celluloid Closet, The (1995) -814 Great Day in Harlem, A (1994) -815 One Fine Day (1996) -816 Candyman: Farewell to the Flesh (1995) -817 Frisk (1995) -818 Girl 6 (1996) -819 Eddie (1996) -820 Space Jam (1996) -821 Mrs. Winterbourne (1996) -822 Faces (1968) -823 Mulholland Falls (1996) -824 Great White Hype, The (1996) -825 Arrival, The (1996) -826 Phantom, The (1996) -827 Daylight (1996) -828 Alaska (1996) -829 Fled (1996) -830 Power 98 (1995) -831 Escape from L.A. (1996) -832 Bogus (1996) -833 Bulletproof (1996) -834 Halloween: The Curse of Michael Myers (1995) -835 Gay Divorcee, The (1934) -836 Ninotchka (1939) -837 Meet John Doe (1941) -838 In the Line of Duty 2 (1987) -839 Loch Ness (1995) -840 Last Man Standing (1996) -841 Glimmer Man, The (1996) -842 Pollyanna (1960) -843 Shaggy Dog, The (1959) -844 Freeway (1996) -845 That Thing You Do! (1996) -846 To Gillian on Her 37th Birthday (1996) -847 Looking for Richard (1996) -848 Murder, My Sweet (1944) -849 Days of Thunder (1990) -850 Perfect Candidate, A (1996) -851 Two or Three Things I Know About Her (1966) -852 Bloody Child, The (1996) -853 Braindead (1992) -854 Bad Taste (1987) -855 Diva (1981) -856 Night on Earth (1991) -857 Paris Was a Woman (1995) -858 Amityville: Dollhouse (1996) -859 April Fool's Day (1986) -860 Believers, The (1987) -861 Nosferatu a Venezia (1986) -862 Jingle All the Way (1996) -863 Garden of Finzi-Contini, The (Giardino dei Finzi-Contini, Il) (1970) -864 My Fellow Americans (1996) -865 Ice Storm, The (1997) -866 Michael (1996) -867 Whole Wide World, The (1996) -868 Hearts and Minds (1996) -869 Fools Rush In (1997) -870 Touch (1997) -871 Vegas Vacation (1997) -872 Love Jones (1997) -873 Picture Perfect (1997) -874 Career Girls (1997) -875 She's So Lovely (1997) -876 Money Talks (1997) -877 Excess Baggage (1997) -878 That Darn Cat! (1997) -879 Peacemaker, The (1997) -880 Soul Food (1997) -881 Money Talks (1997) -882 Washington Square (1997) -883 Telling Lies in America (1997) -884 Year of the Horse (1997) -885 Phantoms (1998) -886 Life Less Ordinary, A (1997) -887 Eve's Bayou (1997) -888 One Night Stand (1997) -889 Tango Lesson, The (1997) -890 Mortal Kombat: Annihilation (1997) -891 Bent (1997) -892 Flubber (1997) -893 For Richer or Poorer (1997) -894 Home Alone 3 (1997) -895 Scream 2 (1997) -896 Sweet Hereafter, The (1997) -897 Time Tracers (1995) -898 Postman, The (1997) -899 Winter Guest, The (1997) -900 Kundun (1997) -901 Mr. Magoo (1997) -902 Big Lebowski, The (1998) -903 Afterglow (1997) -904 Ma vie en rose (My Life in Pink) (1997) -905 Great Expectations (1998) -906 Oscar & Lucinda (1997) -907 Vermin (1998) -908 Half Baked (1998) -909 Dangerous Beauty (1998) -910 Nil By Mouth (1997) -911 Twilight (1998) -912 U.S. Marshalls (1998) -913 Love and Death on Long Island (1997) -914 Wild Things (1998) -915 Primary Colors (1998) -916 Lost in Space (1998) -917 Mercury Rising (1998) -918 City of Angels (1998) -919 City of Lost Children, The (1995) -920 Two Bits (1995) -921 Farewell My Concubine (1993) -922 Dead Man (1995) -923 Raise the Red Lantern (1991) -924 White Squall (1996) -925 Unforgettable (1996) -926 Down Periscope (1996) -927 Flower of My Secret, The (Flor de mi secreto, La) (1995) -928 Craft, The (1996) -929 Harriet the Spy (1996) -930 Chain Reaction (1996) -931 Island of Dr. Moreau, The (1996) -932 First Kid (1996) -933 Funeral, The (1996) -934 Preacher's Wife, The (1996) -935 Paradise Road (1997) -936 Brassed Off (1996) -937 Thousand Acres, A (1997) -938 Smile Like Yours, A (1997) -939 Murder in the First (1995) -940 Airheads (1994) -941 With Honors (1994) -942 What's Love Got to Do with It (1993) -943 Killing Zoe (1994) -944 Renaissance Man (1994) -945 Charade (1963) -946 Fox and the Hound, The (1981) -947 Big Blue, The (Grand bleu, Le) (1988) -948 Booty Call (1997) -949 How to Make an American Quilt (1995) -950 Georgia (1995) -951 Indian in the Cupboard, The (1995) -952 Blue in the Face (1995) -953 Unstrung Heroes (1995) -954 Unzipped (1995) -955 Before Sunrise (1995) -956 Nobody's Fool (1994) -957 Pushing Hands (1992) -958 To Live (Huozhe) (1994) -959 Dazed and Confused (1993) -960 Naked (1993) -961 Orlando (1993) -962 Ruby in Paradise (1993) -963 Some Folks Call It a Sling Blade (1993) -964 Month by the Lake, A (1995) -965 Funny Face (1957) -966 Affair to Remember, An (1957) -967 Little Lord Fauntleroy (1936) -968 Inspector General, The (1949) -969 Winnie the Pooh and the Blustery Day (1968) -970 Hear My Song (1991) -971 Mediterraneo (1991) -972 Passion Fish (1992) -973 Grateful Dead (1995) -974 Eye for an Eye (1996) -975 Fear (1996) -976 Solo (1996) -977 Substitute, The (1996) -978 Heaven's Prisoners (1996) -979 Trigger Effect, The (1996) -980 Mother Night (1996) -981 Dangerous Ground (1997) -982 Maximum Risk (1996) -983 Rich Man's Wife, The (1996) -984 Shadow Conspiracy (1997) -985 Blood & Wine (1997) -986 Turbulence (1997) -987 Underworld (1997) -988 Beautician and the Beast, The (1997) -989 Cats Don't Dance (1997) -990 Anna Karenina (1997) -991 Keys to Tulsa (1997) -992 Head Above Water (1996) -993 Hercules (1997) -994 Last Time I Committed Suicide, The (1997) -995 Kiss Me, Guido (1997) -996 Big Green, The (1995) -997 Stuart Saves His Family (1995) -998 Cabin Boy (1994) -999 Clean Slate (1994) -1000 Lightning Jack (1994) -1001 Stupids, The (1996) -1002 Pest, The (1997) -1003 That Darn Cat! (1997) -1004 Geronimo: An American Legend (1993) -1005 Double vie de Vronique, La (Double Life of Veronique, The) (1991) -1006 Until the End of the World (Bis ans Ende der Welt) (1991) -1007 Waiting for Guffman (1996) -1008 I Shot Andy Warhol (1996) -1009 Stealing Beauty (1996) -1010 Basquiat (1996) -1011 2 Days in the Valley (1996) -1012 Private Parts (1997) -1013 Anaconda (1997) -1014 Romy and Michele's High School Reunion (1997) -1015 Shiloh (1997) -1016 Con Air (1997) -1017 Trees Lounge (1996) -1018 Tie Me Up! Tie Me Down! (1990) -1019 Die xue shuang xiong (Killer, The) (1989) -1020 Gaslight (1944) -1021 8 1/2 (1963) -1022 Fast, Cheap & Out of Control (1997) -1023 Fathers' Day (1997) -1024 Mrs. Dalloway (1997) -1025 Fire Down Below (1997) -1026 Lay of the Land, The (1997) -1027 Shooter, The (1995) -1028 Grumpier Old Men (1995) -1029 Jury Duty (1995) -1030 Beverly Hillbillies, The (1993) -1031 Lassie (1994) -1032 Little Big League (1994) -1033 Homeward Bound II: Lost in San Francisco (1996) -1034 Quest, The (1996) -1035 Cool Runnings (1993) -1036 Drop Dead Fred (1991) -1037 Grease 2 (1982) -1038 Switchback (1997) -1039 Hamlet (1996) -1040 Two if by Sea (1996) -1041 Forget Paris (1995) -1042 Just Cause (1995) -1043 Rent-a-Kid (1995) -1044 Paper, The (1994) -1045 Fearless (1993) -1046 Malice (1993) -1047 Multiplicity (1996) -1048 She's the One (1996) -1049 House Arrest (1996) -1050 Ghost and Mrs. Muir, The (1947) -1051 Associate, The (1996) -1052 Dracula: Dead and Loving It (1995) -1053 Now and Then (1995) -1054 Mr. Wrong (1996) -1055 Simple Twist of Fate, A (1994) -1056 Cronos (1992) -1057 Pallbearer, The (1996) -1058 War, The (1994) -1059 Don't Be a Menace to South Central While Drinking Your Juice in the Hood (1996) -1060 Adventures of Pinocchio, The (1996) -1061 Evening Star, The (1996) -1062 Four Days in September (1997) -1063 Little Princess, A (1995) -1064 Crossfire (1947) -1065 Koyaanisqatsi (1983) -1066 Balto (1995) -1067 Bottle Rocket (1996) -1068 Star Maker, The (Uomo delle stelle, L') (1995) -1069 Amateur (1994) -1070 Living in Oblivion (1995) -1071 Party Girl (1995) -1072 Pyromaniac's Love Story, A (1995) -1073 Shallow Grave (1994) -1074 Reality Bites (1994) -1075 Man of No Importance, A (1994) -1076 Pagemaster, The (1994) -1077 Love and a .45 (1994) -1078 Oliver & Company (1988) -1079 Joe's Apartment (1996) -1080 Celestial Clockwork (1994) -1081 Curdled (1996) -1082 Female Perversions (1996) -1083 Albino Alligator (1996) -1084 Anne Frank Remembered (1995) -1085 Carried Away (1996) -1086 It's My Party (1995) -1087 Bloodsport 2 (1995) -1088 Double Team (1997) -1089 Speed 2: Cruise Control (1997) -1090 Sliver (1993) -1091 Pete's Dragon (1977) -1092 Dear God (1996) -1093 Live Nude Girls (1995) -1094 Thin Line Between Love and Hate, A (1996) -1095 High School High (1996) -1096 Commandments (1997) -1097 Hate (Haine, La) (1995) -1098 Flirting With Disaster (1996) -1099 Red Firecracker, Green Firecracker (1994) -1100 What Happened Was... (1994) -1101 Six Degrees of Separation (1993) -1102 Two Much (1996) -1103 Trust (1990) -1104 C'est arriv prs de chez vous (1992) -1105 Firestorm (1998) -1106 Newton Boys, The (1998) -1107 Beyond Rangoon (1995) -1108 Feast of July (1995) -1109 Death and the Maiden (1994) -1110 Tank Girl (1995) -1111 Double Happiness (1994) -1112 Cobb (1994) -1113 Mrs. Parker and the Vicious Circle (1994) -1114 Faithful (1996) -1115 Twelfth Night (1996) -1116 Mark of Zorro, The (1940) -1117 Surviving Picasso (1996) -1118 Up in Smoke (1978) -1119 Some Kind of Wonderful (1987) -1120 I'm Not Rappaport (1996) -1121 Umbrellas of Cherbourg, The (Parapluies de Cherbourg, Les) (1964) -1122 They Made Me a Criminal (1939) -1123 Last Time I Saw Paris, The (1954) -1124 Farewell to Arms, A (1932) -1125 Innocents, The (1961) -1126 Old Man and the Sea, The (1958) -1127 Truman Show, The (1998) -1128 Heidi Fleiss: Hollywood Madam (1995) -1129 Chungking Express (1994) -1130 Jupiter's Wife (1994) -1131 Safe (1995) -1132 Feeling Minnesota (1996) -1133 Escape to Witch Mountain (1975) -1134 Get on the Bus (1996) -1135 Doors, The (1991) -1136 Ghosts of Mississippi (1996) -1137 Beautiful Thing (1996) -1138 Best Men (1997) -1139 Hackers (1995) -1140 Road to Wellville, The (1994) -1141 War Room, The (1993) -1142 When We Were Kings (1996) -1143 Hard Eight (1996) -1144 Quiet Room, The (1996) -1145 Blue Chips (1994) -1146 Calendar Girl (1993) -1147 My Family (1995) -1148 Tom & Viv (1994) -1149 Walkabout (1971) -1150 Last Dance (1996) -1151 Original Gangstas (1996) -1152 In Love and War (1996) -1153 Backbeat (1993) -1154 Alphaville (1965) -1155 Rendezvous in Paris (Rendez-vous de Paris, Les) (1995) -1156 Cyclo (1995) -1157 Relic, The (1997) -1158 Fille seule, La (A Single Girl) (1995) -1159 Stalker (1979) -1160 Love! Valour! Compassion! (1997) -1161 Palookaville (1996) -1162 Phat Beach (1996) -1163 Portrait of a Lady, The (1996) -1164 Zeus and Roxanne (1997) -1165 Big Bully (1996) -1166 Love & Human Remains (1993) -1167 Sum of Us, The (1994) -1168 Little Buddha (1993) -1169 Fresh (1994) -1170 Spanking the Monkey (1994) -1171 Wild Reeds (1994) -1172 Women, The (1939) -1173 Bliss (1997) -1174 Caught (1996) -1175 Hugo Pool (1997) -1176 Welcome To Sarajevo (1997) -1177 Dunston Checks In (1996) -1178 Major Payne (1994) -1179 Man of the House (1995) -1180 I Love Trouble (1994) -1181 Low Down Dirty Shame, A (1994) -1182 Cops and Robbersons (1994) -1183 Cowboy Way, The (1994) -1184 Endless Summer 2, The (1994) -1185 In the Army Now (1994) -1186 Inkwell, The (1994) -1187 Switchblade Sisters (1975) -1188 Young Guns II (1990) -1189 Prefontaine (1997) -1190 That Old Feeling (1997) -1191 Letter From Death Row, A (1998) -1192 Boys of St. Vincent, The (1993) -1193 Before the Rain (Pred dozhdot) (1994) -1194 Once Were Warriors (1994) -1195 Strawberry and Chocolate (Fresa y chocolate) (1993) -1196 Savage Nights (Nuits fauves, Les) (1992) -1197 Family Thing, A (1996) -1198 Purple Noon (1960) -1199 Cemetery Man (Dellamorte Dellamore) (1994) -1200 Kim (1950) -1201 Marlene Dietrich: Shadow and Light (1996) -1202 Maybe, Maybe Not (Bewegte Mann, Der) (1994) -1203 Top Hat (1935) -1204 To Be or Not to Be (1942) -1205 Secret Agent, The (1996) -1206 Amos & Andrew (1993) -1207 Jade (1995) -1208 Kiss of Death (1995) -1209 Mixed Nuts (1994) -1210 Virtuosity (1995) -1211 Blue Sky (1994) -1212 Flesh and Bone (1993) -1213 Guilty as Sin (1993) -1214 In the Realm of the Senses (Ai no corrida) (1976) -1215 Barb Wire (1996) -1216 Kissed (1996) -1217 Assassins (1995) -1218 Friday (1995) -1219 Goofy Movie, A (1995) -1220 Higher Learning (1995) -1221 When a Man Loves a Woman (1994) -1222 Judgment Night (1993) -1223 King of the Hill (1993) -1224 Scout, The (1994) -1225 Angus (1995) -1226 Night Falls on Manhattan (1997) -1227 Awfully Big Adventure, An (1995) -1228 Under Siege 2: Dark Territory (1995) -1229 Poison Ivy II (1995) -1230 Ready to Wear (Pret-A-Porter) (1994) -1231 Marked for Death (1990) -1232 Madonna: Truth or Dare (1991) -1233 Nnette et Boni (1996) -1234 Chairman of the Board (1998) -1235 Big Bang Theory, The (1994) -1236 Other Voices, Other Rooms (1997) -1237 Twisted (1996) -1238 Full Speed (1996) -1239 Cutthroat Island (1995) -1240 Ghost in the Shell (Kokaku kidotai) (1995) -1241 Van, The (1996) -1242 Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991) -1243 Night Flier (1997) -1244 Metro (1997) -1245 Gridlock'd (1997) -1246 Bushwhacked (1995) -1247 Bad Girls (1994) -1248 Blink (1994) -1249 For Love or Money (1993) -1250 Best of the Best 3: No Turning Back (1995) -1251 A Chef in Love (1996) -1252 Contempt (Mpris, Le) (1963) -1253 Tie That Binds, The (1995) -1254 Gone Fishin' (1997) -1255 Broken English (1996) -1256 Designated Mourner, The (1997) -1257 Designated Mourner, The (1997) -1258 Trial and Error (1997) -1259 Pie in the Sky (1995) -1260 Total Eclipse (1995) -1261 Run of the Country, The (1995) -1262 Walking and Talking (1996) -1263 Foxfire (1996) -1264 Nothing to Lose (1994) -1265 Star Maps (1997) -1266 Bread and Chocolate (Pane e cioccolata) (1973) -1267 Clockers (1995) -1268 Bitter Moon (1992) -1269 Love in the Afternoon (1957) -1270 Life with Mikey (1993) -1271 North (1994) -1272 Talking About Sex (1994) -1273 Color of Night (1994) -1274 Robocop 3 (1993) -1275 Killer (Bulletproof Heart) (1994) -1276 Sunset Park (1996) -1277 Set It Off (1996) -1278 Selena (1997) -1279 Wild America (1997) -1280 Gang Related (1997) -1281 Manny & Lo (1996) -1282 Grass Harp, The (1995) -1283 Out to Sea (1997) -1284 Before and After (1996) -1285 Princess Caraboo (1994) -1286 Shall We Dance? (1937) -1287 Ed (1996) -1288 Denise Calls Up (1995) -1289 Jack and Sarah (1995) -1290 Country Life (1994) -1291 Celtic Pride (1996) -1292 Simple Wish, A (1997) -1293 Star Kid (1997) -1294 Ayn Rand: A Sense of Life (1997) -1295 Kicked in the Head (1997) -1296 Indian Summer (1996) -1297 Love Affair (1994) -1298 Band Wagon, The (1953) -1299 Penny Serenade (1941) -1300 'Til There Was You (1997) -1301 Stripes (1981) -1302 Late Bloomers (1996) -1303 Getaway, The (1994) -1304 New York Cop (1996) -1305 National Lampoon's Senior Trip (1995) -1306 Delta of Venus (1994) -1307 Carmen Miranda: Bananas Is My Business (1994) -1308 Babyfever (1994) -1309 Very Natural Thing, A (1974) -1310 Walk in the Sun, A (1945) -1311 Waiting to Exhale (1995) -1312 Pompatus of Love, The (1996) -1313 Palmetto (1998) -1314 Surviving the Game (1994) -1315 Inventing the Abbotts (1997) -1316 Horse Whisperer, The (1998) -1317 Journey of August King, The (1995) -1318 Catwalk (1995) -1319 Neon Bible, The (1995) -1320 Homage (1995) -1321 Open Season (1996) -1322 Metisse (Caf au Lait) (1993) -1323 Wooden Man's Bride, The (Wu Kui) (1994) -1324 Loaded (1994) -1325 August (1996) -1326 Boys (1996) -1327 Captives (1994) -1328 Of Love and Shadows (1994) -1329 Low Life, The (1994) -1330 An Unforgettable Summer (1994) -1331 Last Klezmer: Leopold Kozlowski, His Life and Music, The (1995) -1332 My Life and Times With Antonin Artaud (En compagnie d'Antonin Artaud) (1993) -1333 Midnight Dancers (Sibak) (1994) -1334 Somebody to Love (1994) -1335 American Buffalo (1996) -1336 Kazaam (1996) -1337 Larger Than Life (1996) -1338 Two Deaths (1995) -1339 Stefano Quantestorie (1993) -1340 Crude Oasis, The (1995) -1341 Hedd Wyn (1992) -1342 Convent, The (Convento, O) (1995) -1343 Lotto Land (1995) -1344 Story of Xinghua, The (1993) -1345 Day the Sun Turned Cold, The (Tianguo niezi) (1994) -1346 Dingo (1992) -1347 Ballad of Narayama, The (Narayama Bushiko) (1958) -1348 Every Other Weekend (1990) -1349 Mille bolle blu (1993) -1350 Crows and Sparrows (1949) -1351 Lover's Knot (1996) -1352 Shadow of Angels (Schatten der Engel) (1976) -1353 1-900 (1994) -1354 Venice/Venice (1992) -1355 Infinity (1996) -1356 Ed's Next Move (1996) -1357 For the Moment (1994) -1358 The Deadly Cure (1996) -1359 Boys in Venice (1996) -1360 Sexual Life of the Belgians, The (1994) -1361 Search for One-eye Jimmy, The (1996) -1362 American Strays (1996) -1363 Leopard Son, The (1996) -1364 Bird of Prey (1996) -1365 Johnny 100 Pesos (1993) -1366 JLG/JLG - autoportrait de dcembre (1994) -1367 Faust (1994) -1368 Mina Tannenbaum (1994) -1369 Forbidden Christ, The (Cristo proibito, Il) (1950) -1370 I Can't Sleep (J'ai pas sommeil) (1994) -1371 Machine, The (1994) -1372 Stranger, The (1994) -1373 Good Morning (1971) -1374 Falling in Love Again (1980) -1375 Cement Garden, The (1993) -1376 Meet Wally Sparks (1997) -1377 Hotel de Love (1996) -1378 Rhyme & Reason (1997) -1379 Love and Other Catastrophes (1996) -1380 Hollow Reed (1996) -1381 Losing Chase (1996) -1382 Bonheur, Le (1965) -1383 Second Jungle Book: Mowgli & Baloo, The (1997) -1384 Squeeze (1996) -1385 Roseanna's Grave (For Roseanna) (1997) -1386 Tetsuo II: Body Hammer (1992) -1387 Fall (1997) -1388 Gabbeh (1996) -1389 Mondo (1996) -1390 Innocent Sleep, The (1995) -1391 For Ever Mozart (1996) -1392 Locusts, The (1997) -1393 Stag (1997) -1394 Swept from the Sea (1997) -1395 Hurricane Streets (1998) -1396 Stonewall (1995) -1397 Of Human Bondage (1934) -1398 Anna (1996) -1399 Stranger in the House (1997) -1400 Picture Bride (1995) -1401 M. Butterfly (1993) -1402 Ciao, Professore! (1993) -1403 Caro Diario (Dear Diary) (1994) -1404 Withnail and I (1987) -1405 Boy's Life 2 (1997) -1406 When Night Is Falling (1995) -1407 Specialist, The (1994) -1408 Gordy (1995) -1409 Swan Princess, The (1994) -1410 Harlem (1993) -1411 Barbarella (1968) -1412 Land Before Time III: The Time of the Great Giving (1995) (V) -1413 Street Fighter (1994) -1414 Coldblooded (1995) -1415 Next Karate Kid, The (1994) -1416 No Escape (1994) -1417 Turning, The (1992) -1418 Joy Luck Club, The (1993) -1419 Highlander III: The Sorcerer (1994) -1420 Gilligan's Island: The Movie (1998) -1421 My Crazy Life (Mi vida loca) (1993) -1422 Suture (1993) -1423 Walking Dead, The (1995) -1424 I Like It Like That (1994) -1425 I'll Do Anything (1994) -1426 Grace of My Heart (1996) -1427 Drunks (1995) -1428 SubUrbia (1997) -1429 Sliding Doors (1998) -1430 Ill Gotten Gains (1997) -1431 Legal Deceit (1997) -1432 Mighty, The (1998) -1433 Men of Means (1998) -1434 Shooting Fish (1997) -1435 Steal Big, Steal Little (1995) -1436 Mr. Jones (1993) -1437 House Party 3 (1994) -1438 Panther (1995) -1439 Jason's Lyric (1994) -1440 Above the Rim (1994) -1441 Moonlight and Valentino (1995) -1442 Scarlet Letter, The (1995) -1443 8 Seconds (1994) -1444 That Darn Cat! (1965) -1445 Ladybird Ladybird (1994) -1446 Bye Bye, Love (1995) -1447 Century (1993) -1448 My Favorite Season (1993) -1449 Pather Panchali (1955) -1450 Golden Earrings (1947) -1451 Foreign Correspondent (1940) -1452 Lady of Burlesque (1943) -1453 Angel on My Shoulder (1946) -1454 Angel and the Badman (1947) -1455 Outlaw, The (1943) -1456 Beat the Devil (1954) -1457 Love Is All There Is (1996) -1458 Damsel in Distress, A (1937) -1459 Madame Butterfly (1995) -1460 Sleepover (1995) -1461 Here Comes Cookie (1935) -1462 Thieves (Voleurs, Les) (1996) -1463 Boys, Les (1997) -1464 Stars Fell on Henrietta, The (1995) -1465 Last Summer in the Hamptons (1995) -1466 Margaret's Museum (1995) -1467 Saint of Fort Washington, The (1993) -1468 Cure, The (1995) -1469 Tom and Huck (1995) -1470 Gumby: The Movie (1995) -1471 Hideaway (1995) -1472 Visitors, The (Visiteurs, Les) (1993) -1473 Little Princess, The (1939) -1474 Nina Takes a Lover (1994) -1475 Bhaji on the Beach (1993) -1476 Raw Deal (1948) -1477 Nightwatch (1997) -1478 Dead Presidents (1995) -1479 Reckless (1995) -1480 Herbie Rides Again (1974) -1481 S.F.W. (1994) -1482 Gate of Heavenly Peace, The (1995) -1483 Man in the Iron Mask, The (1998) -1484 Jerky Boys, The (1994) -1485 Colonel Chabert, Le (1994) -1486 Girl in the Cadillac (1995) -1487 Even Cowgirls Get the Blues (1993) -1488 Germinal (1993) -1489 Chasers (1994) -1490 Fausto (1993) -1491 Tough and Deadly (1995) -1492 Window to Paris (1994) -1493 Modern Affair, A (1995) -1494 Mostro, Il (1994) -1495 Flirt (1995) -1496 Carpool (1996) -1497 Line King: Al Hirschfeld, The (1996) -1498 Farmer & Chase (1995) -1499 Grosse Fatigue (1994) -1500 Santa with Muscles (1996) -1501 Prisoner of the Mountains (Kavkazsky Plennik) (1996) -1502 Naked in New York (1994) -1503 Gold Diggers: The Secret of Bear Mountain (1995) -1504 Bewegte Mann, Der (1994) -1505 Killer: A Journal of Murder (1995) -1506 Nelly & Monsieur Arnaud (1995) -1507 Three Lives and Only One Death (1996) -1508 Babysitter, The (1995) -1509 Getting Even with Dad (1994) -1510 Mad Dog Time (1996) -1511 Children of the Revolution (1996) -1512 World of Apu, The (Apur Sansar) (1959) -1513 Sprung (1997) -1514 Dream With the Fishes (1997) -1515 Wings of Courage (1995) -1516 Wedding Gift, The (1994) -1517 Race the Sun (1996) -1518 Losing Isaiah (1995) -1519 New Jersey Drive (1995) -1520 Fear, The (1995) -1521 Mr. Wonderful (1993) -1522 Trial by Jury (1994) -1523 Good Man in Africa, A (1994) -1524 Kaspar Hauser (1993) -1525 Object of My Affection, The (1998) -1526 Witness (1985) -1527 Senseless (1998) -1528 Nowhere (1997) -1529 Underground (1995) -1530 Jefferson in Paris (1995) -1531 Far From Home: The Adventures of Yellow Dog (1995) -1532 Foreign Student (1994) -1533 I Don't Want to Talk About It (De eso no se habla) (1993) -1534 Twin Town (1997) -1535 Enfer, L' (1994) -1536 Aiqing wansui (1994) -1537 Cosi (1996) -1538 All Over Me (1997) -1539 Being Human (1993) -1540 Amazing Panda Adventure, The (1995) -1541 Beans of Egypt, Maine, The (1994) -1542 Scarlet Letter, The (1926) -1543 Johns (1996) -1544 It Takes Two (1995) -1545 Frankie Starlight (1995) -1546 Shadows (Cienie) (1988) -1547 Show, The (1995) -1548 The Courtyard (1995) -1549 Dream Man (1995) -1550 Destiny Turns on the Radio (1995) -1551 Glass Shield, The (1994) -1552 Hunted, The (1995) -1553 Underneath, The (1995) -1554 Safe Passage (1994) -1555 Secret Adventures of Tom Thumb, The (1993) -1556 Condition Red (1995) -1557 Yankee Zulu (1994) -1558 Aparajito (1956) -1559 Hostile Intentions (1994) -1560 Clean Slate (Coup de Torchon) (1981) -1561 Tigrero: A Film That Was Never Made (1994) -1562 Eye of Vichy, The (Oeil de Vichy, L') (1993) -1563 Promise, The (Versprechen, Das) (1994) -1564 To Cross the Rubicon (1991) -1565 Daens (1992) -1566 Man from Down Under, The (1943) -1567 Careful (1992) -1568 Vermont Is For Lovers (1992) -1569 Vie est belle, La (Life is Rosey) (1987) -1570 Quartier Mozart (1992) -1571 Touki Bouki (Journey of the Hyena) (1973) -1572 Wend Kuuni (God's Gift) (1982) -1573 Spirits of the Dead (Tre passi nel delirio) (1968) -1574 Pharaoh's Army (1995) -1575 I, Worst of All (Yo, la peor de todas) (1990) -1576 Hungarian Fairy Tale, A (1987) -1577 Death in the Garden (Mort en ce jardin, La) (1956) -1578 Collectionneuse, La (1967) -1579 Baton Rouge (1988) -1580 Liebelei (1933) -1581 Woman in Question, The (1950) -1582 T-Men (1947) -1583 Invitation, The (Zaproszenie) (1986) -1584 Symphonie pastorale, La (1946) -1585 American Dream (1990) -1586 Lashou shentan (1992) -1587 Terror in a Texas Town (1958) -1588 Salut cousin! (1996) -1589 Schizopolis (1996) -1590 To Have, or Not (1995) -1591 Duoluo tianshi (1995) -1592 Magic Hour, The (1998) -1593 Death in Brunswick (1991) -1594 Everest (1998) -1595 Shopping (1994) -1596 Nemesis 2: Nebula (1995) -1597 Romper Stomper (1992) -1598 City of Industry (1997) -1599 Someone Else's America (1995) -1600 Guantanamera (1994) -1601 Office Killer (1997) -1602 Price Above Rubies, A (1998) -1603 Angela (1995) -1604 He Walked by Night (1948) -1605 Love Serenade (1996) -1606 Deceiver (1997) -1607 Hurricane Streets (1998) -1608 Buddy (1997) -1609 B*A*P*S (1997) -1610 Truth or Consequences, N.M. (1997) -1611 Intimate Relations (1996) -1612 Leading Man, The (1996) -1613 Tokyo Fist (1995) -1614 Reluctant Debutante, The (1958) -1615 Warriors of Virtue (1997) -1616 Desert Winds (1995) -1617 Hugo Pool (1997) -1618 King of New York (1990) -1619 All Things Fair (1996) -1620 Sixth Man, The (1997) -1621 Butterfly Kiss (1995) -1622 Paris, France (1993) -1623 Crmonie, La (1995) -1624 Hush (1998) -1625 Nightwatch (1997) -1626 Nobody Loves Me (Keiner liebt mich) (1994) -1627 Wife, The (1995) -1628 Lamerica (1994) -1629 Nico Icon (1995) -1630 Silence of the Palace, The (Saimt el Qusur) (1994) -1631 Slingshot, The (1993) -1632 Land and Freedom (Tierra y libertad) (1995) -1633 kldum klaka (Cold Fever) (1994) -1634 Etz Hadomim Tafus (Under the Domin Tree) (1994) -1635 Two Friends (1986) -1636 Brothers in Trouble (1995) -1637 Girls Town (1996) -1638 Normal Life (1996) -1639 Bitter Sugar (Azucar Amargo) (1996) -1640 Eighth Day, The (1996) -1641 Dadetown (1995) -1642 Some Mother's Son (1996) -1643 Angel Baby (1995) -1644 Sudden Manhattan (1996) -1645 Butcher Boy, The (1998) -1646 Men With Guns (1997) -1647 Hana-bi (1997) -1648 Niagara, Niagara (1997) -1649 Big One, The (1997) -1650 Butcher Boy, The (1998) -1651 Spanish Prisoner, The (1997) -1652 Temptress Moon (Feng Yue) (1996) -1653 Entertaining Angels: The Dorothy Day Story (1996) -1654 Chairman of the Board (1998) -1655 Favor, The (1994) -1656 Little City (1998) -1657 Target (1995) -1658 Substance of Fire, The (1996) -1659 Getting Away With Murder (1996) -1660 Small Faces (1995) -1661 New Age, The (1994) -1662 Rough Magic (1995) -1663 Nothing Personal (1995) -1664 8 Heads in a Duffel Bag (1997) -1665 Brother's Kiss, A (1997) -1666 Ripe (1996) -1667 Next Step, The (1995) -1668 Wedding Bell Blues (1996) -1669 MURDER and murder (1996) -1670 Tainted (1998) -1671 Further Gesture, A (1996) -1672 Kika (1993) -1673 Mirage (1995) -1674 Mamma Roma (1962) -1675 Sunchaser, The (1996) -1676 War at Home, The (1996) -1677 Sweet Nothing (1995) -1678 Mat' i syn (1997) -1679 B. Monkey (1998) -1680 Sliding Doors (1998) -1681 You So Crazy (1994) -1682 Scream of Stone (Schrei aus Stein) (1991) diff --git a/assignments/machine-learning-ex8/ex8/multivariateGaussian.m b/assignments/machine-learning-ex8/ex8/multivariateGaussian.m deleted file mode 100644 index a677b2e..0000000 --- a/assignments/machine-learning-ex8/ex8/multivariateGaussian.m +++ /dev/null @@ -1,22 +0,0 @@ -function p = multivariateGaussian(X, mu, Sigma2) -%MULTIVARIATEGAUSSIAN Computes the probability density function of the -%multivariate gaussian distribution. -% p = MULTIVARIATEGAUSSIAN(X, mu, Sigma2) Computes the probability -% density function of the examples X under the multivariate gaussian -% distribution with parameters mu and Sigma2. If Sigma2 is a matrix, it is -% treated as the covariance matrix. If Sigma2 is a vector, it is treated -% as the \sigma^2 values of the variances in each dimension (a diagonal -% covariance matrix) -% - -k = length(mu); - -if (size(Sigma2, 2) == 1) || (size(Sigma2, 1) == 1) - Sigma2 = diag(Sigma2); -end - -X = bsxfun(@minus, X, mu(:)'); -p = (2 * pi) ^ (- k / 2) * det(Sigma2) ^ (-0.5) * ... - exp(-0.5 * sum(bsxfun(@times, X * pinv(Sigma2), X), 2)); - -end \ No newline at end of file diff --git a/assignments/machine-learning-ex8/ex8/normalizeRatings.m b/assignments/machine-learning-ex8/ex8/normalizeRatings.m deleted file mode 100644 index d4e0940..0000000 --- a/assignments/machine-learning-ex8/ex8/normalizeRatings.m +++ /dev/null @@ -1,17 +0,0 @@ -function [Ynorm, Ymean] = normalizeRatings(Y, R) -%NORMALIZERATINGS Preprocess data by subtracting mean rating for every -%movie (every row) -% [Ynorm, Ymean] = NORMALIZERATINGS(Y, R) normalized Y so that each movie -% has a rating of 0 on average, and returns the mean rating in Ymean. -% - -[m, n] = size(Y); -Ymean = zeros(m, 1); -Ynorm = zeros(size(Y)); -for i = 1:m - idx = find(R(i, :) == 1); - Ymean(i) = mean(Y(i, idx)); - Ynorm(i, idx) = Y(i, idx) - Ymean(i); -end - -end diff --git a/assignments/machine-learning-ex8/ex8/octave-workspace b/assignments/machine-learning-ex8/ex8/octave-workspace deleted file mode 100644 index 0e7cd0b..0000000 Binary files a/assignments/machine-learning-ex8/ex8/octave-workspace and /dev/null differ diff --git a/assignments/machine-learning-ex8/ex8/selectThreshold.m b/assignments/machine-learning-ex8/ex8/selectThreshold.m deleted file mode 100644 index 7d8a6f0..0000000 --- a/assignments/machine-learning-ex8/ex8/selectThreshold.m +++ /dev/null @@ -1,40 +0,0 @@ -function [bestEpsilon bestF1] = selectThreshold(yval, pval) -%SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting -%outliers -% [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best -% threshold to use for selecting outliers based on the results from a -% validation set (pval) and the ground truth (yval). -% - -bestEpsilon = 0; -bestF1 = 0; -F1 = 0; - -stepsize = (max(pval) - min(pval)) / 1000; -for epsilon = min(pval):stepsize:max(pval) - - % ====================== YOUR CODE HERE ====================== - % Instructions: Compute the F1 score of choosing epsilon as the - % threshold and place the value in F1. The code at the - % end of the loop will compare the F1 score for this - % choice of epsilon and set it to be the best epsilon if - % it is better than the current choice of epsilon. - % - % Note: You can use predictions = (pval < epsilon) to get a binary vector - % of 0's and 1's of the outlier predictions - predictions = (pval < epsilon); - tp = sum((predictions == 1) & (yval == 1)); - fp = sum((predictions == 1) & (yval == 0)); - fn = sum((predictions == 0) & (yval == 1)); - tn = sum((predictions == 0) & (yval == 0)); - F1 = (2 * (tp / (tp + fp)) * (tp / (tp + fn))) / ((tp / (tp + fp)) + (tp / (tp + fn))); - - % ============================================================= - - if F1 > bestF1 - bestF1 = F1; - bestEpsilon = epsilon; - end -end - -end diff --git a/assignments/machine-learning-ex8/ex8/submit.m b/assignments/machine-learning-ex8/ex8/submit.m deleted file mode 100644 index 57195e8..0000000 --- a/assignments/machine-learning-ex8/ex8/submit.m +++ /dev/null @@ -1,77 +0,0 @@ -function submit() - addpath('./lib'); - - conf.assignmentSlug = 'anomaly-detection-and-recommender-systems'; - conf.itemName = 'Anomaly Detection and Recommender Systems'; - conf.partArrays = { ... - { ... - '1', ... - { 'estimateGaussian.m' }, ... - 'Estimate Gaussian Parameters', ... - }, ... - { ... - '2', ... - { 'selectThreshold.m' }, ... - 'Select Threshold', ... - }, ... - { ... - '3', ... - { 'cofiCostFunc.m' }, ... - 'Collaborative Filtering Cost', ... - }, ... - { ... - '4', ... - { 'cofiCostFunc.m' }, ... - 'Collaborative Filtering Gradient', ... - }, ... - { ... - '5', ... - { 'cofiCostFunc.m' }, ... - 'Regularized Cost', ... - }, ... - { ... - '6', ... - { 'cofiCostFunc.m' }, ... - 'Regularized Gradient', ... - }, ... - }; - conf.output = @output; - - submitWithConfiguration(conf); -end - -function out = output(partId, auxstring) - % Random Test Cases - n_u = 3; n_m = 4; n = 5; - X = reshape(sin(1:n_m*n), n_m, n); - Theta = reshape(cos(1:n_u*n), n_u, n); - Y = reshape(sin(1:2:2*n_m*n_u), n_m, n_u); - R = Y > 0.5; - pval = [abs(Y(:)) ; 0.001; 1]; - Y = (Y .* double(R)); % set 'Y' values to 0 for movies not reviewed - yval = [R(:) ; 1; 0]; - params = [X(:); Theta(:)]; - if partId == '1' - [mu sigma2] = estimateGaussian(X); - out = sprintf('%0.5f ', [mu(:); sigma2(:)]); - elseif partId == '2' - [bestEpsilon bestF1] = selectThreshold(yval, pval); - out = sprintf('%0.5f ', [bestEpsilon(:); bestF1(:)]); - elseif partId == '3' - [J] = cofiCostFunc(params, Y, R, n_u, n_m, ... - n, 0); - out = sprintf('%0.5f ', J(:)); - elseif partId == '4' - [J, grad] = cofiCostFunc(params, Y, R, n_u, n_m, ... - n, 0); - out = sprintf('%0.5f ', grad(:)); - elseif partId == '5' - [J] = cofiCostFunc(params, Y, R, n_u, n_m, ... - n, 1.5); - out = sprintf('%0.5f ', J(:)); - elseif partId == '6' - [J, grad] = cofiCostFunc(params, Y, R, n_u, n_m, ... - n, 1.5); - out = sprintf('%0.5f ', grad(:)); - end -end diff --git a/assignments/machine-learning-ex8/ex8/token.mat b/assignments/machine-learning-ex8/ex8/token.mat deleted file mode 100644 index 476e503..0000000 --- a/assignments/machine-learning-ex8/ex8/token.mat +++ /dev/null @@ -1,15 +0,0 @@ -# Created by Octave 4.2.0, Wed Feb 22 22:30:47 2017 GMT -# name: email -# type: sq_string -# elements: 1 -# length: 17 -scruel@vip.qq.com - - -# name: token -# type: sq_string -# elements: 1 -# length: 16 -pyhIoNiWprdXxRDX - - diff --git a/assignments/machine-learning-ex8/ex8/visualizeFit.m b/assignments/machine-learning-ex8/ex8/visualizeFit.m deleted file mode 100644 index 8917a1e..0000000 --- a/assignments/machine-learning-ex8/ex8/visualizeFit.m +++ /dev/null @@ -1,20 +0,0 @@ -function visualizeFit(X, mu, sigma2) -%VISUALIZEFIT Visualize the dataset and its estimated distribution. -% VISUALIZEFIT(X, p, mu, sigma2) This visualization shows you the -% probability density function of the Gaussian distribution. Each example -% has a location (x1, x2) that depends on its feature values. -% - -[X1,X2] = meshgrid(0:.5:35); -Z = multivariateGaussian([X1(:) X2(:)],mu,sigma2); -Z = reshape(Z,size(X1)); - -plot(X(:, 1), X(:, 2),'bx'); -hold on; -% Do not plot if there are infinities -if (sum(isinf(Z)) == 0) - contour(X1, X2, Z, 10.^(-20:3:0)'); -end -hold off; - -end \ No newline at end of file