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- %% 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);
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