diff --git a/0_numpy_matplotlib_scipy_sympy/matplotlib_ani1.ipynb b/0_numpy_matplotlib_scipy_sympy/matplotlib_ani1.ipynb index 836badb..e28df2b 100644 --- a/0_numpy_matplotlib_scipy_sympy/matplotlib_ani1.ipynb +++ b/0_numpy_matplotlib_scipy_sympy/matplotlib_ani1.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -523,7 +523,7 @@ "" ] }, - "execution_count": 4, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -567,8 +567,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" - }, - "main_language": "python" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/0_numpy_matplotlib_scipy_sympy/matplotlib_ani2.ipynb b/0_numpy_matplotlib_scipy_sympy/matplotlib_ani2.ipynb index 8d1b71c..13c4513 100644 --- a/0_numpy_matplotlib_scipy_sympy/matplotlib_ani2.ipynb +++ b/0_numpy_matplotlib_scipy_sympy/matplotlib_ani2.ipynb @@ -562,7 +562,7 @@ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", - "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", + "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", @@ -788,7 +788,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" diff --git a/0_numpy_matplotlib_scipy_sympy/notebook.tex b/0_numpy_matplotlib_scipy_sympy/notebook.tex new file mode 100644 index 0000000..028bbd0 --- /dev/null +++ b/0_numpy_matplotlib_scipy_sympy/notebook.tex @@ -0,0 +1,322 @@ + +% Default to the notebook output style + + + + +% Inherit from the specified cell style. + + + + + +\documentclass[11pt]{article} + + + + \usepackage[T1]{fontenc} + % Nicer default font (+ math font) than Computer Modern for most use cases + \usepackage{mathpazo} + + % Basic figure setup, for now with no caption control since it's done + % automatically by Pandoc (which extracts ![](path) syntax from Markdown). + \usepackage{graphicx} + % We will generate all images so they have a width \maxwidth. This means + % that they will get their normal width if they fit onto the page, but + % are scaled down if they would overflow the margins. + \makeatletter + \def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth + \else\Gin@nat@width\fi} + \makeatother + \let\Oldincludegraphics\includegraphics + % Set max figure width to be 80% of text width, for now hardcoded. + \renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=.8\maxwidth]{#1}} + % Ensure that by default, figures have no caption (until we provide a + % proper Figure object with a Caption API and a way to capture that + % in the conversion process - todo). + \usepackage{caption} + \DeclareCaptionLabelFormat{nolabel}{} + \captionsetup{labelformat=nolabel} + + \usepackage{adjustbox} % Used to constrain images to a maximum size + \usepackage{xcolor} % Allow colors to be defined + \usepackage{enumerate} % Needed for markdown enumerations to work + \usepackage{geometry} % Used to adjust the document margins + \usepackage{amsmath} % Equations + \usepackage{amssymb} % Equations + \usepackage{textcomp} % defines textquotesingle + % Hack from http://tex.stackexchange.com/a/47451/13684: + \AtBeginDocument{% + \def\PYZsq{\textquotesingle}% Upright quotes in Pygmentized code + } + \usepackage{upquote} % Upright quotes for verbatim code + \usepackage{eurosym} % defines \euro + \usepackage[mathletters]{ucs} % Extended unicode (utf-8) support + \usepackage[utf8x]{inputenc} % Allow utf-8 characters in the tex document + \usepackage{fancyvrb} % verbatim replacement that allows latex + \usepackage{grffile} % extends the file name processing of package graphics + % to support a larger range + % The hyperref package gives us a pdf with properly built + % internal navigation ('pdf bookmarks' for the table of contents, + % internal cross-reference links, web links for URLs, etc.) + \usepackage{hyperref} + \usepackage{longtable} % longtable support required by pandoc >1.10 + \usepackage{booktabs} % table support for pandoc > 1.12.2 + \usepackage[inline]{enumitem} % IRkernel/repr support (it uses the enumerate* environment) + \usepackage[normalem]{ulem} % ulem is needed to support strikethroughs (\sout) + % normalem makes italics be italics, not underlines + + + + + % Colors for the hyperref package + \definecolor{urlcolor}{rgb}{0,.145,.698} + \definecolor{linkcolor}{rgb}{.71,0.21,0.01} + \definecolor{citecolor}{rgb}{.12,.54,.11} + + % ANSI colors + \definecolor{ansi-black}{HTML}{3E424D} + \definecolor{ansi-black-intense}{HTML}{282C36} + \definecolor{ansi-red}{HTML}{E75C58} + \definecolor{ansi-red-intense}{HTML}{B22B31} + \definecolor{ansi-green}{HTML}{00A250} + \definecolor{ansi-green-intense}{HTML}{007427} + \definecolor{ansi-yellow}{HTML}{DDB62B} + \definecolor{ansi-yellow-intense}{HTML}{B27D12} + \definecolor{ansi-blue}{HTML}{208FFB} + \definecolor{ansi-blue-intense}{HTML}{0065CA} + \definecolor{ansi-magenta}{HTML}{D160C4} + \definecolor{ansi-magenta-intense}{HTML}{A03196} + \definecolor{ansi-cyan}{HTML}{60C6C8} + \definecolor{ansi-cyan-intense}{HTML}{258F8F} + \definecolor{ansi-white}{HTML}{C5C1B4} + \definecolor{ansi-white-intense}{HTML}{A1A6B2} + + % commands and environments needed by pandoc snippets + % extracted from the output of `pandoc -s` + \providecommand{\tightlist}{% + \setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}} + \DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}} + % Add ',fontsize=\small' for more characters per line + \newenvironment{Shaded}{}{} + \newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}} + \newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}} + \newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}} + \newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}} + \newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}} + \newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}} + \newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}} + \newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}} + \newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}} + \newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}} + \newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}} + \newcommand{\RegionMarkerTok}[1]{{#1}} + \newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}} + \newcommand{\NormalTok}[1]{{#1}} + + % Additional commands for more recent versions of Pandoc + \newcommand{\ConstantTok}[1]{\textcolor[rgb]{0.53,0.00,0.00}{{#1}}} + \newcommand{\SpecialCharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}} + \newcommand{\VerbatimStringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}} + \newcommand{\SpecialStringTok}[1]{\textcolor[rgb]{0.73,0.40,0.53}{{#1}}} + \newcommand{\ImportTok}[1]{{#1}} + \newcommand{\DocumentationTok}[1]{\textcolor[rgb]{0.73,0.13,0.13}{\textit{{#1}}}} + \newcommand{\AnnotationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}} + \newcommand{\CommentVarTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}} + \newcommand{\VariableTok}[1]{\textcolor[rgb]{0.10,0.09,0.49}{{#1}}} + \newcommand{\ControlFlowTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}} + \newcommand{\OperatorTok}[1]{\textcolor[rgb]{0.40,0.40,0.40}{{#1}}} + \newcommand{\BuiltInTok}[1]{{#1}} + \newcommand{\ExtensionTok}[1]{{#1}} + \newcommand{\PreprocessorTok}[1]{\textcolor[rgb]{0.74,0.48,0.00}{{#1}}} + \newcommand{\AttributeTok}[1]{\textcolor[rgb]{0.49,0.56,0.16}{{#1}}} + \newcommand{\InformationTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}} + \newcommand{\WarningTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textbf{\textit{{#1}}}}} + + + % Define a nice break command that doesn't care if a line doesn't already + % exist. + \def\br{\hspace*{\fill} \\* } + % Math Jax compatability definitions + \def\gt{>} + \def\lt{<} + % Document parameters + \title{matplotlib\_ani2} + + + + + % Pygments definitions + +\makeatletter +\def\PY@reset{\let\PY@it=\relax \let\PY@bf=\relax% + \let\PY@ul=\relax \let\PY@tc=\relax% + \let\PY@bc=\relax \let\PY@ff=\relax} +\def\PY@tok#1{\csname PY@tok@#1\endcsname} +\def\PY@toks#1+{\ifx\relax#1\empty\else% + \PY@tok{#1}\expandafter\PY@toks\fi} +\def\PY@do#1{\PY@bc{\PY@tc{\PY@ul{% + \PY@it{\PY@bf{\PY@ff{#1}}}}}}} +\def\PY#1#2{\PY@reset\PY@toks#1+\relax+\PY@do{#2}} + +\expandafter\def\csname PY@tok@gi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.63,0.00}{##1}}} +\expandafter\def\csname PY@tok@ni\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.60,0.60,0.60}{##1}}} +\expandafter\def\csname PY@tok@ow\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.67,0.13,1.00}{##1}}} +\expandafter\def\csname PY@tok@dl\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} +\expandafter\def\csname PY@tok@vi\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}} 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PY@tok@s\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} +\expandafter\def\csname PY@tok@fm\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}} +\expandafter\def\csname PY@tok@c\endcsname{\let\PY@it=\textit\def\PY@tc##1{\textcolor[rgb]{0.25,0.50,0.50}{##1}}} +\expandafter\def\csname PY@tok@kd\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} +\expandafter\def\csname PY@tok@ge\endcsname{\let\PY@it=\textit} +\expandafter\def\csname PY@tok@kr\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} +\expandafter\def\csname PY@tok@vg\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.10,0.09,0.49}{##1}}} +\expandafter\def\csname PY@tok@go\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.53,0.53}{##1}}} +\expandafter\def\csname PY@tok@no\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.53,0.00,0.00}{##1}}} +\expandafter\def\csname PY@tok@o\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.40,0.40,0.40}{##1}}} +\expandafter\def\csname PY@tok@kc\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.50,0.00}{##1}}} +\expandafter\def\csname PY@tok@gs\endcsname{\let\PY@bf=\textbf} +\expandafter\def\csname PY@tok@sr\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.40,0.53}{##1}}} +\expandafter\def\csname PY@tok@nn\endcsname{\let\PY@bf=\textbf\def\PY@tc##1{\textcolor[rgb]{0.00,0.00,1.00}{##1}}} +\expandafter\def\csname PY@tok@s2\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.73,0.13,0.13}{##1}}} +\expandafter\def\csname PY@tok@kt\endcsname{\def\PY@tc##1{\textcolor[rgb]{0.69,0.00,0.25}{##1}}} + +\def\PYZbs{\char`\\} +\def\PYZus{\char`\_} +\def\PYZob{\char`\{} +\def\PYZcb{\char`\}} +\def\PYZca{\char`\^} +\def\PYZam{\char`\&} +\def\PYZlt{\char`\<} +\def\PYZgt{\char`\>} +\def\PYZsh{\char`\#} +\def\PYZpc{\char`\%} +\def\PYZdl{\char`\$} +\def\PYZhy{\char`\-} +\def\PYZsq{\char`\'} +\def\PYZdq{\char`\"} +\def\PYZti{\char`\~} +% for compatibility with earlier versions +\def\PYZat{@} +\def\PYZlb{[} +\def\PYZrb{]} +\makeatother + + + % Exact colors from NB + \definecolor{incolor}{rgb}{0.0, 0.0, 0.5} + \definecolor{outcolor}{rgb}{0.545, 0.0, 0.0} + + + + + % Prevent overflowing lines due to hard-to-break entities + \sloppy + % Setup hyperref package + \hypersetup{ + breaklinks=true, % so long urls are correctly broken across lines + colorlinks=true, + urlcolor=urlcolor, + linkcolor=linkcolor, + citecolor=citecolor, + } + % Slightly bigger margins than the latex defaults + + \geometry{verbose,tmargin=1in,bmargin=1in,lmargin=1in,rmargin=1in} + + + + \begin{document} + + + \maketitle + + + + + \begin{Verbatim}[commandchars=\\\{\}] +{\color{incolor}In [{\color{incolor}1}]:} \PY{c+c1}{\PYZsh{}\PYZpc{}matplotlib nbagg} + \PY{o}{\PYZpc{}}\PY{k}{matplotlib} nbagg + + \PY{k+kn}{import} \PY{n+nn}{numpy} \PY{k}{as} \PY{n+nn}{np} + \PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{pyplot} \PY{k}{as} \PY{n+nn}{plt} + \PY{k+kn}{import} \PY{n+nn}{matplotlib}\PY{n+nn}{.}\PY{n+nn}{animation} \PY{k}{as} \PY{n+nn}{animation} + + \PY{n}{fig} \PY{o}{=} \PY{n}{plt}\PY{o}{.}\PY{n}{figure}\PY{p}{(}\PY{p}{)} + \PY{n}{x} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{arange}\PY{p}{(}\PY{l+m+mi}{0}\PY{p}{,} \PY{l+m+mi}{10}\PY{p}{,} \PY{l+m+mf}{0.1}\PY{p}{)} + + \PY{n}{ims} \PY{o}{=} \PY{p}{[}\PY{p}{]} + \PY{k}{for} \PY{n}{a} \PY{o+ow}{in} \PY{n+nb}{range}\PY{p}{(}\PY{l+m+mi}{50}\PY{p}{)}\PY{p}{:} + \PY{n}{y} \PY{o}{=} \PY{n}{np}\PY{o}{.}\PY{n}{sin}\PY{p}{(}\PY{n}{x} \PY{o}{\PYZhy{}} \PY{n}{a}\PY{p}{)} + \PY{n}{im} \PY{o}{=} \PY{n}{plt}\PY{o}{.}\PY{n}{plot}\PY{p}{(}\PY{n}{x}\PY{p}{,} \PY{n}{y}\PY{p}{,} \PY{l+s+s2}{\PYZdq{}}\PY{l+s+s2}{r}\PY{l+s+s2}{\PYZdq{}}\PY{p}{)} + \PY{n}{ims}\PY{o}{.}\PY{n}{append}\PY{p}{(}\PY{n}{im}\PY{p}{)} + + \PY{n}{ani} \PY{o}{=} \PY{n}{animation}\PY{o}{.}\PY{n}{ArtistAnimation}\PY{p}{(}\PY{n}{fig}\PY{p}{,} \PY{n}{ims}\PY{p}{)} + \PY{n}{plt}\PY{o}{.}\PY{n}{show}\PY{p}{(}\PY{p}{)} +\end{Verbatim} + + + + \begin{verbatim} + + \end{verbatim} + + + + \begin{verbatim} + + \end{verbatim} + + + + % Add a bibliography block to the postdoc + + + + \end{document} diff --git a/1_kmeans/README.md b/1_kmeans/README.md new file mode 100644 index 0000000..5ce535c --- /dev/null +++ b/1_kmeans/README.md @@ -0,0 +1,4 @@ + + +## References +* [如何使用 Keras 实现无监督聚类](http://m.sohu.com/a/236221126_717210) diff --git a/1_knn/knn_classification.ipynb b/1_knn/knn_classification.ipynb index 1e0266b..ecaa9eb 100644 --- a/1_knn/knn_classification.ipynb +++ b/1_knn/knn_classification.ipynb @@ -41,6 +41,169 @@ }, { "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import operator\n", + "\n", + "class KNN(object):\n", + "\n", + " def __init__(self, k=3):\n", + " self.k = k\n", + "\n", + " def fit(self, x, y):\n", + " self.x = x\n", + " self.y = y\n", + "\n", + " def _square_distance(self, v1, v2):\n", + " return np.sum(np.square(v1-v2))\n", + "\n", + " def _vote(self, ys):\n", + " ys_unique = np.unique(ys)\n", + " vote_dict = {}\n", + " for y in ys:\n", + " if y not in vote_dict.keys():\n", + " vote_dict[y] = 1\n", + " else:\n", + " vote_dict[y] += 1\n", + " sorted_vote_dict = sorted(vote_dict.items(), key=operator.itemgetter(1), reverse=True)\n", + " return sorted_vote_dict[0][0]\n", + "\n", + " def predict(self, x):\n", + " y_pred = []\n", + " for i in range(len(x)):\n", + " dist_arr = [self._square_distance(x[i], self.x[j]) for j in range(len(self.x))]\n", + " sorted_index = np.argsort(dist_arr)\n", + " top_k_index = sorted_index[:self.k]\n", + " y_pred.append(self._vote(ys=self.y[top_k_index]))\n", + " return np.array(y_pred)\n", + "\n", + " def score(self, y_true=None, y_pred=None):\n", + " if y_true is None and y_pred is None:\n", + " y_pred = self.predict(self.x)\n", + " y_true = self.y\n", + " score = 0.0\n", + " for i in range(len(y_true)):\n", + " if y_true[i] == y_pred[i]:\n", + " score += 1\n", + " score /= len(y_true)\n", + " return score" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# data generation\n", + "np.random.seed(314)\n", + "data_size_1 = 300\n", + "x1_1 = np.random.normal(loc=5.0, scale=1.0, size=data_size_1)\n", + "x2_1 = np.random.normal(loc=4.0, scale=1.0, size=data_size_1)\n", + "y_1 = [0 for _ in range(data_size_1)]\n", + "\n", + "data_size_2 = 400\n", + "x1_2 = np.random.normal(loc=10.0, scale=2.0, size=data_size_2)\n", + "x2_2 = np.random.normal(loc=8.0, scale=2.0, size=data_size_2)\n", + "y_2 = [1 for _ in range(data_size_2)]\n", + "\n", + "x1 = np.concatenate((x1_1, x1_2), axis=0)\n", + "x2 = np.concatenate((x2_1, x2_2), axis=0)\n", + "x = np.hstack((x1.reshape(-1,1), x2.reshape(-1,1)))\n", + "y = np.concatenate((y_1, y_2), axis=0)\n", + "\n", + "data_size_all = data_size_1+data_size_2\n", + "shuffled_index = np.random.permutation(data_size_all)\n", + "x = x[shuffled_index]\n", + "y = y[shuffled_index]\n", + "\n", + "split_index = int(data_size_all*0.7)\n", + "x_train = x[:split_index]\n", + "y_train = y[:split_index]\n", + "x_test = x[split_index:]\n", + "y_test = y[split_index:]\n", + "\n", + "# visualize data\n", + "plt.scatter(x_train[:,0], x_train[:,1], c=y_train, marker='.')\n", + "plt.title(\"train data\")\n", + "plt.show()\n", + "plt.scatter(x_test[:,0], x_test[:,1], c=y_test, marker='.')\n", + "plt.title(\"test data\")\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "train accuracy: 0.986\n", + "test accuracy: 0.957\n" + ] + } + ], + "source": [ + "# data preprocessing\n", + "x_train = (x_train - np.min(x_train, axis=0)) / (np.max(x_train, axis=0) - np.min(x_train, axis=0))\n", + "x_test = (x_test - np.min(x_test, axis=0)) / (np.max(x_test, axis=0) - np.min(x_test, axis=0))\n", + "\n", + "# knn classifier\n", + "clf = KNN(k=3)\n", + "clf.fit(x_train, y_train)\n", + "\n", + "print('train accuracy: {:.3}'.format(clf.score()))\n", + "\n", + "y_test_pred = clf.predict(x_test)\n", + "print('test accuracy: {:.3}'.format(clf.score(y_test, y_test_pred)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## sklearn program" + ] + }, + { + "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ @@ -141,7 +304,8 @@ "metadata": {}, "source": [ "## References\n", - "* [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html)\n" + "* [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html)\n", + "* [knn算法的原理与实现](https://zhuanlan.zhihu.com/p/36549000)" ] } ], diff --git a/1_knn/knn_classification.py b/1_knn/knn_classification.py index df44fe4..5ef04bf 100644 --- a/1_knn/knn_classification.py +++ b/1_knn/knn_classification.py @@ -48,6 +48,114 @@ # ## Program # + +import numpy as np +import operator + +class KNN(object): + + def __init__(self, k=3): + self.k = k + + def fit(self, x, y): + self.x = x + self.y = y + + def _square_distance(self, v1, v2): + return np.sum(np.square(v1-v2)) + + def _vote(self, ys): + ys_unique = np.unique(ys) + vote_dict = {} + for y in ys: + if y not in vote_dict.keys(): + vote_dict[y] = 1 + else: + vote_dict[y] += 1 + sorted_vote_dict = sorted(vote_dict.items(), key=operator.itemgetter(1), reverse=True) + return sorted_vote_dict[0][0] + + def predict(self, x): + y_pred = [] + for i in range(len(x)): + dist_arr = [self._square_distance(x[i], self.x[j]) for j in range(len(self.x))] + sorted_index = np.argsort(dist_arr) + top_k_index = sorted_index[:self.k] + y_pred.append(self._vote(ys=self.y[top_k_index])) + return np.array(y_pred) + + def score(self, y_true=None, y_pred=None): + if y_true is None and y_pred is None: + y_pred = self.predict(self.x) + y_true = self.y + score = 0.0 + for i in range(len(y_true)): + if y_true[i] == y_pred[i]: + score += 1 + score /= len(y_true) + return score + +# + +# %matplotlib inline + +import numpy as np +import matplotlib.pyplot as plt + +# data generation +np.random.seed(314) +data_size_1 = 300 +x1_1 = np.random.normal(loc=5.0, scale=1.0, size=data_size_1) +x2_1 = np.random.normal(loc=4.0, scale=1.0, size=data_size_1) +y_1 = [0 for _ in range(data_size_1)] + +data_size_2 = 400 +x1_2 = np.random.normal(loc=10.0, scale=2.0, size=data_size_2) +x2_2 = np.random.normal(loc=8.0, scale=2.0, size=data_size_2) +y_2 = [1 for _ in range(data_size_2)] + +x1 = np.concatenate((x1_1, x1_2), axis=0) +x2 = np.concatenate((x2_1, x2_2), axis=0) +x = np.hstack((x1.reshape(-1,1), x2.reshape(-1,1))) +y = np.concatenate((y_1, y_2), axis=0) + +data_size_all = data_size_1+data_size_2 +shuffled_index = np.random.permutation(data_size_all) +x = x[shuffled_index] +y = y[shuffled_index] + +split_index = int(data_size_all*0.7) +x_train = x[:split_index] +y_train = y[:split_index] +x_test = x[split_index:] +y_test = y[split_index:] + +# visualize data +plt.scatter(x_train[:,0], x_train[:,1], c=y_train, marker='.') +plt.title("train data") +plt.show() +plt.scatter(x_test[:,0], x_test[:,1], c=y_test, marker='.') +plt.title("test data") +plt.show() + + + +# + +# data preprocessing +x_train = (x_train - np.min(x_train, axis=0)) / (np.max(x_train, axis=0) - np.min(x_train, axis=0)) +x_test = (x_test - np.min(x_test, axis=0)) / (np.max(x_test, axis=0) - np.min(x_test, axis=0)) + +# knn classifier +clf = KNN(k=3) +clf.fit(x_train, y_train) + +print('train accuracy: {:.3}'.format(clf.score())) + +y_test_pred = clf.predict(x_test) +print('test accuracy: {:.3}'.format(clf.score(y_test, y_test_pred))) +# - + +# ## sklearn program + +# + % matplotlib inline import matplotlib.pyplot as plt @@ -95,4 +203,4 @@ print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_te # ## References # * [Digits Classification Exercise](http://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html) -# +# * [knn算法的原理与实现](https://zhuanlan.zhihu.com/p/36549000) diff --git a/dataset_circle.csv b/dataset_circle.csv new file mode 100644 index 0000000..c7d9f8f --- /dev/null +++ b/dataset_circle.csv @@ -0,0 +1,400 @@ +-4.998874451622919324e+00,4.727671430051504586e+00,0.000000000000000000e+00 +3.280980164418858092e+00,1.135719744099867690e+01,0.000000000000000000e+00 +-3.989307577792735593e+00,-7.472125124436091781e+00,0.000000000000000000e+00 +-2.845588117474840750e+00,-1.110207598677712149e+01,0.000000000000000000e+00 +-4.736524057786282604e+00,-9.232347813516641466e+00,0.000000000000000000e+00 +-2.997596049424991360e+00,1.045323111011670036e+01,0.000000000000000000e+00 +-7.808569372236178197e+00,6.841137640119772101e+00,0.000000000000000000e+00 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+-1.846386149004602117e+01,-1.188235505038806572e+01,1.000000000000000000e+00 +1.990171207746382009e+01,-1.313517862727534080e+01,1.000000000000000000e+00 diff --git a/nn/Perceptron.ipynb b/nn/Perceptron.ipynb index cf4c788..e5cd598 100644 --- a/nn/Perceptron.ipynb +++ b/nn/Perceptron.ipynb @@ -141,23 +141,11 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "update weight and bias: 1.5 4.0 0.5\n", - "update weight and bias: -0.5 3.5 0.0\n", - "update weight and bias: -2.0 3.0 -0.5\n", - "w = [-2.0, 3.0]\n", - "b = -0.5\n", - "[ 1 1 1 1 -1 -1 -1 -1]\n", - "[1, 1, 1, 1, -1, -1, -1, -1]\n" - ] - } - ], + "execution_count": null, + "metadata": { + "lines_to_end_of_cell_marker": 2 + }, + "outputs": [], "source": [ "import random\n", "import numpy as np\n", @@ -212,8 +200,7 @@ "y_pred = perceptron_pred(train_data, w, b)\n", "\n", "print(train_data[:, 2])\n", - "print(y_pred)\n", - "\n" + "print(y_pred)" ] }, { @@ -244,8 +231,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" - }, - "main_language": "python" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/nn/mlp_bp.ipynb b/nn/mlp_bp.ipynb index 930eee7..d5776a4 100644 --- a/nn/mlp_bp.ipynb +++ b/nn/mlp_bp.ipynb @@ -14,6 +14,7 @@ "## 神经元\n", "\n", "神经元和感知器本质上是一样的，只不过我们说感知器的时候，它的激活函数是阶跃函数；而当我们说神经元时，激活函数往往选择为sigmoid函数或tanh函数。如下图所示：\n", + "\n", "![neuron](images/neuron.gif)\n", "\n", "计算一个神经元的输出的方法和计算一个感知器的输出是一样的。假设神经元的输入是向量$\\vec{x}$，权重向量是$\\vec{w}$(偏置项是$w_0$)，激活函数是sigmoid函数，则其输出y：\n", @@ -31,13 +32,15 @@ "$$\n", "\n", "sigmoid函数是一个非线性函数，值域是(0,1)。函数图像如下图所示\n", + "\n", "![sigmod_function](images/sigmod.jpg)\n", "\n", "sigmoid函数的导数是：\n", - "$$\n", - "y = sigmod(x) \\ \\ \\ \\ \\ \\ (1) \\\\\n", - "y' = y(1-y)\n", - "$$\n", + "\\begin{eqnarray}\n", + "y & = & sigmod(x) \\tag{1} \\\\\n", + "y' & = & y(1-y)\n", + "\\end{eqnarray}\n", + "\n", "可以看到，sigmoid函数的导数非常有趣，它可以用sigmoid函数自身来表示。这样，一旦计算出sigmoid函数的值，计算它的导数的值就非常方便。\n", "\n" ] @@ -83,11 +86,13 @@ "为了计算节点4的输出值，我们必须先得到其所有上游节点（也就是节点1、2、3）的输出值。节点1、2、3是输入层的节点，所以，他们的输出值就是输入向量$\\vec{x}$本身。按照上图画出的对应关系，可以看到节点1、2、3的输出值分别是$x_1$,$x_2$,$x_3$。我们要求输入向量的维度和输入层神经元个数相同，而输入向量的某个元素对应到哪个输入节点是可以自由决定的，你偏非要把$x_1$赋值给节点2也是完全没有问题的，但这样除了把自己弄晕之外，并没有什么价值。\n", "\n", "一旦我们有了节点1、2、3的输出值，我们就可以根据式1计算节点4的输出值$a_4$：\n", + "\n", "![eqn_3_4](images/eqn_3_4.png)\n", "\n", "上式的$w_{4b}$是节点4的偏置项，图中没有画出来。而$w_{41}$,$w_{42}$,$w_{43}$分别为节点1、2、3到节点4连接的权重，在给权重$w_{ji}$编号时，我们把目标节点的编号$j$放在前面，把源节点的编号$i$放在后面。\n", "\n", "同样，我们可以继续计算出节点5、6、7的输出值$a_5$,$a_6$,$a_7$。这样，隐藏层的4个节点的输出值就计算完成了，我们就可以接着计算输出层的节点8的输出值$y_1$：\n", + "\n", "![eqn_5_6](images/eqn_5_6.png)\n", "\n", "同理，我们还可以计算出$y_2$的值。这样输出层所有节点的输出值计算完毕，我们就得到了在输入向量$\\vec{x} = (x_1, x_2, x_3)^T$时，神经网络的输出向量$\\vec{y} = (y_1, y_2)^T$。这里我们也看到，输出向量的维度和输出层神经元个数相同。\n", @@ -103,6 +108,7 @@ "神经网络的计算如果用矩阵来表示会很方便（当然逼格也更高），我们先来看看隐藏层的矩阵表示。\n", "\n", "首先我们把隐藏层4个节点的计算依次排列出来：\n", + "\n", "![eqn_hidden_units](images/eqn_hidden_units.png)\n", "\n", "接着，定义网络的输入向量$\\vec{x}$和隐藏层每个节点的权重向量$\\vec{w}$。令\n", @@ -114,17 +120,21 @@ "![eqn_13_16](images/eqn_13_16.png)\n", "\n", "现在，我们把上述计算$a_4$, $a_5$,$a_6$,$a_7$的四个式子写到一个矩阵里面，每个式子作为矩阵的一行，就可以利用矩阵来表示它们的计算了。令\n", + "\n", "![eqn_matrix1](images/eqn_matrix1.png)\n", "\n", "带入前面的一组式子，得到\n", + "\n", "![formular_2](images/formular_2.png)\n", "\n", "在式2中，$f$是激活函数，在本例中是$sigmod$函数；$W$是某一层的权重矩阵；$\\vec{x}$是某层的输入向量；$\\vec{a}$是某层的输出向量。式2说明神经网络的每一层的作用实际上就是先将输入向量左乘一个数组进行线性变换，得到一个新的向量，然后再对这个向量逐元素应用一个激活函数。\n", "\n", "每一层的算法都是一样的。比如，对于包含一个输入层，一个输出层和三个隐藏层的神经网络，我们假设其权重矩阵分别为$W_1$,$W_2$,$W_3$,$W_4$，每个隐藏层的输出分别是$\\vec{a}_1$,$\\vec{a}_2$,$\\vec{a}_3$，神经网络的输入为$\\vec{x}$，神经网络的输出为$\\vec{y}$，如下图所示：\n", + "\n", "![nn_parameters_demo](images/nn_parameters_demo.png)\n", "\n", "则每一层的输出向量的计算可以表示为：\n", + "\n", "![eqn_17_20](images/eqn_17_20.png)\n", "\n", "\n", @@ -146,17 +156,21 @@ "按照机器学习的通用套路，我们先确定神经网络的目标函数，然后用随机梯度下降优化算法去求目标函数最小值时的参数值。\n", "\n", "我们取网络所有输出层节点的误差平方和作为目标函数：\n", + "\n", "![bp_loss](images/bp_loss.png)\n", "\n", "其中，$E_d$表示是样本$d$的误差。\n", "\n", "然后，使用随机梯度下降算法对目标函数进行优化：\n", + "\n", "![bp_weight_update](images/bp_weight_update.png)\n", "\n", "随机梯度下降算法也就是需要求出误差$E_d$对于每个权重$w_{ji}$的偏导数（也就是梯度），怎么求呢？\n", + "\n", "![nn3](images/nn3.png)\n", "\n", "观察上图，我们发现权重$w_{ji}$仅能通过影响节点$j$的输入值影响网络的其它部分，设$net_j$是节点$j$的加权输入，即\n", + "\n", "![eqn_21_22](images/eqn_21_22.png)\n", "\n", "$E_d$是$net_j$的函数，而$net_j$是$w_{ji}$的函数。根据链式求导法则，可以得到：\n", @@ -179,22 +193,28 @@ "![nn3](images/nn3.png)\n", "\n", "对于输出层来说，$net_j$仅能通过节点$j$的输出值$y_j$来影响网络其它部分，也就是说$E_d$是$y_j$的函数，而$y_j$是$net_j$的函数，其中$y_j = sigmod(net_j)$。所以我们可以再次使用链式求导法则：\n", + "\n", "![eqn_26](images/eqn_26.png)\n", "\n", "考虑上式第一项:\n", + "\n", "![eqn_27_29](images/eqn_27_29.png)\n", "\n", "\n", "考虑上式第二项：\n", + "\n", "![eqn_30_31](images/eqn_30_31.png)\n", "\n", "将第一项和第二项带入，得到：\n", + "\n", "![eqn_ed_net_j.png](images/eqn_ed_net_j.png)\n", "\n", "如果令$\\delta_j = - \\frac{\\partial E_d}{\\partial net_j}$，也就是一个节点的误差项$\\delta$是网络误差对这个节点输入的偏导数的相反数。带入上式，得到：\n", + "\n", "![eqn_delta_j.png](images/eqn_delta_j.png)\n", "\n", "将上述推导带入随机梯度下降公式，得到：\n", + "\n", "![eqn_32_34.png](images/eqn_32_34.png)\n" ] }, @@ -209,9 +229,11 @@ "![nn3](images/nn3.png)\n", "\n", "首先，我们需要定义节点$j$的所有直接下游节点的集合$Downstream(j)$。例如，对于节点4来说，它的直接下游节点是节点8、节点9。可以看到$net_j$只能通过影响$Downstream(j)$再影响$E_d$。设$net_k$是节点$j$的下游节点的输入，则$E_d$是$net_k$的函数，而$net_k$是$net_j$的函数。因为$net_k$有多个，我们应用全导数公式，可以做出如下推导：\n", + "\n", "![eqn_35_40](images/eqn_35_40.png)\n", "\n", "因为$\\delta_j = - \\frac{\\partial E_d}{\\partial net_j}$，带入上式得到：\n", + "\n", "![eqn_delta_hidden.png](images/eqn_delta_hidden.png)\n", "\n", "\n", @@ -233,28 +255,38 @@ "然后，我们按照下面的方法计算出每个节点的误差项$\\delta_i$：\n", "\n", "* **对于输出层节点$i$**\n", + "\n", "![formular_3.png](images/formular_3.png)\n", + "\n", "其中，$\\delta_i$是节点$i$的误差项，$y_i$是节点$i$的输出值，$t_i$是样本对应于节点$i$的目标值。举个例子，根据上图，对于输出层节点8来说，它的输出值是$y_1$，而样本的目标值是$t_1$，带入上面的公式得到节点8的误差项应该是：\n", + "\n", "![forumlar_delta8.png](images/forumlar_delta8.png)\n", "\n", "* **对于隐藏层节点**\n", + "\n", "![formular_4.png](images/formular_4.png)\n", "\n", "其中，$a_i$是节点$i$的输出值，$w_{ki}$是节点$i$到它的下一层节点$k$的连接的权重，$\\delta_k$是节点$i$的下一层节点$k$的误差项。例如，对于隐藏层节点4来说，计算方法如下：\n", + "\n", "![forumlar_delta4.png](images/forumlar_delta4.png)\n", "\n", "\n", + "\n", "最后，更新每个连接上的权值：\n", + "\n", "![formular_5.png](images/formular_5.png)\n", "\n", "其中，$w_{ji}$是节点$i$到节点$j$的权重，$\\eta$是一个成为学习速率的常数，$\\delta_j$是节点$j$的误差项，$x_{ji}$是节点$i$传递给节点$j$的输入。例如，权重$w_{84}$的更新方法如下：\n", + "\n", "![eqn_w84_update.png](images/eqn_w84_update.png)\n", "\n", "类似的，权重$w_{41}$的更新方法如下：\n", + "\n", "![eqn_w41_update.png](images/eqn_w41_update.png)\n", "\n", "\n", "偏置项的输入值永远为1。例如，节点4的偏置项$w_{4b}$应该按照下面的方法计算：\n", + "\n", "![eqn_w4b_update.png](images/eqn_w4b_update.png)\n", "\n", "我们已经介绍了神经网络每个节点误差项的计算和权重更新方法。显然，计算一个节点的误差项，需要先计算每个与其相连的下一层节点的误差项。这就要求误差项的计算顺序必须是从输出层开始，然后反向依次计算每个隐藏层的误差项，直到与输入层相连的那个隐藏层。这就是反向传播算法的名字的含义。当所有节点的误差项计算完毕后，我们就可以根据式5来更新所有的权重。\n", @@ -959,13 +991,7 @@ "epoch [ 578] L = 38.565342, acc = 0.850000\n", "epoch [ 579] L = 38.565098, acc = 0.850000\n", "epoch [ 580] L = 38.564855, acc = 0.850000\n", - "epoch [ 581] L = 38.564613, acc = 0.850000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "epoch [ 581] L = 38.564613, acc = 0.850000\n", "epoch [ 582] L = 38.564371, acc = 0.850000\n", "epoch [ 583] L = 38.564130, acc = 0.850000\n", "epoch [ 584] L = 38.563891, acc = 0.850000\n", @@ -1559,13 +1585,7 @@ "epoch [1172] L = 38.480196, acc = 0.845000\n", "epoch [1173] L = 38.480101, acc = 0.845000\n", "epoch [1174] L = 38.480005, acc = 0.845000\n", - "epoch [1175] L = 38.479909, acc = 0.845000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "epoch [1175] L = 38.479909, acc = 0.845000\n", "epoch [1176] L = 38.479814, acc = 0.845000\n", "epoch [1177] L = 38.479719, acc = 0.845000\n", "epoch [1178] L = 38.479623, acc = 0.845000\n", @@ -2192,13 +2212,7 @@ "epoch [1799] L = 38.432669, acc = 0.845000\n", "epoch [1800] L = 38.432608, acc = 0.845000\n", "epoch [1801] L = 38.432546, acc = 0.845000\n", - "epoch [1802] L = 38.432485, acc = 0.845000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "epoch [1802] L = 38.432485, acc = 0.845000\n", "epoch [1803] L = 38.432423, acc = 0.845000\n", "epoch [1804] L = 38.432362, acc = 0.845000\n", "epoch [1805] L = 38.432301, acc = 0.845000\n", @@ -3229,13 +3243,7 @@ "L = 37.861420, acc = 0.865000\n", "L = 37.857369, acc = 0.865000\n", "L = 37.853295, acc = 0.865000\n", - "L = 37.849197, acc = 0.865000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "L = 37.849197, acc = 0.865000\n", "L = 37.845075, acc = 0.865000\n", "L = 37.840929, acc = 0.865000\n", "L = 37.836759, acc = 0.865000\n", @@ -3846,13 +3854,7 @@ "L = 25.844314, acc = 0.925000\n", "L = 25.817979, acc = 0.925000\n", "L = 25.791672, acc = 0.925000\n", - "L = 25.765391, acc = 0.925000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "L = 25.765391, acc = 0.925000\n", "L = 25.739137, acc = 0.925000\n", "L = 25.712911, acc = 0.930000\n", "L = 25.686712, acc = 0.935000\n", @@ -4467,13 +4469,7 @@ "L = 15.309885, acc = 0.960000\n", "L = 15.301054, acc = 0.960000\n", "L = 15.292240, acc = 0.960000\n", - "L = 15.283445, acc = 0.960000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "L = 15.283445, acc = 0.960000\n", "L = 15.274667, acc = 0.960000\n", "L = 15.265907, acc = 0.960000\n", "L = 15.257165, acc = 0.960000\n", @@ -4761,64 +4757,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Softmax & 交叉熵代价函数\n", - "\n", - "softmax经常被添加在分类任务的神经网络中的输出层，神经网络的反向传播中关键的步骤就是求导，从这个过程也可以更深刻地理解反向传播的过程，还可以对梯度传播的问题有更多的思考。\n", - "\n", - "### softmax 函数\n", - "\n", - "softmax(柔性最大值)函数，一般在神经网络中， softmax可以作为分类任务的输出层。其实可以认为softmax输出的是几个类别选择的概率，比如我有一个分类任务，要分为三个类，softmax函数可以根据它们相对的大小，输出三个类别选取的概率，并且概率和为1。\n", - "\n", - "softmax函数的公式是这种形式：\n", - "![softmax](images/softmax.png)\n", - "\n", - "* $S_i$是经过softmax的类别概率输出\n", - "* $z_k$是神经元的输出\n", - "\n", - "更形象的如下图表示：\n", - "![softmax_demo](images/softmax_demo.png)\n", - "softmax直白来说就是将原来输出是3,1,-3通过softmax函数一作用，就映射成为(0,1)的值，而这些值的累和为1（满足概率的性质），那么我们就可以将它理解成概率，在最后选取输出结点的时候，我们就可以选取概率最大（也就是值对应最大的）结点，作为我们的预测目标！\n", - "\n", - "\n", - "\n", - "首先是神经元的输出，一个神经元如下图：\n", - "![softmax_neuron](images/softmax_neuron.png)\n", - "\n", - "神经元的输出设为：\n", - "![softmax_neuron_output_eqn.png](images/softmax_neuron_output_eqn.png)\n", - "其中$W_{ij}$是第$i$个神经元的第$j$个权重，$b$是偏置。$z_i$表示该网络的第$i$个输出。\n", - "\n", - "给这个输出加上一个softmax函数，那就变成了这样：\n", - "![softmax_neuron_output2_eqn.png](images/softmax_neuron_output2_eqn.png)\n", - "$a_i$代表softmax的第$i$个输出值，右侧套用了softmax函数。\n", - "\n", - "\n", - "### 损失函数 loss function\n", - "\n", - "在神经网络反向传播中，要求一个损失函数，这个损失函数其实表示的是真实值与网络的估计值的误差，知道误差了，才能知道怎样去修改网络中的权重。\n", - "\n", - "损失函数可以有很多形式，这里用的是交叉熵函数，主要是由于这个求导结果比较简单，易于计算，并且交叉熵解决某些损失函数学习缓慢的问题。交叉熵的函数是这样的：\n", - "\n", - "![cross_entropy_loss](images/cross_entropy_loss.png)\n", - "\n", - "其中$y_i$表示真实的分类结果。\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ "## References\n", "* 反向传播算法\n", " * [零基础入门深度学习(3) - 神经网络和反向传播算法](https://www.zybuluo.com/hanbingtao/note/476663)\n", " * [Neural Network Using Python and Numpy](https://www.python-course.eu/neural_networks_with_python_numpy.php)\n", " * http://www.cedar.buffalo.edu/%7Esrihari/CSE574/Chap5/Chap5.3-BackProp.pdf\n", - " * https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/\n", - "* Softmax & 交叉熵\n", - " * [交叉熵代价函数（作用及公式推导）](https://blog.csdn.net/u014313009/article/details/51043064)\n", - " * [手打例子一步一步带你看懂softmax函数以及相关求导过程](https://www.jianshu.com/p/ffa51250ba2e)\n", - " * [简单易懂的softmax交叉熵损失函数求导](https://www.jianshu.com/p/c02a1fbffad6)" + " * https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/\n" ] } ], diff --git a/nn/mlp_bp.py b/nn/mlp_bp.py index 20cd06a..957fdc9 100644 --- a/nn/mlp_bp.py +++ b/nn/mlp_bp.py @@ -24,6 +24,7 @@ # ## 神经元 # # 神经元和感知器本质上是一样的，只不过我们说感知器的时候，它的激活函数是阶跃函数；而当我们说神经元时，激活函数往往选择为sigmoid函数或tanh函数。如下图所示： +# # ![neuron](images/neuron.gif) # # 计算一个神经元的输出的方法和计算一个感知器的输出是一样的。假设神经元的输入是向量$\vec{x}$，权重向量是$\vec{w}$(偏置项是$w_0$)，激活函数是sigmoid函数，则其输出y： @@ -41,13 +42,15 @@ # $$ # # sigmoid函数是一个非线性函数，值域是(0,1)。函数图像如下图所示 +# # ![sigmod_function](images/sigmod.jpg) # # sigmoid函数的导数是： -# $$ -# y = sigmod(x) \ \ \ \ \ \ (1) \\ -# y' = y(1-y) -# $$ +# \begin{eqnarray} +# y & = & sigmod(x) \tag{1} \\ +# y' & = & y(1-y) +# \end{eqnarray} +# # 可以看到，sigmoid函数的导数非常有趣，它可以用sigmoid函数自身来表示。这样，一旦计算出sigmoid函数的值，计算它的导数的值就非常方便。 # # @@ -85,11 +88,13 @@ # 为了计算节点4的输出值，我们必须先得到其所有上游节点（也就是节点1、2、3）的输出值。节点1、2、3是输入层的节点，所以，他们的输出值就是输入向量$\vec{x}$本身。按照上图画出的对应关系，可以看到节点1、2、3的输出值分别是$x_1$,$x_2$,$x_3$。我们要求输入向量的维度和输入层神经元个数相同，而输入向量的某个元素对应到哪个输入节点是可以自由决定的，你偏非要把$x_1$赋值给节点2也是完全没有问题的，但这样除了把自己弄晕之外，并没有什么价值。 # # 一旦我们有了节点1、2、3的输出值，我们就可以根据式1计算节点4的输出值$a_4$： +# # ![eqn_3_4](images/eqn_3_4.png) # # 上式的$w_{4b}$是节点4的偏置项，图中没有画出来。而$w_{41}$,$w_{42}$,$w_{43}$分别为节点1、2、3到节点4连接的权重，在给权重$w_{ji}$编号时，我们把目标节点的编号$j$放在前面，把源节点的编号$i$放在后面。 # # 同样，我们可以继续计算出节点5、6、7的输出值$a_5$,$a_6$,$a_7$。这样，隐藏层的4个节点的输出值就计算完成了，我们就可以接着计算输出层的节点8的输出值$y_1$： +# # ![eqn_5_6](images/eqn_5_6.png) # # 同理，我们还可以计算出$y_2$的值。这样输出层所有节点的输出值计算完毕，我们就得到了在输入向量$\vec{x} = (x_1, x_2, x_3)^T$时，神经网络的输出向量$\vec{y} = (y_1, y_2)^T$。这里我们也看到，输出向量的维度和输出层神经元个数相同。 @@ -101,6 +106,7 @@ # 神经网络的计算如果用矩阵来表示会很方便（当然逼格也更高），我们先来看看隐藏层的矩阵表示。 # # 首先我们把隐藏层4个节点的计算依次排列出来： +# # ![eqn_hidden_units](images/eqn_hidden_units.png) # # 接着，定义网络的输入向量$\vec{x}$和隐藏层每个节点的权重向量$\vec{w}$。令 @@ -112,17 +118,21 @@ # ![eqn_13_16](images/eqn_13_16.png) # # 现在，我们把上述计算$a_4$, $a_5$,$a_6$,$a_7$的四个式子写到一个矩阵里面，每个式子作为矩阵的一行，就可以利用矩阵来表示它们的计算了。令 +# # ![eqn_matrix1](images/eqn_matrix1.png) # # 带入前面的一组式子，得到 +# # ![formular_2](images/formular_2.png) # # 在式2中，$f$是激活函数，在本例中是$sigmod$函数；$W$是某一层的权重矩阵；$\vec{x}$是某层的输入向量；$\vec{a}$是某层的输出向量。式2说明神经网络的每一层的作用实际上就是先将输入向量左乘一个数组进行线性变换，得到一个新的向量，然后再对这个向量逐元素应用一个激活函数。 # # 每一层的算法都是一样的。比如，对于包含一个输入层，一个输出层和三个隐藏层的神经网络，我们假设其权重矩阵分别为$W_1$,$W_2$,$W_3$,$W_4$，每个隐藏层的输出分别是$\vec{a}_1$,$\vec{a}_2$,$\vec{a}_3$，神经网络的输入为$\vec{x}$，神经网络的输出为$\vec{y}$，如下图所示： +# # ![nn_parameters_demo](images/nn_parameters_demo.png) # # 则每一层的输出向量的计算可以表示为： +# # ![eqn_17_20](images/eqn_17_20.png) # # @@ -140,17 +150,21 @@ # 按照机器学习的通用套路，我们先确定神经网络的目标函数，然后用随机梯度下降优化算法去求目标函数最小值时的参数值。 # # 我们取网络所有输出层节点的误差平方和作为目标函数： +# # ![bp_loss](images/bp_loss.png) # # 其中，$E_d$表示是样本$d$的误差。 # # 然后，使用随机梯度下降算法对目标函数进行优化： +# # ![bp_weight_update](images/bp_weight_update.png) # # 随机梯度下降算法也就是需要求出误差$E_d$对于每个权重$w_{ji}$的偏导数（也就是梯度），怎么求呢？ +# # ![nn3](images/nn3.png) # # 观察上图，我们发现权重$w_{ji}$仅能通过影响节点$j$的输入值影响网络的其它部分，设$net_j$是节点$j$的加权输入，即 +# # ![eqn_21_22](images/eqn_21_22.png) # # $E_d$是$net_j$的函数，而$net_j$是$w_{ji}$的函数。根据链式求导法则，可以得到： @@ -169,22 +183,28 @@ # ![nn3](images/nn3.png) # # 对于输出层来说，$net_j$仅能通过节点$j$的输出值$y_j$来影响网络其它部分，也就是说$E_d$是$y_j$的函数，而$y_j$是$net_j$的函数，其中$y_j = sigmod(net_j)$。所以我们可以再次使用链式求导法则： +# # ![eqn_26](images/eqn_26.png) # # 考虑上式第一项: +# # ![eqn_27_29](images/eqn_27_29.png) # # # 考虑上式第二项： +# # ![eqn_30_31](images/eqn_30_31.png) # # 将第一项和第二项带入，得到： +# # ![eqn_ed_net_j.png](images/eqn_ed_net_j.png) # # 如果令$\delta_j = - \frac{\partial E_d}{\partial net_j}$，也就是一个节点的误差项$\delta$是网络误差对这个节点输入的偏导数的相反数。带入上式，得到： +# # ![eqn_delta_j.png](images/eqn_delta_j.png) # # 将上述推导带入随机梯度下降公式，得到： +# # ![eqn_32_34.png](images/eqn_32_34.png) # @@ -195,9 +215,11 @@ # ![nn3](images/nn3.png) # # 首先，我们需要定义节点$j$的所有直接下游节点的集合$Downstream(j)$。例如，对于节点4来说，它的直接下游节点是节点8、节点9。可以看到$net_j$只能通过影响$Downstream(j)$再影响$E_d$。设$net_k$是节点$j$的下游节点的输入，则$E_d$是$net_k$的函数，而$net_k$是$net_j$的函数。因为$net_k$有多个，我们应用全导数公式，可以做出如下推导： +# # ![eqn_35_40](images/eqn_35_40.png) # # 因为$\delta_j = - \frac{\partial E_d}{\partial net_j}$，带入上式得到： +# # ![eqn_delta_hidden.png](images/eqn_delta_hidden.png) # # @@ -215,28 +237,38 @@ # 然后，我们按照下面的方法计算出每个节点的误差项$\delta_i$： # # * **对于输出层节点$i$** +# # ![formular_3.png](images/formular_3.png) +# # 其中，$\delta_i$是节点$i$的误差项，$y_i$是节点$i$的输出值，$t_i$是样本对应于节点$i$的目标值。举个例子，根据上图，对于输出层节点8来说，它的输出值是$y_1$，而样本的目标值是$t_1$，带入上面的公式得到节点8的误差项应该是： +# # ![forumlar_delta8.png](images/forumlar_delta8.png) # # * **对于隐藏层节点** +# # ![formular_4.png](images/formular_4.png) # # 其中，$a_i$是节点$i$的输出值，$w_{ki}$是节点$i$到它的下一层节点$k$的连接的权重，$\delta_k$是节点$i$的下一层节点$k$的误差项。例如，对于隐藏层节点4来说，计算方法如下： +# # ![forumlar_delta4.png](images/forumlar_delta4.png) # # +# # 最后，更新每个连接上的权值： +# # ![formular_5.png](images/formular_5.png) # # 其中，$w_{ji}$是节点$i$到节点$j$的权重，$\eta$是一个成为学习速率的常数，$\delta_j$是节点$j$的误差项，$x_{ji}$是节点$i$传递给节点$j$的输入。例如，权重$w_{84}$的更新方法如下： +# # ![eqn_w84_update.png](images/eqn_w84_update.png) # # 类似的，权重$w_{41}$的更新方法如下： +# # ![eqn_w41_update.png](images/eqn_w41_update.png) # # # 偏置项的输入值永远为1。例如，节点4的偏置项$w_{4b}$应该按照下面的方法计算： +# # ![eqn_w4b_update.png](images/eqn_w4b_update.png) # # 我们已经介绍了神经网络每个节点误差项的计算和权重更新方法。显然，计算一个节点的误差项，需要先计算每个与其相连的下一层节点的误差项。这就要求误差项的计算顺序必须是从输出层开始，然后反向依次计算每个隐藏层的误差项，直到与输入层相连的那个隐藏层。这就是反向传播算法的名字的含义。当所有节点的误差项计算完毕后，我们就可以根据式5来更新所有的权重。 @@ -516,57 +548,10 @@ print(y_res[1:10, :]) # 3. 如何能让神经网络更快的训练好？ # 4. 如何抽象，让神经网络的类支持更多的类型的层 -# ## Softmax & 交叉熵代价函数 -# -# softmax经常被添加在分类任务的神经网络中的输出层，神经网络的反向传播中关键的步骤就是求导，从这个过程也可以更深刻地理解反向传播的过程，还可以对梯度传播的问题有更多的思考。 -# -# ### softmax 函数 -# -# softmax(柔性最大值)函数，一般在神经网络中， softmax可以作为分类任务的输出层。其实可以认为softmax输出的是几个类别选择的概率，比如我有一个分类任务，要分为三个类，softmax函数可以根据它们相对的大小，输出三个类别选取的概率，并且概率和为1。 -# -# softmax函数的公式是这种形式： -# ![softmax](images/softmax.png) -# -# * $S_i$是经过softmax的类别概率输出 -# * $z_k$是神经元的输出 -# -# 更形象的如下图表示： -# ![softmax_demo](images/softmax_demo.png) -# softmax直白来说就是将原来输出是3,1,-3通过softmax函数一作用，就映射成为(0,1)的值，而这些值的累和为1（满足概率的性质），那么我们就可以将它理解成概率，在最后选取输出结点的时候，我们就可以选取概率最大（也就是值对应最大的）结点，作为我们的预测目标！ -# -# -# -# 首先是神经元的输出，一个神经元如下图： -# ![softmax_neuron](images/softmax_neuron.png) -# -# 神经元的输出设为： -# ![softmax_neuron_output_eqn.png](images/softmax_neuron_output_eqn.png) -# 其中$W_{ij}$是第$i$个神经元的第$j$个权重，$b$是偏置。$z_i$表示该网络的第$i$个输出。 -# -# 给这个输出加上一个softmax函数，那就变成了这样： -# ![softmax_neuron_output2_eqn.png](images/softmax_neuron_output2_eqn.png) -# $a_i$代表softmax的第$i$个输出值，右侧套用了softmax函数。 -# -# -# ### 损失函数 loss function -# -# 在神经网络反向传播中，要求一个损失函数，这个损失函数其实表示的是真实值与网络的估计值的误差，知道误差了，才能知道怎样去修改网络中的权重。 -# -# 损失函数可以有很多形式，这里用的是交叉熵函数，主要是由于这个求导结果比较简单，易于计算，并且交叉熵解决某些损失函数学习缓慢的问题。交叉熵的函数是这样的： -# -# ![cross_entropy_loss](images/cross_entropy_loss.png) -# -# 其中$y_i$表示真实的分类结果。 -# -# - # ## References # * 反向传播算法 # * [零基础入门深度学习(3) - 神经网络和反向传播算法](https://www.zybuluo.com/hanbingtao/note/476663) # * [Neural Network Using Python and Numpy](https://www.python-course.eu/neural_networks_with_python_numpy.php) # * http://www.cedar.buffalo.edu/%7Esrihari/CSE574/Chap5/Chap5.3-BackProp.pdf # * https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ -# * Softmax & 交叉熵 -# * [交叉熵代价函数（作用及公式推导）](https://blog.csdn.net/u014313009/article/details/51043064) -# * [手打例子一步一步带你看懂softmax函数以及相关求导过程](https://www.jianshu.com/p/ffa51250ba2e) -# * [简单易懂的softmax交叉熵损失函数求导](https://www.jianshu.com/p/c02a1fbffad6) +# diff --git a/nn/softmax_ce.ipynb b/nn/softmax_ce.ipynb new file mode 100644 index 0000000..cdbd162 --- /dev/null +++ b/nn/softmax_ce.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Softmax & 交叉熵代价函数\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "softmax经常被添加在分类任务的神经网络中的输出层，神经网络的反向传播中关键的步骤就是求导，从这个过程也可以更深刻地理解反向传播的过程，还可以对梯度传播的问题有更多的思考。\n", + "\n", + "## softmax 函数\n", + "\n", + "softmax(柔性最大值)函数，一般在神经网络中， softmax可以作为分类任务的输出层。其实可以认为softmax输出的是几个类别选择的概率，比如我有一个分类任务，要分为三个类，softmax函数可以根据它们相对的大小，输出三个类别选取的概率，并且概率和为1。\n", + "\n", + "softmax函数的公式是这种形式：\n", + "\n", + "$$\n", + "S_i = \\frac{e^{z_i}}{\\sum_k e^{z_k}}\n", + "$$\n", + "\n", + "* $S_i$是经过softmax的类别概率输出\n", + "* $z_k$是神经元的输出\n", + "\n", + "\n", + "更形象的如下图表示：\n", + "\n", + "![softmax_demo](images/softmax_demo.png)\n", + "\n", + "softmax直白来说就是将原来输出是$[3,1,-3]$通过softmax函数一作用，就映射成为(0,1)的值，而这些值的累和为1（满足概率的性质），那么我们就可以将它理解成概率，在最后选取输出结点的时候，我们就可以选取概率最大（也就是值对应最大的）结点，作为我们的预测目标！\n", + "\n", + "\n", + "\n", + "首先是神经元的输出，一个神经元如下图：\n", + "\n", + "![softmax_neuron](images/softmax_neuron.png)\n", + "\n", + "神经元的输出设为：\n", + "\n", + "$$\n", + "z_i = \\sum_{j} w_{ij} x_{j} + b\n", + "$$\n", + "\n", + "其中$W_{ij}$是第$i$个神经元的第$j$个权重，$b$是偏置。$z_i$表示该网络的第$i$个输出。\n", + "\n", + "给这个输出加上一个softmax函数，那就变成了这样：\n", + "\n", + "$$\n", + "a_i = \\frac{e^{z_i}}{\\sum_k e^{z_k}}\n", + "$$\n", + "\n", + "$a_i$代表softmax的第$i$个输出值，右侧套用了softmax函数。\n", + "\n", + "\n", + "### 损失函数 loss function\n", + "\n", + "在神经网络反向传播中，要求一个损失函数，这个损失函数其实表示的是真实值与网络的估计值的误差，知道误差了，才能知道怎样去修改网络中的权重。\n", + "\n", + "损失函数可以有很多形式，这里用的是交叉熵函数，主要是由于这个求导结果比较简单，易于计算，并且交叉熵解决某些损失函数学习缓慢的问题。**[交叉熵函数](https://blog.csdn.net/u014313009/article/details/51043064)**是这样的：\n", + "\n", + "$$\n", + "C = - \\sum_i y_i ln a_i\n", + "$$\n", + "\n", + "其中$y_i$表示真实的分类结果。\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 推导过程\n", + "\n", + "首先，我们要明确一下我们要求什么，我们要求的是我们的$loss$对于神经元输出($z_i$)的梯度，即：\n", + "\n", + "$$\n", + "\\frac{\\partial C}{\\partial z_i}\n", + "$$\n", + "\n", + "根据复合函数求导法则：\n", + "\n", + "$$\n", + "\\frac{\\partial C}{\\partial z_i} = \\frac{\\partial C}{\\partial a_j} \\frac{\\partial a_j}{\\partial z_i}\n", + "$$\n", + "\n", + "有个人可能有疑问了，这里为什么是$a_j$而不是$a_i$，这里要看一下$softmax$的公式了，因为$softmax$公式的特性，它的分母包含了所有神经元的输出，所以，对于不等于i的其他输出里面，也包含着$z_i$，所有的$a$都要纳入到计算范围中，并且后面的计算可以看到需要分为$i = j$和$i \\ne j$两种情况求导。\n", + "\n", + "### 针对$a_j$的偏导\n", + "\n", + "$$\n", + "\\frac{\\partial C}{\\partial a_j} = \\frac{(\\partial -\\sum_j y_j ln a_j)}{\\partial a_j} = -\\sum_j y_j \\frac{1}{a_j}\n", + "$$\n", + "\n", + "### 针对$z_i$的偏导\n", + "\n", + "如果 $i=j$ :\n", + "\n", + "\\begin{eqnarray}\n", + "\\frac{\\partial a_i}{\\partial z_i} & = & \\frac{\\partial (\\frac{e^{z_i}}{\\sum_k e^{z_k}})}{\\partial z_i} \\\\\n", + " & = & \\frac{\\sum_k e^{z_k} e^{z_i} - (e^{z_i})^2}{\\sum_k (e^{z_k})^2} \\\\\n", + " & = & (\\frac{e^{z_i}}{\\sum_k e^{z_k}} ) (1 - \\frac{e^{z_i}}{\\sum_k e^{z_k}} ) \\\\\n", + " & = & a_i (1 - a_i)\n", + "\\end{eqnarray}\n", + "\n", + "如果 $i \\ne j$:\n", + "\\begin{eqnarray}\n", + "\\frac{\\partial a_j}{\\partial z_i} & = & \\frac{\\partial (\\frac{e^{z_j}}{\\sum_k e^{z_k}})}{\\partial z_i} \\\\\n", + " & = & \\frac{0 \\cdot \\sum_k e^{z_k} - e^{z_j} \\cdot e^{z_i} }{(\\sum_k e^{z_k})^2} \\\\\n", + " & = & - \\frac{e^{z_j}}{\\sum_k e^{z_k}} \\cdot \\frac{e^{z_i}}{\\sum_k e^{z_k}} \\\\\n", + " & = & -a_j a_i\n", + "\\end{eqnarray}\n", + "\n", + "当u，v都是变量的函数时的导数推导公式：\n", + "$$\n", + "(\\frac{u}{v})' = \\frac{u'v - uv'}{v^2} \n", + "$$\n", + "\n", + "### 整体的推导\n", + "\n", + "\\begin{eqnarray}\n", + "\\frac{\\partial C}{\\partial z_i} & = & (-\\sum_j y_j \\frac{1}{a_j} ) \\frac{\\partial a_j}{\\partial z_i} \\\\\n", + " & = & - \\frac{y_i}{a_i} a_i ( 1 - a_i) + \\sum_{j \\ne i} \\frac{y_j}{a_j} a_i a_j \\\\\n", + " & = & -y_i + y_i a_i + \\sum_{j \\ne i} y_j a_i \\\\\n", + " & = & -y_i + a_i \\sum_{j} y_j\n", + "\\end{eqnarray}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 问题\n", + "如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的方法中？" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "* Softmax & 交叉熵\n", + " * [交叉熵代价函数（作用及公式推导）](https://blog.csdn.net/u014313009/article/details/51043064)\n", + " * [手打例子一步一步带你看懂softmax函数以及相关求导过程](https://www.jianshu.com/p/ffa51250ba2e)\n", + " * [简单易懂的softmax交叉熵损失函数求导](https://www.jianshu.com/p/c02a1fbffad6)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + }, + "main_language": "python" + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/nn/softmax_ce.py b/nn/softmax_ce.py new file mode 100644 index 0000000..4baf0d2 --- /dev/null +++ b/nn/softmax_ce.py @@ -0,0 +1,146 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext_format_version: '1.2' +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# language_info: +# codemirror_mode: +# name: ipython +# version: 3 +# file_extension: .py +# mimetype: text/x-python +# name: python +# nbconvert_exporter: python +# pygments_lexer: ipython3 +# version: 3.5.2 +# --- + +# # Softmax & 交叉熵代价函数 +# + +# softmax经常被添加在分类任务的神经网络中的输出层，神经网络的反向传播中关键的步骤就是求导，从这个过程也可以更深刻地理解反向传播的过程，还可以对梯度传播的问题有更多的思考。 +# +# ## softmax 函数 +# +# softmax(柔性最大值)函数，一般在神经网络中， softmax可以作为分类任务的输出层。其实可以认为softmax输出的是几个类别选择的概率，比如我有一个分类任务，要分为三个类，softmax函数可以根据它们相对的大小，输出三个类别选取的概率，并且概率和为1。 +# +# softmax函数的公式是这种形式： +# +# $$ +# S_i = \frac{e^{z_i}}{\sum_k e^{z_k}} +# $$ +# +# * $S_i$是经过softmax的类别概率输出 +# * $z_k$是神经元的输出 +# +# +# 更形象的如下图表示： +# +# ![softmax_demo](images/softmax_demo.png) +# +# softmax直白来说就是将原来输出是$[3,1,-3]$通过softmax函数一作用，就映射成为(0,1)的值，而这些值的累和为1（满足概率的性质），那么我们就可以将它理解成概率，在最后选取输出结点的时候，我们就可以选取概率最大（也就是值对应最大的）结点，作为我们的预测目标！ +# +# +# +# 首先是神经元的输出，一个神经元如下图： +# +# ![softmax_neuron](images/softmax_neuron.png) +# +# 神经元的输出设为： +# +# $$ +# z_i = \sum_{j} w_{ij} x_{j} + b +# $$ +# +# 其中$W_{ij}$是第$i$个神经元的第$j$个权重，$b$是偏置。$z_i$表示该网络的第$i$个输出。 +# +# 给这个输出加上一个softmax函数，那就变成了这样： +# +# $$ +# a_i = \frac{e^{z_i}}{\sum_k e^{z_k}} +# $$ +# +# $a_i$代表softmax的第$i$个输出值，右侧套用了softmax函数。 +# +# +# ### 损失函数 loss function +# +# 在神经网络反向传播中，要求一个损失函数，这个损失函数其实表示的是真实值与网络的估计值的误差，知道误差了，才能知道怎样去修改网络中的权重。 +# +# 损失函数可以有很多形式，这里用的是交叉熵函数，主要是由于这个求导结果比较简单，易于计算，并且交叉熵解决某些损失函数学习缓慢的问题。**[交叉熵函数](https://blog.csdn.net/u014313009/article/details/51043064)**是这样的： +# +# $$ +# C = - \sum_i y_i ln a_i +# $$ +# +# 其中$y_i$表示真实的分类结果。 +# +# + +# ## 推导过程 +# +# 首先，我们要明确一下我们要求什么，我们要求的是我们的$loss$对于神经元输出($z_i$)的梯度，即： +# +# $$ +# \frac{\partial C}{\partial z_i} +# $$ +# +# 根据复合函数求导法则： +# +# $$ +# \frac{\partial C}{\partial z_i} = \frac{\partial C}{\partial a_j} \frac{\partial a_j}{\partial z_i} +# $$ +# +# 有个人可能有疑问了，这里为什么是$a_j$而不是$a_i$，这里要看一下$softmax$的公式了，因为$softmax$公式的特性，它的分母包含了所有神经元的输出，所以，对于不等于i的其他输出里面，也包含着$z_i$，所有的$a$都要纳入到计算范围中，并且后面的计算可以看到需要分为$i = j$和$i \ne j$两种情况求导。 +# +# ### 针对$a_j$的偏导 +# +# $$ +# \frac{\partial C}{\partial a_j} = \frac{(\partial -\sum_j y_j ln a_j)}{\partial a_j} = -\sum_j y_j \frac{1}{a_j} +# $$ +# +# ### 针对$z_i$的偏导 +# +# 如果 $i=j$ : +# +# \begin{eqnarray} +# \frac{\partial a_i}{\partial z_i} & = & \frac{\partial (\frac{e^{z_i}}{\sum_k e^{z_k}})}{\partial z_i} \\ +# & = & \frac{\sum_k e^{z_k} e^{z_i} - (e^{z_i})^2}{\sum_k (e^{z_k})^2} \\ +# & = & (\frac{e^{z_i}}{\sum_k e^{z_k}} ) (1 - \frac{e^{z_i}}{\sum_k e^{z_k}} ) \\ +# & = & a_i (1 - a_i) +# \end{eqnarray} +# +# 如果 $i \ne j$: +# \begin{eqnarray} +# \frac{\partial a_j}{\partial z_i} & = & \frac{\partial (\frac{e^{z_j}}{\sum_k e^{z_k}})}{\partial z_i} \\ +# & = & \frac{0 \cdot \sum_k e^{z_k} - e^{z_j} \cdot e^{z_i} }{(\sum_k e^{z_k})^2} \\ +# & = & - \frac{e^{z_j}}{\sum_k e^{z_k}} \cdot \frac{e^{z_i}}{\sum_k e^{z_k}} \\ +# & = & -a_j a_i +# \end{eqnarray} +# +# 当u，v都是变量的函数时的导数推导公式： +# $$ +# (\frac{u}{v})' = \frac{u'v - uv'}{v^2} +# $$ +# +# ### 整体的推导 +# +# \begin{eqnarray} +# \frac{\partial C}{\partial z_i} & = & (-\sum_j y_j \frac{1}{a_j} ) \frac{\partial a_j}{\partial z_i} \\ +# & = & - \frac{y_i}{a_i} a_i ( 1 - a_i) + \sum_{j \ne i} \frac{y_j}{a_j} a_i a_j \\ +# & = & -y_i + y_i a_i + \sum_{j \ne i} y_j a_i \\ +# & = & -y_i + a_i \sum_{j} y_j +# \end{eqnarray} + +# ## 问题 +# 如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的方法中？ + +# ## References +# +# * Softmax & 交叉熵 +# * [交叉熵代价函数（作用及公式推导）](https://blog.csdn.net/u014313009/article/details/51043064) +# * [手打例子一步一步带你看懂softmax函数以及相关求导过程](https://www.jianshu.com/p/ffa51250ba2e) +# * [简单易懂的softmax交叉熵损失函数求导](https://www.jianshu.com/p/c02a1fbffad6) diff --git a/references/Matplotlib.ipynb b/references/Matplotlib.ipynb new file mode 100644 index 0000000..5a9e42a --- /dev/null +++ b/references/Matplotlib.ipynb @@ -0,0 +1,9426 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell, web application servers, and six graphical user interface toolkits.\n", + "\n", + "Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.\n", + "\n", + "Library documentation: http://matplotlib.org/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# needed to display the graphs\n", + "%matplotlib inline\n", + "from pylab import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEgCAYAAACuDOSlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAGq1JREFUeJzt3XuY1HXd//HnW4xIxNA7b8VDkdrxvuvGLM/JWupNaYaK\n", + "p/KQpbfdWXqnHbz9pWJmmeYhq1vDQ0KkYqaokCWpo0Ki4OUptUQvNTyBhicUEuHz++M76ArDMuzu\n", + "7Oc7M8/Hdc3F7OzM7tsR9rXvz+kbKSUkSVrWarkLkCSVkwEhSarJgJAk1WRASJJqMiAkSTUZEJKk\n", + "mgwIqU4R8e6IeDkioovnLImITfqyLqlRDAipCxHxWER8CiCl9PeU0qBU3TwUEZWI+EreCqXGMSCk\n", + 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"cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEgCAYAAACuDOSlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4VGXax/HvQ5HOAgohCog0aSIIiihIlCIqIEhfaYqN\n", + "V9e2urqgEmVXRFdXcFXKgoKslEVFuiAamvQNioCCCEgHpSV0kuf944RIScgQZuY5k/l9rmsuJjNn\n", + "Zm7ONTP33E811lpERETOlst1ACIi4k9KECIikiElCBERyZAShIiIZEgJQkREMqQEISIiGVKCEAmQ\n", + "MaacMSbJGGPOc0yqMaZCOOMSCRUlCJHzMMZsMsbcBmCt/cVaW8SmTR4yxiQYY3q5jVAkdJQgRM7P\n", + "AplVDJplKjmaEoRIJowxHwHlgClpTUvPpjUh5TbG/B1oBPwr7b7BGTw+nzHmH8aYzcaYncaY940x\n", + "+cP9/xDJLiUIkUxYa7sBvwAtrbVFgP/+fpftC8wHHk1rdno8g6d4DagEXJv27xXAS6GPXCQ4lCBE\n", + "Lk6GzU9pHdkPAk9ba/dba5OBAUDncAYncjHyuA5AJMJl1g9REigIrDht0JNBP8okgihBiJzf+Tqi\n", + "z3ffr8ARoLq1dkdwQxIJD/2aETm/XUDFC73PWpsKDAfeNsaUBDDGXGGMaR6SKEVCQAlC5PwGAC8Y\n", + "Y/YC7TizahgEtDfG7DXGvJ3BY58DfgIWG2MOALOBKqEOWCRYTKg2DDLGlAVGA6XwPlTDrLWDjTHx\n", + 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marker = '+', 'o', '*', 's', ',', '.', \n", + "# '1', '2', '3', '4', ...\n", + "ax.plot(x, x+ 9, color=\"green\", lw=2, ls='*', marker='+')\n", + "ax.plot(x, x+10, color=\"green\", lw=2, ls='*', marker='o')\n", + "ax.plot(x, x+11, color=\"green\", lw=2, ls='*', marker='s')\n", + "ax.plot(x, x+12, color=\"green\", lw=2, ls='*', marker='1')\n", + "\n", + "# marker size and color\n", + "ax.plot(x, x+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n", + "ax.plot(x, x+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n", + "ax.plot(x, x+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, \n", + " markerfacecolor=\"red\")\n", + "ax.plot(x, x+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n", + " markerfacecolor=\"yellow\", markeredgewidth=2, markeredgecolor=\"blue\");" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + 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"axes[1].axis('tight')\n", + "axes[1].set_title(\"tight axes\")\n", + "\n", + "axes[2].plot(x, x**2, x, x**3)\n", + "axes[2].set_ylim([0, 60])\n", + "axes[2].set_xlim([2, 5])\n", + "axes[2].set_title(\"custom axes range\");" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAlMAAAEKCAYAAAA2KNBKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd0FGUXwOHfJfRelV6kiyhFECkSKYoUERWxoIi9d0VA\n", + "JahEwAbIhwUFRRGxBAQVqQYJvdfQexfpJZByvz9mAwEDqbuzu7nPOXuyO7szcxfIy5233BFVxRhj\n", + "jDHGpE82twMwxhhjjAlklkwZY4wxxmSAJVPGGGOMMRlgyZQxxhhjTAZYMmWMMcYYkwGWTBljjDHG\n", + "ZIAlU8bnRCRSRB720bkqikiCiNi/dWOCiIiUF5FjIiKX+EyCiFyRSef7VETeyIxjZTCOMBH5NgP7\n", + "vyciz6fic5eLyBoRyZnec2Ul9h9MEBKRrSKyT0TyJtn2iIj85WZcSajnYYwJQJ42pqWbMajqdlUt\n", + "oJ5iid6+SFPVJ1X1XW8dPw3S3XaKSAngfuCzFE+iug/4C3gsvefLSiyZCl7ZgBSvPlIiHpkQjzEm\n", + 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color=\"green\", alpha=0.5);\n", + "axes[3].set_title(\"fill_between\");" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAs4AAAEKCAYAAAACfdMTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X+8pWVd7//XWxHERBDx8BvhW0M6aomUWJpuIjnUMcBz\n", + "UAa/EuXUKam0tHKwjgxZCZ0S6Qecb4nyo0BREkEBmZCdlsJkAaIjh+F8HWUGZtABQVIL5HP+uK/N\n", + "LDb7x5qZtfdas/fr+Xisx773df/63Gvtfd2fdd3Xfd2pKiRJkiTN7EnDDkCSJEnaEZg4S5IkSX0w\n", + "cZYkSZL6YOIsSZIk9cHEWZIkSeqDibMkSZLUBxNnDUWSNyXZlOTBJM/czm2tS3LUIJZP8hNJbt+e\n", + "eCRpmJKsTHLxdqz/xSSvGGRM0+xnq+rurdz2Y8ewPe/HTOsmGUty1wzrnpfk97ZlvxpdJs7aaq2y\n", + "+3ZLeu9P8k9JfjlJ+lz/KcCfAkdV1TOq6v7tDKnaq98K8rHlnzCj6jNV9dzZdri9JyZJC0+S1yf5\n", + "fJJvJbk7ydVJXjaEUPp+QEOSC5K863ErV72gqj49+LCeYNq6uFeSg5M8mqTvnGXSMWzPAyu2ed2q\n", + "elNV/cFsy7Vz6k9u6340v0yctS0KeHVVPQM4CDgTeDtwfp/r7wM8Ffjy3IS3Y0uy07BjkLR1krwV\n", + 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"axes[1].hist(n, cumulative=True, bins=50)\n", + "axes[1].set_title(\"Cumulative detailed histogram\")\n", + "axes[1].set_xlim((min(n), max(n)));" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD7CAYAAABt0P8jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xd4lMX2wPHvpBFCx9AkAipgRZoigkhQSgAhKEWKqIgK\n", + "UizXHxdEUfSKXZR7RaVJV0RARIN0QlcwQIL00CSEBAgJJCQhbX5/zCIRAym7b3Y3ez7Psw9b3n3n\n", + "EJaT2XlnziitNUIIITyHl7MDEEIIUbwk8QshhIeRxC+EEB5GEr8QQngYSfxCCOFhJPELIYSH8XF2\n", + "AJcopWReqRBCFJLWWhX2PS7V49dau9TtzTffdHoMElPJiclV45KY3DemonKpxC+EEMJ6kviFEMLD\n", + "SOK/huDgYGeH8A8SU8G4YkzgmnFJTAXjijEVlbJnnMiRlFLaVWIRQgh3oJRCu/vFXSGEENaTxC+E\n", + "EB5GEr8QQngYSfxCCOFhJPELIYSHkcQvhBAeRhK/EEJ4GEn8QgjhYSTxCyGEh5HEL4QQHkYSvxBC\n", + "eBhJ/EII4WEk8QshhIeRxC+EEB7GrsSvlPpaKRWvlNp1jWP+q5Q6qJSKVEo1tqc9IYQQ9rO3xz8d\n", + 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meshgrid(phi_p, phi_m)\n", + "Z = flux_qubit_potential(X, Y).T\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "p = ax.pcolor(X/(2*pi), Y/(2*pi), Z, \n", + " cmap=cm.RdBu, vmin=abs(Z).min(), vmax=abs(Z).max())\n", + "cb = fig.colorbar(p, ax=ax)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from mpl_toolkits.mplot3d.axes3d import Axes3D" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAwkAAAFdCAYAAABIGyFEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXm0HNV17/+tuYfb3bqSkAVCiEFCWAINIANmiInt2DEE\n", + "24nj5bd+K3m838rPIYCd52E5L/Zz4mU/x3Hyi2PHsY2J87J4/vlhiDHOIw7EJgaMkRBCQkIDCA2g\n", + "GUlIukNPVV3D+f3Rd1efqq7que/tvvd81uol3e7q6qrqrn3OPnvv75YYYxAIBAKBQCAQCAQCQp7p\n", + 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grading and color bar\n", + "ax = fig.add_subplot(1, 2, 2, projection='3d')\n", + "p = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, \n", + " cmap=cm.coolwarm, linewidth=0, antialiased=False)\n", + "cb = fig.colorbar(p, shrink=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FHX6x98zsy2bkFCsoOipqDSlKaCRplTpCOqB5fA8\n", + "xMKhvzt7Pc/zvLMiolhPRAVFERQQQZoBAVGiqKhYUEFFEUOS3ezutN8f42xmazbJpgDf9+vl645k\n", + "MzszO/t9vk/7PJJpmggEAoFAIEiP3NAnIBAIBALBvoAwmAKBQCAQZIAwmAKBQCAQZIAwmAKBQCAQ\n", + "ZIAwmAKBQCAQZIAwmAKBQCAQZICrit+LnhOBQCAQHGhIyX4oPEyBQCAQCDJAGEyBQCAQCDJAGEyB\n", + "QCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQ\n", + "CDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJAGEyBQCAQCDJA\n", + 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[ + "# wire frame\n", + "fig = plt.figure(figsize=(8,6))\n", + "\n", + "ax = fig.add_subplot(1, 1, 1, projection='3d')\n", + "\n", + "p = ax.plot_wireframe(X, Y, Z, rstride=4, cstride=4)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAAFdCAYAAACO4V1gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4XNWdv987vanL6t2Sm4x7wzYYm14WyFLDErKU1E1I\n", + "SDY9+0uyKUsCbMICSQiEBAgQIAkkoWPABXds2ZaNu+Uiy7LVpelzZ+7vD/mOR/JImpFmRjPSeZ+H\n", + "hwRp5pw7unM+99slRVEQCAQCgUAwOJrR3oBAIBAIBKmAEEyBQCAQCCJACKZAIBAIBBEgBFMgEAgE\n", + "gggQgikQCAQCQQQIwRQIBAKBIAJ0Q/xc1JwIBAKBYLwhhfuPwsIUCAQCgSAChGAKBAKBQBABQjAF\n", + "AoFAIIgAIZgCgUAgEESAEEyBQCAQCCJACKZAIBAIBBEgBFMgEAgEgggQgikQCAQCQQQIwRQIBAKB\n", + "IAKEYAoEAoFAEAFCMAUCgUAgiAAhmAKBQCAQRIAQTIFAIBAIIkAIpkAgEAgEESAEUyAQCASCCBCC\n", + 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2*pi);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/references/References.md b/references/References.md index ba172f7..ad6427c 100644 --- a/references/References.md +++ b/references/References.md @@ -43,4 +43,14 @@ http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selec * https://medium.com/@UdacityINDIA/how-to-build-your-first-neural-network-with-python-6819c7f65dbf * https://enlight.nyc/projects/neural-network/ -* https://www.python-course.eu/neural_networks_with_python_numpy.php \ No newline at end of file +* https://www.python-course.eu/neural_networks_with_python_numpy.php + + +## k-Means +* [如何使用 Keras 实现无监督聚类](http://m.sohu.com/a/236221126_717210) + +## AutoEncoder (自编码/非监督学习) +* https://morvanzhou.github.io/tutorials/machine-learning/torch/4-04-autoencoder/ +* https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/404_autoencoder.py +* pytorch AutoEncoder 自编码 https://www.jianshu.com/p/f0929f427d03 +* Adversarial Autoencoders (with Pytorch) https://blog.paperspace.com/adversarial-autoencoders-with-pytorch/ diff --git a/references/SciPy.ipynb b/references/SciPy.ipynb new file mode 100644 index 0000000..876190a --- /dev/null +++ b/references/SciPy.ipynb @@ -0,0 +1,1353 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# SciPy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.\n", + "\n", + "Library documentation: http://www.scipy.org/scipylib/index.html" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# needed to display the graphs\n", + "%matplotlib inline\n", + "from pylab import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from numpy import *\n", + "from scipy.integrate import quad, dblquad, tplquad" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.0, 0.0)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# integration\n", + "val, abserr = quad(lambda x: exp(-x ** 2), Inf, Inf)\n", + "val, abserr" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy.integrate import odeint, ode" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl8U1X6/z8nS9ukS5rue4GyyCKLrIJIAVnc+CGIIFtR\n", + "x8EZF2ZcvoMiWJFRZ9QZBxQHVxQGGVBUFhdcKKAg4ChFsOy0BVq6L+maND2/P24TkrZpcrfkpL3v\n", + "1yuQ5J57zunJvc957nOe8zyEUgoFBQUFha6BytcdUFBQUFDwHorQV1BQUOhCKEJfQUFBoQuhCH0F\n", + "BQWFLoQi9BUUFBS6EIrQV1BQUOhCiBb6hJB3CSFFhJBfXRxPJ4RUEUJ+aXk9LbZNBQUFBQVhaCSo\n", + "4z0AawB80EGZvZTSaRK0paCgoKAgAtGaPqV0P4AKN8WI2HYUFBQUFMTjDZs+BTCaEJJNCPmcENLP\n", + "C20qKCgoKLSDFOYdd/wMIJlSWkcIuRnApwB6e6FdBQUFBYVWyC70KaUmh/dfEELWEkIiKKXljuUI\n", + 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p\n", + " dp = -2 * zeta * w0 * p - w0**2 * x\n", + "\n", + " return [dx, dp]\n", + "\n", + "# initial state\n", + "y0 = [1.0, 0.0]\n", + "\n", + "# time coodinate to solve the ODE for\n", + "t = linspace(0, 10, 1000)\n", + "w0 = 2*pi*1.0\n", + "\n", + "# solve the ODE problem for three different values of the damping ratio\n", + "y1 = odeint(dy, y0, t, args=(0.0, w0)) # undamped\n", + "y2 = odeint(dy, y0, t, args=(0.2, w0)) # under damped\n", + "y3 = odeint(dy, y0, t, args=(1.0, w0)) # critial damping\n", + "y4 = odeint(dy, y0, t, args=(5.0, w0)) # over damped\n", + "\n", + "fig, ax = subplots()\n", + "ax.plot(t, y1[:,0], 'k', label=\"undamped\", linewidth=0.25)\n", + "ax.plot(t, y2[:,0], 'r', label=\"under damped\")\n", + "ax.plot(t, y3[:,0], 'b', label=r\"critical damping\")\n", + "ax.plot(t, y4[:,0], 'g', label=\"over damped\")\n", + "ax.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + 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+ "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.33333333, 0.66666667, 0. ])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# solve a system of linear equations\n", + "x = solve(A, b)\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1.34211023, 0.22314378, -0.11368553])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# eigenvalues and eigenvectors\n", + "A = rand(3,3)\n", + "B = rand(3,3)\n", + "\n", + "evals, evecs = eig(A)\n", + "\n", + "evals" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-0.24560175, -0.93054452, 0.46758953],\n", + " [-0.84521949, -0.00332278, -0.56599975],\n", + " [-0.4746407 , 0.36616371, 0.67897299]])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evecs" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[-0.23411483, 0.96016594, 0.15255034],\n", + " [-0.84815445, -0.12501447, -0.51478677],\n", + " [-0.47520972, -0.24990547, 0.84363676]]),\n", + " array([ 1.3457633 , 0.23442523, 0.1079208 ]),\n", + " array([[-0.21657397, -0.81553512, -0.53665463],\n", + " [ 0.7658064 , 0.19902251, -0.61149865],\n", + " [-0.60550497, 0.54340824, -0.58143892]]))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svd(A)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy import optimize" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAHxdJREFUeJzt3XmYVNWZx/HvyyIIKMgiIIsQBSKKKxIRI+2SiIlCYozG\n", + "zIxGjfGJjMk4WUazIJk8Gk2iMTGT5YkGTVSMS1zQqCjSEREhJKBCQwABpYk2CDayr+/8cartsu2G\n", + "qu7qOrdu/z7Pcx9quVX3paF/dercc841d0dERNKjVewCRESksBTsIiIpo2AXEUkZBbuISMoo2EVE\n", + "UkbBLiKSMjkFu5n1M7PpZrbQzBaY2dcyj3c1s2fNbImZTTWzLlmvuc7MlprZYjP7ZHP9BURE5IMs\n", + "l3HsZtYL6OXu882sE/B34DPApcA77v5jM/sf4CB3v9bMhgL3AScCfYDngMHuvqe5/iIiIhLk1GJ3\n", + "97fdfX7m9iZgESGwxwJ3Z3a7mxD2AOOAye6+091XAsuAEQWsW0REGpB3H7uZDQCOA2YDPd29KvNU\n", + "FdAzc/sQoDLrZZWEDwIREWlmeQV7phvmYeDr7r4x+zkPfTp769fR2gUiIkXQJtcdzawtIdT/6O6P\n", + "Zh6uMrNe7v62mfUG1mQeXw30y3p538xj2e+noBcRaQR3t709n+uoGAPuBCrc/baspx4HLsncvgR4\n", + 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"metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def f(x):\n", + " return 4*x**3 + (x-2)**2 + x**4\n", + "\n", + "fig, ax = subplots()\n", + "x = linspace(-5, 3, 100)\n", + "ax.plot(x, f(x));" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 2.804988\n", + " Iterations: 4\n", + " Function evaluations: 24\n", + " Gradient evaluations: 8\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 0.46961745])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x_min = optimize.fmin_bfgs(f, -0.5)\n", + "x_min" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from scipy import stats" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmYFdW19/HvApwQBBWuGHEgiorGCQUxmNAKKJrEMTdK\n", + "xAGjMTjGmDjmSpt7jXGKRknUOBBnVERF4wRix+FVQEVQEecRHJhEQFGG9f6xq+HQdvc5fbrq1DnV\n", + "v8/z1MMZqk7tortX79619trm7oiISPa0SrsBIiKSDAV4EZGMUoAXEckoBXgRkYxSgBcRySgFeBGR\n", + "jMob4M1skJnNMLO3zOysRvbrZWbLzOzQph4rIiLxazTAm1lrYAQwCNgOGGxmPRrY72Lg0aYeKyIi\n", + "ycjXg+8NvO3u77v7UmAUcGA9+50CjAZmF3GsiIgkIF+A3wT4KOf5x9FrK5nZJoTAfU30Uu3U2LzH\n", + "iohIcvIF+ELqGFwJnO2h5oFFW6HHiohIQtrkeX8msGnO800JPfFcuwKjzAygE7CfmS0t8FjMTL8I\n", + "RESK4O6Wb4cGN8IvgHeALYA1gZeBHo3sPxI4pCnHhiZk1/Dhw9NuQqIq+fref9/9gAPcN9rI/cIL\n", + "3efN++4+ude3ZIn7jTe6b7ONe69e7s8/X7q2JqGSv3aFyPr1RbGz0Rje6BCNuy8DTgYeA6YDd7n7\n", + 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"output_type": "execute_result" + } + ], + "source": [ + "Y.mean(), Y.std(), Y.var()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.193733722708372, 0.23272385793970249)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# t-test example\n", + "t_statistic, p_value = stats.ttest_ind(Y.rvs(size=1000), Y.rvs(size=1000))\n", + "t_statistic, p_value" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/references/Scikit-learn.ipynb b/references/Scikit-learn.ipynb new file mode 100644 index 0000000..93ad0f7 --- /dev/null +++ b/references/Scikit-learn.ipynb @@ -0,0 +1,1069 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Scikit-learn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scikit-learn contains simple and efficient tools for data mining and data analysis. It implements a wide variety of machine learning algorithms and processes to conduct advanced analytics.\n", + "\n", + "Library documentation: http://scikit-learn.org/stable/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### General" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn import datasets\n", + "from sklearn import svm" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0. 0. 5. ..., 0. 0. 0.]\n", + " [ 0. 0. 0. ..., 10. 0. 0.]\n", + " [ 0. 0. 0. ..., 16. 9. 0.]\n", + " ..., \n", + " [ 0. 0. 1. ..., 6. 0. 0.]\n", + " [ 0. 0. 2. ..., 12. 0. 0.]\n", + " [ 0. 0. 10. ..., 12. 1. 0.]]\n" + ] + } + ], + "source": [ + "# import a sample dataset and view the data\n", + "digits = datasets.load_digits()\n", + "print(digits.data)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, ..., 8, 9, 8])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# view the target variable\n", + "digits.target" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,\n", + " gamma=0.001, kernel='rbf', max_iter=-1, probability=False,\n", + " random_state=None, shrinking=True, tol=0.001, verbose=False)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# train a support vector machine using everything but the last example \n", + "classifier = svm.SVC(gamma=0.001, C=100.)\n", + "classifier.fit(digits.data[:-1], digits.target[:-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([8])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# predict the target of the last example\n", + "classifier.predict(digits.data[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([8])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# persist the model and reload\n", + "import pickle\n", + "from sklearn.externals import joblib\n", + "joblib.dump(classifier, 'model.pkl')\n", + "classifier2 = joblib.load('model.pkl')\n", + "classifier2.predict(digits.data[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "os.remove('model.pkl')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.97999999999999998" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# another example with the digits data set\n", + "svc = svm.SVC(C=1, kernel='linear')\n", + "svc.fit(digits.data[:-100], digits.target[:-100]).score(digits.data[-100:], digits.target[-100:])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train: [2 3 4 5] | test: [0 1]\n", + "Train: [0 1 4 5] | test: [2 3]\n", + "Train: [0 1 2 3] | test: [4 5]\n" + ] + } + ], + "source": [ + "# perform cross-validation on the estimator's predictions\n", + "from sklearn import cross_validation\n", + "k_fold = cross_validation.KFold(n=6, n_folds=3)\n", + "for train_indices, test_indices in k_fold:\n", + " print('Train: %s | test: %s' % (train_indices, test_indices))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.93489149, 0.95659432, 0.93989983])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# apply to the model\n", + "kfold = cross_validation.KFold(len(digits.data), n_folds=3)\n", + "cross_validation.cross_val_score(svc, digits.data, digits.target, cv=kfold, n_jobs=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=None,\n", + " estimator=SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n", + " kernel='linear', max_iter=-1, probability=False, random_state=None,\n", + " shrinking=True, tol=0.001, verbose=False),\n", + " fit_params={}, iid=True, loss_func=None, n_jobs=-1,\n", + " param_grid={'gamma': array([ 1.00000e-06, 3.59381e-06, 1.29155e-05, 4.64159e-05,\n", + " 1.66810e-04, 5.99484e-04, 2.15443e-03, 7.74264e-03,\n", + " 2.78256e-02, 1.00000e-01])},\n", + " pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,\n", + " verbose=0)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# use the grid search module to optimize model parameters\n", + "from sklearn.grid_search import GridSearchCV\n", + "gammas = np.logspace(-6, -1, 10)\n", + "classifier = GridSearchCV(estimator=svc, param_grid=dict(gamma=gammas), n_jobs=-1)\n", + "classifier.fit(digits.data[:1000], digits.target[:1000])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.92400000000000004" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier.best_score_" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "9.9999999999999995e-07" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "classifier.best_estimator_.gamma" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.94228356336260977" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# run against the test set\n", + "classifier.score(digits.data[1000:], digits.target[1000:])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.93521595, 0.95826377, 0.93791946])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# nested cross-validation example\n", + "cross_validation.cross_val_score(classifier, digits.data, digits.target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other Classifiers" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# import the iris dataset\n", + "iris = datasets.load_iris()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", + " metric_params=None, n_neighbors=5, p=2, weights='uniform')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# k nearest neighbors\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "knn = KNeighborsClassifier()\n", + "knn.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n", + " max_depth=None, max_features=None, max_leaf_nodes=None,\n", + " min_density=None, min_samples_leaf=1, min_samples_split=2,\n", + " random_state=None, splitter='best')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# decision tree\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "dtree = DecisionTreeClassifier()\n", + "dtree.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "SGDClassifier(alpha=0.0001, class_weight=None, epsilon=0.1, eta0=0.0,\n", + " fit_intercept=True, l1_ratio=0.15, learning_rate='optimal',\n", + " loss='hinge', n_iter=5, n_jobs=1, penalty='l2', power_t=0.5,\n", + " random_state=None, shuffle=False, verbose=0, warm_start=False)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# stochastic gradient descent\n", + "from sklearn.linear_model import SGDClassifier\n", + "sgd = SGDClassifier(loss=\"hinge\", penalty=\"l2\")\n", + "sgd.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of mislabeled points : 6\n" + ] + } + ], + "source": [ + "# naive bayes\n", + "from sklearn.naive_bayes import GaussianNB\n", + "gnb = GaussianNB()\n", + "y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)\n", + "print(\"Number of mislabeled points : %d\" % (iris.target != y_pred).sum())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# load another sample dataset\n", + "diabetes = datasets.load_diabetes()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression(copy_X=True, fit_intercept=True, normalize=False)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# linear regression\n", + "from sklearn import linear_model\n", + "regr = linear_model.LinearRegression()\n", + "regr.fit(diabetes.data, diabetes.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ -10.01219782 -239.81908937 519.83978679 324.39042769 -792.18416163\n", + " 476.74583782 101.04457032 177.06417623 751.27932109 67.62538639]\n" + ] + } + ], + "source": [ + "# regression coefficients\n", + "print(regr.coef_)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2859.6903987680657" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# mean squared error\n", + "np.mean((regr.predict(diabetes.data)-diabetes.target)**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0.51774942541329338" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# explained variance\n", + "regr.score(diabetes.data, diabetes.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Ridge(alpha=0.1, copy_X=True, fit_intercept=True, max_iter=None,\n", + " normalize=False, solver='auto', tol=0.001)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ridge regression\n", + "regr = linear_model.Ridge(alpha=.1)\n", + "regr.fit(diabetes.data, diabetes.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n", + " normalize=False, positive=False, precompute='auto', tol=0.0001,\n", + " warm_start=False)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# lasso regression\n", + "regr = linear_model.Lasso()\n", + "regr.fit(diabetes.data, diabetes.target)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=100000.0, class_weight=None, dual=False,\n", + " fit_intercept=True, intercept_scaling=1, penalty='l2',\n", + " random_state=None, tol=0.0001)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# logistic regression (this is actually a classifier)\n", + "iris = datasets.load_iris()\n", + "logistic = linear_model.LogisticRegression(C=1e5)\n", + "logistic.fit(iris.data, iris.target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# feature scaling\n", + "from sklearn import preprocessing\n", + "X = np.array([[ 1., -1., 2.],\n", + " [ 2., 0., 0.],\n", + " [ 0., 1., -1.]])\n", + "X_scaled = preprocessing.scale(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "StandardScaler(copy=True, with_mean=True, with_std=True)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# save the scaling transform to apply to new data later\n", + "scaler = preprocessing.StandardScaler().fit(X)\n", + "scaler" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0. , -1.22474487, 1.33630621],\n", + " [ 1.22474487, 0. , -0.26726124],\n", + " [-1.22474487, 1.22474487, -1.06904497]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scaler.transform(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.5 , 0. , 1. ],\n", + " [ 1. , 0.5 , 0.33333333],\n", + " [ 0. , 1. , 0. ]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# range scaling\n", + "min_max_scaler = preprocessing.MinMaxScaler()\n", + "X_minmax = min_max_scaler.fit_transform(X)\n", + "X_minmax" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.40824829, -0.40824829, 0.81649658],\n", + " [ 1. , 0. , 0. ],\n", + " [ 0. , 0.70710678, -0.70710678]])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# instance normalization using L2 norm\n", + "X_normalized = preprocessing.normalize(X, norm='l2')\n", + "X_normalized" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 0., 0., 1., 0., 0., 0., 0., 1.]])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# category encoding\n", + "enc = preprocessing.OneHotEncoder()\n", + "enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])\n", + "enc.transform([[0, 1, 3]]).toarray()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1., 0., 1.],\n", + " [ 1., 0., 0.],\n", + " [ 0., 1., 0.]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# binning\n", + "binarizer = preprocessing.Binarizer().fit(X)\n", + "binarizer.transform(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Clustering" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=3, n_init=10,\n", + " n_jobs=1, precompute_distances=True, random_state=None, tol=0.0001,\n", + " verbose=0)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# k means clustering\n", + "from sklearn import cluster\n", + "k_means = cluster.KMeans(n_clusters=3)\n", + "k_means.fit(iris.data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decomposition" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create a signal with 2 useful dimensions\n", + "x1 = np.random.normal(size=100)\n", + "x2 = np.random.normal(size=100)\n", + "x3 = x1 + x2\n", + "X = np.c_[x1, x2, x3]" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "PCA(copy=True, n_components=None, whiten=False)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute principal component analysis\n", + "from sklearn import decomposition\n", + "pca = decomposition.PCA()\n", + "pca.fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.77625101e+00, 9.03048616e-01, 3.02456658e-31])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pca.explained_variance_" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(100L, 2L)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# only the 2 first components are useful\n", + "pca.n_components = 2\n", + "X_reduced = pca.fit_transform(X)\n", + "X_reduced.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# generate more sample data\n", + "time = np.linspace(0, 10, 2000)\n", + "s1 = np.sin(2 * time) # signal 1 : sinusoidal signal\n", + "s2 = np.sign(np.sin(3 * time)) # signal 2 : square signal\n", + "S = np.c_[s1, s2]\n", + "S += 0.2 * np.random.normal(size=S.shape) # Add noise\n", + "S /= S.std(axis=0) # standardize data" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# mix data\n", + "A = np.array([[1, 1], [0.5, 2]]) # mixing matrix\n", + "X = np.dot(S, A.T) # generate observations" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compute independent component analysis\n", + "ica = decomposition.FastICA()\n", + "S_ = ica.fit_transform(X) # get the estimated sources\n", + "A_ = ica.mixing_.T\n", + "np.allclose(X, np.dot(S_, A_) + ica.mean_)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/references/Seaborn.ipynb b/references/Seaborn.ipynb new file mode 100644 index 0000000..3463e49 --- /dev/null +++ b/references/Seaborn.ipynb @@ -0,0 +1,5481 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Seaborn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.\n", + "\n", + "Library documentation: http://stanford.edu/~mwaskom/software/seaborn/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sb\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Themes" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# global config settings to control things like style, font size, color palette etc.\n", + "sb.set(context=\"notebook\", style=\"darkgrid\", palette=\"dark\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAYFJREFUeJzt2EEqRlEAhuF7pUgWYGI9dmFkB0Jm/0zIDozswnpMLECi\n", + "5JhL/+x/r3ie6TmDr269ne48xpgAaGwtPQDgPxFdgJDoAoREFyAkugAh0QUIba87fHv/GLs7a68A\n", + "8M18cjaN+9v5p7O1Rd3d2Z7mw9VGRv0G42k1bZ0fLD1jYz5vnqe7vR+/+59w+jqmi/lh6RkbcT2O\n", + "p8er/aVnbMzR5cs0n5wtPWMRfi8AhEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHR\n", + "BQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJ\n", + "LkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBI\n", + "dAFCogsQEl2AkOgChEQXICS6ACHRBQiJLkBIdAFC8xhj6Q0A/4aXLkBIdAFCogsQEl2AkOgChEQX\n", + "IPQFu2UddlnlgNcAAAAASUVORK5CYII=\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAcNJREFUeJzt2DFqFVEAhtE7khSSXbgBUUFwEzY2gqlSCHYRC9dgIdoJ\n", + "Kawi2Ni4CUEw4gayi0cKhbG3eHzNeDWcU85M8d/q486yrusAAPa7MXsAAPwPBBMAAsEEgEAwASAQ\n", + "TAAIBBMAgoN9L9efv9blcO8nAHCt7E4ux9H7W8ufz/fWcDk8GFfPX223arKbb16O72/vzp6xmTun\n", + "F+Pd+fU937Pji3Hv89PZMzbx7eHZuP/pw+wZm/n66Ml48PHH7Bmb+fL49jh/vZs9YzPHL47G7uRy\n", + "9oy/zi9ZAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwAC\n", + "wQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgE\n", + "EwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBM\n", + "AAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwCCZV3X2RsA4J/nhgkAgWACQCCYABAI\n", + "JgAEggkAgWACQPAbQxckyJWg960AAAAASUVORK5CYII=\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAaRJREFUeJzt2LFN5GAYRdF/RkwnDrcFSiB2SAdoS6CEFR0QOp4SaIHQ\n", + "nSBh8g1GNxkMo3NCO3lfdGUftm0bAMBlx70HAMBvIJgAEAgmAASCCQCBYAJAIJgAENxdevn58bEd\n", + "T6fv2gIAu1vn85iWh8P/zy8G83g6jfVxvt6qnU2vy1jfbvi++2W8rLd739O0jHl93nvGVSzT85jf\n", + "171nXM3yZxrz3xu+7980Xufbve9xmcY6n/ee8e38kgWAQDABIBBMAAgEEwACwQSAQDABIBBMAAgE\n", + "EwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBM\n", + "AAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDAB\n", + "IBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSAQDABIBBMAAgEEwACwQSA\n", + "QDABIDhs27b3BgD48XxhAkAgmAAQCCYABIIJAIFgAkAgmAAQfAHNmSR7wyaIJQAAAABJRU5ErkJg\n", + "gg==\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# seaborn has some nice built-in color palette features\n", + "sb.palplot(sb.color_palette())\n", + "sb.palplot(sb.color_palette(\"husl\", 8))\n", + "sb.palplot(sb.color_palette(\"hls\", 8))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAZQAAABGCAYAAADrRGIwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAbBJREFUeJzt2CFuVVEYRtHzmtdUgGUCtAPoAPCYigp8kxo8kgTVpLIe\n", + "Q4JHVGDwHQAD4DEBLBUEwmUAJDXs2xOatewxn9v5z2ZZlgEA/2pv9gAAHgZBASAhKAAkBAWAhKAA\n", + "kBAUABLbux5//lqW/e3mvrYA8B/YnZ2Mw/cf/4rDnUHZ327G+cW39VZN9u7Nk3F1/Xv2jNW8Ot0b\n", + "nz7/mD1jNc+PD8bX3ZfZM1bx9PBo3N58mD1jNY+evRjf376ePWM1j19ejt3ZyewZ986XFwAJQQEg\n", + "ISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlBASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEB\n", + "ICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJQQEgISgAJAQFgISgAJAQFAASggJAQlAASAgKAAlB\n", + "ASAhKAAkBAWAhKAAkBAUABKCAkBCUABICAoACUEBICEoACQEBYCEoACQEBQAEoICQEJQAEgICgAJ\n", + "QQEgISgAJAQFgISgAJAQFAASm2VZZm8A4AFwoQCQEBQAEoICQEJQAEgICgAJQQEg8Qdo5iEbu2ev\n", + "pAAAAABJRU5ErkJggg==\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# matplotlib colormap of evenly spaced colors\n", + "sb.palplot(sb.color_palette(\"coolwarm\", 7))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAcwAAABGCAYAAABBh6SMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAAcBJREFUeJzt1yGKlVEch+HzXVyBG7grGBExTxMEwWAeMFkMNrlruNgM\n", + "FpMweYIgCDaziOgKZgMuYT674fKWz6P4PPGc8msv/2Vd1wEAnLabPQAA/gWCCQCBYAJAIJgAEAgm\n", + "AASCCQDBrVOf683Nuuw0FYD/x9n+fPy4/rz8/n4ymMtuN35+/7Ldqslu37k/rt9/mD1jM/vHj8a3\n", + "15ezZ2zm7ouL8fHlm9kzNvHw1fPx9uI4e8Zmnl0exuHBYfaMzRw/HceTe09nz9jM1dd342x/PnvG\n", + "H+d8BIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwA\n", + "CAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEg\n", + "EEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBA\n", + "MAEgEEwACAQTAALBBIBAMAEgEEwACAQTAALBBIBAMAEgWNZ1nb0BAP56LkwACAQTAALBBIBAMAEg\n", + "EEwACAQTAIJfldsjGDxlD4AAAAAASUVORK5CYII=\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# sequential palette with linear increase in brightness\n", + "sb.palplot(sb.cubehelix_palette(8))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAfEAAAFkCAYAAAAwmQKNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXeQJNd95/l5LzPLV3VX+/F+egYeA2DgnQgCBAkCpEgt\n", + "yT3ppNuNkI6UKN6uNhgnaY8UT25X0m1Q4kkKcfdutdJR9JRIgiQAgoQHBm7gB9Mzg/GmfXmbme/d\n", + 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sb.load_dataset(\"tips\")\n", + "g = sb.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n", + "bins = np.linspace(0, 60, 13)\n", + "g.map(plt.hist, \"total_bill\", color=\"steelblue\", bins=bins, lw=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAfAAAAHwCAYAAABZrD3mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlwJOd9Hv6nu+fo6bkADIDBvSf2IpekSFGkJJoSaSuy\n", + "ypQUW2YkRrGkKKlIqYpVsVOJk0ri0q9+tqOKIruKVZZjWZaP2LHssmzJkiXa1uW1SYpcitRyL1yL\n", + "+wYGwFw9d3f+GDQWC2J3cczM293zfP6RdjHd810QwIN+3+/7vpJpmiAiIiJnkUUXQERERPvHACci\n", + "InIgBjgREZEDMcCJiIgciAFORETkQJ4DXMO2dSJ7kWp0H35vE9nHXb+v+QRORETkQAxwIiIiB2KA\n", + "ExERORADnIiIyIEY4ERERA50kC50IiKymfLGBhLPPYdKKgVvPI7Ye94D2ecTXRbVkXSAw0y41ITI\n", + "XriMrMnlJicx++yzKK+tbf2dv68PfZ/6FHydnQIro0PgMjIiIjerZDKY+fVfR3l9HR0/+7M48T//\n", + 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"plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAaYAAAGoCAYAAAANe0FzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmU5Hdd7//nd629qveZ6VmTSaaSEBIgEJCEyCrecyUa\n", + "ZJWbIyBHD+g5Jj9FPRFCQA7KAU+4Ry/uoBeucBNvQA0S0IiAkAWzDWTpyUxmn+nptfb67p/fH9Xd\n", + "6aW6uqoy0109836c45FU1ae/36qe/r7q811eX00phRBCCNEr9I1eASGEEGIxCSYhhBA9RYJJCCFE\n", + "T5FgEkII0VMkmIQQQvQUc4OXL6cECiEuNNpGr0Cv2+hg6klRFFEul7sam8lk0HWZiAohRLckmJoo\n", + "l8v809M/IplKdTSuVq1y4+WvIJfLnaM1E0KI858E0yqSqRSpbGajV0MIIS44ss9JCCFET5FgEkII\n", + "0VMkmIQQQvQUCSYhhBA9RYJJCCFET5FgEkII0VMkmIQQQvQUCSYhhBA9RYJJCCFETznvmx+66b0r\n", + "FosoFZ2jNRJCCNHKeR9M3fTeTZ2ZIJXLkj6H6zWv28JYKYsVQpyvNlUwdTv7SSQTHfXeVcuVTlet\n", + 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female=\"#F08080\")\n", + "g = sb.lmplot(\"age\", \"survived\", col=\"sex\", hue=\"sex\", data=df,\n", + " palette=pal, y_jitter=.02, logistic=True)\n", + "g.set(xlim=(0, 80), ylim=(-.05, 1.05))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAfEAAAHxCAYAAAB9MoV9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VPd97//XObNrZrQgxGpWGwYwtsEY7HgFA3bSrHXT\n", + "prnN5iRNYsepb5a6bdL+kvxu/GiT26Zt0pvkxmncNLf3tm62m8WOdxvjBduYxQsewIDZhZA0o9mX\n", + "c879Y4RAIIEkZjQzmvczDwV0NDrne46E3/M95/v9fA3HcRAREZH6Y1a7ASIiIjI2CnEREZE6pRAX\n", + "ERGpUwpxERGROqUQFxERqVPuajfgbIpFy+ntTVe7GeOura2JRjxvaNxz13k3nkY9946OsFHtNkwk\n", + "Nd0Td7td1W5CVTTqeUPjnrvOu/E08rlL+dR0T1zKw7ZtkslE2fcbCoUxzZp+HygiMqEpxBtAMpng\n", + "8CMPE/QHyrbPVDbDjHXraW5uKds+RURkdBTiDSLoDxAOBqvdDBERKSPdCxUREalT6onXkBPPrr1e\n", + 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"output_type": "display_data" + } + ], + "source": [ + "# linear regression with marginal distributions\n", + "sb.set(style=\"darkgrid\")\n", + "tips = sb.load_dataset(\"tips\")\n", + "color = sb.color_palette()[2]\n", + "g = sb.jointplot(\"total_bill\", \"tip\", data=tips, kind=\"reg\",\n", + " xlim=(0, 60), ylim=(0, 12), color=color, size=7)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": [ + "iVBORw0KGgoAAAANSUhEUgAAAhUAAAFhCAYAAADHv1g0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", + "AAALEgAACxIB0t1+/AAAFvVJREFUeJzt3XuQpWWdH/DvjEODuDPAwMh1mEawH1HE8hLXwguyGNRN\n", + "Urgmu1sRdldrAbUs1iRLUFlviRh3N0BKLNdsySrqJmYLVskaoquiKS4VNBq8IPggq4c7zWVuDKA9\n", + "wOSPc0ZaMsP0zHnec/oMn08VRfd73nN+v+ednre/87y3JVu2bAkAwLCWjrsBAGD3IFQAAE0IFQBA\n", + 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diff --git a/references/Statsmodels.ipynb b/references/Statsmodels.ipynb new file mode 100644 index 0000000..9f9e878 --- /dev/null +++ b/references/Statsmodels.ipynb @@ -0,0 +1,781 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Statsmodels" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Statsmodels is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting functions, and result statistics are available for different types of data and each estimator.\n", + "\n", + "Library documentation: http://statsmodels.sourceforge.net/" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Linear Regression Models" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# needed to display the graphs\n", + "%matplotlib inline\n", + "from pylab import *" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import statsmodels.api as sm\n", + "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n", + "np.random.seed(9876789)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# create some artificial data\n", + "nsample = 100\n", + "x = np.linspace(0, 10, 100)\n", + "X = np.column_stack((x, x**2))\n", + "beta = np.array([1, 0.1, 10])\n", + "e = np.random.normal(size=nsample)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# add column of 1s for intercept\n", + "X = sm.add_constant(X)\n", + "y = np.dot(X, beta) + e" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 1.000\n", + "Model: OLS Adj. R-squared: 1.000\n", + "Method: Least Squares F-statistic: 4.020e+06\n", + "Date: Sun, 16 Nov 2014 Prob (F-statistic): 2.83e-239\n", + "Time: 20:59:31 Log-Likelihood: -146.51\n", + "No. Observations: 100 AIC: 299.0\n", + "Df Residuals: 97 BIC: 306.8\n", + "Df Model: 2 \n", + "==============================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "const 1.3423 0.313 4.292 0.000 0.722 1.963\n", + "x1 -0.0402 0.145 -0.278 0.781 -0.327 0.247\n", + "x2 10.0103 0.014 715.745 0.000 9.982 10.038\n", + "==============================================================================\n", + "Omnibus: 2.042 Durbin-Watson: 2.274\n", + "Prob(Omnibus): 0.360 Jarque-Bera (JB): 1.875\n", + "Skew: 0.234 Prob(JB): 0.392\n", + "Kurtosis: 2.519 Cond. No. 144.\n", + "==============================================================================\n" + ] + } + ], + "source": [ + "# fit model and print the summary\n", + "model = sm.OLS(y, X)\n", + "results = model.fit()\n", + "print(results.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Parameters: ', array([ 1.34233516, -0.04024948, 10.01025357]))\n", + "('R2: ', 0.9999879365025871)\n" + ] + } + ], + "source": [ + "# individual results parameters can be accessed\n", + "print('Parameters: ', results.params)\n", + "print('R2: ', results.rsquared)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: y R-squared: 0.933\n", + "Model: OLS Adj. R-squared: 0.928\n", + "Method: Least Squares F-statistic: 211.8\n", + "Date: Sun, 16 Nov 2014 Prob (F-statistic): 6.30e-27\n", + "Time: 20:59:31 Log-Likelihood: -34.438\n", + "No. Observations: 50 AIC: 76.88\n", + "Df Residuals: 46 BIC: 84.52\n", + "Df Model: 3 \n", + "==============================================================================\n", + " coef std err t P>|t| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "x1 0.4687 0.026 17.751 0.000 0.416 0.522\n", + "x2 0.4836 0.104 4.659 0.000 0.275 0.693\n", + "x3 -0.0174 0.002 -7.507 0.000 -0.022 -0.013\n", + "const 5.2058 0.171 30.405 0.000 4.861 5.550\n", + "==============================================================================\n", + "Omnibus: 0.655 Durbin-Watson: 2.896\n", + "Prob(Omnibus): 0.721 Jarque-Bera (JB): 0.360\n", + "Skew: 0.207 Prob(JB): 0.835\n", + "Kurtosis: 3.026 Cond. No. 221.\n", + "==============================================================================\n" + ] + } + ], + "source": [ + "# example with non-linear relationship\n", + "nsample = 50\n", + "sig = 0.5\n", + "x = np.linspace(0, 20, nsample)\n", + "X = np.column_stack((x, np.sin(x), (x-5)**2, np.ones(nsample)))\n", + "beta = [0.5, 0.5, -0.02, 5.]\n", + "\n", + "y_true = np.dot(X, beta)\n", + "y = y_true + sig * np.random.normal(size=nsample)\n", + "\n", + "res = sm.OLS(y, X).fit()\n", + "print(res.summary())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Parameters: ', array([ 0.46872448, 0.48360119, -0.01740479, 5.20584496]))\n", + "('Standard errors: ', array([ 0.02640602, 0.10380518, 0.00231847, 0.17121765]))\n", + "('Predicted values: ', array([ 4.77072516, 5.22213464, 5.63620761, 5.98658823,\n", + " 6.25643234, 6.44117491, 6.54928009, 6.60085051,\n", + " 6.62432454, 6.6518039 , 6.71377946, 6.83412169,\n", + " 7.02615877, 7.29048685, 7.61487206, 7.97626054,\n", + " 8.34456611, 8.68761335, 8.97642389, 9.18997755,\n", + " 9.31866582, 9.36587056, 9.34740836, 9.28893189,\n", + " 9.22171529, 9.17751587, 9.1833565 , 9.25708583,\n", + " 9.40444579, 9.61812821, 9.87897556, 10.15912843,\n", + " 10.42660281, 10.65054491, 10.8063004 , 10.87946503,\n", + " 10.86825119, 10.78378163, 10.64826203, 10.49133265,\n", + " 10.34519853, 10.23933827, 10.19566084, 10.22490593,\n", + " 10.32487947, 10.48081414, 10.66779556, 10.85485568,\n", + " 11.01006072, 11.10575781]))\n" + ] + } + ], + "source": [ + "# look at some quantities of interest\n", + "print('Parameters: ', res.params)\n", + "print('Standard errors: ', res.bse)\n", + "print('Predicted values: ', res.predict())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + 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"metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# generate some data\n", + "np.random.seed(12345)\n", + "arparams = np.array([.75, -.25])\n", + "maparams = np.array([.65, .35])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# set parameters\n", + "arparams = np.r_[1, -arparams]\n", + "maparam = np.r_[1, maparams]\n", + "nobs = 250\n", + "y = arma_generate_sample(arparams, maparams, nobs)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# add some dates information\n", + "dates = sm.tsa.datetools.dates_from_range('1980m1', length=nobs)\n", + "y = pd.TimeSeries(y, index=dates)\n", + "arma_mod = sm.tsa.ARMA(y, order=(2,2))\n", + "arma_res = arma_mod.fit(trend='nc', disp=-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ARMA Model Results \n", + "==============================================================================\n", + "Dep. Variable: y No. Observations: 250\n", + "Model: ARMA(2, 2) Log Likelihood -245.887\n", + "Method: css-mle S.D. of innovations 0.645\n", + "Date: Sun, 16 Nov 2014 AIC 501.773\n", + "Time: 20:59:32 BIC 519.381\n", + "Sample: 01-31-1980 HQIC 508.860\n", + " - 10-31-2000 \n", + "==============================================================================\n", + " coef std err z P>|z| [95.0% Conf. Int.]\n", + "------------------------------------------------------------------------------\n", + "ar.L1.y 0.8411 0.403 2.089 0.038 0.052 1.630\n", + "ar.L2.y -0.2693 0.247 -1.092 0.276 -0.753 0.214\n", + "ma.L1.y 0.5352 0.412 1.299 0.195 -0.273 1.343\n", + "ma.L2.y 0.0157 0.306 0.051 0.959 -0.585 0.616\n", + " Roots \n", + "=============================================================================\n", + " Real Imaginary Modulus Frequency\n", + "-----------------------------------------------------------------------------\n", + "AR.1 1.5618 -1.1289j 1.9271 -0.0996\n", + "AR.2 1.5618 +1.1289j 1.9271 0.0996\n", + "MA.1 -1.9835 +0.0000j 1.9835 0.5000\n", + "MA.2 -32.1812 +0.0000j 32.1812 0.5000\n", + "-----------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "print(arma_res.summary())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + 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Probability in this\n", @@ -262,6 +260,60 @@ "plt.colorbar()\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Circles" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "n = 200\n", + "\n", + "t1 = (np.random.rand(n, 1)*2-1)*np.pi\n", + "r1 = 10 + (np.random.rand(n, 1)*2-1)*4\n", + "x_1 = np.concatenate((r1 * np.cos(t1), r1 * np.sin(t1)), axis=1)\n", + "y_1 = [0 for _ in range(n)]\n", + "\n", + "t2 = (np.random.rand(n, 1)*2-1)*np.pi\n", + "r2 = 20 + (np.random.rand(n, 1)*2-1)*4\n", + "x_2 = np.concatenate((r2 * np.cos(t2), r2 * np.sin(t2)), axis=1)\n", + "y_2 = [1 for _ in range(n)]\n", + "\n", + "x = np.concatenate((x_1, x_2), axis=0)\n", + "y = np.concatenate((y_1, y_2), axis=0)\n", + "\n", + "plt.scatter(x[:, 0], x[:,1], c=y)\n", + "plt.show()\n", + "\n", + "yy = y.reshape(-1, 1)\n", + "data = np.concatenate((x, yy), axis=1)\n", + "np.savetxt(\"dataset_circles.csv\", data, delimiter=\",\")" + ] } ], "metadata": { diff --git a/tips/datasets.py b/tips/datasets.py index 2efe8d2..571f46c 100644 --- a/tips/datasets.py +++ b/tips/datasets.py @@ -128,9 +128,7 @@ plt.show() # + import matplotlib.pyplot as plt -from sklearn.datasets import make_blobs - - +from sklearn.datasets import make_blobsb # Generate 3 blobs with 2 classes where the second blob contains # half positive samples and half negative samples. Probability in this @@ -145,3 +143,34 @@ plt.figure(figsize=(15, 9)) plt.scatter(X[:, 0], X[:, 1], c=y) plt.colorbar() plt.show() +# - + +# ## Circles + +# + +# %matplotlib inline + +import numpy as np +import matplotlib.pyplot as plt + +n = 200 + +t1 = (np.random.rand(n, 1)*2-1)*np.pi +r1 = 10 + (np.random.rand(n, 1)*2-1)*4 +x_1 = np.concatenate((r1 * np.cos(t1), r1 * np.sin(t1)), axis=1) +y_1 = [0 for _ in range(n)] + +t2 = (np.random.rand(n, 1)*2-1)*np.pi +r2 = 20 + (np.random.rand(n, 1)*2-1)*4 +x_2 = np.concatenate((r2 * np.cos(t2), r2 * np.sin(t2)), axis=1) +y_2 = [1 for _ in range(n)] + +x = np.concatenate((x_1, x_2), axis=0) +y = np.concatenate((y_1, y_2), axis=0) + +plt.scatter(x[:, 0], x[:,1], c=y) +plt.show() + +yy = y.reshape(-1, 1) +data = np.concatenate((x, yy), axis=1) +np.savetxt("dataset_circles.csv", data, delimiter=",") diff --git a/tips/notebook_tips.ipynb b/tips/notebook_tips.ipynb index a0f90d2..29e881c 100644 --- a/tips/notebook_tips.ipynb +++ b/tips/notebook_tips.ipynb @@ -10,7 +10,84 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.core.display import HTML\n", + "HTML(\"\"\"\n", + "\n", + "\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -27,7 +104,7 @@ "" ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -44,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -79,6 +156,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "\\begin{align}\n", + "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\\n", + "\\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n", + "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n", + "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n", + "\\end{align}\n", + "\n", + "\\begin{equation}\n", + "E = F \\cdot s \n", + "\\end{equation}\n", + "\n", + "\\begin{eqnarray}\n", + "F & = & sin(x) \\\\\n", + "G & = & cos(x)\n", + "\\end{eqnarray}\n", + "\n", + "\\begin{align}\n", + " g &= \\int_a^b f(x)dx \\label{eq1} \\\\\n", + " a &= b + c \\label{eq2}\n", + "\\end{align}\n", + "\n", + "See (\\ref{eq1})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "## Audio\n" ] }, @@ -201,6 +306,56 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## JupyterLab" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "array([1, 2, 3])\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from pprint import pprint\n", + "\n", + "pp = pprint\n", + "a = np.array([1, 2, 3])\n", + "pp(a)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [jupyter-matplotlib](https://github.com/matplotlib/jupyter-matplotlib)\n", + "\n", + "\n", + "```\n", + "# Installing Node.js 5.x on Ubuntu / Debian\n", + "curl -sL https://deb.nodesource.com/setup_5.x | sudo -E bash -\n", + "sudo apt-get install -y nodejs\n", + "\n", + "pip install ipympl\n", + "\n", + "# If using JupyterLab\n", + "# Install nodejs: https://nodejs.org/en/download/\n", + "jupyter labextension install @jupyter-widgets/jupyterlab-manager\n", + "jupyter labextension install jupyter-matplotlib\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "## References\n", "\n", "* https://nbviewer.jupyter.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/Index.ipynb" @@ -224,8 +379,7 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" - }, - "main_language": "python" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/tips/notebook_tips.py b/tips/notebook_tips.py index ac649ae..98a3c0f 100644 --- a/tips/notebook_tips.py +++ b/tips/notebook_tips.py @@ -21,6 +21,36 @@ # # +from IPython.core.display import HTML +HTML(""" + +""") + from IPython.display import Latex Latex(r"""\begin{eqnarray} \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\ @@ -37,6 +67,29 @@ Latex(r"""\begin{eqnarray} \nabla \cdot \vec{\mathbf{B}} & = 0 \end{align} +# \begin{align} +# \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\ +# \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\ +# \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\ +# \nabla \cdot \vec{\mathbf{B}} & = 0 +# \end{align} +# +# \begin{equation} +# E = F \cdot s +# \end{equation} +# +# \begin{eqnarray} +# F & = & sin(x) \\ +# G & = & cos(x) +# \end{eqnarray} +# +# \begin{align} +# g &= \int_a^b f(x)dx \label{eq1} \\ +# a &= b + c \label{eq2} +# \end{align} +# +# See (\ref{eq1}) + # ## Audio # @@ -63,6 +116,34 @@ from IPython.display import IFrame IFrame('https://jupyter.org', width='100%', height=350) # - +# ## JupyterLab + +# + +import numpy as np +from pprint import pprint + +pp = pprint +a = np.array([1, 2, 3]) +pp(a) + +# - + +# ### [jupyter-matplotlib](https://github.com/matplotlib/jupyter-matplotlib) +# +# +# ``` +# # Installing Node.js 5.x on Ubuntu / Debian +# curl -sL https://deb.nodesource.com/setup_5.x | sudo -E bash - +# sudo apt-get install -y nodejs +# +# pip install ipympl +# +# # If using JupyterLab +# # Install nodejs: https://nodejs.org/en/download/ +# jupyter labextension install @jupyter-widgets/jupyterlab-manager +# jupyter labextension install jupyter-matplotlib +# ``` + # ## References # # * https://nbviewer.jupyter.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/Index.ipynb