remove the code of assigments

README.md

@@ -11,8 +11,7 @@ https://www.coursera.org/learn/machine-learning
 本项目包含课程中的所有课后作业以及笔记，目前正在将手写笔记进行电子化。
 
 1. 笔记(notes)都为中文，为了便于复习和扩充等，尽量会按照视频目录，以及视频内容进行提炼整理。
-
-2. 所有课后作业(assignments)都已提交并通过 Coursera 编程测验。仅供参考，请勿抄袭。
+2. 遵循[荣誉准则][honor code]，现已移除已通过的课后编程作业的源代码，之后会改为指导笔记形式陆续更新。
 
 
 For Andrew Ng's machine learning course on Coursera.
@@ -26,11 +25,11 @@ You can read it by Typora or any other similar markdown editor.
 
 1. **在线阅读地址**：http://scruel.gitee.io/ml-andrewng-notes/
 
-2. 直接在 GitHub Page 上阅读，需安装 Chrome 插件 —— [GitHub with MathJax][1]（部分公式仍无法正常解析）。
+2. 直接在 GitHub Page 上阅读，需安装 Chrome 插件 —— [GitHub with MathJax][GitHub with MathJax]（部分公式仍无法正常解析）。
 
 3. 下载 .html 网页文件及 笔记图片 image 文件夹，浏览器打开阅读。
 
-4. 笔记源码(.md)基于支持 LaTeX 的 markdown 编辑器 [Typora][2]，其他类似的编辑器也可以阅读及编辑，注意其他编辑器可能会产生排版问题。
+4. 笔记源码(.md)基于支持 LaTeX 的 markdown 编辑器 [Typora][Typora]，其他类似的编辑器也可以阅读及编辑，注意其他编辑器可能会产生排版问题。
 
 
 
@@ -39,12 +38,16 @@ You can read it by Typora or any other similar markdown editor.
 
 方便网络不方便的同学学习
 
-[百度网盘下载](https://pan.baidu.com/s/1mkmnRIC) | 密码：fdzc（包含视频，讲义，以及编程作业题原题压缩包）
+[百度网盘下载][baidupan] | 密码：fdzc（包含视频，讲义，以及编程作业题原题压缩包）
 
-[B站在线中英双语](http://www.bilibili.com/video/av9912938?bbid=F8173D95-FF96-47EF-B7F4-0779D698B8051978infoc) | [B站在线英文版1](https://www.bilibili.com/video/av17624209/?from=search&seid=15848135050308500663) | [B站在线英文版2](https://www.bilibili.com/video/av17624412/?from=search&seid=15848135050308500663)
+[B站在线中英双语][bilibili_zh] | [B站在线英文版1][bilibili_en1] | [B站在线英文版2][bilibili_en2]
 
 当然，还是建议克服一下困难，最好能上 Coursera 网站学习，官网安排的贴心小测试可以检验你对知识的掌握情况。
 
+
+
+如果遇到 Coursera 上提交编程作业失败的问题，以 [submitWithConfiguration.m](https://github.com/scruel/ML-AndrewNg-Notes/blob/master/assignments/submitWithConfiguration.m) 覆盖编程作业中 lib 文件夹下的同名文件即可解决。
+
 ## Thanks
 
 - Coursera 官网
@@ -55,18 +58,24 @@ You can read it by Typora or any other similar markdown editor.
 注：由于手写笔记成稿时间较早，有所参考无法一一致谢，在此统一表示谢意！
 
 ## License
-[![Creative Commons License](https://i.creativecommons.org/l/by-nc/4.0/88x31.png)][3]
+[![Creative Commons License](https://i.creativecommons.org/l/by-nc/4.0/88x31.png)][CC BY-NC 4.0]
 
-This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][3].
+This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License][CC BY-NC 4.0].
 
 
 
-[知乎文章](https://zhuanlan.zhihu.com/p/32781741)
+[知乎文章][zhihu]
 
 
 
 By: Scruel
 
-[1]: https://chrome.google.com/webstore/detail/ioemnmodlmafdkllaclgeombjnmnbima
-[2]: https://typora.io/
-[3]: http://creativecommons.org/licenses/by-nc/4.0/
+[zhihu]: https://zhuanlan.zhihu.com/p/32781741
+[baidupan]: https://pan.baidu.com/s/1mkmnRIC
+[bilibili_zh]: http://www.bilibili.com/video/av9912938?bbid=F8173D95-FF96-47EF-B7F4-0779D698B8051978infoc
+[bilibili_en1]: https://www.bilibili.com/video/av17624209/?from=search&seid=15848135050308500663
+[bilibili_en2]: https://www.bilibili.com/video/av17624412/?from=search&seid=15848135050308500663
+[GitHub with MathJax]: https://chrome.google.com/webstore/detail/ioemnmodlmafdkllaclgeombjnmnbima
+[Typora]: https://typora.io/
+[honor code]: https://www.coursera.org/learn/machine-learning/supplement/nh65Z/machine-learning-honor-code
+[CC BY-NC 4.0]: http://creativecommons.org/licenses/by-nc/4.0/

assignments/convert(MNIST2Matlab).m

@@ -1,99 +0,0 @@
-% Version 1.000
-%
-% Code provided by Ruslan Salakhutdinov and Geoff Hinton
-%
-% Permission is granted for anyone to copy, use, modify, or distribute this
-% program and accompanying programs and documents for any purpose, provided
-% this copyright notice is retained and prominently displayed, along with
-% a note saying that the original programs are available from our
-% web page.
-% The programs and documents are distributed without any warranty, express or
-% implied.  As the programs were written for research purposes only, they have
-% not been tested to the degree that would be advisable in any important
-% application.  All use of these programs is entirely at the user's own risk.
-
-% This program reads raw MNIST files available at 
-% http://yann.lecun.com/exdb/mnist/ 
-% and converts them to files in matlab format 
-% Before using this program you first need to download files:
-% train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz 
-% t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz
-% and gunzip them. You need to allocate some space for this.  
-
-% This program was originally written by Yee Whye Teh 
-
-% Work with test files first 
-fprintf(1,'You first need to download files:\n train-images-idx3-ubyte.gz\n train-labels-idx1-ubyte.gz\n t10k-images-idx3-ubyte.gz\n t10k-labels-idx1-ubyte.gz\n from http://yann.lecun.com/exdb/mnist/\n and gunzip them \n'); 
-
-f = fopen('t10k-images-idx3-ubyte','r');
-[a,count] = fread(f,4,'int32');
-  
-g = fopen('t10k-labels-idx1-ubyte','r');
-[l,count] = fread(g,2,'int32');
-
-fprintf(1,'Starting to convert Test MNIST images (prints 10 dots) \n'); 
-n = 1000;
-
-Df = cell(1,10);
-for d=0:9,
-  Df{d+1} = fopen(['test' num2str(d) '.ascii'],'w');
-end;
-  
-for i=1:10,
-  fprintf('.');
-  rawimages = fread(f,28*28*n,'uchar');
-  rawlabels = fread(g,n,'uchar');
-  rawimages = reshape(rawimages,28*28,n);
-
-  for j=1:n,
-    fprintf(Df{rawlabels(j)+1},'%3d ',rawimages(:,j));
-    fprintf(Df{rawlabels(j)+1},'\n');
-  end;
-end;
-
-fprintf(1,'\n');
-for d=0:9,
-  fclose(Df{d+1});
-  D = load(['test' num2str(d) '.ascii'],'-ascii');
-  fprintf('%5d Digits of class %d\n',size(D,1),d);
-  save(['test' num2str(d) '.mat'],'D','-mat');
-end;
-
-
-% Work with trainig files second  
-f = fopen('train-images-idx3-ubyte','r');
-[a,count] = fread(f,4,'int32');
-
-g = fopen('train-labels-idx1-ubyte','r');
-[l,count] = fread(g,2,'int32');
-
-fprintf(1,'Starting to convert Training MNIST images (prints 60 dots)\n'); 
-n = 1000;
-
-Df = cell(1,10);
-for d=0:9,
-  Df{d+1} = fopen(['digit' num2str(d) '.ascii'],'w');
-end;
-
-for i=1:60,
-  fprintf('.');
-  rawimages = fread(f,28*28*n,'uchar');
-  rawlabels = fread(g,n,'uchar');
-  rawimages = reshape(rawimages,28*28,n);
-
-  for j=1:n,
-    fprintf(Df{rawlabels(j)+1},'%3d ',rawimages(:,j));
-    fprintf(Df{rawlabels(j)+1},'\n');
-  end;
-end;
-
-fprintf(1,'\n');
-for d=0:9,
-  fclose(Df{d+1});
-  D = load(['digit' num2str(d) '.ascii'],'-ascii');
-  fprintf('%5d Digits of class %d\n',size(D,1),d);
-  save(['digit' num2str(d) '.mat'],'D','-mat');
-end;
-
-dos('rm *.ascii');
-

assignments/machine-learning-ex1/ex1/computeCost.m

@@ -1,21 +0,0 @@
-function J = computeCost(X, y, theta)
-%COMPUTECOST Compute cost for linear regression
-%   J = COMPUTECOST(X, y, theta) computes the cost of using theta as the
-%   parameter for linear regression to fit the data points in X and y
-
-% Initialize some useful values
-m = length(y); % number of training examples
-% You need to return the following variables correctly 
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the cost of a particular choice of theta
-%               You should set J to the cost.
-
-J = 1 / 2 / m * sum((X * theta - y) .^ 2);
-
-
-%JVal = 1 / 2 / m * (X * theta - y)' * (X * theta -y);
-%gradient = theta - 1 / m * (X' * (X * theta - y));
-% =========================================================================
-
-end

assignments/machine-learning-ex1/ex1/computeCostMulti.m

@@ -1,22 +0,0 @@
-function J = computeCostMulti(X, y, theta)
-%COMPUTECOSTMULTI Compute cost for linear regression with multiple variables
-%   J = COMPUTECOSTMULTI(X, y, theta) computes the cost of using theta as the
-%   parameter for linear regression to fit the data points in X and y
-
-% Initialize some useful values
-m = length(y); % number of training examples
-
-% You need to return the following variables correctly 
-J = 0;
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the cost of a particular choice of theta
-%               You should set J to the cost.
-J = 1 / 2 / m * sum((X * theta - y) .^ 2);
-%J = 1 / 2 / m * (X * theta - y)' * (X * theta - y);
-
-
-
-% =========================================================================
-
-end

assignments/machine-learning-ex1/ex1/ex1.m

@@ -1,122 +0,0 @@
-%% Machine Learning Online Class - Exercise 1: Linear Regression
-
-%  Instructions
-%  ------------
-% 
-%  This file contains code that helps you get started on the
-%  linear exercise. You will need to complete the following functions 
-%  in this exericse:
-%
-%     warmUpExercise.m
-%     plotData.m
-%     gradientDescent.m
-%     computeCost.m
-%     gradientDescentMulti.m
-%     computeCostMulti.m
-%     featureNormalize.m
-%     normalEqn.m
-%
-%  For this exercise, you will not need to change any code in this file,
-%  or any other files other than those mentioned above.
-%
-% x refers to the population size in 10,000s
-% y refers to the profit in $10,000s
-%
-
-%% Initialization
-clear ; close all; clc
-
-%% ==================== Part 1: Basic Function ====================
-% Complete warmUpExercise.m 
-fprintf('Running warmUpExercise ... \n');
-fprintf('5x5 Identity Matrix: \n');
-warmUpExercise()
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-
-%% ======================= Part 2: Plotting =======================
-fprintf('Plotting Data ...\n')
-data = load('ex1data1.txt');
-X = data(:, 1); y = data(:, 2);
-m = length(y); % number of training examples
-
-% Plot Data
-% Note: You have to complete the code in plotData.m
-plotData(X, y);
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-%% =================== Part 3: Gradient descent ===================
-fprintf('Running Gradient Descent ...\n')
-
-X = [ones(m, 1), data(:,1)]; % Add a column of ones to x
-theta = zeros(2, 1); % initialize fitting parameters
-
-% Some gradient descent settings
-iterations = 1500;
-alpha = 0.01;
-
-% compute and display initial cost
-computeCost(X, y, theta)
-
-% run gradient descent
-theta = gradientDescent(X, y, theta, alpha, iterations);
-
-% print theta to screen
-fprintf('Theta found by gradient descent: ');
-fprintf('%f %f \n', theta(1), theta(2));
-
-% Plot the linear fit
-hold on; % keep previous plot visible
-plot(X(:,2), X*theta, '-')
-legend('Training data', 'Linear regression')
-hold off % don't overlay any more plots on this figure
-
-% Predict values for population sizes of 35,000 and 70,000
-predict1 = [1, 3.5] *theta;
-fprintf('For population = 35,000, we predict a profit of %f\n',...
-    predict1*10000);
-predict2 = [1, 7] * theta;
-fprintf('For population = 70,000, we predict a profit of %f\n',...
-    predict2*10000);
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-%% ============= Part 4: Visualizing J(theta_0, theta_1) =============
-fprintf('Visualizing J(theta_0, theta_1) ...\n')
-
-% Grid over which we will calculate J
-theta0_vals = linspace(-10, 10, 100);
-theta1_vals = linspace(-1, 4, 100);%从-1到4之间取100个数组成一个向量
-
-% initialize J_vals to a matrix of 0's
-J_vals = zeros(length(theta0_vals), length(theta1_vals));
-
-% Fill out J_vals
-for i = 1:length(theta0_vals)
-    for j = 1:length(theta1_vals)
-	  t = [theta0_vals(i); theta1_vals(j)];    
-	  J_vals(i,j) = computeCost(X, y, t);
-    end
-end
-
-
-% Because of the way meshgrids work in the surf command, we need to 
-% transpose J_vals before calling surf, or else the axes will be flipped
-J_vals = J_vals';
-% Surface plot
-figure;
-surf(theta0_vals, theta1_vals, J_vals)%画出三维图形
-xlabel('\theta_0'); ylabel('\theta_1');
-
-% Contour plot 轮廓图
-figure;
-% Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100
-contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20))
-xlabel('\theta_0'); ylabel('\theta_1');
-hold on;
-plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2);

assignments/machine-learning-ex1/ex1/ex1_multi.m

@@ -1,201 +0,0 @@
-%% Machine Learning Online Class
-%  Exercise 1: Linear regression with multiple variables
-%
-%  Instructions
-%  ------------
-% 
-%  This file contains code that helps you get started on the
-%  linear regression exercise. 
-%
-%  You will need to complete the following functions in this 
-%  exericse:
-%
-%     warmUpExercise.m
-%     plotData.m
-%     gradientDescent.m
-%     computeCost.m
-%     gradientDescentMulti.m
-%     computeCostMulti.m
-%     featureNormalize.m
-%     normalEqn.m
-%
-%  For this part of the exercise, you will need to change some
-%  parts of the code below for various experiments (e.g., changing
-%  learning rates).
-%
-
-%% Initialization
-
-%% ================ Part 1: Feature Normalization ================
-
-%% Clear and Close Figures
-clear ; close all; clc
-
-fprintf('Loading data ...\n');
-
-%% Load Data
-data = load('ex1data2.txt');
-X = data(:, 1:2);
-y = data(:, 3);
-m = length(y);
-
-% Print out some data points
-fprintf('First 10 examples from the dataset: \n');
-fprintf(' x = [%.0f %.0f], y = %.0f \n', [X(1:10,:) y(1:10,:)]');
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-% Scale features and set them to zero mean
-fprintf('Normalizing Features ...\n');
-
-[X mu sigma] = featureNormalize(X);
-
-% Add intercept term to X
-X = [ones(m, 1) X];
-
-
-%% ================ Part 2: Gradient Descent ================
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: We have provided you with the following starter
-%               code that runs gradient descent with a particular
-%               learning rate (alpha). 
-%
-%               Your task is to first make sure that your functions - 
-%               computeCost and gradientDescent already work with 
-%               this starter code and support multiple variables.
-%
-%               After that, try running gradient descent with 
-%               different values of alpha and see which one gives
-%               you the best result.
-%
-%               Finally, you should complete the code at the end
-%               to predict the price of a 1650 sq-ft, 3 br house.
-%
-% Hint: By using the 'hold on' command, you can plot multiple
-%       graphs on the same figure.
-%
-% Hint: At prediction, make sure you do the same feature normalization.
-%
-
-fprintf('Running gradient descent ...\n');
-
-% Choose some alpha value
-alpha = 0.001;
-num_iters = 4000;
-
-% Init Theta and Run Gradient Descent 
-theta = zeros(3, 1);
-[theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters);
-%[theta, J_history] = gradientDescentMulti(X, y, zeros(3, 1), 0.001, 4000);
-%plot(1:numel(J_history), J_history, '-bc', 'LineWidth', 2);
-%price = theta(1) + (1650 - mu(1)) / sigma(1) * theta(2) + (3 - mu(2)) / sigma(2) * theta(3) % You should change this
-%price = theta(1) + (15 - mu(1)) / sigma(1) * theta(2) + (1 - mu(2)) / sigma(2) * theta(3) + (2 - mu(3)) / sigma(3) * theta(4) % You should change this
-
-fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
-         '(using gradient descent):\n $%f\n'], price);
-		 
-% Plot the convergence graph
-figure;
-plot(1:numel(J_history), J_history, '-b', 'LineWidth', 2);
-xlabel('Number of iterations');
-ylabel('Cost J');
-
-% Display gradient descent's result
-fprintf('Theta computed from gradient descent: \n');
-fprintf(' %f \n', theta);
-fprintf('\n');
-
-% Estimate the price of a 1650 sq-ft, 3 br house
-% ====================== YOUR CODE HERE ======================
-% Recall that the first column of X is all-ones. Thus, it does
-% not need to be normalized.
-price = 0; % You should change this
-
-
-% ============================================================
-
-fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
-         '(using gradient descent):\n $%f\n'], price);
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-%% ================ Part 3: Normal Equations ================
-
-fprintf('Solving with normal equations...\n');
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: The following code computes the closed form 
-%               solution for linear regression using the normal
-%               equations. You should complete the code in 
-%               normalEqn.m
-%
-%               After doing so, you should complete this code 
-%               to predict the price of a 1650 sq-ft, 3 br house.
-%
-
-%% Load Data
-data = csvread('ex1data2.txt');
-X = data(:, 1:2);
-y = data(:, 3);
-m = length(y);
-
-% Add intercept term to X
-X = [ones(m, 1) X];
-
-% Calculate the parameters from the normal equation
-theta = normalEqn(X, y);
-
-% Display normal equation's result
-fprintf('Theta computed from the normal equations: \n');
-fprintf(' %f \n', theta);
-fprintf('\n');
-
-
-% Estimate the price of a 1650 sq-ft, 3 br house
-% ====================== YOUR CODE HERE ======================
-price = theta(1) + 1650 * theta(2) + 3 * theta(3); % You should change this
-
-
-% ============================================================
-
-fprintf(['Predicted price of a 1650 sq-ft, 3 br house ' ...
-         '(using normal equations):\n $%f\n'], price);
-
-%% ============= Part 4: Visualizing J(theta_0, theta_1) =============
-fprintf('Visualizing J(theta_0, theta_1) ...\n')
-
-% Grid over which we will calculate J
-theta0_vals = linspace(-10, 10, 100);
-theta1_vals = linspace(-1, 4, 100);%从-1到4之间取100个数组成一个向量
-theta2_vals = linspace(-1, 4, 100);%从-1到4之间取100个数组成一个向量
-
-% initialize J_vals to a matrix of 0's
-J_vals = zeros(length(theta0_vals), length(theta1_vals), length(theta1_vals));
-
-% Fill out J_vals
-for i = 1:length(theta0_vals)
-    for j = 1:length(theta1_vals)
-	  t = [theta0_vals(i); theta1_vals(j)];    
-	  J_vals(i,j) = computeCostMulti(X, y, t);
-    end
-end
-
-
-% Because of the way meshgrids work in the surf command, we need to 
-% transpose J_vals before calling surf, or else the axes will be flipped
-J_vals = J_vals';
-% Surface plot
-figure;
-surf(theta0_vals, theta1_vals, J_vals)%画出三维图形
-xlabel('\theta_0'); ylabel('\theta_1');
-
-% Contour plot 轮廓图
-figure;
-% Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100
-contour(theta0_vals, theta1_vals, J_vals, logspace(-2, 3, 20))
-xlabel('\theta_0'); ylabel('\theta_1');
-hold on;
-plot(theta(1), theta(2), 'rx', 'MarkerSize', 10, 'LineWidth', 2);

assignments/machine-learning-ex1/ex1/ex1data1.txt

@@ -1,97 +0,0 @@
-6.1101,17.592
-5.5277,9.1302
-8.5186,13.662
-7.0032,11.854
-5.8598,6.8233
-8.3829,11.886
-7.4764,4.3483
-8.5781,12
-6.4862,6.5987
-5.0546,3.8166
-5.7107,3.2522
-14.164,15.505
-5.734,3.1551
-8.4084,7.2258
-5.6407,0.71618
-5.3794,3.5129
-6.3654,5.3048
-5.1301,0.56077
-6.4296,3.6518
-7.0708,5.3893
-6.1891,3.1386
-20.27,21.767
-5.4901,4.263
-6.3261,5.1875
-5.5649,3.0825
-18.945,22.638
-12.828,13.501
-10.957,7.0467
-13.176,14.692
-22.203,24.147
-5.2524,-1.22
-6.5894,5.9966
-9.2482,12.134
-5.8918,1.8495
-8.2111,6.5426
-7.9334,4.5623
-8.0959,4.1164
-5.6063,3.3928
-12.836,10.117
-6.3534,5.4974
-5.4069,0.55657
-6.8825,3.9115
-11.708,5.3854
-5.7737,2.4406
-7.8247,6.7318
-7.0931,1.0463
-5.0702,5.1337
-5.8014,1.844
-11.7,8.0043
-5.5416,1.0179
-7.5402,6.7504
-5.3077,1.8396
-7.4239,4.2885
-7.6031,4.9981
-6.3328,1.4233
-6.3589,-1.4211
-6.2742,2.4756
-5.6397,4.6042
-9.3102,3.9624
-9.4536,5.4141
-8.8254,5.1694
-5.1793,-0.74279
-21.279,17.929
-14.908,12.054
-18.959,17.054
-7.2182,4.8852
-8.2951,5.7442
-10.236,7.7754
-5.4994,1.0173
-20.341,20.992
-10.136,6.6799
-7.3345,4.0259
-6.0062,1.2784
-7.2259,3.3411
-5.0269,-2.6807
-6.5479,0.29678
-7.5386,3.8845
-5.0365,5.7014
-10.274,6.7526
-5.1077,2.0576
-5.7292,0.47953
-5.1884,0.20421
-6.3557,0.67861
-9.7687,7.5435
-6.5159,5.3436
-8.5172,4.2415
-9.1802,6.7981
-6.002,0.92695
-5.5204,0.152
-5.0594,2.8214
-5.7077,1.8451
-7.6366,4.2959
-5.8707,7.2029
-5.3054,1.9869
-8.2934,0.14454
-13.394,9.0551
-5.4369,0.61705

assignments/machine-learning-ex1/ex1/ex1data2 - 副本.txt

@@ -1,47 +0,0 @@
-2104,3,399900
-1600,3,329900
-2400,3,369000
-1416,2,232000
-3000,4,539900
-1985,4,299900
-1534,3,314900
-1427,3,198999
-1380,3,212000
-1494,3,242500
-1940,4,239999
-2000,3,347000
-1890,3,329999
-4478,5,699900
-1268,3,259900
-2300,4,449900
-1320,2,299900
-1236,3,199900
-2609,4,499998
-3031,4,599000
-1767,3,252900
-1888,2,255000
-1604,3,242900
-1962,4,259900
-3890,3,573900
-1100,3,249900
-1458,3,464500
-2526,3,469000
-2200,3,475000
-2637,3,299900
-1839,2,349900
-1000,1,169900
-2040,4,314900
-3137,3,579900
-1811,4,285900
-1437,3,249900
-1239,3,229900
-2132,4,345000
-4215,4,549000
-2162,4,287000
-1664,2,368500
-2238,3,329900
-2567,4,314000
-1200,3,299000
-852,2,179900
-1852,4,299900
-1203,3,239500

assignments/machine-learning-ex1/ex1/ex1data2.txt

@@ -1,47 +0,0 @@
-2104,3,399900
-1600,3,329900
-2400,3,369000
-1416,2,232000
-3000,4,539900
-1985,4,299900
-1534,3,314900
-1427,3,198999
-1380,3,212000
-1494,3,242500
-1940,4,239999
-2000,3,347000
-1890,3,329999
-4478,5,699900
-1268,3,259900
-2300,4,449900
-1320,2,299900
-1236,3,199900
-2609,4,499998
-3031,4,599000
-1767,3,252900
-1888,2,255000
-1604,3,242900
-1962,4,259900
-3890,3,573900
-1100,3,249900
-1458,3,464500
-2526,3,469000
-2200,3,475000
-2637,3,299900
-1839,2,349900
-1000,1,169900
-2040,4,314900
-3137,3,579900
-1811,4,285900
-1437,3,249900
-1239,3,229900
-2132,4,345000
-4215,4,549000
-2162,4,287000
-1664,2,368500
-2238,3,329900
-2567,4,314000
-1200,3,299000
-852,2,179900
-1852,4,299900
-1203,3,239500

assignments/machine-learning-ex1/ex1/featureNormalize.m

@@ -1,40 +0,0 @@
-function [X_norm, mu, sigma] = featureNormalize(X)
-%FEATURENORMALIZE Normalizes the features in X 
-%   FEATURENORMALIZE(X) returns a normalized version of X where
-%   the mean value of each feature is 0 and the standard deviation
-%   is 1. This is often a good preprocessing step to do when
-%   working with learning algorithms.
-
-% You need to set these values correctly
-X_norm = X;
-mu = zeros(1, size(X, 2));
-sigma = zeros(1, size(X, 2));
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: First, for each feature dimension, compute the mean
-%               of the feature and subtract it from the dataset,
-%               storing the mean value in mu. Next, compute the 
-%               standard deviation of each feature and divide
-%               each feature by it's standard deviation, storing
-%               the standard deviation in sigma. 
-%
-%               Note that X is a matrix where each column is a 
-%               feature and each row is an example. You need 
-%               to perform the normalization separately for 
-%               each feature. 
-%
-% Hint: You might find the 'mean' and 'std' functions useful.
-%       
-
-mu = mean(X_norm);
-sigma = std(X_norm);
-X_norm = (X_norm .- mu) ./ sigma;
-
-
-
-
-
-
-% ============================================================
-
-end

assignments/machine-learning-ex1/ex1/gradientDescent.m

@@ -1,31 +0,0 @@
-function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
-	%GRADIENTDESCENT Performs gradient descent to learn theta
-	%   theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by 
-	%   taking num_iters gradient steps with learning rate alpha
-
-	% Initialize some useful values
-	m = length(y); % number of training examples
-	J_history = zeros(num_iters, 1);
-
-	for iter = 1:num_iters
-
-		% ====================== YOUR CODE HERE ======================
-		% Instructions: Perform a single gradient step on the parameter vector
-		%               theta. 
-		%
-		% Hint: While debugging, it can be useful to print out the values
-		%       of the cost function (computeCost) and gradient here.
-		%
-		theta = theta - alpha / m * (X' * (X * theta - y));
-		%X * theta 代表了hθ
-		%X' (hθ-y)表示将每一个X中的元素(n * m)与(hθ-y)(m * 1)相乘，从而得到新的theta矩阵(n * 1)
-
-
-		% ============================================================
-
-		% Save the cost J in every iteration    
-		J_history(iter) = computeCost(X, y, theta);
-
-	end
-
-end

assignments/machine-learning-ex1/ex1/gradientDescentMulti.m

@@ -1,31 +0,0 @@
-function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
-%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
-%   theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
-%   taking num_iters gradient steps with learning rate alpha
-
-% Initialize some useful values
-m = length(y); % number of training examples
-J_history = zeros(num_iters, 1);
-
-	for iter = 1:num_iters
-
-		% ====================== YOUR CODE HERE ======================
-		% Instructions: Perform a single gradient step on the parameter vector
-		%               theta. 
-		%
-		% Hint: While debugging, it can be useful to print out the values
-		%       of the cost function (computeCostMulti) and gradient here.
-		%
-		theta = theta - alpha / m * (X' * (X * theta - y));
-		%X * theta 代表了hθ
-		%X' (hθ-y)表示将每一个X中的元素(n * m)与(hθ-y)(m * 1)相乘，从而得到新的theta矩阵(n * 1)
-
-
-    % ============================================================
-
-		% Save the cost J in every iteration    
-		J_history(iter) = computeCostMulti(X, y, theta);
-
-	end
-
-end

assignments/machine-learning-ex1/ex1/lib/jsonlab/AUTHORS.txt

@@ -1,41 +0,0 @@
-The author of "jsonlab" toolbox is Qianqian Fang. Qianqian
-is currently an Assistant Professor at Massachusetts General Hospital, 
-Harvard Medical School.
-
-Address: Martinos Center for Biomedical Imaging, 
-         Massachusetts General Hospital, 
-         Harvard Medical School
-         Bldg 149, 13th St, Charlestown, MA 02129, USA
-URL: http://nmr.mgh.harvard.edu/~fangq/
-Email: <fangq at nmr.mgh.harvard.edu> or <fangqq at gmail.com>
-
-
-The script loadjson.m was built upon previous works by
-
-- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-       date: 2009/11/02
-- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-       date: 2009/03/22
-- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565
-       date: 2008/07/03
-
-
-This toolbox contains patches submitted by the following contributors:
-
-- Blake Johnson <bjohnso at bbn.com>
-  part of revision 341
-
-- Niclas Borlin <Niclas.Borlin at cs.umu.se>
-  various fixes in revision 394, including
-  - loadjson crashes for all-zero sparse matrix.
-  - loadjson crashes for empty sparse matrix.
-  - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson.
-  - loadjson crashes for sparse real column vector.
-  - loadjson crashes for sparse complex column vector.
-  - Data is corrupted by savejson for sparse real row vector.
-  - savejson crashes for sparse complex row vector. 
-
-- Yul Kang <yul.kang.on at gmail.com>
-  patches for svn revision 415.
-  - savejson saves an empty cell array as [] instead of null
-  - loadjson differentiates an empty struct from an empty array

assignments/machine-learning-ex1/ex1/lib/jsonlab/ChangeLog.txt

@@ -1,74 +0,0 @@
-============================================================================
-
-   JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave
-
-----------------------------------------------------------------------------
-
-JSONlab ChangeLog (key features marked by *):
-
-== JSONlab 1.0 (codename: Optimus - Final), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2015/01/02 polish help info for all major functions, update examples, finalize 1.0
- 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson
-
-== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/11/22 show progress bar in loadjson ('ShowProgress') 
- 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact')
- 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels
- 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab
-
-== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/09/17  fix several compatibility issues when running on octave versions 3.2-3.8
- 2014/09/17  support 2D cell and struct arrays in both savejson and saveubjson
- 2014/08/04  escape special characters in a JSON string
- 2014/02/16  fix a bug when saving ubjson files
-
-== JSONlab 0.9.9 (codename: Optimus - beta), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/01/22  use binary read and write in saveubjson and loadubjson
-
-== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang)
-
-== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
- 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson
-
-== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
- 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin)
-
-== JSONlab 0.9.0 (codename: Rodimus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson
- 2012/06/01  support JSONP in savejson
- 2012/05/25  fix the empty cell bug (reported by Cyril Davin)
- 2012/04/05  savejson can save to a file (suggested by Patrick Rapin)
-
-== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/02/28  loadjson quotation mark escape bug, see http://bit.ly/yyk1nS
- 2012/01/25  patch to handle root-less objects, contributed by Blake Johnson
-
-== JSONlab 0.8.0 (codename: Sentiel), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab
- 2012/01/11  remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer
- 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson
- 2011/11/18  fix struct array bug reported by Mykel Kochenderfer
-
-== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2011/10/21  fix a bug in loadjson, previous code does not use any of the acceleration
- 2011/10/20  loadjson supports JSON collections - concatenated JSON objects
-
-== JSONlab 0.5.0 (codename: Nexus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2011/10/16  package and release jsonlab 0.5.0
- 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug
- 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level
- 2011/10/10  create jsonlab project, start jsonlab website, add online documentation
- 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support
- 2011/10/06 *savejson works for structs, cells and arrays
- 2011/09/09  derive loadjson from JSON parser from MATLAB Central, draft savejson.m

assignments/machine-learning-ex1/ex1/lib/jsonlab/LICENSE_BSD.txt

@@ -1,25 +0,0 @@
-Copyright 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>. All rights reserved.
-
-Redistribution and use in source and binary forms, with or without modification, are
-permitted provided that the following conditions are met:
-
-   1. Redistributions of source code must retain the above copyright notice, this list of
-      conditions and the following disclaimer.
-
-   2. Redistributions in binary form must reproduce the above copyright notice, this list
-      of conditions and the following disclaimer in the documentation and/or other materials
-      provided with the distribution.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED
-WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
-FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS 
-OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
-ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-The views and conclusions contained in the software and documentation are those of the
-authors and should not be interpreted as representing official policies, either expressed
-or implied, of the copyright holders.

assignments/machine-learning-ex1/ex1/lib/jsonlab/README.txt

@@ -1,394 +0,0 @@
-===============================================================================
-=                                 JSONLab                                     =
-=           An open-source MATLAB/Octave JSON encoder and decoder             =
-===============================================================================
-
-*Copyright (C) 2011-2015  Qianqian Fang <fangq at nmr.mgh.harvard.edu>
-*License: BSD License, see License_BSD.txt for details
-*Version: 1.0 (Optimus - Final)
-
--------------------------------------------------------------------------------
-
-Table of Content:
-
-I.  Introduction
-II. Installation
-III.Using JSONLab
-IV. Known Issues and TODOs
-V.  Contribution and feedback
-
--------------------------------------------------------------------------------
-
-I.  Introduction
-
-JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, 
-human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format 
-to represent complex and hierarchical data. It is as powerful as 
-[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely 
-used for data-exchange in applications, and is essential for the wild success 
-of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and 
-[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. 
-
-UBJSON (Universal Binary JSON) is a binary JSON format, specifically 
-optimized for compact file size and better performance while keeping
-the semantics as simple as the text-based JSON format. Using the UBJSON
-format allows to wrap complex binary data in a flexible and extensible
-structure, making it possible to process complex and large dataset 
-without accuracy loss due to text conversions.
-
-We envision that both JSON and its binary version will serve as part of 
-the mainstream data-exchange formats for scientific research in the future. 
-It will provide the flexibility and generality achieved by other popular 
-general-purpose file specifications, such as
-[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly 
-reduced complexity and enhanced performance.
-
-JSONLab is a free and open-source implementation of a JSON/UBJSON encoder 
-and a decoder in the native MATLAB language. It can be used to convert a MATLAB 
-data structure (array, struct, cell, struct array and cell array) into 
-JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB 
-data structure. JSONLab supports both MATLAB and  
-[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone).
-
--------------------------------------------------------------------------------
-
-II. Installation
-
-The installation of JSONLab is no different than any other simple
-MATLAB toolbox. You only need to download/unzip the JSONLab package
-to a folder, and add the folder's path to MATLAB/Octave's path list
-by using the following command:
-
-    addpath('/path/to/jsonlab');
-
-If you want to add this path permanently, you need to type "pathtool", 
-browse to the jsonlab root folder and add to the list, then click "Save".
-Then, run "rehash" in MATLAB, and type "which loadjson", if you see an 
-output, that means JSONLab is installed for MATLAB/Octave.
-
--------------------------------------------------------------------------------
-
-III.Using JSONLab
-
-JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, 
-and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and 
-two equivallent functions -- loadubjson and saveubjson for the binary 
-JSON. The detailed help info for the four functions can be found below:
-
-=== loadjson.m ===
-<pre>
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-</pre>
-
-=== savejson.m ===
-
-<pre>
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
- </pre>
-
-=== loadubjson.m ===
-
-<pre>
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-</pre>
-
-=== saveubjson.m ===
-
-<pre>
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-</pre>
-
-
-=== examples ===
-
-Under the "examples" folder, you can find several scripts to demonstrate the
-basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you 
-will see the conversions from MATLAB data structure to JSON text and backward.
-In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet
-and validate the loadjson/savejson functions for regression testing purposes.
-Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson
-and loadubjson pairs for various matlab data structures.
-
-Please run these examples and understand how JSONLab works before you use
-it to process your data.
-
--------------------------------------------------------------------------------
-
-IV. Known Issues and TODOs
-
-JSONLab has several known limitations. We are striving to make it more general
-and robust. Hopefully in a few future releases, the limitations become less.
-
-Here are the known issues:
-
-# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays;
-# When processing names containing multi-byte characters, Octave and MATLAB \
-can give different field-names; you can use feature('DefaultCharacterSet','latin1') \
-in MATLAB to get consistant results
-# savejson can not handle class and dataset.
-# saveubjson converts a logical array into a uint8 ([U]) array
-# an unofficial N-D array count syntax is implemented in saveubjson. We are \
-actively communicating with the UBJSON spec maintainer to investigate the \
-possibility of making it upstream
-# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \
-files, however, it can parse all UBJSON files produced by saveubjson.
-
--------------------------------------------------------------------------------
-
-V. Contribution and feedback
-
-JSONLab is an open-source project. This means you can not only use it and modify
-it as you wish, but also you can contribute your changes back to JSONLab so
-that everyone else can enjoy the improvement. For anyone who want to contribute,
-please download JSONLab source code from it's subversion repository by using the
-following command:
-
- svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab
-
-You can make changes to the files as needed. Once you are satisfied with your
-changes, and ready to share it with others, please cd the root directory of 
-JSONLab, and type
-
- svn diff > yourname_featurename.patch
-
-You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at
-the email address shown in the beginning of this file. Qianqian will review 
-the changes and commit it to the subversion if they are satisfactory.
-
-We appreciate any suggestions and feedbacks from you. Please use iso2mesh's
-mailing list to report any questions you may have with JSONLab:
-
-http://groups.google.com/group/iso2mesh-users?hl=en&pli=1
-
-(Subscription to the mailing list is needed in order to post messages).

assignments/machine-learning-ex1/ex1/lib/jsonlab/jsonopt.m

@@ -1,32 +0,0 @@
-function val=jsonopt(key,default,varargin)
-%
-% val=jsonopt(key,default,optstruct)
-%
-% setting options based on a struct. The struct can be produced
-% by varargin2struct from a list of 'param','value' pairs
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-%
-% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $
-%
-% input:
-%      key: a string with which one look up a value from a struct
-%      default: if the key does not exist, return default
-%      optstruct: a struct where each sub-field is a key 
-%
-% output:
-%      val: if key exists, val=optstruct.key; otherwise val=default
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-% 
-
-val=default;
-if(nargin<=2) return; end
-opt=varargin{1};
-if(isstruct(opt) && isfield(opt,key))
-    val=getfield(opt,key);
-end
-

assignments/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m

@@ -1,566 +0,0 @@
-function data = loadjson(fname,varargin)
-%
-% data=loadjson(fname,opt)
-%    or
-% data=loadjson(fname,'param1',value1,'param2',value2,...)
-%
-% parse a JSON (JavaScript Object Notation) file or string
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2011/09/09, including previous works from 
-%
-%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-%            created on 2009/11/02
-%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-%            created on  2009/03/22
-%         Joel Feenstra:
-%         http://www.mathworks.com/matlabcentral/fileexchange/20565
-%            created on 2008/07/03
-%
-% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      fname: input file name, if fname contains "{}" or "[]", fname
-%             will be interpreted as a JSON string
-%      opt: a struct to store parsing options, opt can be replaced by 
-%           a list of ('param',value) pairs - the param string is equivallent
-%           to a field in opt. opt can have the following 
-%           fields (first in [.|.] is the default)
-%
-%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-%                         for each element of the JSON data, and group 
-%                         arrays based on the cell2mat rules.
-%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-%                         speed-optimized array parser when loading an 
-%                         array object. The fast array parser may 
-%                         collapse block arrays into a single large
-%                         array similar to rules defined in cell2mat; 0 to 
-%                         use a legacy parser; if set to a larger-than-1
-%                         value, this option will specify the minimum
-%                         dimension to enable the fast array parser. For
-%                         example, if the input is a 3D array, setting
-%                         FastArrayParser to 1 will return a 3D array;
-%                         setting to 2 will return a cell array of 2D
-%                         arrays; setting to 3 will return to a 2D cell
-%                         array of 1D vectors; setting to 4 will return a
-%                         3D cell array.
-%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
-%
-% output:
-%      dat: a cell array, where {...} blocks are converted into cell arrays,
-%           and [...] are converted to arrays
-%
-% examples:
-%      dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-%      dat=loadjson(['examples' filesep 'example1.json'])
-%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-global pos inStr len  esc index_esc len_esc isoct arraytoken
-
-if(regexp(fname,'[\{\}\]\[]','once'))
-   string=fname;
-elseif(exist(fname,'file'))
-   fid = fopen(fname,'rb');
-   string = fread(fid,inf,'uint8=>char')';
-   fclose(fid);
-else
-   error('input file does not exist');
-end
-
-pos = 1; len = length(string); inStr = string;
-isoct=exist('OCTAVE_VERSION','builtin');
-arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
-jstr=regexprep(inStr,'\\\\','  ');
-escquote=regexp(jstr,'\\"');
-arraytoken=sort([arraytoken escquote]);
-
-% String delimiters and escape chars identified to improve speed:
-esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
-index_esc = 1; len_esc = length(esc);
-
-opt=varargin2struct(varargin{:});
-
-if(jsonopt('ShowProgress',0,opt)==1)
-    opt.progressbar_=waitbar(0,'loading ...');
-end
-jsoncount=1;
-while pos <= len
-    switch(next_char)
-        case '{'
-            data{jsoncount} = parse_object(opt);
-        case '['
-            data{jsoncount} = parse_array(opt);
-        otherwise
-            error_pos('Outer level structure must be an object or an array');
-    end
-    jsoncount=jsoncount+1;
-end % while
-
-jsoncount=length(data);
-if(jsoncount==1 && iscell(data))
-    data=data{1};
-end
-
-if(~isempty(data))
-      if(isstruct(data)) % data can be a struct array
-          data=jstruct2array(data);
-      elseif(iscell(data))
-          data=jcell2array(data);
-      end
-end
-if(isfield(opt,'progressbar_'))
-    close(opt.progressbar_);
-end
-
-%%
-function newdata=jcell2array(data)
-len=length(data);
-newdata=data;
-for i=1:len
-      if(isstruct(data{i}))
-          newdata{i}=jstruct2array(data{i});
-      elseif(iscell(data{i}))
-          newdata{i}=jcell2array(data{i});
-      end
-end
-
-%%-------------------------------------------------------------------------
-function newdata=jstruct2array(data)
-fn=fieldnames(data);
-newdata=data;
-len=length(data);
-for i=1:length(fn) % depth-first
-    for j=1:len
-        if(isstruct(getfield(data(j),fn{i})))
-            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
-        end
-    end
-end
-if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
-  newdata=cell(len,1);
-  for j=1:len
-    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
-    iscpx=0;
-    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
-        if(data(j).x0x5F_ArrayIsComplex_)
-           iscpx=1;
-        end
-    end
-    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
-        if(data(j).x0x5F_ArrayIsSparse_)
-            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-                dim=data(j).x0x5F_ArraySize_;
-                if(iscpx && size(ndata,2)==4-any(dim==1))
-                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
-                end
-                if isempty(ndata)
-                    % All-zeros sparse
-                    ndata=sparse(dim(1),prod(dim(2:end)));
-                elseif dim(1)==1
-                    % Sparse row vector
-                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
-                elseif dim(2)==1
-                    % Sparse column vector
-                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
-                else
-                    % Generic sparse array.
-                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
-                end
-            else
-                if(iscpx && size(ndata,2)==4)
-                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));
-                end
-                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
-            end
-        end
-    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-        if(iscpx && size(ndata,2)==2)
-             ndata=complex(ndata(:,1),ndata(:,2));
-        end
-        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
-    end
-    newdata{j}=ndata;
-  end
-  if(len==1)
-      newdata=newdata{1};
-  end
-end
-
-%%-------------------------------------------------------------------------
-function object = parse_object(varargin)
-    parse_char('{');
-    object = [];
-    if next_char ~= '}'
-        while 1
-            str = parseStr(varargin{:});
-            if isempty(str)
-                error_pos('Name of value at position %d cannot be empty');
-            end
-            parse_char(':');
-            val = parse_value(varargin{:});
-            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );
-            if next_char == '}'
-                break;
-            end
-            parse_char(',');
-        end
-    end
-    parse_char('}');
-
-%%-------------------------------------------------------------------------
-
-function object = parse_array(varargin) % JSON array is written in row-major order
-global pos inStr isoct
-    parse_char('[');
-    object = cell(0, 1);
-    dim2=[];
-    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
-    pbar=jsonopt('progressbar_',-1,varargin{:});
-
-    if next_char ~= ']'
-	if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
-            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
-            arraystr=['[' inStr(pos:endpos)];
-            arraystr=regexprep(arraystr,'"_NaN_"','NaN');
-            arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
-            arraystr(arraystr==sprintf('\n'))=[];
-            arraystr(arraystr==sprintf('\r'))=[];
-            %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
-            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
-        	astr=inStr((e1l+1):(e1r-1));
-        	astr=regexprep(astr,'"_NaN_"','NaN');
-        	astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
-        	astr(astr==sprintf('\n'))=[];
-        	astr(astr==sprintf('\r'))=[];
-        	astr(astr==' ')='';
-        	if(isempty(find(astr=='[', 1))) % array is 2D
-                    dim2=length(sscanf(astr,'%f,',[1 inf]));
-        	end
-            else % array is 1D
-        	astr=arraystr(2:end-1);
-        	astr(astr==' ')='';
-        	[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
-        	if(nextidx>=length(astr)-1)
-                    object=obj;
-                    pos=endpos;
-                    parse_char(']');
-                    return;
-        	end
-            end
-            if(~isempty(dim2))
-        	astr=arraystr;
-        	astr(astr=='[')='';
-        	astr(astr==']')='';
-        	astr(astr==' ')='';
-        	[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
-        	if(nextidx>=length(astr)-1)
-                    object=reshape(obj,dim2,numel(obj)/dim2)';
-                    pos=endpos;
-                    parse_char(']');
-                    if(pbar>0)
-                        waitbar(pos/length(inStr),pbar,'loading ...');
-                    end
-                    return;
-        	end
-            end
-            arraystr=regexprep(arraystr,'\]\s*,','];');
-	else
-            arraystr='[';
-	end
-        try
-           if(isoct && regexp(arraystr,'"','once'))
-                error('Octave eval can produce empty cells for JSON-like input');
-           end
-           object=eval(arraystr);
-           pos=endpos;
-        catch
-         while 1
-            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
-            val = parse_value(newopt);
-            object{end+1} = val;
-            if next_char == ']'
-                break;
-            end
-            parse_char(',');
-         end
-        end
-    end
-    if(jsonopt('SimplifyCell',0,varargin{:})==1)
-      try
-        oldobj=object;
-        object=cell2mat(object')';
-        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
-            object=oldobj;
-        elseif(size(object,1)>1 && ndims(object)==2)
-            object=object';
-        end
-      catch
-      end
-    end
-    parse_char(']');
-    
-    if(pbar>0)
-        waitbar(pos/length(inStr),pbar,'loading ...');
-    end
-%%-------------------------------------------------------------------------
-
-function parse_char(c)
-    global pos inStr len
-    skip_whitespace;
-    if pos > len || inStr(pos) ~= c
-        error_pos(sprintf('Expected %c at position %%d', c));
-    else
-        pos = pos + 1;
-        skip_whitespace;
-    end
-
-%%-------------------------------------------------------------------------
-
-function c = next_char
-    global pos inStr len
-    skip_whitespace;
-    if pos > len
-        c = [];
-    else
-        c = inStr(pos);
-    end
-
-%%-------------------------------------------------------------------------
-
-function skip_whitespace
-    global pos inStr len
-    while pos <= len && isspace(inStr(pos))
-        pos = pos + 1;
-    end
-
-%%-------------------------------------------------------------------------
-function str = parseStr(varargin)
-    global pos inStr len  esc index_esc len_esc
- % len, ns = length(inStr), keyboard
-    if inStr(pos) ~= '"'
-        error_pos('String starting with " expected at position %d');
-    else
-        pos = pos + 1;
-    end
-    str = '';
-    while pos <= len
-        while index_esc <= len_esc && esc(index_esc) < pos
-            index_esc = index_esc + 1;
-        end
-        if index_esc > len_esc
-            str = [str inStr(pos:len)];
-            pos = len + 1;
-            break;
-        else
-            str = [str inStr(pos:esc(index_esc)-1)];
-            pos = esc(index_esc);
-        end
-        nstr = length(str); switch inStr(pos)
-            case '"'
-                pos = pos + 1;
-                if(~isempty(str))
-                    if(strcmp(str,'_Inf_'))
-                        str=Inf;
-                    elseif(strcmp(str,'-_Inf_'))
-                        str=-Inf;
-                    elseif(strcmp(str,'_NaN_'))
-                        str=NaN;
-                    end
-                end
-                return;
-            case '\'
-                if pos+1 > len
-                    error_pos('End of file reached right after escape character');
-                end
-                pos = pos + 1;
-                switch inStr(pos)
-                    case {'"' '\' '/'}
-                        str(nstr+1) = inStr(pos);
-                        pos = pos + 1;
-                    case {'b' 'f' 'n' 'r' 't'}
-                        str(nstr+1) = sprintf(['\' inStr(pos)]);
-                        pos = pos + 1;
-                    case 'u'
-                        if pos+4 > len
-                            error_pos('End of file reached in escaped unicode character');
-                        end
-                        str(nstr+(1:6)) = inStr(pos-1:pos+4);
-                        pos = pos + 5;
-                end
-            otherwise % should never happen
-                str(nstr+1) = inStr(pos), keyboard
-                pos = pos + 1;
-        end
-    end
-    error_pos('End of file while expecting end of inStr');
-
-%%-------------------------------------------------------------------------
-
-function num = parse_number(varargin)
-    global pos inStr len isoct
-    currstr=inStr(pos:end);
-    numstr=0;
-    if(isoct~=0)
-        numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
-        [num, one] = sscanf(currstr, '%f', 1);
-        delta=numstr+1;
-    else
-        [num, one, err, delta] = sscanf(currstr, '%f', 1);
-        if ~isempty(err)
-            error_pos('Error reading number at position %d');
-        end
-    end
-    pos = pos + delta-1;
-
-%%-------------------------------------------------------------------------
-
-function val = parse_value(varargin)
-    global pos inStr len
-    true = 1; false = 0;
-    
-    pbar=jsonopt('progressbar_',-1,varargin{:});
-    if(pbar>0)
-        waitbar(pos/len,pbar,'loading ...');
-    end
-    
-    switch(inStr(pos))
-        case '"'
-            val = parseStr(varargin{:});
-            return;
-        case '['
-            val = parse_array(varargin{:});
-            return;
-        case '{'
-            val = parse_object(varargin{:});
-            if isstruct(val)
-                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
-                    val=jstruct2array(val);
-                end
-            elseif isempty(val)
-                val = struct;
-            end
-            return;
-        case {'-','0','1','2','3','4','5','6','7','8','9'}
-            val = parse_number(varargin{:});
-            return;
-        case 't'
-            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
-                val = true;
-                pos = pos + 4;
-                return;
-            end
-        case 'f'
-            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
-                val = false;
-                pos = pos + 5;
-                return;
-            end
-        case 'n'
-            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
-                val = [];
-                pos = pos + 4;
-                return;
-            end
-    end
-    error_pos('Value expected at position %d');
-%%-------------------------------------------------------------------------
-
-function error_pos(msg)
-    global pos inStr len
-    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
-    if poShow(3) == poShow(2)
-        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after
-    end
-    msg = [sprintf(msg, pos) ': ' ...
-    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
-    error( ['JSONparser:invalidFormat: ' msg] );
-
-%%-------------------------------------------------------------------------
-
-function str = valid_field(str)
-global isoct
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
-
-%%-------------------------------------------------------------------------
-function endpos = matching_quote(str,pos)
-len=length(str);
-while(pos<len)
-    if(str(pos)=='"')
-        if(~(pos>1 && str(pos-1)=='\'))
-            endpos=pos;
-            return;
-        end        
-    end
-    pos=pos+1;
-end
-error('unmatched quotation mark');
-%%-------------------------------------------------------------------------
-function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
-global arraytoken
-level=1;
-maxlevel=level;
-endpos=0;
-bpos=arraytoken(arraytoken>=pos);
-tokens=str(bpos);
-len=length(tokens);
-pos=1;
-e1l=[];
-e1r=[];
-while(pos<=len)
-    c=tokens(pos);
-    if(c==']')
-        level=level-1;
-        if(isempty(e1r)) e1r=bpos(pos); end
-        if(level==0)
-            endpos=bpos(pos);
-            return
-        end
-    end
-    if(c=='[')
-        if(isempty(e1l)) e1l=bpos(pos); end
-        level=level+1;
-        maxlevel=max(maxlevel,level);
-    end
-    if(c=='"')
-        pos=matching_quote(tokens,pos+1);
-    end
-    pos=pos+1;
-end
-if(endpos==0) 
-    error('unmatched "]"');
-end
-

assignments/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m

@@ -1,528 +0,0 @@
-function data = loadubjson(fname,varargin)
-%
-% data=loadubjson(fname,opt)
-%    or
-% data=loadubjson(fname,'param1',value1,'param2',value2,...)
-%
-% parse a JSON (JavaScript Object Notation) file or string
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2013/08/01
-%
-% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      fname: input file name, if fname contains "{}" or "[]", fname
-%             will be interpreted as a UBJSON string
-%      opt: a struct to store parsing options, opt can be replaced by 
-%           a list of ('param',value) pairs - the param string is equivallent
-%           to a field in opt. opt can have the following 
-%           fields (first in [.|.] is the default)
-%
-%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-%                         for each element of the JSON data, and group 
-%                         arrays based on the cell2mat rules.
-%           opt.IntEndian [B|L]: specify the endianness of the integer fields
-%                         in the UBJSON input data. B - Big-Endian format for 
-%                         integers (as required in the UBJSON specification); 
-%                         L - input integer fields are in Little-Endian order.
-%
-% output:
-%      dat: a cell array, where {...} blocks are converted into cell arrays,
-%           and [...] are converted to arrays
-%
-% examples:
-%      obj=struct('string','value','array',[1 2 3]);
-%      ubjdata=saveubjson('obj',obj);
-%      dat=loadubjson(ubjdata)
-%      dat=loadubjson(['examples' filesep 'example1.ubj'])
-%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-global pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian
-
-if(regexp(fname,'[\{\}\]\[]','once'))
-   string=fname;
-elseif(exist(fname,'file'))
-   fid = fopen(fname,'rb');
-   string = fread(fid,inf,'uint8=>char')';
-   fclose(fid);
-else
-   error('input file does not exist');
-end
-
-pos = 1; len = length(string); inStr = string;
-isoct=exist('OCTAVE_VERSION','builtin');
-arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
-jstr=regexprep(inStr,'\\\\','  ');
-escquote=regexp(jstr,'\\"');
-arraytoken=sort([arraytoken escquote]);
-
-% String delimiters and escape chars identified to improve speed:
-esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
-index_esc = 1; len_esc = length(esc);
-
-opt=varargin2struct(varargin{:});
-fileendian=upper(jsonopt('IntEndian','B',opt));
-[os,maxelem,systemendian]=computer;
-
-jsoncount=1;
-while pos <= len
-    switch(next_char)
-        case '{'
-            data{jsoncount} = parse_object(opt);
-        case '['
-            data{jsoncount} = parse_array(opt);
-        otherwise
-            error_pos('Outer level structure must be an object or an array');
-    end
-    jsoncount=jsoncount+1;
-end % while
-
-jsoncount=length(data);
-if(jsoncount==1 && iscell(data))
-    data=data{1};
-end
-
-if(~isempty(data))
-      if(isstruct(data)) % data can be a struct array
-          data=jstruct2array(data);
-      elseif(iscell(data))
-          data=jcell2array(data);
-      end
-end
-
-
-%%
-function newdata=parse_collection(id,data,obj)
-
-if(jsoncount>0 && exist('data','var')) 
-    if(~iscell(data))
-       newdata=cell(1);
-       newdata{1}=data;
-       data=newdata;
-    end
-end
-
-%%
-function newdata=jcell2array(data)
-len=length(data);
-newdata=data;
-for i=1:len
-      if(isstruct(data{i}))
-          newdata{i}=jstruct2array(data{i});
-      elseif(iscell(data{i}))
-          newdata{i}=jcell2array(data{i});
-      end
-end
-
-%%-------------------------------------------------------------------------
-function newdata=jstruct2array(data)
-fn=fieldnames(data);
-newdata=data;
-len=length(data);
-for i=1:length(fn) % depth-first
-    for j=1:len
-        if(isstruct(getfield(data(j),fn{i})))
-            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
-        end
-    end
-end
-if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
-  newdata=cell(len,1);
-  for j=1:len
-    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
-    iscpx=0;
-    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
-        if(data(j).x0x5F_ArrayIsComplex_)
-           iscpx=1;
-        end
-    end
-    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
-        if(data(j).x0x5F_ArrayIsSparse_)
-            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-                dim=double(data(j).x0x5F_ArraySize_);
-                if(iscpx && size(ndata,2)==4-any(dim==1))
-                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
-                end
-                if isempty(ndata)
-                    % All-zeros sparse
-                    ndata=sparse(dim(1),prod(dim(2:end)));
-                elseif dim(1)==1
-                    % Sparse row vector
-                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
-                elseif dim(2)==1
-                    % Sparse column vector
-                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
-                else
-                    % Generic sparse array.
-                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
-                end
-            else
-                if(iscpx && size(ndata,2)==4)
-                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));
-                end
-                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
-            end
-        end
-    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-        if(iscpx && size(ndata,2)==2)
-             ndata=complex(ndata(:,1),ndata(:,2));
-        end
-        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
-    end
-    newdata{j}=ndata;
-  end
-  if(len==1)
-      newdata=newdata{1};
-  end
-end
-
-%%-------------------------------------------------------------------------
-function object = parse_object(varargin)
-    parse_char('{');
-    object = [];
-    type='';
-    count=-1;
-    if(next_char == '$')
-        type=inStr(pos+1); % TODO
-        pos=pos+2;
-    end
-    if(next_char == '#')
-        pos=pos+1;
-        count=double(parse_number());
-    end
-    if next_char ~= '}'
-        num=0;
-        while 1
-            str = parseStr(varargin{:});
-            if isempty(str)
-                error_pos('Name of value at position %d cannot be empty');
-            end
-            %parse_char(':');
-            val = parse_value(varargin{:});
-            num=num+1;
-            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );
-            if next_char == '}' || (count>=0 && num>=count)
-                break;
-            end
-            %parse_char(',');
-        end
-    end
-    if(count==-1)
-        parse_char('}');
-    end
-
-%%-------------------------------------------------------------------------
-function [cid,len]=elem_info(type)
-id=strfind('iUIlLdD',type);
-dataclass={'int8','uint8','int16','int32','int64','single','double'};
-bytelen=[1,1,2,4,8,4,8];
-if(id>0)
-    cid=dataclass{id};
-    len=bytelen(id);
-else
-    error_pos('unsupported type at position %d');
-end
-%%-------------------------------------------------------------------------
-
-
-function [data adv]=parse_block(type,count,varargin)
-global pos inStr isoct fileendian systemendian
-[cid,len]=elem_info(type);
-datastr=inStr(pos:pos+len*count-1);
-if(isoct)
-    newdata=int8(datastr);
-else
-    newdata=uint8(datastr);
-end
-id=strfind('iUIlLdD',type);
-if(id<=5 && fileendian~=systemendian)
-    newdata=swapbytes(typecast(newdata,cid));
-end
-data=typecast(newdata,cid);
-adv=double(len*count);
-
-%%-------------------------------------------------------------------------
-
-
-function object = parse_array(varargin) % JSON array is written in row-major order
-global pos inStr isoct
-    parse_char('[');
-    object = cell(0, 1);
-    dim=[];
-    type='';
-    count=-1;
-    if(next_char == '$')
-        type=inStr(pos+1);
-        pos=pos+2;
-    end
-    if(next_char == '#')
-        pos=pos+1;
-        if(next_char=='[')
-            dim=parse_array(varargin{:});
-            count=prod(double(dim));
-        else
-            count=double(parse_number());
-        end
-    end
-    if(~isempty(type))
-        if(count>=0)
-            [object adv]=parse_block(type,count,varargin{:});
-            if(~isempty(dim))
-                object=reshape(object,dim);
-            end
-            pos=pos+adv;
-            return;
-        else
-            endpos=matching_bracket(inStr,pos);
-            [cid,len]=elem_info(type);
-            count=(endpos-pos)/len;
-            [object adv]=parse_block(type,count,varargin{:});
-            pos=pos+adv;
-            parse_char(']');
-            return;
-        end
-    end
-    if next_char ~= ']'
-         while 1
-            val = parse_value(varargin{:});
-            object{end+1} = val;
-            if next_char == ']'
-                break;
-            end
-            %parse_char(',');
-         end
-    end
-    if(jsonopt('SimplifyCell',0,varargin{:})==1)
-      try
-        oldobj=object;
-        object=cell2mat(object')';
-        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
-            object=oldobj;
-        elseif(size(object,1)>1 && ndims(object)==2)
-            object=object';
-        end
-      catch
-      end
-    end
-    if(count==-1)
-        parse_char(']');
-    end
-
-%%-------------------------------------------------------------------------
-
-function parse_char(c)
-    global pos inStr len
-    skip_whitespace;
-    if pos > len || inStr(pos) ~= c
-        error_pos(sprintf('Expected %c at position %%d', c));
-    else
-        pos = pos + 1;
-        skip_whitespace;
-    end
-
-%%-------------------------------------------------------------------------
-
-function c = next_char
-    global pos inStr len
-    skip_whitespace;
-    if pos > len
-        c = [];
-    else
-        c = inStr(pos);
-    end
-
-%%-------------------------------------------------------------------------
-
-function skip_whitespace
-    global pos inStr len
-    while pos <= len && isspace(inStr(pos))
-        pos = pos + 1;
-    end
-
-%%-------------------------------------------------------------------------
-function str = parseStr(varargin)
-    global pos inStr esc index_esc len_esc
- % len, ns = length(inStr), keyboard
-    type=inStr(pos);
-    if type ~= 'S' && type ~= 'C' && type ~= 'H'
-        error_pos('String starting with S expected at position %d');
-    else
-        pos = pos + 1;
-    end
-    if(type == 'C')
-        str=inStr(pos);
-        pos=pos+1;
-        return;
-    end
-    bytelen=double(parse_number());
-    if(length(inStr)>=pos+bytelen-1)
-        str=inStr(pos:pos+bytelen-1);
-        pos=pos+bytelen;
-    else
-        error_pos('End of file while expecting end of inStr');
-    end
-
-%%-------------------------------------------------------------------------
-
-function num = parse_number(varargin)
-    global pos inStr len isoct fileendian systemendian
-    id=strfind('iUIlLdD',inStr(pos));
-    if(isempty(id))
-        error_pos('expecting a number at position %d');
-    end
-    type={'int8','uint8','int16','int32','int64','single','double'};
-    bytelen=[1,1,2,4,8,4,8];
-    datastr=inStr(pos+1:pos+bytelen(id));
-    if(isoct)
-        newdata=int8(datastr);
-    else
-        newdata=uint8(datastr);
-    end
-    if(id<=5 && fileendian~=systemendian)
-        newdata=swapbytes(typecast(newdata,type{id}));
-    end
-    num=typecast(newdata,type{id});
-    pos = pos + bytelen(id)+1;
-
-%%-------------------------------------------------------------------------
-
-function val = parse_value(varargin)
-    global pos inStr len
-    true = 1; false = 0;
-
-    switch(inStr(pos))
-        case {'S','C','H'}
-            val = parseStr(varargin{:});
-            return;
-        case '['
-            val = parse_array(varargin{:});
-            return;
-        case '{'
-            val = parse_object(varargin{:});
-            if isstruct(val)
-                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
-                    val=jstruct2array(val);
-                end
-            elseif isempty(val)
-                val = struct;
-            end
-            return;
-        case {'i','U','I','l','L','d','D'}
-            val = parse_number(varargin{:});
-            return;
-        case 'T'
-            val = true;
-            pos = pos + 1;
-            return;
-        case 'F'
-            val = false;
-            pos = pos + 1;
-            return;
-        case {'Z','N'}
-            val = [];
-            pos = pos + 1;
-            return;
-    end
-    error_pos('Value expected at position %d');
-%%-------------------------------------------------------------------------
-
-function error_pos(msg)
-    global pos inStr len
-    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
-    if poShow(3) == poShow(2)
-        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after
-    end
-    msg = [sprintf(msg, pos) ': ' ...
-    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
-    error( ['JSONparser:invalidFormat: ' msg] );
-
-%%-------------------------------------------------------------------------
-
-function str = valid_field(str)
-global isoct
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
-
-%%-------------------------------------------------------------------------
-function endpos = matching_quote(str,pos)
-len=length(str);
-while(pos<len)
-    if(str(pos)=='"')
-        if(~(pos>1 && str(pos-1)=='\'))
-            endpos=pos;
-            return;
-        end        
-    end
-    pos=pos+1;
-end
-error('unmatched quotation mark');
-%%-------------------------------------------------------------------------
-function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
-global arraytoken
-level=1;
-maxlevel=level;
-endpos=0;
-bpos=arraytoken(arraytoken>=pos);
-tokens=str(bpos);
-len=length(tokens);
-pos=1;
-e1l=[];
-e1r=[];
-while(pos<=len)
-    c=tokens(pos);
-    if(c==']')
-        level=level-1;
-        if(isempty(e1r)) e1r=bpos(pos); end
-        if(level==0)
-            endpos=bpos(pos);
-            return
-        end
-    end
-    if(c=='[')
-        if(isempty(e1l)) e1l=bpos(pos); end
-        level=level+1;
-        maxlevel=max(maxlevel,level);
-    end
-    if(c=='"')
-        pos=matching_quote(tokens,pos+1);
-    end
-    pos=pos+1;
-end
-if(endpos==0) 
-    error('unmatched "]"');
-end
-

assignments/machine-learning-ex1/ex1/lib/jsonlab/mergestruct.m

@@ -1,33 +0,0 @@
-function s=mergestruct(s1,s2)
-%
-% s=mergestruct(s1,s2)
-%
-% merge two struct objects into one
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% date: 2012/12/22
-%
-% input:
-%      s1,s2: a struct object, s1 and s2 can not be arrays
-%
-% output:
-%      s: the merged struct object. fields in s1 and s2 will be combined in s.
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(~isstruct(s1) || ~isstruct(s2))
-    error('input parameters contain non-struct');
-end
-if(length(s1)>1 || length(s2)>1)
-    error('can not merge struct arrays');
-end
-fn=fieldnames(s2);
-s=s1;
-for i=1:length(fn)              
-    s=setfield(s,fn{i},getfield(s2,fn{i}));
-end
-

assignments/machine-learning-ex1/ex1/lib/jsonlab/savejson.m

@@ -1,475 +0,0 @@
-function json=savejson(rootname,obj,varargin)
-%
-% json=savejson(rootname,obj,filename)
-%    or
-% json=savejson(rootname,obj,opt)
-% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
-%
-% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-% Object Notation) string
-%
-% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2011/09/09
-%
-% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      rootname: the name of the root-object, when set to '', the root name
-%        is ignored, however, when opt.ForceRootName is set to 1 (see below),
-%        the MATLAB variable name will be used as the root name.
-%      obj: a MATLAB object (array, cell, cell array, struct, struct array).
-%      filename: a string for the file name to save the output JSON data.
-%      opt: a struct for additional options, ignore to use default values.
-%        opt can have the following fields (first in [.|.] is the default)
-%
-%        opt.FileName [''|string]: a file name to save the output JSON data
-%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-%                         of a 1D/2D array;
-%        opt.ArrayIndent [1|0]: if 1, output explicit data array with
-%                         precedent indentation; if 0, no indentation
-%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-%                         array in JSON array format; if sets to 1, an
-%                         array will be shown as a struct with fields
-%                         "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-%                         sparse arrays, the non-zero elements will be
-%                         saved to _ArrayData_ field in triplet-format i.e.
-%                         (ix,iy,val) and "_ArrayIsSparse_" will be added
-%                         with a value of 1; for a complex array, the 
-%                         _ArrayData_ array will include two columns 
-%                         (4 for sparse) to record the real and imaginary 
-%                         parts, and also "_ArrayIsComplex_":1 is added. 
-%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-%                         will use true/false rather than 1/0.
-%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-%                         numerical element will be shown without a square
-%                         bracket, unless it is the root object; if 0, square
-%                         brackets are forced for any numerical arrays.
-%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-%                         will use the name of the passed obj variable as the 
-%                         root object name; if obj is an expression and 
-%                         does not have a name, 'root' will be used; if this 
-%                         is set to 0 and rootname is empty, the root level 
-%                         will be merged down to the lower level.
-%        opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-%                         and $1 represents the sign. For those who want to use
-%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'
-%        opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-%                         to represent NaN
-%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-%                         for example, if opt.JSONP='foo', the JSON data is
-%                         wrapped inside a function call as 'foo(...);'
-%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-%                         back to the string form
-%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
-%
-%        opt can be replaced by a list of ('param',value) pairs. The param 
-%        string is equivallent to a field in opt and is case sensitive.
-% output:
-%      json: a string in the JSON format (see http://json.org)
-%
-% examples:
-%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-%               'SpecialData',[nan, inf, -inf]);
-%      savejson('jmesh',jsonmesh)
-%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(nargin==1)
-   varname=inputname(1);
-   obj=rootname;
-   if(isempty(varname)) 
-      varname='root';
-   end
-   rootname=varname;
-else
-   varname=inputname(2);
-end
-if(length(varargin)==1 && ischar(varargin{1}))
-   opt=struct('FileName',varargin{1});
-else
-   opt=varargin2struct(varargin{:});
-end
-opt.IsOctave=exist('OCTAVE_VERSION','builtin');
-rootisarray=0;
-rootlevel=1;
-forceroot=jsonopt('ForceRootName',0,opt);
-if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
-    rootisarray=1;
-    rootlevel=0;
-else
-    if(isempty(rootname))
-        rootname=varname;
-    end
-end
-if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
-    rootname='root';
-end
-
-whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-if(jsonopt('Compact',0,opt)==1)
-    whitespaces=struct('tab','','newline','','sep',',');
-end
-if(~isfield(opt,'whitespaces_'))
-    opt.whitespaces_=whitespaces;
-end
-
-nl=whitespaces.newline;
-
-json=obj2json(rootname,obj,rootlevel,opt);
-if(rootisarray)
-    json=sprintf('%s%s',json,nl);
-else
-    json=sprintf('{%s%s%s}\n',nl,json,nl);
-end
-
-jsonp=jsonopt('JSONP','',opt);
-if(~isempty(jsonp))
-    json=sprintf('%s(%s);%s',jsonp,json,nl);
-end
-
-% save to a file if FileName is set, suggested by Patrick Rapin
-if(~isempty(jsonopt('FileName','',opt)))
-    if(jsonopt('SaveBinary',0,opt)==1)
-	    fid = fopen(opt.FileName, 'wb');
-	    fwrite(fid,json);
-    else
-	    fid = fopen(opt.FileName, 'wt');
-	    fwrite(fid,json,'char');
-    end
-    fclose(fid);
-end
-
-%%-------------------------------------------------------------------------
-function txt=obj2json(name,item,level,varargin)
-
-if(iscell(item))
-    txt=cell2json(name,item,level,varargin{:});
-elseif(isstruct(item))
-    txt=struct2json(name,item,level,varargin{:});
-elseif(ischar(item))
-    txt=str2json(name,item,level,varargin{:});
-else
-    txt=mat2json(name,item,level,varargin{:});
-end
-
-%%-------------------------------------------------------------------------
-function txt=cell2json(name,item,level,varargin)
-txt='';
-if(~iscell(item))
-        error('input is not a cell');
-end
-
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
-padding0=repmat(ws.tab,1,level);
-padding2=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-if(len>1)
-    if(~isempty(name))
-        txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; 
-    else
-        txt=sprintf('%s[%s',padding0,nl); 
-    end
-elseif(len==0)
-    if(~isempty(name))
-        txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; 
-    else
-        txt=sprintf('%s[]',padding0); 
-    end
-end
-for j=1:dim(2)
-    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
-    for i=1:dim(1)
-       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
-       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-    end
-    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
-    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-end
-if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
-
-%%-------------------------------------------------------------------------
-function txt=struct2json(name,item,level,varargin)
-txt='';
-if(~isstruct(item))
-	error('input is not a struct');
-end
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding0=repmat(ws.tab,1,level);
-padding2=repmat(ws.tab,1,level+1);
-padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
-nl=ws.newline;
-
-if(~isempty(name)) 
-    if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
-else
-    if(len>1) txt=sprintf('%s[%s',padding0,nl); end
-end
-for j=1:dim(2)
-  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
-  for i=1:dim(1)
-    names = fieldnames(item(i,j));
-    if(~isempty(name) && len==1)
-        txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); 
-    else
-        txt=sprintf('%s%s{%s',txt,padding1,nl); 
-    end
-    if(~isempty(names))
-      for e=1:length(names)
-	    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
-             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
-        if(e<length(names)) txt=sprintf('%s%s',txt,','); end
-        txt=sprintf('%s%s',txt,nl);
-      end
-    end
-    txt=sprintf('%s%s}',txt,padding1);
-    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-  end
-  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
-  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-end
-if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
-
-%%-------------------------------------------------------------------------
-function txt=str2json(name,item,level,varargin)
-txt='';
-if(~ischar(item))
-        error('input is not a string');
-end
-item=reshape(item, max(size(item),[1 0]));
-len=size(item,1);
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding1=repmat(ws.tab,1,level);
-padding0=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-sep=ws.sep;
-
-if(~isempty(name)) 
-    if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
-else
-    if(len>1) txt=sprintf('%s[%s',padding1,nl); end
-end
-isoct=jsonopt('IsOctave',0,varargin{:});
-for e=1:len
-    if(isoct)
-        val=regexprep(item(e,:),'\\','\\');
-        val=regexprep(val,'"','\"');
-        val=regexprep(val,'^"','\"');
-    else
-        val=regexprep(item(e,:),'\\','\\\\');
-        val=regexprep(val,'"','\\"');
-        val=regexprep(val,'^"','\\"');
-    end
-    val=escapejsonstring(val);
-    if(len==1)
-        obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
-	if(isempty(name)) obj=['"',val,'"']; end
-        txt=sprintf('%s%s%s%s',txt,padding1,obj);
-    else
-        txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
-    end
-    if(e==len) sep=''; end
-    txt=sprintf('%s%s',txt,sep);
-end
-if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
-
-%%-------------------------------------------------------------------------
-function txt=mat2json(name,item,level,varargin)
-if(~isnumeric(item) && ~islogical(item))
-        error('input is not an array');
-end
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding1=repmat(ws.tab,1,level);
-padding0=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-sep=ws.sep;
-
-if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
-   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
-    if(isempty(name))
-    	txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
-              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
-    else
-    	txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
-              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
-    end
-else
-    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
-        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
-    else
-        numtxt=matdata2json(item,level+1,varargin{:});
-    end
-    if(isempty(name))
-    	txt=sprintf('%s%s',padding1,numtxt);
-    else
-        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
-           	txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
-        else
-    	    txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
-        end
-    end
-    return;
-end
-dataformat='%s%s%s%s%s';
-
-if(issparse(item))
-    [ix,iy]=find(item);
-    data=full(item(find(item)));
-    if(~isreal(item))
-       data=[real(data(:)),imag(data(:))];
-       if(size(item,1)==1)
-           % Kludge to have data's 'transposedness' match item's.
-           % (Necessary for complex row vector handling below.)
-           data=data';
-       end
-       txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
-    end
-    txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
-    if(size(item,1)==1)
-        % Row vector, store only column indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([iy(:),data'],level+2,varargin{:}), nl);
-    elseif(size(item,2)==1)
-        % Column vector, store only row indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([ix,data],level+2,varargin{:}), nl);
-    else
-        % General case, store row and column indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([ix,iy,data],level+2,varargin{:}), nl);
-    end
-else
-    if(isreal(item))
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-            matdata2json(item(:)',level+2,varargin{:}), nl);
-    else
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
-    end
-end
-txt=sprintf('%s%s%s',txt,padding1,'}');
-
-%%-------------------------------------------------------------------------
-function txt=matdata2json(mat,level,varargin)
-
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-tab=ws.tab;
-nl=ws.newline;
-
-if(size(mat,1)==1)
-    pre='';
-    post='';
-    level=level-1;
-else
-    pre=sprintf('[%s',nl);
-    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
-end
-
-if(isempty(mat))
-    txt='null';
-    return;
-end
-floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
-%if(numel(mat)>1)
-    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
-%else
-%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
-%end
-
-if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
-    formatstr=[repmat(tab,1,level) formatstr];
-end
-
-txt=sprintf(formatstr,mat');
-txt(end-length(nl):end)=[];
-if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
-   txt=regexprep(txt,'1','true');
-   txt=regexprep(txt,'0','false');
-end
-%txt=regexprep(mat2str(mat),'\s+',',');
-%txt=regexprep(txt,';',sprintf('],\n['));
-% if(nargin>=2 && size(mat,1)>1)
-%     txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
-% end
-txt=[pre txt post];
-if(any(isinf(mat(:))))
-    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
-end
-if(any(isnan(mat(:))))
-    txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
-end
-
-%%-------------------------------------------------------------------------
-function newname=checkname(name,varargin)
-isunpack=jsonopt('UnpackHex',1,varargin{:});
-newname=name;
-if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
-    return
-end
-if(isunpack)
-    isoct=jsonopt('IsOctave',0,varargin{:});
-    if(~isoct)
-        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
-    else
-        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
-        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
-        if(isempty(pos)) return; end
-        str0=name;
-        pos0=[0 pend(:)' length(name)];
-        newname='';
-        for i=1:length(pos)
-            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
-        end
-        if(pos(end)~=length(name))
-            newname=[newname str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-end
-
-%%-------------------------------------------------------------------------
-function newstr=escapejsonstring(str)
-newstr=str;
-isoct=exist('OCTAVE_VERSION','builtin');
-if(isoct)
-   vv=sscanf(OCTAVE_VERSION,'%f');
-   if(vv(1)>=3.8) isoct=0; end
-end
-if(isoct)
-  escapechars={'\a','\f','\n','\r','\t','\v'};
-  for i=1:length(escapechars);
-    newstr=regexprep(newstr,escapechars{i},escapechars{i});
-  end
-else
-  escapechars={'\a','\b','\f','\n','\r','\t','\v'};
-  for i=1:length(escapechars);
-    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
-  end
-end

assignments/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m

@@ -1,504 +0,0 @@
-function json=saveubjson(rootname,obj,varargin)
-%
-% json=saveubjson(rootname,obj,filename)
-%    or
-% json=saveubjson(rootname,obj,opt)
-% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
-%
-% convert a MATLAB object (cell, struct or array) into a Universal 
-% Binary JSON (UBJSON) binary string
-%
-% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2013/08/17
-%
-% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      rootname: the name of the root-object, when set to '', the root name
-%        is ignored, however, when opt.ForceRootName is set to 1 (see below),
-%        the MATLAB variable name will be used as the root name.
-%      obj: a MATLAB object (array, cell, cell array, struct, struct array)
-%      filename: a string for the file name to save the output UBJSON data
-%      opt: a struct for additional options, ignore to use default values.
-%        opt can have the following fields (first in [.|.] is the default)
-%
-%        opt.FileName [''|string]: a file name to save the output JSON data
-%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-%                         array in JSON array format; if sets to 1, an
-%                         array will be shown as a struct with fields
-%                         "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-%                         sparse arrays, the non-zero elements will be
-%                         saved to _ArrayData_ field in triplet-format i.e.
-%                         (ix,iy,val) and "_ArrayIsSparse_" will be added
-%                         with a value of 1; for a complex array, the 
-%                         _ArrayData_ array will include two columns 
-%                         (4 for sparse) to record the real and imaginary 
-%                         parts, and also "_ArrayIsComplex_":1 is added. 
-%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-%                         will use true/false rather than 1/0.
-%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-%                         numerical element will be shown without a square
-%                         bracket, unless it is the root object; if 0, square
-%                         brackets are forced for any numerical arrays.
-%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-%                         will use the name of the passed obj variable as the 
-%                         root object name; if obj is an expression and 
-%                         does not have a name, 'root' will be used; if this 
-%                         is set to 0 and rootname is empty, the root level 
-%                         will be merged down to the lower level.
-%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-%                         for example, if opt.JSON='foo', the JSON data is
-%                         wrapped inside a function call as 'foo(...);'
-%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-%                         back to the string form
-%
-%        opt can be replaced by a list of ('param',value) pairs. The param 
-%        string is equivallent to a field in opt and is case sensitive.
-% output:
-%      json: a binary string in the UBJSON format (see http://ubjson.org)
-%
-% examples:
-%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-%               'SpecialData',[nan, inf, -inf]);
-%      saveubjson('jsonmesh',jsonmesh)
-%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(nargin==1)
-   varname=inputname(1);
-   obj=rootname;
-   if(isempty(varname)) 
-      varname='root';
-   end
-   rootname=varname;
-else
-   varname=inputname(2);
-end
-if(length(varargin)==1 && ischar(varargin{1}))
-   opt=struct('FileName',varargin{1});
-else
-   opt=varargin2struct(varargin{:});
-end
-opt.IsOctave=exist('OCTAVE_VERSION','builtin');
-rootisarray=0;
-rootlevel=1;
-forceroot=jsonopt('ForceRootName',0,opt);
-if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
-    rootisarray=1;
-    rootlevel=0;
-else
-    if(isempty(rootname))
-        rootname=varname;
-    end
-end
-if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
-    rootname='root';
-end
-json=obj2ubjson(rootname,obj,rootlevel,opt);
-if(~rootisarray)
-    json=['{' json '}'];
-end
-
-jsonp=jsonopt('JSONP','',opt);
-if(~isempty(jsonp))
-    json=[jsonp '(' json ')'];
-end
-
-% save to a file if FileName is set, suggested by Patrick Rapin
-if(~isempty(jsonopt('FileName','',opt)))
-    fid = fopen(opt.FileName, 'wb');
-    fwrite(fid,json);
-    fclose(fid);
-end
-
-%%-------------------------------------------------------------------------
-function txt=obj2ubjson(name,item,level,varargin)
-
-if(iscell(item))
-    txt=cell2ubjson(name,item,level,varargin{:});
-elseif(isstruct(item))
-    txt=struct2ubjson(name,item,level,varargin{:});
-elseif(ischar(item))
-    txt=str2ubjson(name,item,level,varargin{:});
-else
-    txt=mat2ubjson(name,item,level,varargin{:});
-end
-
-%%-------------------------------------------------------------------------
-function txt=cell2ubjson(name,item,level,varargin)
-txt='';
-if(~iscell(item))
-        error('input is not a cell');
-end
-
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item); % let's handle 1D cell first
-if(len>1) 
-    if(~isempty(name))
-        txt=[S_(checkname(name,varargin{:})) '[']; name=''; 
-    else
-        txt='['; 
-    end
-elseif(len==0)
-    if(~isempty(name))
-        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; 
-    else
-        txt='Z'; 
-    end
-end
-for j=1:dim(2)
-    if(dim(1)>1) txt=[txt '[']; end
-    for i=1:dim(1)
-       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
-    end
-    if(dim(1)>1) txt=[txt ']']; end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=struct2ubjson(name,item,level,varargin)
-txt='';
-if(~isstruct(item))
-	error('input is not a struct');
-end
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-
-if(~isempty(name)) 
-    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
-else
-    if(len>1) txt='['; end
-end
-for j=1:dim(2)
-  if(dim(1)>1) txt=[txt '[']; end
-  for i=1:dim(1)
-     names = fieldnames(item(i,j));
-     if(~isempty(name) && len==1)
-        txt=[txt S_(checkname(name,varargin{:})) '{']; 
-     else
-        txt=[txt '{']; 
-     end
-     if(~isempty(names))
-       for e=1:length(names)
-	     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
-             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
-       end
-     end
-     txt=[txt '}'];
-  end
-  if(dim(1)>1) txt=[txt ']']; end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=str2ubjson(name,item,level,varargin)
-txt='';
-if(~ischar(item))
-        error('input is not a string');
-end
-item=reshape(item, max(size(item),[1 0]));
-len=size(item,1);
-
-if(~isempty(name)) 
-    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
-else
-    if(len>1) txt='['; end
-end
-isoct=jsonopt('IsOctave',0,varargin{:});
-for e=1:len
-    val=item(e,:);
-    if(len==1)
-        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
-	if(isempty(name)) obj=['',S_(val),'']; end
-        txt=[txt,'',obj];
-    else
-        txt=[txt,'',['',S_(val),'']];
-    end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=mat2ubjson(name,item,level,varargin)
-if(~isnumeric(item) && ~islogical(item))
-        error('input is not an array');
-end
-
-if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
-   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
-      cid=I_(uint32(max(size(item))));
-      if(isempty(name))
-    	txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
-      else
-          if(isempty(item))
-              txt=[S_(checkname(name,varargin{:})),'Z'];
-              return;
-          else
-    	      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
-          end
-      end
-else
-    if(isempty(name))
-    	txt=matdata2ubjson(item,level+1,varargin{:});
-    else
-        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
-            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
-           	txt=[S_(checkname(name,varargin{:})) numtxt];
-        else
-    	    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
-        end
-    end
-    return;
-end
-if(issparse(item))
-    [ix,iy]=find(item);
-    data=full(item(find(item)));
-    if(~isreal(item))
-       data=[real(data(:)),imag(data(:))];
-       if(size(item,1)==1)
-           % Kludge to have data's 'transposedness' match item's.
-           % (Necessary for complex row vector handling below.)
-           data=data';
-       end
-       txt=[txt,S_('_ArrayIsComplex_'),'T'];
-    end
-    txt=[txt,S_('_ArrayIsSparse_'),'T'];
-    if(size(item,1)==1)
-        % Row vector, store only column indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([iy(:),data'],level+2,varargin{:})];
-    elseif(size(item,2)==1)
-        % Column vector, store only row indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([ix,data],level+2,varargin{:})];
-    else
-        % General case, store row and column indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([ix,iy,data],level+2,varargin{:})];
-    end
-else
-    if(isreal(item))
-        txt=[txt,S_('_ArrayData_'),...
-            matdata2ubjson(item(:)',level+2,varargin{:})];
-    else
-        txt=[txt,S_('_ArrayIsComplex_'),'T'];
-        txt=[txt,S_('_ArrayData_'),...
-            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
-    end
-end
-txt=[txt,'}'];
-
-%%-------------------------------------------------------------------------
-function txt=matdata2ubjson(mat,level,varargin)
-if(isempty(mat))
-    txt='Z';
-    return;
-end
-if(size(mat,1)==1)
-    level=level-1;
-end
-type='';
-hasnegtive=(mat<0);
-if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
-    if(isempty(hasnegtive))
-       if(max(mat(:))<=2^8)
-           type='U';
-       end
-    end
-    if(isempty(type))
-        % todo - need to consider negative ones separately
-        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
-        if(isempty(find(id)))
-            error('high-precision data is not yet supported');
-        end
-        key='iIlL';
-	type=key(find(id));
-    end
-    txt=[I_a(mat(:),type,size(mat))];
-elseif(islogical(mat))
-    logicalval='FT';
-    if(numel(mat)==1)
-        txt=logicalval(mat+1);
-    else
-        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
-    end
-else
-    if(numel(mat)==1)
-        txt=['[' D_(mat) ']'];
-    else
-        txt=D_a(mat(:),'D',size(mat));
-    end
-end
-
-%txt=regexprep(mat2str(mat),'\s+',',');
-%txt=regexprep(txt,';',sprintf('],['));
-% if(nargin>=2 && size(mat,1)>1)
-%     txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
-% end
-if(any(isinf(mat(:))))
-    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
-end
-if(any(isnan(mat(:))))
-    txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
-end
-
-%%-------------------------------------------------------------------------
-function newname=checkname(name,varargin)
-isunpack=jsonopt('UnpackHex',1,varargin{:});
-newname=name;
-if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
-    return
-end
-if(isunpack)
-    isoct=jsonopt('IsOctave',0,varargin{:});
-    if(~isoct)
-        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
-    else
-        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
-        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
-        if(isempty(pos)) return; end
-        str0=name;
-        pos0=[0 pend(:)' length(name)];
-        newname='';
-        for i=1:length(pos)
-            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
-        end
-        if(pos(end)~=length(name))
-            newname=[newname str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-end
-%%-------------------------------------------------------------------------
-function val=S_(str)
-if(length(str)==1)
-  val=['C' str];
-else
-  val=['S' I_(int32(length(str))) str];
-end
-%%-------------------------------------------------------------------------
-function val=I_(num)
-if(~isinteger(num))
-    error('input is not an integer');
-end
-if(num>=0 && num<255)
-   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
-   return;
-end
-key='iIlL';
-cid={'int8','int16','int32','int64'};
-for i=1:4
-  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
-    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
-    return;
-  end
-end
-error('unsupported integer');
-
-%%-------------------------------------------------------------------------
-function val=D_(num)
-if(~isfloat(num))
-    error('input is not a float');
-end
-
-if(isa(num,'single'))
-  val=['d' data2byte(num,'uint8')];
-else
-  val=['D' data2byte(num,'uint8')];
-end
-%%-------------------------------------------------------------------------
-function data=I_a(num,type,dim,format)
-id=find(ismember('iUIlL',type));
-
-if(id==0)
-  error('unsupported integer array');
-end
-
-% based on UBJSON specs, all integer types are stored in big endian format
-
-if(id==1)
-  data=data2byte(swapbytes(int8(num)),'uint8');
-  blen=1;
-elseif(id==2)
-  data=data2byte(swapbytes(uint8(num)),'uint8');
-  blen=1;
-elseif(id==3)
-  data=data2byte(swapbytes(int16(num)),'uint8');
-  blen=2;
-elseif(id==4)
-  data=data2byte(swapbytes(int32(num)),'uint8');
-  blen=4;
-elseif(id==5)
-  data=data2byte(swapbytes(int64(num)),'uint8');
-  blen=8;
-end
-
-if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
-  format='opt';
-end
-if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
-  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
-      cid=I_(uint32(max(dim)));
-      data=['$' type '#' I_a(dim,cid(1)) data(:)'];
-  else
-      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
-  end
-  data=['[' data(:)'];
-else
-  data=reshape(data,blen,numel(data)/blen);
-  data(2:blen+1,:)=data;
-  data(1,:)=type;
-  data=data(:)';
-  data=['[' data(:)' ']'];
-end
-%%-------------------------------------------------------------------------
-function data=D_a(num,type,dim,format)
-id=find(ismember('dD',type));
-
-if(id==0)
-  error('unsupported float array');
-end
-
-if(id==1)
-  data=data2byte(single(num),'uint8');
-elseif(id==2)
-  data=data2byte(double(num),'uint8');
-end
-
-if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
-  format='opt';
-end
-if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
-  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
-      cid=I_(uint32(max(dim)));
-      data=['$' type '#' I_a(dim,cid(1)) data(:)'];
-  else
-      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
-  end
-  data=['[' data];
-else
-  data=reshape(data,(id*4),length(data)/(id*4));
-  data(2:(id*4+1),:)=data;
-  data(1,:)=type;
-  data=data(:)';
-  data=['[' data(:)' ']'];
-end
-%%-------------------------------------------------------------------------
-function bytes=data2byte(varargin)
-bytes=typecast(varargin{:});
-bytes=bytes(:)';

assignments/machine-learning-ex1/ex1/lib/jsonlab/varargin2struct.m

@@ -1,40 +0,0 @@
-function opt=varargin2struct(varargin)
-%
-% opt=varargin2struct('param1',value1,'param2',value2,...)
-%   or
-% opt=varargin2struct(...,optstruct,...)
-%
-% convert a series of input parameters into a structure
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% date: 2012/12/22
-%
-% input:
-%      'param', value: the input parameters should be pairs of a string and a value
-%       optstruct: if a parameter is a struct, the fields will be merged to the output struct
-%
-% output:
-%      opt: a struct where opt.param1=value1, opt.param2=value2 ...
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-len=length(varargin);
-opt=struct;
-if(len==0) return; end
-i=1;
-while(i<=len)
-    if(isstruct(varargin{i}))
-        opt=mergestruct(opt,varargin{i});
-    elseif(ischar(varargin{i}) && i<len)
-        opt=setfield(opt,varargin{i},varargin{i+1});
-        i=i+1;
-    else
-        error('input must be in the form of ...,''name'',value,... pairs or structs');
-    end
-    i=i+1;
-end
-

assignments/machine-learning-ex1/ex1/lib/makeValidFieldName.m

@@ -1,30 +0,0 @@
-function str = makeValidFieldName(str)
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    isoct=exist('OCTAVE_VERSION','builtin');
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end

assignments/machine-learning-ex1/ex1/lib/submitWithConfiguration.m

@@ -1,126 +0,0 @@
-function submitWithConfiguration(conf)
-  addpath('./lib/jsonlab');
-
-  parts = parts(conf);
-
-  fprintf('== Submitting solutions | %s...\n', conf.itemName);
-
-  tokenFile = 'token.mat';
-  if exist(tokenFile, 'file')
-    load(tokenFile);
-    [email token] = promptToken(email, token, tokenFile);
-  else
-    [email token] = promptToken('', '', tokenFile);
-  end
-
-  if isempty(token)
-    fprintf('!! Submission Cancelled\n');
-    return
-  end
-
-  try
-    response = submitParts(conf, email, token, parts);
-  catch
-    e = lasterror();
-    fprintf( ...
-      '!! Submission failed: unexpected error: %s\n', ...
-      e.message);
-    fprintf('!! Please try again later.\n');
-    return
-  end
-
-  if isfield(response, 'errorMessage')
-    fprintf('!! Submission failed: %s\n', response.errorMessage);
-  else
-    showFeedback(parts, response);
-    save(tokenFile, 'email', 'token');
-  end
-end
-
-function [email token] = promptToken(email, existingToken, tokenFile)
-  if (~isempty(email) && ~isempty(existingToken))
-    prompt = sprintf( ...
-      'Use token from last successful submission (%s)? (Y/n): ', ...
-      email);
-    reenter = input(prompt, 's');
-
-    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
-      token = existingToken;
-      return;
-    else
-      delete(tokenFile);
-    end
-  end
-  email = input('Login (email address): ', 's');
-  token = input('Token: ', 's');
-end
-
-function isValid = isValidPartOptionIndex(partOptions, i)
-  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
-end
-
-function response = submitParts(conf, email, token, parts)
-  body = makePostBody(conf, email, token, parts);
-  submissionUrl = submissionUrl();
-  params = {'jsonBody', body};
-  % responseBody = urlread(submissionUrl, 'post', params);
-  [code, responseBody] = system(sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, submissionUrl));
-  response = loadjson(responseBody);
-end
-
-function body = makePostBody(conf, email, token, parts)
-  bodyStruct.assignmentSlug = conf.assignmentSlug;
-  bodyStruct.submitterEmail = email;
-  bodyStruct.secret = token;
-  bodyStruct.parts = makePartsStruct(conf, parts);
-
-  opt.Compact = 1;
-  body = savejson('', bodyStruct, opt);
-end
-
-function partsStruct = makePartsStruct(conf, parts)
-  for part = parts
-    partId = part{:}.id;
-    fieldName = makeValidFieldName(partId);
-    outputStruct.output = conf.output(partId);
-    partsStruct.(fieldName) = outputStruct;
-  end
-end
-
-function [parts] = parts(conf)
-  parts = {};
-  for partArray = conf.partArrays
-    part.id = partArray{:}{1};
-    part.sourceFiles = partArray{:}{2};
-    part.name = partArray{:}{3};
-    parts{end + 1} = part;
-  end
-end
-
-function showFeedback(parts, response)
-  fprintf('== \n');
-  fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
-  fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
-  for part = parts
-    score = '';
-    partFeedback = '';
-    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
-    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
-    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
-    fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
-  end
-  evaluation = response.evaluation;
-  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
-  fprintf('==                                   --------------------------------\n');
-  fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
-  fprintf('== \n');
-end
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%
-% Service configuration
-%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-function submissionUrl = submissionUrl()
-  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
-end

assignments/machine-learning-ex1/ex1/normalEqn.m

@@ -1,23 +0,0 @@
-function [theta] = normalEqn(X, y)
-%NORMALEQN Computes the closed-form solution to linear regression 
-%   NORMALEQN(X,y) computes the closed-form solution to linear 
-%   regression using the normal equations.
-
-theta = zeros(size(X, 2), 1);
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Complete the code to compute the closed form solution
-%               to linear regression and put the result in theta.
-%
-
-% ---------------------- Sample Solution ----------------------
-
-theta = pinv(X' * X) * X' * y;
-
-
-% -------------------------------------------------------------
-
-
-% ============================================================
-
-end

assignments/machine-learning-ex1/ex1/plotData.m

@@ -1,29 +0,0 @@
-function plotData(x, y)
-%PLOTDATA Plots the data points x and y into a new figure 
-%   PLOTDATA(x,y) plots the data points and gives the figure axes labels of
-%   population and profit.
-
-% ====================== YOUR CODE HERE ======================
-
-% Instructions: Plot the training data into a figure using the 
-%               "figure" and "plot" commands. Set the axes labels using
-%               the "xlabel" and "ylabel" commands. Assume the 
-%               population and revenue data have been passed in
-%               as the x and y arguments of this function.
-%
-% Hint: You can use the 'rx' option with plot to have the markers
-%       appear as red crosses. Furthermore, you can make the
-%       markers larger by using plot(..., 'rx', 'MarkerSize', 10);
-
-figure; % open a new figure window
-plot(x, y, 'rx', 'MarkerSize', 10);
-xlabel('xlabel');
-ylabel('ylabel');
-
-
-
-
-
-% ============================================================
-
-end

assignments/machine-learning-ex1/ex1/submit.m

@@ -1,69 +0,0 @@
-function submit()
-  addpath('./lib');
-
-  conf.assignmentSlug = 'linear-regression';
-  conf.itemName = 'Linear Regression with Multiple Variables';
-  conf.partArrays = { ...
-    { ...
-      '1', ...
-      { 'warmUpExercise.m' }, ...
-      'Warm-up Exercise', ...
-    }, ...
-    { ...
-      '2', ...
-      { 'computeCost.m' }, ...
-      'Computing Cost (for One Variable)', ...
-    }, ...
-    { ...
-      '3', ...
-      { 'gradientDescent.m' }, ...
-      'Gradient Descent (for One Variable)', ...
-    }, ...
-    { ...
-      '4', ...
-      { 'featureNormalize.m' }, ...
-      'Feature Normalization', ...
-    }, ...
-    { ...
-      '5', ...
-      { 'computeCostMulti.m' }, ...
-      'Computing Cost (for Multiple Variables)', ...
-    }, ...
-    { ...
-      '6', ...
-      { 'gradientDescentMulti.m' }, ...
-      'Gradient Descent (for Multiple Variables)', ...
-    }, ...
-    { ...
-      '7', ...
-      { 'normalEqn.m' }, ...
-      'Normal Equations', ...
-    }, ...
-  };
-  conf.output = @output;
-
-  submitWithConfiguration(conf);
-end
-
-function out = output(partId)
-  % Random Test Cases
-  X1 = [ones(20,1) (exp(1) + exp(2) * (0.1:0.1:2))'];
-  Y1 = X1(:,2) + sin(X1(:,1)) + cos(X1(:,2));
-  X2 = [X1 X1(:,2).^0.5 X1(:,2).^0.25];
-  Y2 = Y1.^0.5 + Y1;
-  if partId == '1'
-    out = sprintf('%0.5f ', warmUpExercise());
-  elseif partId == '2'
-    out = sprintf('%0.5f ', computeCost(X1, Y1, [0.5 -0.5]'));
-  elseif partId == '3'
-    out = sprintf('%0.5f ', gradientDescent(X1, Y1, [0.5 -0.5]', 0.01, 10));
-  elseif partId == '4'
-    out = sprintf('%0.5f ', featureNormalize(X2(:,2:4)));
-  elseif partId == '5'
-    out = sprintf('%0.5f ', computeCostMulti(X2, Y2, [0.1 0.2 0.3 0.4]'));
-  elseif partId == '6'
-    out = sprintf('%0.5f ', gradientDescentMulti(X2, Y2, [-0.1 -0.2 -0.3 -0.4]', 0.01, 10));
-  elseif partId == '7'
-    out = sprintf('%0.5f ', normalEqn(X2, Y2));
-  end 
-end

assignments/machine-learning-ex1/ex1/test.m

@@ -1,6 +0,0 @@
-function [JVal, gradient] = functionCost(theta)
-JVal = theta' * theta;
-gradient = theta;
-% =========================================================================
-
-end

assignments/machine-learning-ex1/ex1/token.mat

@@ -1,15 +0,0 @@
-# Created by Octave 4.0.3, Mon Nov 14 10:25:22 2016 GMT <unknown@DESKTOP-1S2UOSP>
-# name: email
-# type: sq_string
-# elements: 1
-# length: 16
-scruel@vip.qq.com
-
-
-# name: token
-# type: sq_string
-# elements: 1
-# length: 16
-A3Wc4pI1qYjCUaRB
-
-

assignments/machine-learning-ex1/ex1/warmUpExercise.m

@@ -1,21 +0,0 @@
-function A = warmUpExercise()
-%WARMUPEXERCISE Example function in octave
-%   A = WARMUPEXERCISE() is an example function that returns the 5x5 identity matrix
-
-A = [];
-% ============= YOUR CODE HERE ==============
-% Instructions: Return the 5x5 identity matrix 
-%               In octave, we return values by defining which variables
-%               represent the return values (at the top of the file)
-%               and then set them accordingly. 
-
-
-
-A = eye(5);
-
-
-
-% ===========================================
-
-
-end

assignments/machine-learning-ex2/ex2/costFunction.m

@@ -1,32 +0,0 @@
-function [J, grad] = costFunction(theta, X, y)
-%COSTFUNCTION Compute cost and gradient for logistic regression
-%   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
-%   parameter for logistic regression and the gradient of the cost
-%   w.r.t. to the parameters.
-
-% Initialize some useful values
-m = length(y); % number of training examples
-
-% You need to return the following variables correctly 
-J = 0;
-grad = zeros(size(theta));
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the cost of a particular choice of theta.
-%               You should set J to the cost.
-%               Compute the partial derivatives and set grad to the partial
-%               derivatives of the cost w.r.t. each parameter in theta
-%
-% Note: grad should have the same dimensions as theta
-%
-
-hx = sigmoid(X * theta); %hypothesis, m * 1
-J = 1 / m * sum(-y' * log(hx) - (1 .- y)' * log(1 -hx));
-grad = 1 / m * X' *(hx - y);
-
-
-
-
-% =============================================================
-
-end

assignments/machine-learning-ex2/ex2/costFunctionReg.m

@@ -1,32 +0,0 @@
-function [J, grad] = costFunctionReg(theta, X, y, lambda)
-%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
-%   J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
-%   theta as the parameter for regularized logistic regression and the
-%   gradient of the cost w.r.t. to the parameters. 
-
-% Initialize some useful values
-m = length(y); % number of training examples
-
-% You need to return the following variables correctly 
-J = 0;
-grad = zeros(size(theta));
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the cost of a particular choice of theta.
-%               You should set J to the cost.
-%               Compute the partial derivatives and set grad to the partial
-%               derivatives of the cost w.r.t. each parameter in theta
-
-hx = sigmoid(X * theta); %hypothesis, m * 1
-
-J = 1 / m * sum(-y' * log(hx) - (1 - y)' * log(1 - hx)) + lambda / (2 * m) * theta(2:end)' * theta(2:end);
-
-gradf = (1 / m) * (X(:, 1)' * (hx - y));
-gradb = (1 / m) * (X(:, 2:end)' * (hx - y)) + lambda * theta(2:end) / m;
-
-grad = [gradf;gradb];
-
-
-% =============================================================
-
-end

assignments/machine-learning-ex2/ex2/ex2.m

@@ -1,135 +0,0 @@
-%% Machine Learning Online Class - Exercise 2: Logistic Regression
-%
-%  Instructions
-%  ------------
-% 
-%  This file contains code that helps you get started on the logistic
-%  regression exercise. You will need to complete the following functions 
-%  in this exericse:
-%
-%     sigmoid.m
-%     costFunction.m
-%     predict.m
-%     costFunctionReg.m
-%
-%  For this exercise, you will not need to change any code in this file,
-%  or any other files other than those mentioned above.
-%
-
-%% Initialization
-clear ; close all; clc
-
-%% Load Data
-%  The first two columns contains the exam scores and the third column
-%  contains the label.
-
-data = load('ex2data1.txt');
-X = data(:, [1, 2]); y = data(:, 3);
-
-%% ==================== Part 1: Plotting ====================
-%  We start the exercise by first plotting the data to understand the 
-%  the problem we are working with.
-
-fprintf(['Plotting data with + indicating (y = 1) examples and o ' ...
-         'indicating (y = 0) examples.\n']);
-
-plotData(X, y);
-
-% Put some labels 
-hold on;
-% Labels and Legend
-xlabel('Exam 1 score')
-ylabel('Exam 2 score')
-
-% Specified in plot order
-legend('Admitted', 'Not admitted')
-hold off;
-
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
-
-
-%% ============ Part 2: Compute Cost and Gradient ============
-%  In this part of the exercise, you will implement the cost and gradient
-%  for logistic regression. You neeed to complete the code in 
-%  costFunction.m
-
-%  Setup the data matrix appropriately, and add ones for the intercept term
-[m, n] = size(X);
-
-% Add intercept term to x and X_test
-X = [ones(m, 1) X];
-
-% Initialize fitting parameters
-initial_theta = zeros(n + 1, 1);
-
-% Compute and display initial cost and gradient
-[cost, grad] = costFunction(initial_theta, X, y);
-
-fprintf('Cost at initial theta (zeros): %f\n', cost);
-fprintf('Gradient at initial theta (zeros): \n');
-fprintf(' %f \n', grad);
-
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
-
-
-%% ============= Part 3: Optimizing using fminunc  =============
-%  In this exercise, you will use a built-in function (fminunc) to find the
-%  optimal parameters theta.
-
-%  Set options for fminunc
-options = optimset('GradObj', 'on', 'MaxIter', 400);
-
-%  Run fminunc to obtain the optimal theta
-%  This function will return theta and the cost 
-[theta, cost] = ...
-	fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
-
-% Print theta to screen
-fprintf('Cost at theta found by fminunc: %f\n', cost);
-fprintf('theta: \n');
-fprintf(' %f \n', theta);
-
-% Plot Boundary
-plotDecisionBoundary(theta, X, y);
-
-% Put some labels 
-hold on;
-% Labels and Legend
-xlabel('Exam 1 score')
-ylabel('Exam 2 score')
-
-% Specified in plot order
-legend('Admitted', 'Not admitted')
-hold off;
-
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
-
-%% ============== Part 4: Predict and Accuracies ==============
-%  After learning the parameters, you'll like to use it to predict the outcomes
-%  on unseen data. In this part, you will use the logistic regression model
-%  to predict the probability that a student with score 45 on exam 1 and 
-%  score 85 on exam 2 will be admitted.
-%
-%  Furthermore, you will compute the training and test set accuracies of 
-%  our model.
-%
-%  Your task is to complete the code in predict.m
-
-%  Predict probability for a student with score 45 on exam 1 
-%  and score 85 on exam 2 
-
-prob = sigmoid([1 45 85] * theta);
-fprintf(['For a student with scores 45 and 85, we predict an admission ' ...
-         'probability of %f\n\n'], prob);
-
-% Compute accuracy on our training set
-p = predict(theta, X);
-
-fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
-
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
-

assignments/machine-learning-ex2/ex2/ex2_reg.m

@@ -1,116 +0,0 @@
-%% Machine Learning Online Class - Exercise 2: Logistic Regression
-%
-%  Instructions
-%  ------------
-% 
-%  This file contains code that helps you get started on the second part
-%  of the exercise which covers regularization with logistic regression.
-%
-%  You will need to complete the following functions in this exericse:
-%
-%     sigmoid.m
-%     costFunction.m
-%     predict.m
-%     costFunctionReg.m
-%
-%  For this exercise, you will not need to change any code in this file,
-%  or any other files other than those mentioned above.
-%
-
-%% Initialization
-clear ; close all; clc
-
-%% Load Data
-%  The first two columns contains the X values and the third column
-%  contains the label (y).
-
-data = load('ex2data2.txt');
-X = data(:, [1, 2]); y = data(:, 3);
-
-plotData(X, y);
-
-% Put some labels 
-hold on;
-
-% Labels and Legend
-xlabel('Microchip Test 1')
-ylabel('Microchip Test 2')
-
-% Specified in plot order
-legend('y = 1', 'y = 0')
-hold off;
-
-
-%% =========== Part 1: Regularized Logistic Regression ============
-%  In this part, you are given a dataset with data points that are not
-%  linearly separable. However, you would still like to use logistic 
-%  regression to classify the data points. 
-%
-%  To do so, you introduce more features to use -- in particular, you add
-%  polynomial features to our data matrix (similar to polynomial
-%  regression).
-%
-
-% Add Polynomial Features
-
-% Note that mapFeature also adds a column of ones for us, so the intercept
-% term is handled
-X = mapFeature(X(:,1), X(:,2));
-
-% Initialize fitting parameters
-initial_theta = zeros(size(X, 2), 1);
-
-% Set regularization parameter lambda to 1
-lambda = 1;
-
-% Compute and display initial cost and gradient for regularized logistic
-% regression
-[cost, grad] = costFunctionReg(initial_theta, X, y, lambda);
-
-fprintf('Cost at initial theta (zeros): %f\n', cost);
-
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
-
-%% ============= Part 2: Regularization and Accuracies =============
-%  Optional Exercise:
-%  In this part, you will get to try different values of lambda and 
-%  see how regularization affects the decision coundart
-%
-%  Try the following values of lambda (0, 1, 10, 100).
-%
-%  How does the decision boundary change when you vary lambda? How does
-%  the training set accuracy vary?
-%
-
-% Initialize fitting parameters
-initial_theta = zeros(size(X, 2), 1);
-
-% Set regularization parameter lambda to 1 (you should vary this)
-lambda = 100;
-
-% Set Options
-options = optimset('GradObj', 'on', 'MaxIter', 400);
-
-% Optimize
-[theta, J, exit_flag] = ...
-	fminunc(@(t)(costFunctionReg(t, X, y, 1)), initial_theta, options);
-
-% Plot Boundary
-plotDecisionBoundary(theta, X, y);
-hold on;
-title(sprintf('lambda = %g', lambda))
-
-% Labels and Legend
-xlabel('Microchip Test 1')
-ylabel('Microchip Test 2')
-
-legend('y = 1', 'y = 0', 'Decision boundary')
-hold off;
-
-% Compute accuracy on our training set
-p = predict(theta, X);
-
-fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
-
-

assignments/machine-learning-ex2/ex2/ex2data.csv

@@ -1,100 +0,0 @@
-0,34.62365962,78.02469282
-0,30.28671077,43.89499752
-0,35.84740877,72.90219803
-1,60.18259939,86.3085521
-1,79.03273605,75.34437644
-0,45.08327748,56.31637178
-1,61.10666454,96.51142588
-1,75.02474557,46.55401354
-1,76.0987867,87.42056972
-1,84.43281996,43.53339331
-0,95.86155507,38.22527806
-0,75.01365839,30.60326323
-1,82.30705337,76.4819633
-1,69.36458876,97.71869196
-0,39.53833914,76.03681085
-1,53.97105215,89.20735014
-1,69.07014406,52.74046973
-0,67.94685548,46.67857411
-1,70.66150955,92.92713789
-1,76.97878373,47.57596365
-0,67.37202755,42.83843832
-1,89.67677575,65.79936593
-0,50.53478829,48.85581153
-0,34.21206098,44.2095286
-1,77.92409145,68.97235999
-1,62.27101367,69.95445795
-1,80.19018075,44.82162893
-0,93.1143888,38.80067034
-0,61.83020602,50.25610789
-0,38.7858038,64.99568096
-1,61.37928945,72.80788731
-1,85.40451939,57.05198398
-0,52.10797973,63.12762377
-1,52.04540477,69.43286012
-0,40.23689374,71.16774802
-0,54.63510555,52.21388588
-0,33.91550011,98.86943574
-1,64.17698887,80.90806059
-0,74.78925296,41.57341523
-0,34.18364003,75.23772034
-1,83.90239366,56.30804622
-0,51.54772027,46.85629026
-1,94.44336777,65.56892161
-0,82.36875376,40.61825516
-0,51.04775177,45.82270146
-0,62.22267576,52.06099195
-1,77.19303493,70.4582
-1,97.77159928,86.72782233
-1,62.0730638,96.76882412
-1,91.5649745,88.69629255
-1,79.94481794,74.16311935
-1,99.27252693,60.999031
-1,90.54671411,43.39060181
-0,34.52451385,60.39634246
-0,50.28649612,49.80453881
-0,49.58667722,59.80895099
-1,97.64563396,68.86157272
-0,32.57720017,95.59854761
-1,74.24869137,69.82457123
-1,71.79646206,78.45356225
-1,75.39561147,85.75993667
-0,35.28611282,47.02051395
-0,56.2538175,39.26147251
-0,30.05882245,49.59297387
-0,44.66826172,66.45008615
-0,66.56089447,41.09209808
-1,40.45755098,97.53518549
-0,49.07256322,51.88321182
-1,80.27957401,92.11606081
-1,66.74671857,60.99139403
-0,32.72283304,43.30717306
-1,64.03932042,78.03168802
-1,72.34649423,96.22759297
-1,60.45788574,73.0949981
-1,58.84095622,75.85844831
-1,99.8278578,72.36925193
-1,47.26426911,88.475865
-1,50.4581598,75.80985953
-0,60.45555629,42.50840944
-0,82.22666158,42.71987854
-1,88.91389642,69.8037889
-1,94.83450672,45.6943068
-1,67.31925747,66.58935318
-1,57.23870632,59.51428198
-1,80.366756,90.9601479
-1,68.46852179,85.5943071
-0,42.07545454,78.844786
-1,75.47770201,90.424539
-1,78.63542435,96.64742717
-0,52.34800399,60.76950526
-1,94.09433113,77.15910509
-1,90.44855097,87.50879176
-0,55.48216114,35.57070347
-1,74.49269242,84.84513685
-1,89.84580671,45.35828361
-1,83.48916274,48.3802858
-1,42.26170081,87.10385094
-1,99.31500881,68.77540947
-1,55.34001756,64.93193801
-1,74.775893,89.5298129

assignments/machine-learning-ex2/ex2/ex2data1.txt

@@ -1,100 +0,0 @@
-34.62365962451697,78.0246928153624,0
-30.28671076822607,43.89499752400101,0
-35.84740876993872,72.90219802708364,0
-60.18259938620976,86.30855209546826,1
-79.0327360507101,75.3443764369103,1
-45.08327747668339,56.3163717815305,0
-61.10666453684766,96.51142588489624,1
-75.02474556738889,46.55401354116538,1
-76.09878670226257,87.42056971926803,1
-84.43281996120035,43.53339331072109,1
-95.86155507093572,38.22527805795094,0
-75.01365838958247,30.60326323428011,0
-82.30705337399482,76.48196330235604,1
-69.36458875970939,97.71869196188608,1
-39.53833914367223,76.03681085115882,0
-53.9710521485623,89.20735013750205,1
-69.07014406283025,52.74046973016765,1
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assignments/machine-learning-ex2/ex2/ex2data2.csv

@@ -1,118 +0,0 @@
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assignments/machine-learning-ex2/ex2/ex2data2.txt

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assignments/machine-learning-ex2/ex2/ex2test.csv

@@ -1,5 +0,0 @@
-0,98.83450677,10.6943068
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-0,70.23870632,48.51428198
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assignments/machine-learning-ex2/ex2/ex2test.txt

@@ -1,5 +0,0 @@
-98.83450676630196,10.69430680250754,0
-65.31925746917527,65.58935317747915,1
-70.23870631569862,48.51428198012956,0
-80.36675600171273,70.96014789746954,1
-60.46852178591112,60.59430710452014,0

assignments/machine-learning-ex2/ex2/lib/jsonlab/AUTHORS.txt

@@ -1,41 +0,0 @@
-The author of "jsonlab" toolbox is Qianqian Fang. Qianqian
-is currently an Assistant Professor at Massachusetts General Hospital, 
-Harvard Medical School.
-
-Address: Martinos Center for Biomedical Imaging, 
-         Massachusetts General Hospital, 
-         Harvard Medical School
-         Bldg 149, 13th St, Charlestown, MA 02129, USA
-URL: http://nmr.mgh.harvard.edu/~fangq/
-Email: <fangq at nmr.mgh.harvard.edu> or <fangqq at gmail.com>
-
-
-The script loadjson.m was built upon previous works by
-
-- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-       date: 2009/11/02
-- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-       date: 2009/03/22
-- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565
-       date: 2008/07/03
-
-
-This toolbox contains patches submitted by the following contributors:
-
-- Blake Johnson <bjohnso at bbn.com>
-  part of revision 341
-
-- Niclas Borlin <Niclas.Borlin at cs.umu.se>
-  various fixes in revision 394, including
-  - loadjson crashes for all-zero sparse matrix.
-  - loadjson crashes for empty sparse matrix.
-  - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson.
-  - loadjson crashes for sparse real column vector.
-  - loadjson crashes for sparse complex column vector.
-  - Data is corrupted by savejson for sparse real row vector.
-  - savejson crashes for sparse complex row vector. 
-
-- Yul Kang <yul.kang.on at gmail.com>
-  patches for svn revision 415.
-  - savejson saves an empty cell array as [] instead of null
-  - loadjson differentiates an empty struct from an empty array

assignments/machine-learning-ex2/ex2/lib/jsonlab/ChangeLog.txt

@@ -1,74 +0,0 @@
-============================================================================
-
-   JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave
-
-----------------------------------------------------------------------------
-
-JSONlab ChangeLog (key features marked by *):
-
-== JSONlab 1.0 (codename: Optimus - Final), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2015/01/02 polish help info for all major functions, update examples, finalize 1.0
- 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson
-
-== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/11/22 show progress bar in loadjson ('ShowProgress') 
- 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact')
- 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels
- 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab
-
-== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/09/17  fix several compatibility issues when running on octave versions 3.2-3.8
- 2014/09/17  support 2D cell and struct arrays in both savejson and saveubjson
- 2014/08/04  escape special characters in a JSON string
- 2014/02/16  fix a bug when saving ubjson files
-
-== JSONlab 0.9.9 (codename: Optimus - beta), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/01/22  use binary read and write in saveubjson and loadubjson
-
-== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang)
-
-== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
- 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson
-
-== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
- 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin)
-
-== JSONlab 0.9.0 (codename: Rodimus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson
- 2012/06/01  support JSONP in savejson
- 2012/05/25  fix the empty cell bug (reported by Cyril Davin)
- 2012/04/05  savejson can save to a file (suggested by Patrick Rapin)
-
-== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/02/28  loadjson quotation mark escape bug, see http://bit.ly/yyk1nS
- 2012/01/25  patch to handle root-less objects, contributed by Blake Johnson
-
-== JSONlab 0.8.0 (codename: Sentiel), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab
- 2012/01/11  remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer
- 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson
- 2011/11/18  fix struct array bug reported by Mykel Kochenderfer
-
-== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2011/10/21  fix a bug in loadjson, previous code does not use any of the acceleration
- 2011/10/20  loadjson supports JSON collections - concatenated JSON objects
-
-== JSONlab 0.5.0 (codename: Nexus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2011/10/16  package and release jsonlab 0.5.0
- 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug
- 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level
- 2011/10/10  create jsonlab project, start jsonlab website, add online documentation
- 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support
- 2011/10/06 *savejson works for structs, cells and arrays
- 2011/09/09  derive loadjson from JSON parser from MATLAB Central, draft savejson.m

assignments/machine-learning-ex2/ex2/lib/jsonlab/LICENSE_BSD.txt

@@ -1,25 +0,0 @@
-Copyright 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>. All rights reserved.
-
-Redistribution and use in source and binary forms, with or without modification, are
-permitted provided that the following conditions are met:
-
-   1. Redistributions of source code must retain the above copyright notice, this list of
-      conditions and the following disclaimer.
-
-   2. Redistributions in binary form must reproduce the above copyright notice, this list
-      of conditions and the following disclaimer in the documentation and/or other materials
-      provided with the distribution.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED
-WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
-FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS 
-OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
-ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-The views and conclusions contained in the software and documentation are those of the
-authors and should not be interpreted as representing official policies, either expressed
-or implied, of the copyright holders.

assignments/machine-learning-ex2/ex2/lib/jsonlab/README.txt

@@ -1,394 +0,0 @@
-===============================================================================
-=                                 JSONLab                                     =
-=           An open-source MATLAB/Octave JSON encoder and decoder             =
-===============================================================================
-
-*Copyright (C) 2011-2015  Qianqian Fang <fangq at nmr.mgh.harvard.edu>
-*License: BSD License, see License_BSD.txt for details
-*Version: 1.0 (Optimus - Final)
-
--------------------------------------------------------------------------------
-
-Table of Content:
-
-I.  Introduction
-II. Installation
-III.Using JSONLab
-IV. Known Issues and TODOs
-V.  Contribution and feedback
-
--------------------------------------------------------------------------------
-
-I.  Introduction
-
-JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, 
-human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format 
-to represent complex and hierarchical data. It is as powerful as 
-[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely 
-used for data-exchange in applications, and is essential for the wild success 
-of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and 
-[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. 
-
-UBJSON (Universal Binary JSON) is a binary JSON format, specifically 
-optimized for compact file size and better performance while keeping
-the semantics as simple as the text-based JSON format. Using the UBJSON
-format allows to wrap complex binary data in a flexible and extensible
-structure, making it possible to process complex and large dataset 
-without accuracy loss due to text conversions.
-
-We envision that both JSON and its binary version will serve as part of 
-the mainstream data-exchange formats for scientific research in the future. 
-It will provide the flexibility and generality achieved by other popular 
-general-purpose file specifications, such as
-[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly 
-reduced complexity and enhanced performance.
-
-JSONLab is a free and open-source implementation of a JSON/UBJSON encoder 
-and a decoder in the native MATLAB language. It can be used to convert a MATLAB 
-data structure (array, struct, cell, struct array and cell array) into 
-JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB 
-data structure. JSONLab supports both MATLAB and  
-[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone).
-
--------------------------------------------------------------------------------
-
-II. Installation
-
-The installation of JSONLab is no different than any other simple
-MATLAB toolbox. You only need to download/unzip the JSONLab package
-to a folder, and add the folder's path to MATLAB/Octave's path list
-by using the following command:
-
-    addpath('/path/to/jsonlab');
-
-If you want to add this path permanently, you need to type "pathtool", 
-browse to the jsonlab root folder and add to the list, then click "Save".
-Then, run "rehash" in MATLAB, and type "which loadjson", if you see an 
-output, that means JSONLab is installed for MATLAB/Octave.
-
--------------------------------------------------------------------------------
-
-III.Using JSONLab
-
-JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, 
-and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and 
-two equivallent functions -- loadubjson and saveubjson for the binary 
-JSON. The detailed help info for the four functions can be found below:
-
-=== loadjson.m ===
-<pre>
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-</pre>
-
-=== savejson.m ===
-
-<pre>
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
- </pre>
-
-=== loadubjson.m ===
-
-<pre>
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-</pre>
-
-=== saveubjson.m ===
-
-<pre>
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-</pre>
-
-
-=== examples ===
-
-Under the "examples" folder, you can find several scripts to demonstrate the
-basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you 
-will see the conversions from MATLAB data structure to JSON text and backward.
-In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet
-and validate the loadjson/savejson functions for regression testing purposes.
-Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson
-and loadubjson pairs for various matlab data structures.
-
-Please run these examples and understand how JSONLab works before you use
-it to process your data.
-
--------------------------------------------------------------------------------
-
-IV. Known Issues and TODOs
-
-JSONLab has several known limitations. We are striving to make it more general
-and robust. Hopefully in a few future releases, the limitations become less.
-
-Here are the known issues:
-
-# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays;
-# When processing names containing multi-byte characters, Octave and MATLAB \
-can give different field-names; you can use feature('DefaultCharacterSet','latin1') \
-in MATLAB to get consistant results
-# savejson can not handle class and dataset.
-# saveubjson converts a logical array into a uint8 ([U]) array
-# an unofficial N-D array count syntax is implemented in saveubjson. We are \
-actively communicating with the UBJSON spec maintainer to investigate the \
-possibility of making it upstream
-# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \
-files, however, it can parse all UBJSON files produced by saveubjson.
-
--------------------------------------------------------------------------------
-
-V. Contribution and feedback
-
-JSONLab is an open-source project. This means you can not only use it and modify
-it as you wish, but also you can contribute your changes back to JSONLab so
-that everyone else can enjoy the improvement. For anyone who want to contribute,
-please download JSONLab source code from it's subversion repository by using the
-following command:
-
- svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab
-
-You can make changes to the files as needed. Once you are satisfied with your
-changes, and ready to share it with others, please cd the root directory of 
-JSONLab, and type
-
- svn diff > yourname_featurename.patch
-
-You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at
-the email address shown in the beginning of this file. Qianqian will review 
-the changes and commit it to the subversion if they are satisfactory.
-
-We appreciate any suggestions and feedbacks from you. Please use iso2mesh's
-mailing list to report any questions you may have with JSONLab:
-
-http://groups.google.com/group/iso2mesh-users?hl=en&pli=1
-
-(Subscription to the mailing list is needed in order to post messages).

assignments/machine-learning-ex2/ex2/lib/jsonlab/jsonopt.m

@@ -1,32 +0,0 @@
-function val=jsonopt(key,default,varargin)
-%
-% val=jsonopt(key,default,optstruct)
-%
-% setting options based on a struct. The struct can be produced
-% by varargin2struct from a list of 'param','value' pairs
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-%
-% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $
-%
-% input:
-%      key: a string with which one look up a value from a struct
-%      default: if the key does not exist, return default
-%      optstruct: a struct where each sub-field is a key 
-%
-% output:
-%      val: if key exists, val=optstruct.key; otherwise val=default
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-% 
-
-val=default;
-if(nargin<=2) return; end
-opt=varargin{1};
-if(isstruct(opt) && isfield(opt,key))
-    val=getfield(opt,key);
-end
-

assignments/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m

@@ -1,566 +0,0 @@
-function data = loadjson(fname,varargin)
-%
-% data=loadjson(fname,opt)
-%    or
-% data=loadjson(fname,'param1',value1,'param2',value2,...)
-%
-% parse a JSON (JavaScript Object Notation) file or string
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2011/09/09, including previous works from 
-%
-%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-%            created on 2009/11/02
-%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-%            created on  2009/03/22
-%         Joel Feenstra:
-%         http://www.mathworks.com/matlabcentral/fileexchange/20565
-%            created on 2008/07/03
-%
-% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      fname: input file name, if fname contains "{}" or "[]", fname
-%             will be interpreted as a JSON string
-%      opt: a struct to store parsing options, opt can be replaced by 
-%           a list of ('param',value) pairs - the param string is equivallent
-%           to a field in opt. opt can have the following 
-%           fields (first in [.|.] is the default)
-%
-%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-%                         for each element of the JSON data, and group 
-%                         arrays based on the cell2mat rules.
-%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-%                         speed-optimized array parser when loading an 
-%                         array object. The fast array parser may 
-%                         collapse block arrays into a single large
-%                         array similar to rules defined in cell2mat; 0 to 
-%                         use a legacy parser; if set to a larger-than-1
-%                         value, this option will specify the minimum
-%                         dimension to enable the fast array parser. For
-%                         example, if the input is a 3D array, setting
-%                         FastArrayParser to 1 will return a 3D array;
-%                         setting to 2 will return a cell array of 2D
-%                         arrays; setting to 3 will return to a 2D cell
-%                         array of 1D vectors; setting to 4 will return a
-%                         3D cell array.
-%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
-%
-% output:
-%      dat: a cell array, where {...} blocks are converted into cell arrays,
-%           and [...] are converted to arrays
-%
-% examples:
-%      dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-%      dat=loadjson(['examples' filesep 'example1.json'])
-%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-global pos inStr len  esc index_esc len_esc isoct arraytoken
-
-if(regexp(fname,'[\{\}\]\[]','once'))
-   string=fname;
-elseif(exist(fname,'file'))
-   fid = fopen(fname,'rb');
-   string = fread(fid,inf,'uint8=>char')';
-   fclose(fid);
-else
-   error('input file does not exist');
-end
-
-pos = 1; len = length(string); inStr = string;
-isoct=exist('OCTAVE_VERSION','builtin');
-arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
-jstr=regexprep(inStr,'\\\\','  ');
-escquote=regexp(jstr,'\\"');
-arraytoken=sort([arraytoken escquote]);
-
-% String delimiters and escape chars identified to improve speed:
-esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
-index_esc = 1; len_esc = length(esc);
-
-opt=varargin2struct(varargin{:});
-
-if(jsonopt('ShowProgress',0,opt)==1)
-    opt.progressbar_=waitbar(0,'loading ...');
-end
-jsoncount=1;
-while pos <= len
-    switch(next_char)
-        case '{'
-            data{jsoncount} = parse_object(opt);
-        case '['
-            data{jsoncount} = parse_array(opt);
-        otherwise
-            error_pos('Outer level structure must be an object or an array');
-    end
-    jsoncount=jsoncount+1;
-end % while
-
-jsoncount=length(data);
-if(jsoncount==1 && iscell(data))
-    data=data{1};
-end
-
-if(~isempty(data))
-      if(isstruct(data)) % data can be a struct array
-          data=jstruct2array(data);
-      elseif(iscell(data))
-          data=jcell2array(data);
-      end
-end
-if(isfield(opt,'progressbar_'))
-    close(opt.progressbar_);
-end
-
-%%
-function newdata=jcell2array(data)
-len=length(data);
-newdata=data;
-for i=1:len
-      if(isstruct(data{i}))
-          newdata{i}=jstruct2array(data{i});
-      elseif(iscell(data{i}))
-          newdata{i}=jcell2array(data{i});
-      end
-end
-
-%%-------------------------------------------------------------------------
-function newdata=jstruct2array(data)
-fn=fieldnames(data);
-newdata=data;
-len=length(data);
-for i=1:length(fn) % depth-first
-    for j=1:len
-        if(isstruct(getfield(data(j),fn{i})))
-            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
-        end
-    end
-end
-if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
-  newdata=cell(len,1);
-  for j=1:len
-    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
-    iscpx=0;
-    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
-        if(data(j).x0x5F_ArrayIsComplex_)
-           iscpx=1;
-        end
-    end
-    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
-        if(data(j).x0x5F_ArrayIsSparse_)
-            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-                dim=data(j).x0x5F_ArraySize_;
-                if(iscpx && size(ndata,2)==4-any(dim==1))
-                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
-                end
-                if isempty(ndata)
-                    % All-zeros sparse
-                    ndata=sparse(dim(1),prod(dim(2:end)));
-                elseif dim(1)==1
-                    % Sparse row vector
-                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
-                elseif dim(2)==1
-                    % Sparse column vector
-                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
-                else
-                    % Generic sparse array.
-                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
-                end
-            else
-                if(iscpx && size(ndata,2)==4)
-                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));
-                end
-                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
-            end
-        end
-    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-        if(iscpx && size(ndata,2)==2)
-             ndata=complex(ndata(:,1),ndata(:,2));
-        end
-        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
-    end
-    newdata{j}=ndata;
-  end
-  if(len==1)
-      newdata=newdata{1};
-  end
-end
-
-%%-------------------------------------------------------------------------
-function object = parse_object(varargin)
-    parse_char('{');
-    object = [];
-    if next_char ~= '}'
-        while 1
-            str = parseStr(varargin{:});
-            if isempty(str)
-                error_pos('Name of value at position %d cannot be empty');
-            end
-            parse_char(':');
-            val = parse_value(varargin{:});
-            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );
-            if next_char == '}'
-                break;
-            end
-            parse_char(',');
-        end
-    end
-    parse_char('}');
-
-%%-------------------------------------------------------------------------
-
-function object = parse_array(varargin) % JSON array is written in row-major order
-global pos inStr isoct
-    parse_char('[');
-    object = cell(0, 1);
-    dim2=[];
-    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
-    pbar=jsonopt('progressbar_',-1,varargin{:});
-
-    if next_char ~= ']'
-	if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
-            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
-            arraystr=['[' inStr(pos:endpos)];
-            arraystr=regexprep(arraystr,'"_NaN_"','NaN');
-            arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
-            arraystr(arraystr==sprintf('\n'))=[];
-            arraystr(arraystr==sprintf('\r'))=[];
-            %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
-            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
-        	astr=inStr((e1l+1):(e1r-1));
-        	astr=regexprep(astr,'"_NaN_"','NaN');
-        	astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
-        	astr(astr==sprintf('\n'))=[];
-        	astr(astr==sprintf('\r'))=[];
-        	astr(astr==' ')='';
-        	if(isempty(find(astr=='[', 1))) % array is 2D
-                    dim2=length(sscanf(astr,'%f,',[1 inf]));
-        	end
-            else % array is 1D
-        	astr=arraystr(2:end-1);
-        	astr(astr==' ')='';
-        	[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
-        	if(nextidx>=length(astr)-1)
-                    object=obj;
-                    pos=endpos;
-                    parse_char(']');
-                    return;
-        	end
-            end
-            if(~isempty(dim2))
-        	astr=arraystr;
-        	astr(astr=='[')='';
-        	astr(astr==']')='';
-        	astr(astr==' ')='';
-        	[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
-        	if(nextidx>=length(astr)-1)
-                    object=reshape(obj,dim2,numel(obj)/dim2)';
-                    pos=endpos;
-                    parse_char(']');
-                    if(pbar>0)
-                        waitbar(pos/length(inStr),pbar,'loading ...');
-                    end
-                    return;
-        	end
-            end
-            arraystr=regexprep(arraystr,'\]\s*,','];');
-	else
-            arraystr='[';
-	end
-        try
-           if(isoct && regexp(arraystr,'"','once'))
-                error('Octave eval can produce empty cells for JSON-like input');
-           end
-           object=eval(arraystr);
-           pos=endpos;
-        catch
-         while 1
-            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
-            val = parse_value(newopt);
-            object{end+1} = val;
-            if next_char == ']'
-                break;
-            end
-            parse_char(',');
-         end
-        end
-    end
-    if(jsonopt('SimplifyCell',0,varargin{:})==1)
-      try
-        oldobj=object;
-        object=cell2mat(object')';
-        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
-            object=oldobj;
-        elseif(size(object,1)>1 && ndims(object)==2)
-            object=object';
-        end
-      catch
-      end
-    end
-    parse_char(']');
-    
-    if(pbar>0)
-        waitbar(pos/length(inStr),pbar,'loading ...');
-    end
-%%-------------------------------------------------------------------------
-
-function parse_char(c)
-    global pos inStr len
-    skip_whitespace;
-    if pos > len || inStr(pos) ~= c
-        error_pos(sprintf('Expected %c at position %%d', c));
-    else
-        pos = pos + 1;
-        skip_whitespace;
-    end
-
-%%-------------------------------------------------------------------------
-
-function c = next_char
-    global pos inStr len
-    skip_whitespace;
-    if pos > len
-        c = [];
-    else
-        c = inStr(pos);
-    end
-
-%%-------------------------------------------------------------------------
-
-function skip_whitespace
-    global pos inStr len
-    while pos <= len && isspace(inStr(pos))
-        pos = pos + 1;
-    end
-
-%%-------------------------------------------------------------------------
-function str = parseStr(varargin)
-    global pos inStr len  esc index_esc len_esc
- % len, ns = length(inStr), keyboard
-    if inStr(pos) ~= '"'
-        error_pos('String starting with " expected at position %d');
-    else
-        pos = pos + 1;
-    end
-    str = '';
-    while pos <= len
-        while index_esc <= len_esc && esc(index_esc) < pos
-            index_esc = index_esc + 1;
-        end
-        if index_esc > len_esc
-            str = [str inStr(pos:len)];
-            pos = len + 1;
-            break;
-        else
-            str = [str inStr(pos:esc(index_esc)-1)];
-            pos = esc(index_esc);
-        end
-        nstr = length(str); switch inStr(pos)
-            case '"'
-                pos = pos + 1;
-                if(~isempty(str))
-                    if(strcmp(str,'_Inf_'))
-                        str=Inf;
-                    elseif(strcmp(str,'-_Inf_'))
-                        str=-Inf;
-                    elseif(strcmp(str,'_NaN_'))
-                        str=NaN;
-                    end
-                end
-                return;
-            case '\'
-                if pos+1 > len
-                    error_pos('End of file reached right after escape character');
-                end
-                pos = pos + 1;
-                switch inStr(pos)
-                    case {'"' '\' '/'}
-                        str(nstr+1) = inStr(pos);
-                        pos = pos + 1;
-                    case {'b' 'f' 'n' 'r' 't'}
-                        str(nstr+1) = sprintf(['\' inStr(pos)]);
-                        pos = pos + 1;
-                    case 'u'
-                        if pos+4 > len
-                            error_pos('End of file reached in escaped unicode character');
-                        end
-                        str(nstr+(1:6)) = inStr(pos-1:pos+4);
-                        pos = pos + 5;
-                end
-            otherwise % should never happen
-                str(nstr+1) = inStr(pos), keyboard
-                pos = pos + 1;
-        end
-    end
-    error_pos('End of file while expecting end of inStr');
-
-%%-------------------------------------------------------------------------
-
-function num = parse_number(varargin)
-    global pos inStr len isoct
-    currstr=inStr(pos:end);
-    numstr=0;
-    if(isoct~=0)
-        numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
-        [num, one] = sscanf(currstr, '%f', 1);
-        delta=numstr+1;
-    else
-        [num, one, err, delta] = sscanf(currstr, '%f', 1);
-        if ~isempty(err)
-            error_pos('Error reading number at position %d');
-        end
-    end
-    pos = pos + delta-1;
-
-%%-------------------------------------------------------------------------
-
-function val = parse_value(varargin)
-    global pos inStr len
-    true = 1; false = 0;
-    
-    pbar=jsonopt('progressbar_',-1,varargin{:});
-    if(pbar>0)
-        waitbar(pos/len,pbar,'loading ...');
-    end
-    
-    switch(inStr(pos))
-        case '"'
-            val = parseStr(varargin{:});
-            return;
-        case '['
-            val = parse_array(varargin{:});
-            return;
-        case '{'
-            val = parse_object(varargin{:});
-            if isstruct(val)
-                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
-                    val=jstruct2array(val);
-                end
-            elseif isempty(val)
-                val = struct;
-            end
-            return;
-        case {'-','0','1','2','3','4','5','6','7','8','9'}
-            val = parse_number(varargin{:});
-            return;
-        case 't'
-            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
-                val = true;
-                pos = pos + 4;
-                return;
-            end
-        case 'f'
-            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
-                val = false;
-                pos = pos + 5;
-                return;
-            end
-        case 'n'
-            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
-                val = [];
-                pos = pos + 4;
-                return;
-            end
-    end
-    error_pos('Value expected at position %d');
-%%-------------------------------------------------------------------------
-
-function error_pos(msg)
-    global pos inStr len
-    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
-    if poShow(3) == poShow(2)
-        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after
-    end
-    msg = [sprintf(msg, pos) ': ' ...
-    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
-    error( ['JSONparser:invalidFormat: ' msg] );
-
-%%-------------------------------------------------------------------------
-
-function str = valid_field(str)
-global isoct
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
-
-%%-------------------------------------------------------------------------
-function endpos = matching_quote(str,pos)
-len=length(str);
-while(pos<len)
-    if(str(pos)=='"')
-        if(~(pos>1 && str(pos-1)=='\'))
-            endpos=pos;
-            return;
-        end        
-    end
-    pos=pos+1;
-end
-error('unmatched quotation mark');
-%%-------------------------------------------------------------------------
-function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
-global arraytoken
-level=1;
-maxlevel=level;
-endpos=0;
-bpos=arraytoken(arraytoken>=pos);
-tokens=str(bpos);
-len=length(tokens);
-pos=1;
-e1l=[];
-e1r=[];
-while(pos<=len)
-    c=tokens(pos);
-    if(c==']')
-        level=level-1;
-        if(isempty(e1r)) e1r=bpos(pos); end
-        if(level==0)
-            endpos=bpos(pos);
-            return
-        end
-    end
-    if(c=='[')
-        if(isempty(e1l)) e1l=bpos(pos); end
-        level=level+1;
-        maxlevel=max(maxlevel,level);
-    end
-    if(c=='"')
-        pos=matching_quote(tokens,pos+1);
-    end
-    pos=pos+1;
-end
-if(endpos==0) 
-    error('unmatched "]"');
-end
-

assignments/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m

@@ -1,528 +0,0 @@
-function data = loadubjson(fname,varargin)
-%
-% data=loadubjson(fname,opt)
-%    or
-% data=loadubjson(fname,'param1',value1,'param2',value2,...)
-%
-% parse a JSON (JavaScript Object Notation) file or string
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2013/08/01
-%
-% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      fname: input file name, if fname contains "{}" or "[]", fname
-%             will be interpreted as a UBJSON string
-%      opt: a struct to store parsing options, opt can be replaced by 
-%           a list of ('param',value) pairs - the param string is equivallent
-%           to a field in opt. opt can have the following 
-%           fields (first in [.|.] is the default)
-%
-%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-%                         for each element of the JSON data, and group 
-%                         arrays based on the cell2mat rules.
-%           opt.IntEndian [B|L]: specify the endianness of the integer fields
-%                         in the UBJSON input data. B - Big-Endian format for 
-%                         integers (as required in the UBJSON specification); 
-%                         L - input integer fields are in Little-Endian order.
-%
-% output:
-%      dat: a cell array, where {...} blocks are converted into cell arrays,
-%           and [...] are converted to arrays
-%
-% examples:
-%      obj=struct('string','value','array',[1 2 3]);
-%      ubjdata=saveubjson('obj',obj);
-%      dat=loadubjson(ubjdata)
-%      dat=loadubjson(['examples' filesep 'example1.ubj'])
-%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-global pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian
-
-if(regexp(fname,'[\{\}\]\[]','once'))
-   string=fname;
-elseif(exist(fname,'file'))
-   fid = fopen(fname,'rb');
-   string = fread(fid,inf,'uint8=>char')';
-   fclose(fid);
-else
-   error('input file does not exist');
-end
-
-pos = 1; len = length(string); inStr = string;
-isoct=exist('OCTAVE_VERSION','builtin');
-arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
-jstr=regexprep(inStr,'\\\\','  ');
-escquote=regexp(jstr,'\\"');
-arraytoken=sort([arraytoken escquote]);
-
-% String delimiters and escape chars identified to improve speed:
-esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
-index_esc = 1; len_esc = length(esc);
-
-opt=varargin2struct(varargin{:});
-fileendian=upper(jsonopt('IntEndian','B',opt));
-[os,maxelem,systemendian]=computer;
-
-jsoncount=1;
-while pos <= len
-    switch(next_char)
-        case '{'
-            data{jsoncount} = parse_object(opt);
-        case '['
-            data{jsoncount} = parse_array(opt);
-        otherwise
-            error_pos('Outer level structure must be an object or an array');
-    end
-    jsoncount=jsoncount+1;
-end % while
-
-jsoncount=length(data);
-if(jsoncount==1 && iscell(data))
-    data=data{1};
-end
-
-if(~isempty(data))
-      if(isstruct(data)) % data can be a struct array
-          data=jstruct2array(data);
-      elseif(iscell(data))
-          data=jcell2array(data);
-      end
-end
-
-
-%%
-function newdata=parse_collection(id,data,obj)
-
-if(jsoncount>0 && exist('data','var')) 
-    if(~iscell(data))
-       newdata=cell(1);
-       newdata{1}=data;
-       data=newdata;
-    end
-end
-
-%%
-function newdata=jcell2array(data)
-len=length(data);
-newdata=data;
-for i=1:len
-      if(isstruct(data{i}))
-          newdata{i}=jstruct2array(data{i});
-      elseif(iscell(data{i}))
-          newdata{i}=jcell2array(data{i});
-      end
-end
-
-%%-------------------------------------------------------------------------
-function newdata=jstruct2array(data)
-fn=fieldnames(data);
-newdata=data;
-len=length(data);
-for i=1:length(fn) % depth-first
-    for j=1:len
-        if(isstruct(getfield(data(j),fn{i})))
-            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
-        end
-    end
-end
-if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
-  newdata=cell(len,1);
-  for j=1:len
-    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
-    iscpx=0;
-    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
-        if(data(j).x0x5F_ArrayIsComplex_)
-           iscpx=1;
-        end
-    end
-    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
-        if(data(j).x0x5F_ArrayIsSparse_)
-            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-                dim=double(data(j).x0x5F_ArraySize_);
-                if(iscpx && size(ndata,2)==4-any(dim==1))
-                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
-                end
-                if isempty(ndata)
-                    % All-zeros sparse
-                    ndata=sparse(dim(1),prod(dim(2:end)));
-                elseif dim(1)==1
-                    % Sparse row vector
-                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
-                elseif dim(2)==1
-                    % Sparse column vector
-                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
-                else
-                    % Generic sparse array.
-                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
-                end
-            else
-                if(iscpx && size(ndata,2)==4)
-                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));
-                end
-                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
-            end
-        end
-    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-        if(iscpx && size(ndata,2)==2)
-             ndata=complex(ndata(:,1),ndata(:,2));
-        end
-        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
-    end
-    newdata{j}=ndata;
-  end
-  if(len==1)
-      newdata=newdata{1};
-  end
-end
-
-%%-------------------------------------------------------------------------
-function object = parse_object(varargin)
-    parse_char('{');
-    object = [];
-    type='';
-    count=-1;
-    if(next_char == '$')
-        type=inStr(pos+1); % TODO
-        pos=pos+2;
-    end
-    if(next_char == '#')
-        pos=pos+1;
-        count=double(parse_number());
-    end
-    if next_char ~= '}'
-        num=0;
-        while 1
-            str = parseStr(varargin{:});
-            if isempty(str)
-                error_pos('Name of value at position %d cannot be empty');
-            end
-            %parse_char(':');
-            val = parse_value(varargin{:});
-            num=num+1;
-            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );
-            if next_char == '}' || (count>=0 && num>=count)
-                break;
-            end
-            %parse_char(',');
-        end
-    end
-    if(count==-1)
-        parse_char('}');
-    end
-
-%%-------------------------------------------------------------------------
-function [cid,len]=elem_info(type)
-id=strfind('iUIlLdD',type);
-dataclass={'int8','uint8','int16','int32','int64','single','double'};
-bytelen=[1,1,2,4,8,4,8];
-if(id>0)
-    cid=dataclass{id};
-    len=bytelen(id);
-else
-    error_pos('unsupported type at position %d');
-end
-%%-------------------------------------------------------------------------
-
-
-function [data adv]=parse_block(type,count,varargin)
-global pos inStr isoct fileendian systemendian
-[cid,len]=elem_info(type);
-datastr=inStr(pos:pos+len*count-1);
-if(isoct)
-    newdata=int8(datastr);
-else
-    newdata=uint8(datastr);
-end
-id=strfind('iUIlLdD',type);
-if(id<=5 && fileendian~=systemendian)
-    newdata=swapbytes(typecast(newdata,cid));
-end
-data=typecast(newdata,cid);
-adv=double(len*count);
-
-%%-------------------------------------------------------------------------
-
-
-function object = parse_array(varargin) % JSON array is written in row-major order
-global pos inStr isoct
-    parse_char('[');
-    object = cell(0, 1);
-    dim=[];
-    type='';
-    count=-1;
-    if(next_char == '$')
-        type=inStr(pos+1);
-        pos=pos+2;
-    end
-    if(next_char == '#')
-        pos=pos+1;
-        if(next_char=='[')
-            dim=parse_array(varargin{:});
-            count=prod(double(dim));
-        else
-            count=double(parse_number());
-        end
-    end
-    if(~isempty(type))
-        if(count>=0)
-            [object adv]=parse_block(type,count,varargin{:});
-            if(~isempty(dim))
-                object=reshape(object,dim);
-            end
-            pos=pos+adv;
-            return;
-        else
-            endpos=matching_bracket(inStr,pos);
-            [cid,len]=elem_info(type);
-            count=(endpos-pos)/len;
-            [object adv]=parse_block(type,count,varargin{:});
-            pos=pos+adv;
-            parse_char(']');
-            return;
-        end
-    end
-    if next_char ~= ']'
-         while 1
-            val = parse_value(varargin{:});
-            object{end+1} = val;
-            if next_char == ']'
-                break;
-            end
-            %parse_char(',');
-         end
-    end
-    if(jsonopt('SimplifyCell',0,varargin{:})==1)
-      try
-        oldobj=object;
-        object=cell2mat(object')';
-        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
-            object=oldobj;
-        elseif(size(object,1)>1 && ndims(object)==2)
-            object=object';
-        end
-      catch
-      end
-    end
-    if(count==-1)
-        parse_char(']');
-    end
-
-%%-------------------------------------------------------------------------
-
-function parse_char(c)
-    global pos inStr len
-    skip_whitespace;
-    if pos > len || inStr(pos) ~= c
-        error_pos(sprintf('Expected %c at position %%d', c));
-    else
-        pos = pos + 1;
-        skip_whitespace;
-    end
-
-%%-------------------------------------------------------------------------
-
-function c = next_char
-    global pos inStr len
-    skip_whitespace;
-    if pos > len
-        c = [];
-    else
-        c = inStr(pos);
-    end
-
-%%-------------------------------------------------------------------------
-
-function skip_whitespace
-    global pos inStr len
-    while pos <= len && isspace(inStr(pos))
-        pos = pos + 1;
-    end
-
-%%-------------------------------------------------------------------------
-function str = parseStr(varargin)
-    global pos inStr esc index_esc len_esc
- % len, ns = length(inStr), keyboard
-    type=inStr(pos);
-    if type ~= 'S' && type ~= 'C' && type ~= 'H'
-        error_pos('String starting with S expected at position %d');
-    else
-        pos = pos + 1;
-    end
-    if(type == 'C')
-        str=inStr(pos);
-        pos=pos+1;
-        return;
-    end
-    bytelen=double(parse_number());
-    if(length(inStr)>=pos+bytelen-1)
-        str=inStr(pos:pos+bytelen-1);
-        pos=pos+bytelen;
-    else
-        error_pos('End of file while expecting end of inStr');
-    end
-
-%%-------------------------------------------------------------------------
-
-function num = parse_number(varargin)
-    global pos inStr len isoct fileendian systemendian
-    id=strfind('iUIlLdD',inStr(pos));
-    if(isempty(id))
-        error_pos('expecting a number at position %d');
-    end
-    type={'int8','uint8','int16','int32','int64','single','double'};
-    bytelen=[1,1,2,4,8,4,8];
-    datastr=inStr(pos+1:pos+bytelen(id));
-    if(isoct)
-        newdata=int8(datastr);
-    else
-        newdata=uint8(datastr);
-    end
-    if(id<=5 && fileendian~=systemendian)
-        newdata=swapbytes(typecast(newdata,type{id}));
-    end
-    num=typecast(newdata,type{id});
-    pos = pos + bytelen(id)+1;
-
-%%-------------------------------------------------------------------------
-
-function val = parse_value(varargin)
-    global pos inStr len
-    true = 1; false = 0;
-
-    switch(inStr(pos))
-        case {'S','C','H'}
-            val = parseStr(varargin{:});
-            return;
-        case '['
-            val = parse_array(varargin{:});
-            return;
-        case '{'
-            val = parse_object(varargin{:});
-            if isstruct(val)
-                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
-                    val=jstruct2array(val);
-                end
-            elseif isempty(val)
-                val = struct;
-            end
-            return;
-        case {'i','U','I','l','L','d','D'}
-            val = parse_number(varargin{:});
-            return;
-        case 'T'
-            val = true;
-            pos = pos + 1;
-            return;
-        case 'F'
-            val = false;
-            pos = pos + 1;
-            return;
-        case {'Z','N'}
-            val = [];
-            pos = pos + 1;
-            return;
-    end
-    error_pos('Value expected at position %d');
-%%-------------------------------------------------------------------------
-
-function error_pos(msg)
-    global pos inStr len
-    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
-    if poShow(3) == poShow(2)
-        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after
-    end
-    msg = [sprintf(msg, pos) ': ' ...
-    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
-    error( ['JSONparser:invalidFormat: ' msg] );
-
-%%-------------------------------------------------------------------------
-
-function str = valid_field(str)
-global isoct
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
-
-%%-------------------------------------------------------------------------
-function endpos = matching_quote(str,pos)
-len=length(str);
-while(pos<len)
-    if(str(pos)=='"')
-        if(~(pos>1 && str(pos-1)=='\'))
-            endpos=pos;
-            return;
-        end        
-    end
-    pos=pos+1;
-end
-error('unmatched quotation mark');
-%%-------------------------------------------------------------------------
-function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
-global arraytoken
-level=1;
-maxlevel=level;
-endpos=0;
-bpos=arraytoken(arraytoken>=pos);
-tokens=str(bpos);
-len=length(tokens);
-pos=1;
-e1l=[];
-e1r=[];
-while(pos<=len)
-    c=tokens(pos);
-    if(c==']')
-        level=level-1;
-        if(isempty(e1r)) e1r=bpos(pos); end
-        if(level==0)
-            endpos=bpos(pos);
-            return
-        end
-    end
-    if(c=='[')
-        if(isempty(e1l)) e1l=bpos(pos); end
-        level=level+1;
-        maxlevel=max(maxlevel,level);
-    end
-    if(c=='"')
-        pos=matching_quote(tokens,pos+1);
-    end
-    pos=pos+1;
-end
-if(endpos==0) 
-    error('unmatched "]"');
-end
-

assignments/machine-learning-ex2/ex2/lib/jsonlab/mergestruct.m

@@ -1,33 +0,0 @@
-function s=mergestruct(s1,s2)
-%
-% s=mergestruct(s1,s2)
-%
-% merge two struct objects into one
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% date: 2012/12/22
-%
-% input:
-%      s1,s2: a struct object, s1 and s2 can not be arrays
-%
-% output:
-%      s: the merged struct object. fields in s1 and s2 will be combined in s.
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(~isstruct(s1) || ~isstruct(s2))
-    error('input parameters contain non-struct');
-end
-if(length(s1)>1 || length(s2)>1)
-    error('can not merge struct arrays');
-end
-fn=fieldnames(s2);
-s=s1;
-for i=1:length(fn)              
-    s=setfield(s,fn{i},getfield(s2,fn{i}));
-end
-

assignments/machine-learning-ex2/ex2/lib/jsonlab/savejson.m

@@ -1,475 +0,0 @@
-function json=savejson(rootname,obj,varargin)
-%
-% json=savejson(rootname,obj,filename)
-%    or
-% json=savejson(rootname,obj,opt)
-% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
-%
-% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-% Object Notation) string
-%
-% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2011/09/09
-%
-% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      rootname: the name of the root-object, when set to '', the root name
-%        is ignored, however, when opt.ForceRootName is set to 1 (see below),
-%        the MATLAB variable name will be used as the root name.
-%      obj: a MATLAB object (array, cell, cell array, struct, struct array).
-%      filename: a string for the file name to save the output JSON data.
-%      opt: a struct for additional options, ignore to use default values.
-%        opt can have the following fields (first in [.|.] is the default)
-%
-%        opt.FileName [''|string]: a file name to save the output JSON data
-%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-%                         of a 1D/2D array;
-%        opt.ArrayIndent [1|0]: if 1, output explicit data array with
-%                         precedent indentation; if 0, no indentation
-%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-%                         array in JSON array format; if sets to 1, an
-%                         array will be shown as a struct with fields
-%                         "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-%                         sparse arrays, the non-zero elements will be
-%                         saved to _ArrayData_ field in triplet-format i.e.
-%                         (ix,iy,val) and "_ArrayIsSparse_" will be added
-%                         with a value of 1; for a complex array, the 
-%                         _ArrayData_ array will include two columns 
-%                         (4 for sparse) to record the real and imaginary 
-%                         parts, and also "_ArrayIsComplex_":1 is added. 
-%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-%                         will use true/false rather than 1/0.
-%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-%                         numerical element will be shown without a square
-%                         bracket, unless it is the root object; if 0, square
-%                         brackets are forced for any numerical arrays.
-%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-%                         will use the name of the passed obj variable as the 
-%                         root object name; if obj is an expression and 
-%                         does not have a name, 'root' will be used; if this 
-%                         is set to 0 and rootname is empty, the root level 
-%                         will be merged down to the lower level.
-%        opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-%                         and $1 represents the sign. For those who want to use
-%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'
-%        opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-%                         to represent NaN
-%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-%                         for example, if opt.JSONP='foo', the JSON data is
-%                         wrapped inside a function call as 'foo(...);'
-%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-%                         back to the string form
-%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
-%
-%        opt can be replaced by a list of ('param',value) pairs. The param 
-%        string is equivallent to a field in opt and is case sensitive.
-% output:
-%      json: a string in the JSON format (see http://json.org)
-%
-% examples:
-%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-%               'SpecialData',[nan, inf, -inf]);
-%      savejson('jmesh',jsonmesh)
-%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(nargin==1)
-   varname=inputname(1);
-   obj=rootname;
-   if(isempty(varname)) 
-      varname='root';
-   end
-   rootname=varname;
-else
-   varname=inputname(2);
-end
-if(length(varargin)==1 && ischar(varargin{1}))
-   opt=struct('FileName',varargin{1});
-else
-   opt=varargin2struct(varargin{:});
-end
-opt.IsOctave=exist('OCTAVE_VERSION','builtin');
-rootisarray=0;
-rootlevel=1;
-forceroot=jsonopt('ForceRootName',0,opt);
-if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
-    rootisarray=1;
-    rootlevel=0;
-else
-    if(isempty(rootname))
-        rootname=varname;
-    end
-end
-if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
-    rootname='root';
-end
-
-whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-if(jsonopt('Compact',0,opt)==1)
-    whitespaces=struct('tab','','newline','','sep',',');
-end
-if(~isfield(opt,'whitespaces_'))
-    opt.whitespaces_=whitespaces;
-end
-
-nl=whitespaces.newline;
-
-json=obj2json(rootname,obj,rootlevel,opt);
-if(rootisarray)
-    json=sprintf('%s%s',json,nl);
-else
-    json=sprintf('{%s%s%s}\n',nl,json,nl);
-end
-
-jsonp=jsonopt('JSONP','',opt);
-if(~isempty(jsonp))
-    json=sprintf('%s(%s);%s',jsonp,json,nl);
-end
-
-% save to a file if FileName is set, suggested by Patrick Rapin
-if(~isempty(jsonopt('FileName','',opt)))
-    if(jsonopt('SaveBinary',0,opt)==1)
-	    fid = fopen(opt.FileName, 'wb');
-	    fwrite(fid,json);
-    else
-	    fid = fopen(opt.FileName, 'wt');
-	    fwrite(fid,json,'char');
-    end
-    fclose(fid);
-end
-
-%%-------------------------------------------------------------------------
-function txt=obj2json(name,item,level,varargin)
-
-if(iscell(item))
-    txt=cell2json(name,item,level,varargin{:});
-elseif(isstruct(item))
-    txt=struct2json(name,item,level,varargin{:});
-elseif(ischar(item))
-    txt=str2json(name,item,level,varargin{:});
-else
-    txt=mat2json(name,item,level,varargin{:});
-end
-
-%%-------------------------------------------------------------------------
-function txt=cell2json(name,item,level,varargin)
-txt='';
-if(~iscell(item))
-        error('input is not a cell');
-end
-
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
-padding0=repmat(ws.tab,1,level);
-padding2=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-if(len>1)
-    if(~isempty(name))
-        txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; 
-    else
-        txt=sprintf('%s[%s',padding0,nl); 
-    end
-elseif(len==0)
-    if(~isempty(name))
-        txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; 
-    else
-        txt=sprintf('%s[]',padding0); 
-    end
-end
-for j=1:dim(2)
-    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
-    for i=1:dim(1)
-       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
-       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-    end
-    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
-    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-end
-if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
-
-%%-------------------------------------------------------------------------
-function txt=struct2json(name,item,level,varargin)
-txt='';
-if(~isstruct(item))
-	error('input is not a struct');
-end
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding0=repmat(ws.tab,1,level);
-padding2=repmat(ws.tab,1,level+1);
-padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
-nl=ws.newline;
-
-if(~isempty(name)) 
-    if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
-else
-    if(len>1) txt=sprintf('%s[%s',padding0,nl); end
-end
-for j=1:dim(2)
-  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
-  for i=1:dim(1)
-    names = fieldnames(item(i,j));
-    if(~isempty(name) && len==1)
-        txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); 
-    else
-        txt=sprintf('%s%s{%s',txt,padding1,nl); 
-    end
-    if(~isempty(names))
-      for e=1:length(names)
-	    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
-             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
-        if(e<length(names)) txt=sprintf('%s%s',txt,','); end
-        txt=sprintf('%s%s',txt,nl);
-      end
-    end
-    txt=sprintf('%s%s}',txt,padding1);
-    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-  end
-  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
-  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-end
-if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
-
-%%-------------------------------------------------------------------------
-function txt=str2json(name,item,level,varargin)
-txt='';
-if(~ischar(item))
-        error('input is not a string');
-end
-item=reshape(item, max(size(item),[1 0]));
-len=size(item,1);
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding1=repmat(ws.tab,1,level);
-padding0=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-sep=ws.sep;
-
-if(~isempty(name)) 
-    if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
-else
-    if(len>1) txt=sprintf('%s[%s',padding1,nl); end
-end
-isoct=jsonopt('IsOctave',0,varargin{:});
-for e=1:len
-    if(isoct)
-        val=regexprep(item(e,:),'\\','\\');
-        val=regexprep(val,'"','\"');
-        val=regexprep(val,'^"','\"');
-    else
-        val=regexprep(item(e,:),'\\','\\\\');
-        val=regexprep(val,'"','\\"');
-        val=regexprep(val,'^"','\\"');
-    end
-    val=escapejsonstring(val);
-    if(len==1)
-        obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
-	if(isempty(name)) obj=['"',val,'"']; end
-        txt=sprintf('%s%s%s%s',txt,padding1,obj);
-    else
-        txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
-    end
-    if(e==len) sep=''; end
-    txt=sprintf('%s%s',txt,sep);
-end
-if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
-
-%%-------------------------------------------------------------------------
-function txt=mat2json(name,item,level,varargin)
-if(~isnumeric(item) && ~islogical(item))
-        error('input is not an array');
-end
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding1=repmat(ws.tab,1,level);
-padding0=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-sep=ws.sep;
-
-if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
-   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
-    if(isempty(name))
-    	txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
-              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
-    else
-    	txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
-              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
-    end
-else
-    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
-        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
-    else
-        numtxt=matdata2json(item,level+1,varargin{:});
-    end
-    if(isempty(name))
-    	txt=sprintf('%s%s',padding1,numtxt);
-    else
-        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
-           	txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
-        else
-    	    txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
-        end
-    end
-    return;
-end
-dataformat='%s%s%s%s%s';
-
-if(issparse(item))
-    [ix,iy]=find(item);
-    data=full(item(find(item)));
-    if(~isreal(item))
-       data=[real(data(:)),imag(data(:))];
-       if(size(item,1)==1)
-           % Kludge to have data's 'transposedness' match item's.
-           % (Necessary for complex row vector handling below.)
-           data=data';
-       end
-       txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
-    end
-    txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
-    if(size(item,1)==1)
-        % Row vector, store only column indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([iy(:),data'],level+2,varargin{:}), nl);
-    elseif(size(item,2)==1)
-        % Column vector, store only row indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([ix,data],level+2,varargin{:}), nl);
-    else
-        % General case, store row and column indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([ix,iy,data],level+2,varargin{:}), nl);
-    end
-else
-    if(isreal(item))
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-            matdata2json(item(:)',level+2,varargin{:}), nl);
-    else
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
-    end
-end
-txt=sprintf('%s%s%s',txt,padding1,'}');
-
-%%-------------------------------------------------------------------------
-function txt=matdata2json(mat,level,varargin)
-
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-tab=ws.tab;
-nl=ws.newline;
-
-if(size(mat,1)==1)
-    pre='';
-    post='';
-    level=level-1;
-else
-    pre=sprintf('[%s',nl);
-    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
-end
-
-if(isempty(mat))
-    txt='null';
-    return;
-end
-floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
-%if(numel(mat)>1)
-    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
-%else
-%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
-%end
-
-if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
-    formatstr=[repmat(tab,1,level) formatstr];
-end
-
-txt=sprintf(formatstr,mat');
-txt(end-length(nl):end)=[];
-if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
-   txt=regexprep(txt,'1','true');
-   txt=regexprep(txt,'0','false');
-end
-%txt=regexprep(mat2str(mat),'\s+',',');
-%txt=regexprep(txt,';',sprintf('],\n['));
-% if(nargin>=2 && size(mat,1)>1)
-%     txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
-% end
-txt=[pre txt post];
-if(any(isinf(mat(:))))
-    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
-end
-if(any(isnan(mat(:))))
-    txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
-end
-
-%%-------------------------------------------------------------------------
-function newname=checkname(name,varargin)
-isunpack=jsonopt('UnpackHex',1,varargin{:});
-newname=name;
-if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
-    return
-end
-if(isunpack)
-    isoct=jsonopt('IsOctave',0,varargin{:});
-    if(~isoct)
-        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
-    else
-        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
-        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
-        if(isempty(pos)) return; end
-        str0=name;
-        pos0=[0 pend(:)' length(name)];
-        newname='';
-        for i=1:length(pos)
-            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
-        end
-        if(pos(end)~=length(name))
-            newname=[newname str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-end
-
-%%-------------------------------------------------------------------------
-function newstr=escapejsonstring(str)
-newstr=str;
-isoct=exist('OCTAVE_VERSION','builtin');
-if(isoct)
-   vv=sscanf(OCTAVE_VERSION,'%f');
-   if(vv(1)>=3.8) isoct=0; end
-end
-if(isoct)
-  escapechars={'\a','\f','\n','\r','\t','\v'};
-  for i=1:length(escapechars);
-    newstr=regexprep(newstr,escapechars{i},escapechars{i});
-  end
-else
-  escapechars={'\a','\b','\f','\n','\r','\t','\v'};
-  for i=1:length(escapechars);
-    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
-  end
-end

assignments/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m

@@ -1,504 +0,0 @@
-function json=saveubjson(rootname,obj,varargin)
-%
-% json=saveubjson(rootname,obj,filename)
-%    or
-% json=saveubjson(rootname,obj,opt)
-% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
-%
-% convert a MATLAB object (cell, struct or array) into a Universal 
-% Binary JSON (UBJSON) binary string
-%
-% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2013/08/17
-%
-% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      rootname: the name of the root-object, when set to '', the root name
-%        is ignored, however, when opt.ForceRootName is set to 1 (see below),
-%        the MATLAB variable name will be used as the root name.
-%      obj: a MATLAB object (array, cell, cell array, struct, struct array)
-%      filename: a string for the file name to save the output UBJSON data
-%      opt: a struct for additional options, ignore to use default values.
-%        opt can have the following fields (first in [.|.] is the default)
-%
-%        opt.FileName [''|string]: a file name to save the output JSON data
-%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-%                         array in JSON array format; if sets to 1, an
-%                         array will be shown as a struct with fields
-%                         "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-%                         sparse arrays, the non-zero elements will be
-%                         saved to _ArrayData_ field in triplet-format i.e.
-%                         (ix,iy,val) and "_ArrayIsSparse_" will be added
-%                         with a value of 1; for a complex array, the 
-%                         _ArrayData_ array will include two columns 
-%                         (4 for sparse) to record the real and imaginary 
-%                         parts, and also "_ArrayIsComplex_":1 is added. 
-%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-%                         will use true/false rather than 1/0.
-%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-%                         numerical element will be shown without a square
-%                         bracket, unless it is the root object; if 0, square
-%                         brackets are forced for any numerical arrays.
-%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-%                         will use the name of the passed obj variable as the 
-%                         root object name; if obj is an expression and 
-%                         does not have a name, 'root' will be used; if this 
-%                         is set to 0 and rootname is empty, the root level 
-%                         will be merged down to the lower level.
-%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-%                         for example, if opt.JSON='foo', the JSON data is
-%                         wrapped inside a function call as 'foo(...);'
-%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-%                         back to the string form
-%
-%        opt can be replaced by a list of ('param',value) pairs. The param 
-%        string is equivallent to a field in opt and is case sensitive.
-% output:
-%      json: a binary string in the UBJSON format (see http://ubjson.org)
-%
-% examples:
-%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-%               'SpecialData',[nan, inf, -inf]);
-%      saveubjson('jsonmesh',jsonmesh)
-%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(nargin==1)
-   varname=inputname(1);
-   obj=rootname;
-   if(isempty(varname)) 
-      varname='root';
-   end
-   rootname=varname;
-else
-   varname=inputname(2);
-end
-if(length(varargin)==1 && ischar(varargin{1}))
-   opt=struct('FileName',varargin{1});
-else
-   opt=varargin2struct(varargin{:});
-end
-opt.IsOctave=exist('OCTAVE_VERSION','builtin');
-rootisarray=0;
-rootlevel=1;
-forceroot=jsonopt('ForceRootName',0,opt);
-if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
-    rootisarray=1;
-    rootlevel=0;
-else
-    if(isempty(rootname))
-        rootname=varname;
-    end
-end
-if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
-    rootname='root';
-end
-json=obj2ubjson(rootname,obj,rootlevel,opt);
-if(~rootisarray)
-    json=['{' json '}'];
-end
-
-jsonp=jsonopt('JSONP','',opt);
-if(~isempty(jsonp))
-    json=[jsonp '(' json ')'];
-end
-
-% save to a file if FileName is set, suggested by Patrick Rapin
-if(~isempty(jsonopt('FileName','',opt)))
-    fid = fopen(opt.FileName, 'wb');
-    fwrite(fid,json);
-    fclose(fid);
-end
-
-%%-------------------------------------------------------------------------
-function txt=obj2ubjson(name,item,level,varargin)
-
-if(iscell(item))
-    txt=cell2ubjson(name,item,level,varargin{:});
-elseif(isstruct(item))
-    txt=struct2ubjson(name,item,level,varargin{:});
-elseif(ischar(item))
-    txt=str2ubjson(name,item,level,varargin{:});
-else
-    txt=mat2ubjson(name,item,level,varargin{:});
-end
-
-%%-------------------------------------------------------------------------
-function txt=cell2ubjson(name,item,level,varargin)
-txt='';
-if(~iscell(item))
-        error('input is not a cell');
-end
-
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item); % let's handle 1D cell first
-if(len>1) 
-    if(~isempty(name))
-        txt=[S_(checkname(name,varargin{:})) '[']; name=''; 
-    else
-        txt='['; 
-    end
-elseif(len==0)
-    if(~isempty(name))
-        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; 
-    else
-        txt='Z'; 
-    end
-end
-for j=1:dim(2)
-    if(dim(1)>1) txt=[txt '[']; end
-    for i=1:dim(1)
-       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
-    end
-    if(dim(1)>1) txt=[txt ']']; end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=struct2ubjson(name,item,level,varargin)
-txt='';
-if(~isstruct(item))
-	error('input is not a struct');
-end
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-
-if(~isempty(name)) 
-    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
-else
-    if(len>1) txt='['; end
-end
-for j=1:dim(2)
-  if(dim(1)>1) txt=[txt '[']; end
-  for i=1:dim(1)
-     names = fieldnames(item(i,j));
-     if(~isempty(name) && len==1)
-        txt=[txt S_(checkname(name,varargin{:})) '{']; 
-     else
-        txt=[txt '{']; 
-     end
-     if(~isempty(names))
-       for e=1:length(names)
-	     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
-             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
-       end
-     end
-     txt=[txt '}'];
-  end
-  if(dim(1)>1) txt=[txt ']']; end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=str2ubjson(name,item,level,varargin)
-txt='';
-if(~ischar(item))
-        error('input is not a string');
-end
-item=reshape(item, max(size(item),[1 0]));
-len=size(item,1);
-
-if(~isempty(name)) 
-    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
-else
-    if(len>1) txt='['; end
-end
-isoct=jsonopt('IsOctave',0,varargin{:});
-for e=1:len
-    val=item(e,:);
-    if(len==1)
-        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
-	if(isempty(name)) obj=['',S_(val),'']; end
-        txt=[txt,'',obj];
-    else
-        txt=[txt,'',['',S_(val),'']];
-    end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=mat2ubjson(name,item,level,varargin)
-if(~isnumeric(item) && ~islogical(item))
-        error('input is not an array');
-end
-
-if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
-   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
-      cid=I_(uint32(max(size(item))));
-      if(isempty(name))
-    	txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
-      else
-          if(isempty(item))
-              txt=[S_(checkname(name,varargin{:})),'Z'];
-              return;
-          else
-    	      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
-          end
-      end
-else
-    if(isempty(name))
-    	txt=matdata2ubjson(item,level+1,varargin{:});
-    else
-        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
-            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
-           	txt=[S_(checkname(name,varargin{:})) numtxt];
-        else
-    	    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
-        end
-    end
-    return;
-end
-if(issparse(item))
-    [ix,iy]=find(item);
-    data=full(item(find(item)));
-    if(~isreal(item))
-       data=[real(data(:)),imag(data(:))];
-       if(size(item,1)==1)
-           % Kludge to have data's 'transposedness' match item's.
-           % (Necessary for complex row vector handling below.)
-           data=data';
-       end
-       txt=[txt,S_('_ArrayIsComplex_'),'T'];
-    end
-    txt=[txt,S_('_ArrayIsSparse_'),'T'];
-    if(size(item,1)==1)
-        % Row vector, store only column indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([iy(:),data'],level+2,varargin{:})];
-    elseif(size(item,2)==1)
-        % Column vector, store only row indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([ix,data],level+2,varargin{:})];
-    else
-        % General case, store row and column indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([ix,iy,data],level+2,varargin{:})];
-    end
-else
-    if(isreal(item))
-        txt=[txt,S_('_ArrayData_'),...
-            matdata2ubjson(item(:)',level+2,varargin{:})];
-    else
-        txt=[txt,S_('_ArrayIsComplex_'),'T'];
-        txt=[txt,S_('_ArrayData_'),...
-            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
-    end
-end
-txt=[txt,'}'];
-
-%%-------------------------------------------------------------------------
-function txt=matdata2ubjson(mat,level,varargin)
-if(isempty(mat))
-    txt='Z';
-    return;
-end
-if(size(mat,1)==1)
-    level=level-1;
-end
-type='';
-hasnegtive=(mat<0);
-if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
-    if(isempty(hasnegtive))
-       if(max(mat(:))<=2^8)
-           type='U';
-       end
-    end
-    if(isempty(type))
-        % todo - need to consider negative ones separately
-        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
-        if(isempty(find(id)))
-            error('high-precision data is not yet supported');
-        end
-        key='iIlL';
-	type=key(find(id));
-    end
-    txt=[I_a(mat(:),type,size(mat))];
-elseif(islogical(mat))
-    logicalval='FT';
-    if(numel(mat)==1)
-        txt=logicalval(mat+1);
-    else
-        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
-    end
-else
-    if(numel(mat)==1)
-        txt=['[' D_(mat) ']'];
-    else
-        txt=D_a(mat(:),'D',size(mat));
-    end
-end
-
-%txt=regexprep(mat2str(mat),'\s+',',');
-%txt=regexprep(txt,';',sprintf('],['));
-% if(nargin>=2 && size(mat,1)>1)
-%     txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
-% end
-if(any(isinf(mat(:))))
-    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
-end
-if(any(isnan(mat(:))))
-    txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
-end
-
-%%-------------------------------------------------------------------------
-function newname=checkname(name,varargin)
-isunpack=jsonopt('UnpackHex',1,varargin{:});
-newname=name;
-if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
-    return
-end
-if(isunpack)
-    isoct=jsonopt('IsOctave',0,varargin{:});
-    if(~isoct)
-        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
-    else
-        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
-        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
-        if(isempty(pos)) return; end
-        str0=name;
-        pos0=[0 pend(:)' length(name)];
-        newname='';
-        for i=1:length(pos)
-            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
-        end
-        if(pos(end)~=length(name))
-            newname=[newname str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-end
-%%-------------------------------------------------------------------------
-function val=S_(str)
-if(length(str)==1)
-  val=['C' str];
-else
-  val=['S' I_(int32(length(str))) str];
-end
-%%-------------------------------------------------------------------------
-function val=I_(num)
-if(~isinteger(num))
-    error('input is not an integer');
-end
-if(num>=0 && num<255)
-   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
-   return;
-end
-key='iIlL';
-cid={'int8','int16','int32','int64'};
-for i=1:4
-  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
-    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
-    return;
-  end
-end
-error('unsupported integer');
-
-%%-------------------------------------------------------------------------
-function val=D_(num)
-if(~isfloat(num))
-    error('input is not a float');
-end
-
-if(isa(num,'single'))
-  val=['d' data2byte(num,'uint8')];
-else
-  val=['D' data2byte(num,'uint8')];
-end
-%%-------------------------------------------------------------------------
-function data=I_a(num,type,dim,format)
-id=find(ismember('iUIlL',type));
-
-if(id==0)
-  error('unsupported integer array');
-end
-
-% based on UBJSON specs, all integer types are stored in big endian format
-
-if(id==1)
-  data=data2byte(swapbytes(int8(num)),'uint8');
-  blen=1;
-elseif(id==2)
-  data=data2byte(swapbytes(uint8(num)),'uint8');
-  blen=1;
-elseif(id==3)
-  data=data2byte(swapbytes(int16(num)),'uint8');
-  blen=2;
-elseif(id==4)
-  data=data2byte(swapbytes(int32(num)),'uint8');
-  blen=4;
-elseif(id==5)
-  data=data2byte(swapbytes(int64(num)),'uint8');
-  blen=8;
-end
-
-if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
-  format='opt';
-end
-if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
-  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
-      cid=I_(uint32(max(dim)));
-      data=['$' type '#' I_a(dim,cid(1)) data(:)'];
-  else
-      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
-  end
-  data=['[' data(:)'];
-else
-  data=reshape(data,blen,numel(data)/blen);
-  data(2:blen+1,:)=data;
-  data(1,:)=type;
-  data=data(:)';
-  data=['[' data(:)' ']'];
-end
-%%-------------------------------------------------------------------------
-function data=D_a(num,type,dim,format)
-id=find(ismember('dD',type));
-
-if(id==0)
-  error('unsupported float array');
-end
-
-if(id==1)
-  data=data2byte(single(num),'uint8');
-elseif(id==2)
-  data=data2byte(double(num),'uint8');
-end
-
-if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
-  format='opt';
-end
-if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
-  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
-      cid=I_(uint32(max(dim)));
-      data=['$' type '#' I_a(dim,cid(1)) data(:)'];
-  else
-      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
-  end
-  data=['[' data];
-else
-  data=reshape(data,(id*4),length(data)/(id*4));
-  data(2:(id*4+1),:)=data;
-  data(1,:)=type;
-  data=data(:)';
-  data=['[' data(:)' ']'];
-end
-%%-------------------------------------------------------------------------
-function bytes=data2byte(varargin)
-bytes=typecast(varargin{:});
-bytes=bytes(:)';

assignments/machine-learning-ex2/ex2/lib/jsonlab/varargin2struct.m

@@ -1,40 +0,0 @@
-function opt=varargin2struct(varargin)
-%
-% opt=varargin2struct('param1',value1,'param2',value2,...)
-%   or
-% opt=varargin2struct(...,optstruct,...)
-%
-% convert a series of input parameters into a structure
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% date: 2012/12/22
-%
-% input:
-%      'param', value: the input parameters should be pairs of a string and a value
-%       optstruct: if a parameter is a struct, the fields will be merged to the output struct
-%
-% output:
-%      opt: a struct where opt.param1=value1, opt.param2=value2 ...
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-len=length(varargin);
-opt=struct;
-if(len==0) return; end
-i=1;
-while(i<=len)
-    if(isstruct(varargin{i}))
-        opt=mergestruct(opt,varargin{i});
-    elseif(ischar(varargin{i}) && i<len)
-        opt=setfield(opt,varargin{i},varargin{i+1});
-        i=i+1;
-    else
-        error('input must be in the form of ...,''name'',value,... pairs or structs');
-    end
-    i=i+1;
-end
-

assignments/machine-learning-ex2/ex2/lib/makeValidFieldName.m

@@ -1,30 +0,0 @@
-function str = makeValidFieldName(str)
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    isoct=exist('OCTAVE_VERSION','builtin');
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end

assignments/machine-learning-ex2/ex2/lib/submitWithConfiguration.m

@@ -1,125 +0,0 @@
-function submitWithConfiguration(conf)
-  addpath('./lib/jsonlab');
-
-  parts = parts(conf);
-
-  fprintf('== Submitting solutions | %s...\n', conf.itemName);
-
-  tokenFile = 'token.mat';
-  if exist(tokenFile, 'file')
-    load(tokenFile);
-    [email token] = promptToken(email, token, tokenFile);
-  else
-    [email token] = promptToken('', '', tokenFile);
-  end
-
-  if isempty(token)
-    fprintf('!! Submission Cancelled\n');
-    return
-  end
-
-  try
-    response = submitParts(conf, email, token, parts);
-  catch
-    e = lasterror();
-    fprintf( ...
-      '!! Submission failed: unexpected error: %s\n', ...
-      e.message);
-    fprintf('!! Please try again later.\n');
-    return
-  end
-
-  if isfield(response, 'errorMessage')
-    fprintf('!! Submission failed: %s\n', response.errorMessage);
-  else
-    showFeedback(parts, response);
-    save(tokenFile, 'email', 'token');
-  end
-end
-
-function [email token] = promptToken(email, existingToken, tokenFile)
-  if (~isempty(email) && ~isempty(existingToken))
-    prompt = sprintf( ...
-      'Use token from last successful submission (%s)? (Y/n): ', ...
-      email);
-    reenter = input(prompt, 's');
-
-    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
-      token = existingToken;
-      return;
-    else
-      delete(tokenFile);
-    end
-  end
-  email = input('Login (email address): ', 's');
-  token = input('Token: ', 's');
-end
-
-function isValid = isValidPartOptionIndex(partOptions, i)
-  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
-end
-
-function response = submitParts(conf, email, token, parts)
-  body = makePostBody(conf, email, token, parts);
-  submissionUrl = submissionUrl();
-  params = {'jsonBody', body};
-[code, responseBody] = system(sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, submissionUrl));
-  response = loadjson(responseBody);
-end
-
-function body = makePostBody(conf, email, token, parts)
-  bodyStruct.assignmentSlug = conf.assignmentSlug;
-  bodyStruct.submitterEmail = email;
-  bodyStruct.secret = token;
-  bodyStruct.parts = makePartsStruct(conf, parts);
-
-  opt.Compact = 1;
-  body = savejson('', bodyStruct, opt);
-end
-
-function partsStruct = makePartsStruct(conf, parts)
-  for part = parts
-    partId = part{:}.id;
-    fieldName = makeValidFieldName(partId);
-    outputStruct.output = conf.output(partId);
-    partsStruct.(fieldName) = outputStruct;
-  end
-end
-
-function [parts] = parts(conf)
-  parts = {};
-  for partArray = conf.partArrays
-    part.id = partArray{:}{1};
-    part.sourceFiles = partArray{:}{2};
-    part.name = partArray{:}{3};
-    parts{end + 1} = part;
-  end
-end
-
-function showFeedback(parts, response)
-  fprintf('== \n');
-  fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
-  fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
-  for part = parts
-    score = '';
-    partFeedback = '';
-    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
-    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
-    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
-    fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
-  end
-  evaluation = response.evaluation;
-  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
-  fprintf('==                                   --------------------------------\n');
-  fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
-  fprintf('== \n');
-end
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%
-% Service configuration
-%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-function submissionUrl = submissionUrl()
-  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
-end

assignments/machine-learning-ex2/ex2/mapFeature.m

@@ -1,21 +0,0 @@
-function out = mapFeature(X1, X2)
-% MAPFEATURE Feature mapping function to polynomial features
-%
-%   MAPFEATURE(X1, X2) maps the two input features
-%   to quadratic features used in the regularization exercise.
-%
-%   Returns a new feature array with more features, comprising of 
-%   X1, X2, X1.^2, X2.^2, X1*X2, X1*X2.^2, etc..
-%
-%   Inputs X1, X2 must be the same size
-%
-
-degree = 6;
-out = ones(size(X1(:,1)));
-for i = 1:degree
-    for j = 0:i
-        out(:, end+1) = (X1.^(i-j)).*(X2.^j);
-    end
-end
-
-end

assignments/machine-learning-ex2/ex2/plotData.m

@@ -1,30 +0,0 @@
-function plotData(X, y)
-%PLOTDATA Plots the data points X and y into a new figure 
-%   PLOTDATA(x,y) plots the data points with + for the positive examples
-%   and o for the negative examples. X is assumed to be a Mx2 matrix.
-
-% Create New Figure
-figure; hold on;
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Plot the positive and negative examples on a
-%               2D plot, using the option 'k+' for the positive
-%               examples and 'ko' for the negative examples.
-%
-
-pos = find(y == 1);
-neg = find(y == 0);
-plot(X(pos, 1), X(pos, 2), 'k+' ,'LineWidth', 2, 'MarkerSize', 7);
-plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', 'MarkerSize', 7);
-
-
-
-
-
-% =========================================================================
-
-
-
-hold off;
-
-end

assignments/machine-learning-ex2/ex2/plotDecisionBoundary.m

@@ -1,48 +0,0 @@
-function plotDecisionBoundary(theta, X, y)
-%PLOTDECISIONBOUNDARY Plots the data points X and y into a new figure with
-%the decision boundary defined by theta
-%   PLOTDECISIONBOUNDARY(theta, X,y) plots the data points with + for the 
-%   positive examples and o for the negative examples. X is assumed to be 
-%   a either 
-%   1) Mx3 matrix, where the first column is an all-ones column for the 
-%      intercept.
-%   2) MxN, N>3 matrix, where the first column is all-ones
-
-% Plot Data
-plotData(X(:,2:3), y);
-hold on
-
-if size(X, 2) <= 3
-    % Only need 2 points to define a line, so choose two endpoints
-    plot_x = [min(X(:,2))-2,  max(X(:,2))+2];
-
-    % Calculate the decision boundary line
-    plot_y = (-1./theta(3)).*(theta(2).*plot_x + theta(1));
-
-    % Plot, and adjust axes for better viewing
-    plot(plot_x, plot_y)
-    
-    % Legend, specific for the exercise
-    legend('Admitted', 'Not admitted', 'Decision Boundary')
-    axis([30, 100, 30, 100])
-else
-    % Here is the grid range
-    u = linspace(-1, 1.5, 50);
-    v = linspace(-1, 1.5, 50);
-
-    z = zeros(length(u), length(v));
-    % Evaluate z = theta*x over the grid
-    for i = 1:length(u)
-        for j = 1:length(v)
-            z(i,j) = mapFeature(u(i), v(j))*theta;
-        end
-    end
-    z = z'; % important to transpose z before calling contour
-
-    % Plot z = 0
-    % Notice you need to specify the range [0, 0]
-    contour(u, v, z, [0, 0], 'LineWidth', 2)
-end
-hold off
-
-end

assignments/machine-learning-ex2/ex2/predict.m

@@ -1,27 +0,0 @@
-function p = predict(theta, X)
-%PREDICT Predict whether the label is 0 or 1 using learned logistic 
-%regression parameters theta
-%   p = PREDICT(theta, X) computes the predictions for X using a 
-%   threshold at 0.5 (i.e., if sigmoid(theta'*x) >= 0.5, predict 1)
-
-m = size(X, 1); % Number of training examples
-
-% You need to return the following variables correctly
-p = zeros(m, 1);
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Complete the following code to make predictions using
-%               your learned logistic regression parameters. 
-%               You should set p to a vector of 0's and 1's
-%
-
-prob = sigmoid(X * theta);
-pos = find(prob >= 0.5);
-p(pos,1) = 1;
-
-
-
-% =========================================================================
-
-
-end

assignments/machine-learning-ex2/ex2/sigmoid.m

@@ -1,19 +0,0 @@
-function g = sigmoid(z)
-%SIGMOID Compute sigmoid functoon
-%   J = SIGMOID(z) computes the sigmoid of z.
-
-% You need to return the following variables correctly 
-g = zeros(size(z));
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the sigmoid of each value of z (z can be a matrix,
-%               vector or scalar).
-
-%g = 1 ./ (1 .+ e .^ -z);
-g = 1.0 ./ (1.0 + exp(-z));%exp(n)-->e的n次方
-
-
-
-% =============================================================
-
-end

assignments/machine-learning-ex2/ex2/submit.m

@@ -1,62 +0,0 @@
-function submit()
-  addpath('./lib');
-
-  conf.assignmentSlug = 'logistic-regression';
-  conf.itemName = 'Logistic Regression';
-  conf.partArrays = { ...
-    { ...
-      '1', ...
-      { 'sigmoid.m' }, ...
-      'Sigmoid Function', ...
-    }, ...
-    { ...
-      '2', ...
-      { 'costFunction.m' }, ...
-      'Logistic Regression Cost', ...
-    }, ...
-    { ...
-      '3', ...
-      { 'costFunction.m' }, ...
-      'Logistic Regression Gradient', ...
-    }, ...
-    { ...
-      '4', ...
-      { 'predict.m' }, ...
-      'Predict', ...
-    }, ...
-    { ...
-      '5', ...
-      { 'costFunctionReg.m' }, ...
-      'Regularized Logistic Regression Cost', ...
-    }, ...
-    { ...
-      '6', ...
-      { 'costFunctionReg.m' }, ...
-      'Regularized Logistic Regression Gradient', ...
-    }, ...
-  };
-  conf.output = @output;
-
-  submitWithConfiguration(conf);
-end
-
-function out = output(partId, auxstring)
-  % Random Test Cases
-  X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
-  y = sin(X(:,1) + X(:,2)) > 0;
-  if partId == '1'
-    out = sprintf('%0.5f ', sigmoid(X));
-  elseif partId == '2'
-    out = sprintf('%0.5f ', costFunction([0.25 0.5 -0.5]', X, y));
-  elseif partId == '3'
-    [cost, grad] = costFunction([0.25 0.5 -0.5]', X, y);
-    out = sprintf('%0.5f ', grad);
-  elseif partId == '4'
-    out = sprintf('%0.5f ', predict([0.25 0.5 -0.5]', X));
-  elseif partId == '5'
-    out = sprintf('%0.5f ', costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1));
-  elseif partId == '6'
-    [cost, grad] = costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1);
-    out = sprintf('%0.5f ', grad);
-  end 
-end

assignments/machine-learning-ex2/ex2/token.mat

@@ -1,15 +0,0 @@
-# Created by Octave 4.2.0, Sat Nov 19 20:17:18 2016 GMT <unknown@unknown>
-# name: email
-# type: sq_string
-# elements: 1
-# length: 17
-scruel@vip.qq.com
-
-
-# name: token
-# type: sq_string
-# elements: 1
-# length: 16
-A3Wc4pI1qYjCUaRB
-
-

assignments/machine-learning-ex3/ex3/displayData.m

@@ -1,59 +0,0 @@
-function [h, display_array] = displayData(X, example_width)
-%DISPLAYDATA Display 2D data in a nice grid
-%   [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
-%   stored in X in a nice grid. It returns the figure handle h and the 
-%   displayed array if requested.
-
-% Set example_width automatically if not passed in
-if ~exist('example_width', 'var') || isempty(example_width) 
-	example_width = round(sqrt(size(X, 2)));
-end
-
-% Gray Image
-colormap(gray);
-
-% Compute rows, cols
-[m n] = size(X);
-example_height = (n / example_width);
-
-% Compute number of items to display
-display_rows = floor(sqrt(m));
-display_cols = ceil(m / display_rows);
-
-% Between images padding
-pad = 1;
-
-% Setup blank display
-display_array = - ones(pad + display_rows * (example_height + pad), ...
-                       pad + display_cols * (example_width + pad));
-
-% Copy each example into a patch on the display array
-curr_ex = 1;
-for j = 1:display_rows
-	for i = 1:display_cols
-		if curr_ex > m, 
-			break; 
-		end
-		% Copy the patch
-		
-		% Get the max value of the patch
-		max_val = max(abs(X(curr_ex, :)));
-		display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ...
-		              pad + (i - 1) * (example_width + pad) + (1:example_width)) = ...
-						reshape(X(curr_ex, :), example_height, example_width) / max_val;
-		curr_ex = curr_ex + 1;
-	end
-	if curr_ex > m, 
-		break; 
-	end
-end
-
-% Display Image
-h = imagesc(display_array, [-1 1]);
-
-% Do not show axis
-axis image off
-
-drawnow;
-
-end

assignments/machine-learning-ex3/ex3/ex3.m

@@ -1,69 +0,0 @@
-%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all
-
-%  Instructions
-%  ------------
-% 
-%  This file contains code that helps you get started on the
-%  linear exercise. You will need to complete the following functions 
-%  in this exericse:
-%
-%     lrCostFunction.m (logistic regression cost function)
-%     oneVsAll.m
-%     predictOneVsAll.m
-%     predict.m
-%
-%  For this exercise, you will not need to change any code in this file,
-%  or any other files other than those mentioned above.
-%
-
-%% Initialization
-clear ; close all; clc
-
-%% Setup the parameters you will use for this part of the exercise
-input_layer_size  = 400;  % 20x20 Input Images of Digits
-num_labels = 10;          % 10 labels, from 1 to 10   ~k
-                          % (note that we have mapped "0" to label 10)
-
-%% =========== Part 1: Loading and Visualizing Data =============
-%  We start the exercise by first loading and visualizing the dataset. 
-%  You will be working with a dataset that contains handwritten digits.
-%
-
-% Load Training Data
-fprintf('Loading and Visualizing Data ...\n')
-
-load('ex3data1.mat'); % training data stored in arrays X, y
-m = size(X, 1);
-
-% Randomly select 100 data points to display
-%rand_indices = randperm(m);
-%sel = X(rand_indices(1:100), :);
-
-%displayData(sel);
-
-%fprintf('Program paused. Press enter to continue.\n');
-%pause;
-
-%% ============ Part 2: Vectorize Logistic Regression ============
-%  In this part of the exercise, you will reuse your logistic regression
-%  code from the last exercise. You task here is to make sure that your
-%  regularized logistic regression implementation is vectorized. After
-%  that, you will implement one-vs-all classification for the handwritten
-%  digit dataset.
-%
-
-fprintf('\nTraining One-vs-All Logistic Regression...\n')
-
-lambda = 0.1;
-[all_theta] = oneVsAll(X, y, num_labels, lambda);
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-
-%% ================ Part 3: Predict for One-Vs-All ================
-%  After ...
-pred = predictOneVsAll(all_theta, X);
-
-fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
-

assignments/machine-learning-ex3/ex3/ex3_nn.m

@@ -1,88 +0,0 @@
-%% Machine Learning Online Class - Exercise 3 | Part 2: Neural Networks
-
-%  Instructions
-%  ------------
-% 
-%  This file contains code that helps you get started on the
-%  linear exercise. You will need to complete the following functions 
-%  in this exericse:
-%
-%     lrCostFunction.m (logistic regression cost function)
-%     oneVsAll.m
-%     predictOneVsAll.m
-%     predict.m
-%
-%  For this exercise, you will not need to change any code in this file,
-%  or any other files other than those mentioned above.
-%
-
-%% Initialization
-clear ; close all; clc
-
-%% Setup the parameters you will use for this exercise
-input_layer_size  = 400;  % 20x20 Input Images of Digits
-hidden_layer_size = 25;   % 25 hidden units
-num_labels = 10;          % 10 labels, from 1 to 10   
-                          % (note that we have mapped "0" to label 10)
-
-%% =========== Part 1: Loading and Visualizing Data =============
-%  We start the exercise by first loading and visualizing the dataset. 
-%  You will be working with a dataset that contains handwritten digits.
-%
-
-% Load Training Data
-fprintf('Loading and Visualizing Data ...\n')
-
-load('ex3data1.mat');
-m = size(X, 1);
-
-% Randomly select 100 data points to display
-sel = randperm(size(X, 1));
-sel = sel(1:100);
-
-displayData(X(sel, :));
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-%% ================ Part 2: Loading Pameters ================
-% In this part of the exercise, we load some pre-initialized 
-% neural network parameters.
-
-fprintf('\nLoading Saved Neural Network Parameters ...\n')
-
-% Load the weights into variables Theta1 and Theta2
-load('ex3weights.mat');
-
-%% ================= Part 3: Implement Predict =================
-%  After training the neural network, we would like to use it to predict
-%  the labels. You will now implement the "predict" function to use the
-%  neural network to predict the labels of the training set. This lets
-%  you compute the training set accuracy.
-%Theta1,Theta2 = fmincg(@CostFunction, initial_theta, option)
-pred = predict(Theta1, Theta2, X);
-
-fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-%  To give you an idea of the network's output, you can also run
-%  through the examples one at the a time to see what it is predicting.
-
-%  Randomly permute examples
-rp = randperm(m);
-
-for i = 1:m
-    % Display 
-    fprintf('\nDisplaying Example Image\n');
-    displayData(X(rp(i), :));
-
-    pred = predict(Theta1, Theta2, X(rp(i),:));
-    fprintf('\nNeural Network Prediction: %d (digit %d)\n', pred, mod(pred, 10));
-    
-    % Pause
-    fprintf('Program paused. Press enter to continue.\n');
-    pause;
-end
-

assignments/machine-learning-ex3/ex3/fmincg.m

@@ -1,175 +0,0 @@
-function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
-% Minimize a continuous differentialble multivariate function. Starting point
-% is given by "X" (D by 1), and the function named in the string "f", must
-% return a function value and a vector of partial derivatives. The Polack-
-% Ribiere flavour of conjugate gradients is used to compute search directions,
-% and a line search using quadratic and cubic polynomial approximations and the
-% Wolfe-Powell stopping criteria is used together with the slope ratio method
-% for guessing initial step sizes. Additionally a bunch of checks are made to
-% make sure that exploration is taking place and that extrapolation will not
-% be unboundedly large. The "length" gives the length of the run: if it is
-% positive, it gives the maximum number of line searches, if negative its
-% absolute gives the maximum allowed number of function evaluations. You can
-% (optionally) give "length" a second component, which will indicate the
-% reduction in function value to be expected in the first line-search (defaults
-% to 1.0). The function returns when either its length is up, or if no further
-% progress can be made (ie, we are at a minimum, or so close that due to
-% numerical problems, we cannot get any closer). If the function terminates
-% within a few iterations, it could be an indication that the function value
-% and derivatives are not consistent (ie, there may be a bug in the
-% implementation of your "f" function). The function returns the found
-% solution "X", a vector of function values "fX" indicating the progress made
-% and "i" the number of iterations (line searches or function evaluations,
-% depending on the sign of "length") used.
-%
-% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
-%
-% See also: checkgrad 
-%
-% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13
-%
-%
-% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen
-% 
-% Permission is granted for anyone to copy, use, or modify these
-% programs and accompanying documents for purposes of research or
-% education, provided this copyright notice is retained, and note is
-% made of any changes that have been made.
-% 
-% These programs and documents are distributed without any warranty,
-% express or implied.  As the programs were written for research
-% purposes only, they have not been tested to the degree that would be
-% advisable in any important application.  All use of these programs is
-% entirely at the user's own risk.
-%
-% [ml-class] Changes Made:
-% 1) Function name and argument specifications
-% 2) Output display
-%
-
-% Read options
-if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter')
-    length = options.MaxIter;
-else
-    length = 100;
-end
-
-
-RHO = 0.01;                            % a bunch of constants for line searches
-SIG = 0.5;       % RHO and SIG are the constants in the Wolfe-Powell conditions
-INT = 0.1;    % don't reevaluate within 0.1 of the limit of the current bracket
-EXT = 3.0;                    % extrapolate maximum 3 times the current bracket
-MAX = 20;                         % max 20 function evaluations per line search
-RATIO = 100;                                      % maximum allowed slope ratio
-
-argstr = ['feval(f, X'];                      % compose string used to call function
-for i = 1:(nargin - 3)
-  argstr = [argstr, ',P', int2str(i)];
-end
-argstr = [argstr, ')'];
-
-if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end
-S=['Iteration '];
-
-i = 0;                                            % zero the run length counter
-ls_failed = 0;                             % no previous line search has failed
-fX = [];
-[f1 df1] = eval(argstr);                      % get function value and gradient
-i = i + (length<0);                                            % count epochs?!
-s = -df1;                                        % search direction is steepest
-d1 = -s'*s;                                                 % this is the slope
-z1 = red/(1-d1);                                  % initial step is red/(|s|+1)
-
-while i < abs(length)                                      % while not finished
-  i = i + (length>0);                                      % count iterations?!
-
-  X0 = X; f0 = f1; df0 = df1;                   % make a copy of current values
-  X = X + z1*s;                                             % begin line search
-  [f2 df2] = eval(argstr);
-  i = i + (length<0);                                          % count epochs?!
-  d2 = df2'*s;
-  f3 = f1; d3 = d1; z3 = -z1;             % initialize point 3 equal to point 1
-  if length>0, M = MAX; else M = min(MAX, -length-i); end
-  success = 0; limit = -1;                     % initialize quanteties
-  while 1
-    while ((f2 > f1+z1*RHO*d1) || (d2 > -SIG*d1)) && (M > 0) 
-      limit = z1;                                         % tighten the bracket
-      if f2 > f1
-        z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3);                 % quadratic fit
-      else
-        A = 6*(f2-f3)/z3+3*(d2+d3);                                 % cubic fit
-        B = 3*(f3-f2)-z3*(d3+2*d2);
-        z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A;       % numerical error possible - ok!
-      end
-      if isnan(z2) || isinf(z2)
-        z2 = z3/2;                  % if we had a numerical problem then bisect
-      end
-      z2 = max(min(z2, INT*z3),(1-INT)*z3);  % don't accept too close to limits
-      z1 = z1 + z2;                                           % update the step
-      X = X + z2*s;
-      [f2 df2] = eval(argstr);
-      M = M - 1; i = i + (length<0);                           % count epochs?!
-      d2 = df2'*s;
-      z3 = z3-z2;                    % z3 is now relative to the location of z2
-    end
-    if f2 > f1+z1*RHO*d1 || d2 > -SIG*d1
-      break;                                                % this is a failure
-    elseif d2 > SIG*d1
-      success = 1; break;                                             % success
-    elseif M == 0
-      break;                                                          % failure
-    end
-    A = 6*(f2-f3)/z3+3*(d2+d3);                      % make cubic extrapolation
-    B = 3*(f3-f2)-z3*(d3+2*d2);
-    z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3));        % num. error possible - ok!
-    if ~isreal(z2) || isnan(z2) || isinf(z2) || z2 < 0 % num prob or wrong sign?
-      if limit < -0.5                               % if we have no upper limit
-        z2 = z1 * (EXT-1);                 % the extrapolate the maximum amount
-      else
-        z2 = (limit-z1)/2;                                   % otherwise bisect
-      end
-    elseif (limit > -0.5) && (z2+z1 > limit)         % extraplation beyond max?
-      z2 = (limit-z1)/2;                                               % bisect
-    elseif (limit < -0.5) && (z2+z1 > z1*EXT)       % extrapolation beyond limit
-      z2 = z1*(EXT-1.0);                           % set to extrapolation limit
-    elseif z2 < -z3*INT
-      z2 = -z3*INT;
-    elseif (limit > -0.5) && (z2 < (limit-z1)*(1.0-INT))  % too close to limit?
-      z2 = (limit-z1)*(1.0-INT);
-    end
-    f3 = f2; d3 = d2; z3 = -z2;                  % set point 3 equal to point 2
-    z1 = z1 + z2; X = X + z2*s;                      % update current estimates
-    [f2 df2] = eval(argstr);
-    M = M - 1; i = i + (length<0);                             % count epochs?!
-    d2 = df2'*s;
-  end                                                      % end of line search
-
-  if success                                         % if line search succeeded
-    f1 = f2; fX = [fX' f1]';
-    fprintf('%s %4i | Cost: %4.6e\r', S, i, f1);
-    s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2;      % Polack-Ribiere direction
-    tmp = df1; df1 = df2; df2 = tmp;                         % swap derivatives
-    d2 = df1'*s;
-    if d2 > 0                                      % new slope must be negative
-      s = -df1;                              % otherwise use steepest direction
-      d2 = -s'*s;    
-    end
-    z1 = z1 * min(RATIO, d1/(d2-realmin));          % slope ratio but max RATIO
-    d1 = d2;
-    ls_failed = 0;                              % this line search did not fail
-  else
-    X = X0; f1 = f0; df1 = df0;  % restore point from before failed line search
-    if ls_failed || i > abs(length)          % line search failed twice in a row
-      break;                             % or we ran out of time, so we give up
-    end
-    tmp = df1; df1 = df2; df2 = tmp;                         % swap derivatives
-    s = -df1;                                                    % try steepest
-    d1 = -s'*s;
-    z1 = 1/(1-d1);                     
-    ls_failed = 1;                                    % this line search failed
-  end
-  if exist('OCTAVE_VERSION')
-    fflush(stdout);
-  end
-end
-fprintf('\n');

assignments/machine-learning-ex3/ex3/lib/jsonlab/AUTHORS.txt

@@ -1,41 +0,0 @@
-The author of "jsonlab" toolbox is Qianqian Fang. Qianqian
-is currently an Assistant Professor at Massachusetts General Hospital, 
-Harvard Medical School.
-
-Address: Martinos Center for Biomedical Imaging, 
-         Massachusetts General Hospital, 
-         Harvard Medical School
-         Bldg 149, 13th St, Charlestown, MA 02129, USA
-URL: http://nmr.mgh.harvard.edu/~fangq/
-Email: <fangq at nmr.mgh.harvard.edu> or <fangqq at gmail.com>
-
-
-The script loadjson.m was built upon previous works by
-
-- Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-       date: 2009/11/02
-- François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-       date: 2009/03/22
-- Joel Feenstra: http://www.mathworks.com/matlabcentral/fileexchange/20565
-       date: 2008/07/03
-
-
-This toolbox contains patches submitted by the following contributors:
-
-- Blake Johnson <bjohnso at bbn.com>
-  part of revision 341
-
-- Niclas Borlin <Niclas.Borlin at cs.umu.se>
-  various fixes in revision 394, including
-  - loadjson crashes for all-zero sparse matrix.
-  - loadjson crashes for empty sparse matrix.
-  - Non-zero size of 0-by-N and N-by-0 empty matrices is lost after savejson/loadjson.
-  - loadjson crashes for sparse real column vector.
-  - loadjson crashes for sparse complex column vector.
-  - Data is corrupted by savejson for sparse real row vector.
-  - savejson crashes for sparse complex row vector. 
-
-- Yul Kang <yul.kang.on at gmail.com>
-  patches for svn revision 415.
-  - savejson saves an empty cell array as [] instead of null
-  - loadjson differentiates an empty struct from an empty array

assignments/machine-learning-ex3/ex3/lib/jsonlab/ChangeLog.txt

@@ -1,74 +0,0 @@
-============================================================================
-
-   JSONlab - a toolbox to encode/decode JSON/UBJSON files in MATLAB/Octave
-
-----------------------------------------------------------------------------
-
-JSONlab ChangeLog (key features marked by *):
-
-== JSONlab 1.0 (codename: Optimus - Final), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2015/01/02 polish help info for all major functions, update examples, finalize 1.0
- 2014/12/19 fix a bug to strictly respect NoRowBracket in savejson
-
-== JSONlab 1.0.0-RC2 (codename: Optimus - RC2), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/11/22 show progress bar in loadjson ('ShowProgress') 
- 2014/11/17 add Compact option in savejson to output compact JSON format ('Compact')
- 2014/11/17 add FastArrayParser in loadjson to specify fast parser applicable levels
- 2014/09/18 start official github mirror: https://github.com/fangq/jsonlab
-
-== JSONlab 1.0.0-RC1 (codename: Optimus - RC1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/09/17  fix several compatibility issues when running on octave versions 3.2-3.8
- 2014/09/17  support 2D cell and struct arrays in both savejson and saveubjson
- 2014/08/04  escape special characters in a JSON string
- 2014/02/16  fix a bug when saving ubjson files
-
-== JSONlab 0.9.9 (codename: Optimus - beta), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2014/01/22  use binary read and write in saveubjson and loadubjson
-
-== JSONlab 0.9.8-1 (codename: Optimus - alpha update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2013/10/07 better round-trip conservation for empty arrays and structs (patch submitted by Yul Kang)
-
-== JSONlab 0.9.8 (codename: Optimus - alpha), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
- 2013/08/23 *universal Binary JSON (UBJSON) support, including both saveubjson and loadubjson
-
-== JSONlab 0.9.1 (codename: Rodimus, update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
- 2012/12/18 *handling of various empty and sparse matrices (fixes submitted by Niclas Borlin)
-
-== JSONlab 0.9.0 (codename: Rodimus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/06/17 *new format for an invalid leading char, unpacking hex code in savejson
- 2012/06/01  support JSONP in savejson
- 2012/05/25  fix the empty cell bug (reported by Cyril Davin)
- 2012/04/05  savejson can save to a file (suggested by Patrick Rapin)
-
-== JSONlab 0.8.1 (codename: Sentiel, Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/02/28  loadjson quotation mark escape bug, see http://bit.ly/yyk1nS
- 2012/01/25  patch to handle root-less objects, contributed by Blake Johnson
-
-== JSONlab 0.8.0 (codename: Sentiel), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2012/01/13 *speed up loadjson by 20 fold when parsing large data arrays in matlab
- 2012/01/11  remove row bracket if an array has 1 element, suggested by Mykel Kochenderfer
- 2011/12/22 *accept sequence of 'param',value input in savejson and loadjson
- 2011/11/18  fix struct array bug reported by Mykel Kochenderfer
-
-== JSONlab 0.5.1 (codename: Nexus Update 1), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2011/10/21  fix a bug in loadjson, previous code does not use any of the acceleration
- 2011/10/20  loadjson supports JSON collections - concatenated JSON objects
-
-== JSONlab 0.5.0 (codename: Nexus), FangQ <fangq (at) nmr.mgh.harvard.edu> ==
-
- 2011/10/16  package and release jsonlab 0.5.0
- 2011/10/15 *add json demo and regression test, support cpx numbers, fix double quote bug
- 2011/10/11 *speed up readjson dramatically, interpret _Array* tags, show data in root level
- 2011/10/10  create jsonlab project, start jsonlab website, add online documentation
- 2011/10/07 *speed up savejson by 25x using sprintf instead of mat2str, add options support
- 2011/10/06 *savejson works for structs, cells and arrays
- 2011/09/09  derive loadjson from JSON parser from MATLAB Central, draft savejson.m

assignments/machine-learning-ex3/ex3/lib/jsonlab/LICENSE_BSD.txt

@@ -1,25 +0,0 @@
-Copyright 2011-2015 Qianqian Fang <fangq at nmr.mgh.harvard.edu>. All rights reserved.
-
-Redistribution and use in source and binary forms, with or without modification, are
-permitted provided that the following conditions are met:
-
-   1. Redistributions of source code must retain the above copyright notice, this list of
-      conditions and the following disclaimer.
-
-   2. Redistributions in binary form must reproduce the above copyright notice, this list
-      of conditions and the following disclaimer in the documentation and/or other materials
-      provided with the distribution.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ''AS IS'' AND ANY EXPRESS OR IMPLIED
-WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
-FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS 
-OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
-CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
-ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
-NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
-ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-The views and conclusions contained in the software and documentation are those of the
-authors and should not be interpreted as representing official policies, either expressed
-or implied, of the copyright holders.

assignments/machine-learning-ex3/ex3/lib/jsonlab/README.txt

@@ -1,394 +0,0 @@
-===============================================================================
-=                                 JSONLab                                     =
-=           An open-source MATLAB/Octave JSON encoder and decoder             =
-===============================================================================
-
-*Copyright (C) 2011-2015  Qianqian Fang <fangq at nmr.mgh.harvard.edu>
-*License: BSD License, see License_BSD.txt for details
-*Version: 1.0 (Optimus - Final)
-
--------------------------------------------------------------------------------
-
-Table of Content:
-
-I.  Introduction
-II. Installation
-III.Using JSONLab
-IV. Known Issues and TODOs
-V.  Contribution and feedback
-
--------------------------------------------------------------------------------
-
-I.  Introduction
-
-JSON ([http://www.json.org/ JavaScript Object Notation]) is a highly portable, 
-human-readable and "[http://en.wikipedia.org/wiki/JSON fat-free]" text format 
-to represent complex and hierarchical data. It is as powerful as 
-[http://en.wikipedia.org/wiki/XML XML], but less verbose. JSON format is widely 
-used for data-exchange in applications, and is essential for the wild success 
-of [http://en.wikipedia.org/wiki/Ajax_(programming) Ajax] and 
-[http://en.wikipedia.org/wiki/Web_2.0 Web2.0]. 
-
-UBJSON (Universal Binary JSON) is a binary JSON format, specifically 
-optimized for compact file size and better performance while keeping
-the semantics as simple as the text-based JSON format. Using the UBJSON
-format allows to wrap complex binary data in a flexible and extensible
-structure, making it possible to process complex and large dataset 
-without accuracy loss due to text conversions.
-
-We envision that both JSON and its binary version will serve as part of 
-the mainstream data-exchange formats for scientific research in the future. 
-It will provide the flexibility and generality achieved by other popular 
-general-purpose file specifications, such as
-[http://www.hdfgroup.org/HDF5/whatishdf5.html HDF5], with significantly 
-reduced complexity and enhanced performance.
-
-JSONLab is a free and open-source implementation of a JSON/UBJSON encoder 
-and a decoder in the native MATLAB language. It can be used to convert a MATLAB 
-data structure (array, struct, cell, struct array and cell array) into 
-JSON/UBJSON formatted strings, or to decode a JSON/UBJSON file into MATLAB 
-data structure. JSONLab supports both MATLAB and  
-[http://www.gnu.org/software/octave/ GNU Octave] (a free MATLAB clone).
-
--------------------------------------------------------------------------------
-
-II. Installation
-
-The installation of JSONLab is no different than any other simple
-MATLAB toolbox. You only need to download/unzip the JSONLab package
-to a folder, and add the folder's path to MATLAB/Octave's path list
-by using the following command:
-
-    addpath('/path/to/jsonlab');
-
-If you want to add this path permanently, you need to type "pathtool", 
-browse to the jsonlab root folder and add to the list, then click "Save".
-Then, run "rehash" in MATLAB, and type "which loadjson", if you see an 
-output, that means JSONLab is installed for MATLAB/Octave.
-
--------------------------------------------------------------------------------
-
-III.Using JSONLab
-
-JSONLab provides two functions, loadjson.m -- a MATLAB->JSON decoder, 
-and savejson.m -- a MATLAB->JSON encoder, for the text-based JSON, and 
-two equivallent functions -- loadubjson and saveubjson for the binary 
-JSON. The detailed help info for the four functions can be found below:
-
-=== loadjson.m ===
-<pre>
-  data=loadjson(fname,opt)
-     or
-  data=loadjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2011/09/09, including previous works from 
- 
-          Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-             created on 2009/11/02
-          François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-             created on  2009/03/22
-          Joel Feenstra:
-          http://www.mathworks.com/matlabcentral/fileexchange/20565
-             created on 2008/07/03
- 
-  $Id: loadjson.m 452 2014-11-22 16:43:33Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a JSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-                          speed-optimized array parser when loading an 
-                          array object. The fast array parser may 
-                          collapse block arrays into a single large
-                          array similar to rules defined in cell2mat; 0 to 
-                          use a legacy parser; if set to a larger-than-1
-                          value, this option will specify the minimum
-                          dimension to enable the fast array parser. For
-                          example, if the input is a 3D array, setting
-                          FastArrayParser to 1 will return a 3D array;
-                          setting to 2 will return a cell array of 2D
-                          arrays; setting to 3 will return to a 2D cell
-                          array of 1D vectors; setting to 4 will return a
-                          3D cell array.
-            opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-       dat=loadjson(['examples' filesep 'example1.json'])
-       dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-</pre>
-
-=== savejson.m ===
-
-<pre>
-  json=savejson(rootname,obj,filename)
-     or
-  json=savejson(rootname,obj,opt)
-  json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-  Object Notation) string
- 
-  author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2011/09/09
- 
-  $Id: savejson.m 458 2014-12-19 22:17:17Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array).
-       filename: a string for the file name to save the output JSON data.
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-                          of a 1D/2D array;
-         opt.ArrayIndent [1|0]: if 1, output explicit data array with
-                          precedent indentation; if 0, no indentation
-         opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-                          to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-                          and $1 represents the sign. For those who want to use
-                          1e999 to represent Inf, they can set opt.Inf to '$11e999'
-         opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-                          to represent NaN
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSONP='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
-         opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-         opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a string in the JSON format (see http://json.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       savejson('jmesh',jsonmesh)
-       savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
- </pre>
-
-=== loadubjson.m ===
-
-<pre>
-  data=loadubjson(fname,opt)
-     or
-  data=loadubjson(fname,'param1',value1,'param2',value2,...)
- 
-  parse a JSON (JavaScript Object Notation) file or string
- 
-  authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2013/08/01
- 
-  $Id: loadubjson.m 436 2014-08-05 20:51:40Z fangq $
- 
-  input:
-       fname: input file name, if fname contains "{}" or "[]", fname
-              will be interpreted as a UBJSON string
-       opt: a struct to store parsing options, opt can be replaced by 
-            a list of ('param',value) pairs - the param string is equivallent
-            to a field in opt. opt can have the following 
-            fields (first in [.|.] is the default)
- 
-            opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-                          for each element of the JSON data, and group 
-                          arrays based on the cell2mat rules.
-            opt.IntEndian [B|L]: specify the endianness of the integer fields
-                          in the UBJSON input data. B - Big-Endian format for 
-                          integers (as required in the UBJSON specification); 
-                          L - input integer fields are in Little-Endian order.
- 
-  output:
-       dat: a cell array, where {...} blocks are converted into cell arrays,
-            and [...] are converted to arrays
- 
-  examples:
-       obj=struct('string','value','array',[1 2 3]);
-       ubjdata=saveubjson('obj',obj);
-       dat=loadubjson(ubjdata)
-       dat=loadubjson(['examples' filesep 'example1.ubj'])
-       dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-</pre>
-
-=== saveubjson.m ===
-
-<pre>
-  json=saveubjson(rootname,obj,filename)
-     or
-  json=saveubjson(rootname,obj,opt)
-  json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
- 
-  convert a MATLAB object (cell, struct or array) into a Universal 
-  Binary JSON (UBJSON) binary string
- 
-  author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-  created on 2013/08/17
- 
-  $Id: saveubjson.m 440 2014-09-17 19:59:45Z fangq $
- 
-  input:
-       rootname: the name of the root-object, when set to '', the root name
-         is ignored, however, when opt.ForceRootName is set to 1 (see below),
-         the MATLAB variable name will be used as the root name.
-       obj: a MATLAB object (array, cell, cell array, struct, struct array)
-       filename: a string for the file name to save the output UBJSON data
-       opt: a struct for additional options, ignore to use default values.
-         opt can have the following fields (first in [.|.] is the default)
- 
-         opt.FileName [''|string]: a file name to save the output JSON data
-         opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-                          array in JSON array format; if sets to 1, an
-                          array will be shown as a struct with fields
-                          "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-                          sparse arrays, the non-zero elements will be
-                          saved to _ArrayData_ field in triplet-format i.e.
-                          (ix,iy,val) and "_ArrayIsSparse_" will be added
-                          with a value of 1; for a complex array, the 
-                          _ArrayData_ array will include two columns 
-                          (4 for sparse) to record the real and imaginary 
-                          parts, and also "_ArrayIsComplex_":1 is added. 
-         opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-                          will use true/false rather than 1/0.
-         opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-                          numerical element will be shown without a square
-                          bracket, unless it is the root object; if 0, square
-                          brackets are forced for any numerical arrays.
-         opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-                          will use the name of the passed obj variable as the 
-                          root object name; if obj is an expression and 
-                          does not have a name, 'root' will be used; if this 
-                          is set to 0 and rootname is empty, the root level 
-                          will be merged down to the lower level.
-         opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-                          for example, if opt.JSON='foo', the JSON data is
-                          wrapped inside a function call as 'foo(...);'
-         opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-                          back to the string form
- 
-         opt can be replaced by a list of ('param',value) pairs. The param 
-         string is equivallent to a field in opt and is case sensitive.
-  output:
-       json: a binary string in the UBJSON format (see http://ubjson.org)
- 
-  examples:
-       jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-                'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-                'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-                           2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-                'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-                'SpecialData',[nan, inf, -inf]);
-       saveubjson('jsonmesh',jsonmesh)
-       saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-</pre>
-
-
-=== examples ===
-
-Under the "examples" folder, you can find several scripts to demonstrate the
-basic utilities of JSONLab. Running the "demo_jsonlab_basic.m" script, you 
-will see the conversions from MATLAB data structure to JSON text and backward.
-In "jsonlab_selftest.m", we load complex JSON files downloaded from the Internet
-and validate the loadjson/savejson functions for regression testing purposes.
-Similarly, a "demo_ubjson_basic.m" script is provided to test the saveubjson
-and loadubjson pairs for various matlab data structures.
-
-Please run these examples and understand how JSONLab works before you use
-it to process your data.
-
--------------------------------------------------------------------------------
-
-IV. Known Issues and TODOs
-
-JSONLab has several known limitations. We are striving to make it more general
-and robust. Hopefully in a few future releases, the limitations become less.
-
-Here are the known issues:
-
-# 3D or higher dimensional cell/struct-arrays will be converted to 2D arrays;
-# When processing names containing multi-byte characters, Octave and MATLAB \
-can give different field-names; you can use feature('DefaultCharacterSet','latin1') \
-in MATLAB to get consistant results
-# savejson can not handle class and dataset.
-# saveubjson converts a logical array into a uint8 ([U]) array
-# an unofficial N-D array count syntax is implemented in saveubjson. We are \
-actively communicating with the UBJSON spec maintainer to investigate the \
-possibility of making it upstream
-# loadubjson can not parse all UBJSON Specification (Draft 9) compliant \
-files, however, it can parse all UBJSON files produced by saveubjson.
-
--------------------------------------------------------------------------------
-
-V. Contribution and feedback
-
-JSONLab is an open-source project. This means you can not only use it and modify
-it as you wish, but also you can contribute your changes back to JSONLab so
-that everyone else can enjoy the improvement. For anyone who want to contribute,
-please download JSONLab source code from it's subversion repository by using the
-following command:
-
- svn checkout svn://svn.code.sf.net/p/iso2mesh/code/trunk/jsonlab jsonlab
-
-You can make changes to the files as needed. Once you are satisfied with your
-changes, and ready to share it with others, please cd the root directory of 
-JSONLab, and type
-
- svn diff > yourname_featurename.patch
-
-You then email the .patch file to JSONLab's maintainer, Qianqian Fang, at
-the email address shown in the beginning of this file. Qianqian will review 
-the changes and commit it to the subversion if they are satisfactory.
-
-We appreciate any suggestions and feedbacks from you. Please use iso2mesh's
-mailing list to report any questions you may have with JSONLab:
-
-http://groups.google.com/group/iso2mesh-users?hl=en&pli=1
-
-(Subscription to the mailing list is needed in order to post messages).

assignments/machine-learning-ex3/ex3/lib/jsonlab/jsonopt.m

@@ -1,32 +0,0 @@
-function val=jsonopt(key,default,varargin)
-%
-% val=jsonopt(key,default,optstruct)
-%
-% setting options based on a struct. The struct can be produced
-% by varargin2struct from a list of 'param','value' pairs
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-%
-% $Id: loadjson.m 371 2012-06-20 12:43:06Z fangq $
-%
-% input:
-%      key: a string with which one look up a value from a struct
-%      default: if the key does not exist, return default
-%      optstruct: a struct where each sub-field is a key 
-%
-% output:
-%      val: if key exists, val=optstruct.key; otherwise val=default
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-% 
-
-val=default;
-if(nargin<=2) return; end
-opt=varargin{1};
-if(isstruct(opt) && isfield(opt,key))
-    val=getfield(opt,key);
-end
-

assignments/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m

@@ -1,566 +0,0 @@
-function data = loadjson(fname,varargin)
-%
-% data=loadjson(fname,opt)
-%    or
-% data=loadjson(fname,'param1',value1,'param2',value2,...)
-%
-% parse a JSON (JavaScript Object Notation) file or string
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2011/09/09, including previous works from 
-%
-%         Nedialko Krouchev: http://www.mathworks.com/matlabcentral/fileexchange/25713
-%            created on 2009/11/02
-%         François Glineur: http://www.mathworks.com/matlabcentral/fileexchange/23393
-%            created on  2009/03/22
-%         Joel Feenstra:
-%         http://www.mathworks.com/matlabcentral/fileexchange/20565
-%            created on 2008/07/03
-%
-% $Id: loadjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      fname: input file name, if fname contains "{}" or "[]", fname
-%             will be interpreted as a JSON string
-%      opt: a struct to store parsing options, opt can be replaced by 
-%           a list of ('param',value) pairs - the param string is equivallent
-%           to a field in opt. opt can have the following 
-%           fields (first in [.|.] is the default)
-%
-%           opt.SimplifyCell [0|1]: if set to 1, loadjson will call cell2mat
-%                         for each element of the JSON data, and group 
-%                         arrays based on the cell2mat rules.
-%           opt.FastArrayParser [1|0 or integer]: if set to 1, use a
-%                         speed-optimized array parser when loading an 
-%                         array object. The fast array parser may 
-%                         collapse block arrays into a single large
-%                         array similar to rules defined in cell2mat; 0 to 
-%                         use a legacy parser; if set to a larger-than-1
-%                         value, this option will specify the minimum
-%                         dimension to enable the fast array parser. For
-%                         example, if the input is a 3D array, setting
-%                         FastArrayParser to 1 will return a 3D array;
-%                         setting to 2 will return a cell array of 2D
-%                         arrays; setting to 3 will return to a 2D cell
-%                         array of 1D vectors; setting to 4 will return a
-%                         3D cell array.
-%           opt.ShowProgress [0|1]: if set to 1, loadjson displays a progress bar.
-%
-% output:
-%      dat: a cell array, where {...} blocks are converted into cell arrays,
-%           and [...] are converted to arrays
-%
-% examples:
-%      dat=loadjson('{"obj":{"string":"value","array":[1,2,3]}}')
-%      dat=loadjson(['examples' filesep 'example1.json'])
-%      dat=loadjson(['examples' filesep 'example1.json'],'SimplifyCell',1)
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-global pos inStr len  esc index_esc len_esc isoct arraytoken
-
-if(regexp(fname,'[\{\}\]\[]','once'))
-   string=fname;
-elseif(exist(fname,'file'))
-   fid = fopen(fname,'rb');
-   string = fread(fid,inf,'uint8=>char')';
-   fclose(fid);
-else
-   error('input file does not exist');
-end
-
-pos = 1; len = length(string); inStr = string;
-isoct=exist('OCTAVE_VERSION','builtin');
-arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
-jstr=regexprep(inStr,'\\\\','  ');
-escquote=regexp(jstr,'\\"');
-arraytoken=sort([arraytoken escquote]);
-
-% String delimiters and escape chars identified to improve speed:
-esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
-index_esc = 1; len_esc = length(esc);
-
-opt=varargin2struct(varargin{:});
-
-if(jsonopt('ShowProgress',0,opt)==1)
-    opt.progressbar_=waitbar(0,'loading ...');
-end
-jsoncount=1;
-while pos <= len
-    switch(next_char)
-        case '{'
-            data{jsoncount} = parse_object(opt);
-        case '['
-            data{jsoncount} = parse_array(opt);
-        otherwise
-            error_pos('Outer level structure must be an object or an array');
-    end
-    jsoncount=jsoncount+1;
-end % while
-
-jsoncount=length(data);
-if(jsoncount==1 && iscell(data))
-    data=data{1};
-end
-
-if(~isempty(data))
-      if(isstruct(data)) % data can be a struct array
-          data=jstruct2array(data);
-      elseif(iscell(data))
-          data=jcell2array(data);
-      end
-end
-if(isfield(opt,'progressbar_'))
-    close(opt.progressbar_);
-end
-
-%%
-function newdata=jcell2array(data)
-len=length(data);
-newdata=data;
-for i=1:len
-      if(isstruct(data{i}))
-          newdata{i}=jstruct2array(data{i});
-      elseif(iscell(data{i}))
-          newdata{i}=jcell2array(data{i});
-      end
-end
-
-%%-------------------------------------------------------------------------
-function newdata=jstruct2array(data)
-fn=fieldnames(data);
-newdata=data;
-len=length(data);
-for i=1:length(fn) % depth-first
-    for j=1:len
-        if(isstruct(getfield(data(j),fn{i})))
-            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
-        end
-    end
-end
-if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
-  newdata=cell(len,1);
-  for j=1:len
-    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
-    iscpx=0;
-    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
-        if(data(j).x0x5F_ArrayIsComplex_)
-           iscpx=1;
-        end
-    end
-    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
-        if(data(j).x0x5F_ArrayIsSparse_)
-            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-                dim=data(j).x0x5F_ArraySize_;
-                if(iscpx && size(ndata,2)==4-any(dim==1))
-                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
-                end
-                if isempty(ndata)
-                    % All-zeros sparse
-                    ndata=sparse(dim(1),prod(dim(2:end)));
-                elseif dim(1)==1
-                    % Sparse row vector
-                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
-                elseif dim(2)==1
-                    % Sparse column vector
-                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
-                else
-                    % Generic sparse array.
-                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
-                end
-            else
-                if(iscpx && size(ndata,2)==4)
-                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));
-                end
-                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
-            end
-        end
-    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-        if(iscpx && size(ndata,2)==2)
-             ndata=complex(ndata(:,1),ndata(:,2));
-        end
-        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
-    end
-    newdata{j}=ndata;
-  end
-  if(len==1)
-      newdata=newdata{1};
-  end
-end
-
-%%-------------------------------------------------------------------------
-function object = parse_object(varargin)
-    parse_char('{');
-    object = [];
-    if next_char ~= '}'
-        while 1
-            str = parseStr(varargin{:});
-            if isempty(str)
-                error_pos('Name of value at position %d cannot be empty');
-            end
-            parse_char(':');
-            val = parse_value(varargin{:});
-            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );
-            if next_char == '}'
-                break;
-            end
-            parse_char(',');
-        end
-    end
-    parse_char('}');
-
-%%-------------------------------------------------------------------------
-
-function object = parse_array(varargin) % JSON array is written in row-major order
-global pos inStr isoct
-    parse_char('[');
-    object = cell(0, 1);
-    dim2=[];
-    arraydepth=jsonopt('JSONLAB_ArrayDepth_',1,varargin{:});
-    pbar=jsonopt('progressbar_',-1,varargin{:});
-
-    if next_char ~= ']'
-	if(jsonopt('FastArrayParser',1,varargin{:})>=1 && arraydepth>=jsonopt('FastArrayParser',1,varargin{:}))
-            [endpos, e1l, e1r, maxlevel]=matching_bracket(inStr,pos);
-            arraystr=['[' inStr(pos:endpos)];
-            arraystr=regexprep(arraystr,'"_NaN_"','NaN');
-            arraystr=regexprep(arraystr,'"([-+]*)_Inf_"','$1Inf');
-            arraystr(arraystr==sprintf('\n'))=[];
-            arraystr(arraystr==sprintf('\r'))=[];
-            %arraystr=regexprep(arraystr,'\s*,',','); % this is slow,sometimes needed
-            if(~isempty(e1l) && ~isempty(e1r)) % the array is in 2D or higher D
-        	astr=inStr((e1l+1):(e1r-1));
-        	astr=regexprep(astr,'"_NaN_"','NaN');
-        	astr=regexprep(astr,'"([-+]*)_Inf_"','$1Inf');
-        	astr(astr==sprintf('\n'))=[];
-        	astr(astr==sprintf('\r'))=[];
-        	astr(astr==' ')='';
-        	if(isempty(find(astr=='[', 1))) % array is 2D
-                    dim2=length(sscanf(astr,'%f,',[1 inf]));
-        	end
-            else % array is 1D
-        	astr=arraystr(2:end-1);
-        	astr(astr==' ')='';
-        	[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',[1,inf]);
-        	if(nextidx>=length(astr)-1)
-                    object=obj;
-                    pos=endpos;
-                    parse_char(']');
-                    return;
-        	end
-            end
-            if(~isempty(dim2))
-        	astr=arraystr;
-        	astr(astr=='[')='';
-        	astr(astr==']')='';
-        	astr(astr==' ')='';
-        	[obj, count, errmsg, nextidx]=sscanf(astr,'%f,',inf);
-        	if(nextidx>=length(astr)-1)
-                    object=reshape(obj,dim2,numel(obj)/dim2)';
-                    pos=endpos;
-                    parse_char(']');
-                    if(pbar>0)
-                        waitbar(pos/length(inStr),pbar,'loading ...');
-                    end
-                    return;
-        	end
-            end
-            arraystr=regexprep(arraystr,'\]\s*,','];');
-	else
-            arraystr='[';
-	end
-        try
-           if(isoct && regexp(arraystr,'"','once'))
-                error('Octave eval can produce empty cells for JSON-like input');
-           end
-           object=eval(arraystr);
-           pos=endpos;
-        catch
-         while 1
-            newopt=varargin2struct(varargin{:},'JSONLAB_ArrayDepth_',arraydepth+1);
-            val = parse_value(newopt);
-            object{end+1} = val;
-            if next_char == ']'
-                break;
-            end
-            parse_char(',');
-         end
-        end
-    end
-    if(jsonopt('SimplifyCell',0,varargin{:})==1)
-      try
-        oldobj=object;
-        object=cell2mat(object')';
-        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
-            object=oldobj;
-        elseif(size(object,1)>1 && ndims(object)==2)
-            object=object';
-        end
-      catch
-      end
-    end
-    parse_char(']');
-    
-    if(pbar>0)
-        waitbar(pos/length(inStr),pbar,'loading ...');
-    end
-%%-------------------------------------------------------------------------
-
-function parse_char(c)
-    global pos inStr len
-    skip_whitespace;
-    if pos > len || inStr(pos) ~= c
-        error_pos(sprintf('Expected %c at position %%d', c));
-    else
-        pos = pos + 1;
-        skip_whitespace;
-    end
-
-%%-------------------------------------------------------------------------
-
-function c = next_char
-    global pos inStr len
-    skip_whitespace;
-    if pos > len
-        c = [];
-    else
-        c = inStr(pos);
-    end
-
-%%-------------------------------------------------------------------------
-
-function skip_whitespace
-    global pos inStr len
-    while pos <= len && isspace(inStr(pos))
-        pos = pos + 1;
-    end
-
-%%-------------------------------------------------------------------------
-function str = parseStr(varargin)
-    global pos inStr len  esc index_esc len_esc
- % len, ns = length(inStr), keyboard
-    if inStr(pos) ~= '"'
-        error_pos('String starting with " expected at position %d');
-    else
-        pos = pos + 1;
-    end
-    str = '';
-    while pos <= len
-        while index_esc <= len_esc && esc(index_esc) < pos
-            index_esc = index_esc + 1;
-        end
-        if index_esc > len_esc
-            str = [str inStr(pos:len)];
-            pos = len + 1;
-            break;
-        else
-            str = [str inStr(pos:esc(index_esc)-1)];
-            pos = esc(index_esc);
-        end
-        nstr = length(str); switch inStr(pos)
-            case '"'
-                pos = pos + 1;
-                if(~isempty(str))
-                    if(strcmp(str,'_Inf_'))
-                        str=Inf;
-                    elseif(strcmp(str,'-_Inf_'))
-                        str=-Inf;
-                    elseif(strcmp(str,'_NaN_'))
-                        str=NaN;
-                    end
-                end
-                return;
-            case '\'
-                if pos+1 > len
-                    error_pos('End of file reached right after escape character');
-                end
-                pos = pos + 1;
-                switch inStr(pos)
-                    case {'"' '\' '/'}
-                        str(nstr+1) = inStr(pos);
-                        pos = pos + 1;
-                    case {'b' 'f' 'n' 'r' 't'}
-                        str(nstr+1) = sprintf(['\' inStr(pos)]);
-                        pos = pos + 1;
-                    case 'u'
-                        if pos+4 > len
-                            error_pos('End of file reached in escaped unicode character');
-                        end
-                        str(nstr+(1:6)) = inStr(pos-1:pos+4);
-                        pos = pos + 5;
-                end
-            otherwise % should never happen
-                str(nstr+1) = inStr(pos), keyboard
-                pos = pos + 1;
-        end
-    end
-    error_pos('End of file while expecting end of inStr');
-
-%%-------------------------------------------------------------------------
-
-function num = parse_number(varargin)
-    global pos inStr len isoct
-    currstr=inStr(pos:end);
-    numstr=0;
-    if(isoct~=0)
-        numstr=regexp(currstr,'^\s*-?(?:0|[1-9]\d*)(?:\.\d+)?(?:[eE][+\-]?\d+)?','end');
-        [num, one] = sscanf(currstr, '%f', 1);
-        delta=numstr+1;
-    else
-        [num, one, err, delta] = sscanf(currstr, '%f', 1);
-        if ~isempty(err)
-            error_pos('Error reading number at position %d');
-        end
-    end
-    pos = pos + delta-1;
-
-%%-------------------------------------------------------------------------
-
-function val = parse_value(varargin)
-    global pos inStr len
-    true = 1; false = 0;
-    
-    pbar=jsonopt('progressbar_',-1,varargin{:});
-    if(pbar>0)
-        waitbar(pos/len,pbar,'loading ...');
-    end
-    
-    switch(inStr(pos))
-        case '"'
-            val = parseStr(varargin{:});
-            return;
-        case '['
-            val = parse_array(varargin{:});
-            return;
-        case '{'
-            val = parse_object(varargin{:});
-            if isstruct(val)
-                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
-                    val=jstruct2array(val);
-                end
-            elseif isempty(val)
-                val = struct;
-            end
-            return;
-        case {'-','0','1','2','3','4','5','6','7','8','9'}
-            val = parse_number(varargin{:});
-            return;
-        case 't'
-            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'true')
-                val = true;
-                pos = pos + 4;
-                return;
-            end
-        case 'f'
-            if pos+4 <= len && strcmpi(inStr(pos:pos+4), 'false')
-                val = false;
-                pos = pos + 5;
-                return;
-            end
-        case 'n'
-            if pos+3 <= len && strcmpi(inStr(pos:pos+3), 'null')
-                val = [];
-                pos = pos + 4;
-                return;
-            end
-    end
-    error_pos('Value expected at position %d');
-%%-------------------------------------------------------------------------
-
-function error_pos(msg)
-    global pos inStr len
-    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
-    if poShow(3) == poShow(2)
-        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after
-    end
-    msg = [sprintf(msg, pos) ': ' ...
-    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
-    error( ['JSONparser:invalidFormat: ' msg] );
-
-%%-------------------------------------------------------------------------
-
-function str = valid_field(str)
-global isoct
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
-
-%%-------------------------------------------------------------------------
-function endpos = matching_quote(str,pos)
-len=length(str);
-while(pos<len)
-    if(str(pos)=='"')
-        if(~(pos>1 && str(pos-1)=='\'))
-            endpos=pos;
-            return;
-        end        
-    end
-    pos=pos+1;
-end
-error('unmatched quotation mark');
-%%-------------------------------------------------------------------------
-function [endpos, e1l, e1r, maxlevel] = matching_bracket(str,pos)
-global arraytoken
-level=1;
-maxlevel=level;
-endpos=0;
-bpos=arraytoken(arraytoken>=pos);
-tokens=str(bpos);
-len=length(tokens);
-pos=1;
-e1l=[];
-e1r=[];
-while(pos<=len)
-    c=tokens(pos);
-    if(c==']')
-        level=level-1;
-        if(isempty(e1r)) e1r=bpos(pos); end
-        if(level==0)
-            endpos=bpos(pos);
-            return
-        end
-    end
-    if(c=='[')
-        if(isempty(e1l)) e1l=bpos(pos); end
-        level=level+1;
-        maxlevel=max(maxlevel,level);
-    end
-    if(c=='"')
-        pos=matching_quote(tokens,pos+1);
-    end
-    pos=pos+1;
-end
-if(endpos==0) 
-    error('unmatched "]"');
-end
-

assignments/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m

@@ -1,528 +0,0 @@
-function data = loadubjson(fname,varargin)
-%
-% data=loadubjson(fname,opt)
-%    or
-% data=loadubjson(fname,'param1',value1,'param2',value2,...)
-%
-% parse a JSON (JavaScript Object Notation) file or string
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2013/08/01
-%
-% $Id: loadubjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      fname: input file name, if fname contains "{}" or "[]", fname
-%             will be interpreted as a UBJSON string
-%      opt: a struct to store parsing options, opt can be replaced by 
-%           a list of ('param',value) pairs - the param string is equivallent
-%           to a field in opt. opt can have the following 
-%           fields (first in [.|.] is the default)
-%
-%           opt.SimplifyCell [0|1]: if set to 1, loadubjson will call cell2mat
-%                         for each element of the JSON data, and group 
-%                         arrays based on the cell2mat rules.
-%           opt.IntEndian [B|L]: specify the endianness of the integer fields
-%                         in the UBJSON input data. B - Big-Endian format for 
-%                         integers (as required in the UBJSON specification); 
-%                         L - input integer fields are in Little-Endian order.
-%
-% output:
-%      dat: a cell array, where {...} blocks are converted into cell arrays,
-%           and [...] are converted to arrays
-%
-% examples:
-%      obj=struct('string','value','array',[1 2 3]);
-%      ubjdata=saveubjson('obj',obj);
-%      dat=loadubjson(ubjdata)
-%      dat=loadubjson(['examples' filesep 'example1.ubj'])
-%      dat=loadubjson(['examples' filesep 'example1.ubj'],'SimplifyCell',1)
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-global pos inStr len  esc index_esc len_esc isoct arraytoken fileendian systemendian
-
-if(regexp(fname,'[\{\}\]\[]','once'))
-   string=fname;
-elseif(exist(fname,'file'))
-   fid = fopen(fname,'rb');
-   string = fread(fid,inf,'uint8=>char')';
-   fclose(fid);
-else
-   error('input file does not exist');
-end
-
-pos = 1; len = length(string); inStr = string;
-isoct=exist('OCTAVE_VERSION','builtin');
-arraytoken=find(inStr=='[' | inStr==']' | inStr=='"');
-jstr=regexprep(inStr,'\\\\','  ');
-escquote=regexp(jstr,'\\"');
-arraytoken=sort([arraytoken escquote]);
-
-% String delimiters and escape chars identified to improve speed:
-esc = find(inStr=='"' | inStr=='\' ); % comparable to: regexp(inStr, '["\\]');
-index_esc = 1; len_esc = length(esc);
-
-opt=varargin2struct(varargin{:});
-fileendian=upper(jsonopt('IntEndian','B',opt));
-[os,maxelem,systemendian]=computer;
-
-jsoncount=1;
-while pos <= len
-    switch(next_char)
-        case '{'
-            data{jsoncount} = parse_object(opt);
-        case '['
-            data{jsoncount} = parse_array(opt);
-        otherwise
-            error_pos('Outer level structure must be an object or an array');
-    end
-    jsoncount=jsoncount+1;
-end % while
-
-jsoncount=length(data);
-if(jsoncount==1 && iscell(data))
-    data=data{1};
-end
-
-if(~isempty(data))
-      if(isstruct(data)) % data can be a struct array
-          data=jstruct2array(data);
-      elseif(iscell(data))
-          data=jcell2array(data);
-      end
-end
-
-
-%%
-function newdata=parse_collection(id,data,obj)
-
-if(jsoncount>0 && exist('data','var')) 
-    if(~iscell(data))
-       newdata=cell(1);
-       newdata{1}=data;
-       data=newdata;
-    end
-end
-
-%%
-function newdata=jcell2array(data)
-len=length(data);
-newdata=data;
-for i=1:len
-      if(isstruct(data{i}))
-          newdata{i}=jstruct2array(data{i});
-      elseif(iscell(data{i}))
-          newdata{i}=jcell2array(data{i});
-      end
-end
-
-%%-------------------------------------------------------------------------
-function newdata=jstruct2array(data)
-fn=fieldnames(data);
-newdata=data;
-len=length(data);
-for i=1:length(fn) % depth-first
-    for j=1:len
-        if(isstruct(getfield(data(j),fn{i})))
-            newdata(j)=setfield(newdata(j),fn{i},jstruct2array(getfield(data(j),fn{i})));
-        end
-    end
-end
-if(~isempty(strmatch('x0x5F_ArrayType_',fn)) && ~isempty(strmatch('x0x5F_ArrayData_',fn)))
-  newdata=cell(len,1);
-  for j=1:len
-    ndata=cast(data(j).x0x5F_ArrayData_,data(j).x0x5F_ArrayType_);
-    iscpx=0;
-    if(~isempty(strmatch('x0x5F_ArrayIsComplex_',fn)))
-        if(data(j).x0x5F_ArrayIsComplex_)
-           iscpx=1;
-        end
-    end
-    if(~isempty(strmatch('x0x5F_ArrayIsSparse_',fn)))
-        if(data(j).x0x5F_ArrayIsSparse_)
-            if(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-                dim=double(data(j).x0x5F_ArraySize_);
-                if(iscpx && size(ndata,2)==4-any(dim==1))
-                    ndata(:,end-1)=complex(ndata(:,end-1),ndata(:,end));
-                end
-                if isempty(ndata)
-                    % All-zeros sparse
-                    ndata=sparse(dim(1),prod(dim(2:end)));
-                elseif dim(1)==1
-                    % Sparse row vector
-                    ndata=sparse(1,ndata(:,1),ndata(:,2),dim(1),prod(dim(2:end)));
-                elseif dim(2)==1
-                    % Sparse column vector
-                    ndata=sparse(ndata(:,1),1,ndata(:,2),dim(1),prod(dim(2:end)));
-                else
-                    % Generic sparse array.
-                    ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3),dim(1),prod(dim(2:end)));
-                end
-            else
-                if(iscpx && size(ndata,2)==4)
-                    ndata(:,3)=complex(ndata(:,3),ndata(:,4));
-                end
-                ndata=sparse(ndata(:,1),ndata(:,2),ndata(:,3));
-            end
-        end
-    elseif(~isempty(strmatch('x0x5F_ArraySize_',fn)))
-        if(iscpx && size(ndata,2)==2)
-             ndata=complex(ndata(:,1),ndata(:,2));
-        end
-        ndata=reshape(ndata(:),data(j).x0x5F_ArraySize_);
-    end
-    newdata{j}=ndata;
-  end
-  if(len==1)
-      newdata=newdata{1};
-  end
-end
-
-%%-------------------------------------------------------------------------
-function object = parse_object(varargin)
-    parse_char('{');
-    object = [];
-    type='';
-    count=-1;
-    if(next_char == '$')
-        type=inStr(pos+1); % TODO
-        pos=pos+2;
-    end
-    if(next_char == '#')
-        pos=pos+1;
-        count=double(parse_number());
-    end
-    if next_char ~= '}'
-        num=0;
-        while 1
-            str = parseStr(varargin{:});
-            if isempty(str)
-                error_pos('Name of value at position %d cannot be empty');
-            end
-            %parse_char(':');
-            val = parse_value(varargin{:});
-            num=num+1;
-            eval( sprintf( 'object.%s  = val;', valid_field(str) ) );
-            if next_char == '}' || (count>=0 && num>=count)
-                break;
-            end
-            %parse_char(',');
-        end
-    end
-    if(count==-1)
-        parse_char('}');
-    end
-
-%%-------------------------------------------------------------------------
-function [cid,len]=elem_info(type)
-id=strfind('iUIlLdD',type);
-dataclass={'int8','uint8','int16','int32','int64','single','double'};
-bytelen=[1,1,2,4,8,4,8];
-if(id>0)
-    cid=dataclass{id};
-    len=bytelen(id);
-else
-    error_pos('unsupported type at position %d');
-end
-%%-------------------------------------------------------------------------
-
-
-function [data adv]=parse_block(type,count,varargin)
-global pos inStr isoct fileendian systemendian
-[cid,len]=elem_info(type);
-datastr=inStr(pos:pos+len*count-1);
-if(isoct)
-    newdata=int8(datastr);
-else
-    newdata=uint8(datastr);
-end
-id=strfind('iUIlLdD',type);
-if(id<=5 && fileendian~=systemendian)
-    newdata=swapbytes(typecast(newdata,cid));
-end
-data=typecast(newdata,cid);
-adv=double(len*count);
-
-%%-------------------------------------------------------------------------
-
-
-function object = parse_array(varargin) % JSON array is written in row-major order
-global pos inStr isoct
-    parse_char('[');
-    object = cell(0, 1);
-    dim=[];
-    type='';
-    count=-1;
-    if(next_char == '$')
-        type=inStr(pos+1);
-        pos=pos+2;
-    end
-    if(next_char == '#')
-        pos=pos+1;
-        if(next_char=='[')
-            dim=parse_array(varargin{:});
-            count=prod(double(dim));
-        else
-            count=double(parse_number());
-        end
-    end
-    if(~isempty(type))
-        if(count>=0)
-            [object adv]=parse_block(type,count,varargin{:});
-            if(~isempty(dim))
-                object=reshape(object,dim);
-            end
-            pos=pos+adv;
-            return;
-        else
-            endpos=matching_bracket(inStr,pos);
-            [cid,len]=elem_info(type);
-            count=(endpos-pos)/len;
-            [object adv]=parse_block(type,count,varargin{:});
-            pos=pos+adv;
-            parse_char(']');
-            return;
-        end
-    end
-    if next_char ~= ']'
-         while 1
-            val = parse_value(varargin{:});
-            object{end+1} = val;
-            if next_char == ']'
-                break;
-            end
-            %parse_char(',');
-         end
-    end
-    if(jsonopt('SimplifyCell',0,varargin{:})==1)
-      try
-        oldobj=object;
-        object=cell2mat(object')';
-        if(iscell(oldobj) && isstruct(object) && numel(object)>1 && jsonopt('SimplifyCellArray',1,varargin{:})==0)
-            object=oldobj;
-        elseif(size(object,1)>1 && ndims(object)==2)
-            object=object';
-        end
-      catch
-      end
-    end
-    if(count==-1)
-        parse_char(']');
-    end
-
-%%-------------------------------------------------------------------------
-
-function parse_char(c)
-    global pos inStr len
-    skip_whitespace;
-    if pos > len || inStr(pos) ~= c
-        error_pos(sprintf('Expected %c at position %%d', c));
-    else
-        pos = pos + 1;
-        skip_whitespace;
-    end
-
-%%-------------------------------------------------------------------------
-
-function c = next_char
-    global pos inStr len
-    skip_whitespace;
-    if pos > len
-        c = [];
-    else
-        c = inStr(pos);
-    end
-
-%%-------------------------------------------------------------------------
-
-function skip_whitespace
-    global pos inStr len
-    while pos <= len && isspace(inStr(pos))
-        pos = pos + 1;
-    end
-
-%%-------------------------------------------------------------------------
-function str = parseStr(varargin)
-    global pos inStr esc index_esc len_esc
- % len, ns = length(inStr), keyboard
-    type=inStr(pos);
-    if type ~= 'S' && type ~= 'C' && type ~= 'H'
-        error_pos('String starting with S expected at position %d');
-    else
-        pos = pos + 1;
-    end
-    if(type == 'C')
-        str=inStr(pos);
-        pos=pos+1;
-        return;
-    end
-    bytelen=double(parse_number());
-    if(length(inStr)>=pos+bytelen-1)
-        str=inStr(pos:pos+bytelen-1);
-        pos=pos+bytelen;
-    else
-        error_pos('End of file while expecting end of inStr');
-    end
-
-%%-------------------------------------------------------------------------
-
-function num = parse_number(varargin)
-    global pos inStr len isoct fileendian systemendian
-    id=strfind('iUIlLdD',inStr(pos));
-    if(isempty(id))
-        error_pos('expecting a number at position %d');
-    end
-    type={'int8','uint8','int16','int32','int64','single','double'};
-    bytelen=[1,1,2,4,8,4,8];
-    datastr=inStr(pos+1:pos+bytelen(id));
-    if(isoct)
-        newdata=int8(datastr);
-    else
-        newdata=uint8(datastr);
-    end
-    if(id<=5 && fileendian~=systemendian)
-        newdata=swapbytes(typecast(newdata,type{id}));
-    end
-    num=typecast(newdata,type{id});
-    pos = pos + bytelen(id)+1;
-
-%%-------------------------------------------------------------------------
-
-function val = parse_value(varargin)
-    global pos inStr len
-    true = 1; false = 0;
-
-    switch(inStr(pos))
-        case {'S','C','H'}
-            val = parseStr(varargin{:});
-            return;
-        case '['
-            val = parse_array(varargin{:});
-            return;
-        case '{'
-            val = parse_object(varargin{:});
-            if isstruct(val)
-                if(~isempty(strmatch('x0x5F_ArrayType_',fieldnames(val), 'exact')))
-                    val=jstruct2array(val);
-                end
-            elseif isempty(val)
-                val = struct;
-            end
-            return;
-        case {'i','U','I','l','L','d','D'}
-            val = parse_number(varargin{:});
-            return;
-        case 'T'
-            val = true;
-            pos = pos + 1;
-            return;
-        case 'F'
-            val = false;
-            pos = pos + 1;
-            return;
-        case {'Z','N'}
-            val = [];
-            pos = pos + 1;
-            return;
-    end
-    error_pos('Value expected at position %d');
-%%-------------------------------------------------------------------------
-
-function error_pos(msg)
-    global pos inStr len
-    poShow = max(min([pos-15 pos-1 pos pos+20],len),1);
-    if poShow(3) == poShow(2)
-        poShow(3:4) = poShow(2)+[0 -1];  % display nothing after
-    end
-    msg = [sprintf(msg, pos) ': ' ...
-    inStr(poShow(1):poShow(2)) '<error>' inStr(poShow(3):poShow(4)) ];
-    error( ['JSONparser:invalidFormat: ' msg] );
-
-%%-------------------------------------------------------------------------
-
-function str = valid_field(str)
-global isoct
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-    %str(~isletter(str) & ~('0' <= str & str <= '9')) = '_';
-
-%%-------------------------------------------------------------------------
-function endpos = matching_quote(str,pos)
-len=length(str);
-while(pos<len)
-    if(str(pos)=='"')
-        if(~(pos>1 && str(pos-1)=='\'))
-            endpos=pos;
-            return;
-        end        
-    end
-    pos=pos+1;
-end
-error('unmatched quotation mark');
-%%-------------------------------------------------------------------------
-function [endpos e1l e1r maxlevel] = matching_bracket(str,pos)
-global arraytoken
-level=1;
-maxlevel=level;
-endpos=0;
-bpos=arraytoken(arraytoken>=pos);
-tokens=str(bpos);
-len=length(tokens);
-pos=1;
-e1l=[];
-e1r=[];
-while(pos<=len)
-    c=tokens(pos);
-    if(c==']')
-        level=level-1;
-        if(isempty(e1r)) e1r=bpos(pos); end
-        if(level==0)
-            endpos=bpos(pos);
-            return
-        end
-    end
-    if(c=='[')
-        if(isempty(e1l)) e1l=bpos(pos); end
-        level=level+1;
-        maxlevel=max(maxlevel,level);
-    end
-    if(c=='"')
-        pos=matching_quote(tokens,pos+1);
-    end
-    pos=pos+1;
-end
-if(endpos==0) 
-    error('unmatched "]"');
-end
-

assignments/machine-learning-ex3/ex3/lib/jsonlab/mergestruct.m

@@ -1,33 +0,0 @@
-function s=mergestruct(s1,s2)
-%
-% s=mergestruct(s1,s2)
-%
-% merge two struct objects into one
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% date: 2012/12/22
-%
-% input:
-%      s1,s2: a struct object, s1 and s2 can not be arrays
-%
-% output:
-%      s: the merged struct object. fields in s1 and s2 will be combined in s.
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(~isstruct(s1) || ~isstruct(s2))
-    error('input parameters contain non-struct');
-end
-if(length(s1)>1 || length(s2)>1)
-    error('can not merge struct arrays');
-end
-fn=fieldnames(s2);
-s=s1;
-for i=1:length(fn)              
-    s=setfield(s,fn{i},getfield(s2,fn{i}));
-end
-

assignments/machine-learning-ex3/ex3/lib/jsonlab/savejson.m

@@ -1,475 +0,0 @@
-function json=savejson(rootname,obj,varargin)
-%
-% json=savejson(rootname,obj,filename)
-%    or
-% json=savejson(rootname,obj,opt)
-% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
-%
-% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
-% Object Notation) string
-%
-% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2011/09/09
-%
-% $Id: savejson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      rootname: the name of the root-object, when set to '', the root name
-%        is ignored, however, when opt.ForceRootName is set to 1 (see below),
-%        the MATLAB variable name will be used as the root name.
-%      obj: a MATLAB object (array, cell, cell array, struct, struct array).
-%      filename: a string for the file name to save the output JSON data.
-%      opt: a struct for additional options, ignore to use default values.
-%        opt can have the following fields (first in [.|.] is the default)
-%
-%        opt.FileName [''|string]: a file name to save the output JSON data
-%        opt.FloatFormat ['%.10g'|string]: format to show each numeric element
-%                         of a 1D/2D array;
-%        opt.ArrayIndent [1|0]: if 1, output explicit data array with
-%                         precedent indentation; if 0, no indentation
-%        opt.ArrayToStruct[0|1]: when set to 0, savejson outputs 1D/2D
-%                         array in JSON array format; if sets to 1, an
-%                         array will be shown as a struct with fields
-%                         "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-%                         sparse arrays, the non-zero elements will be
-%                         saved to _ArrayData_ field in triplet-format i.e.
-%                         (ix,iy,val) and "_ArrayIsSparse_" will be added
-%                         with a value of 1; for a complex array, the 
-%                         _ArrayData_ array will include two columns 
-%                         (4 for sparse) to record the real and imaginary 
-%                         parts, and also "_ArrayIsComplex_":1 is added. 
-%        opt.ParseLogical [0|1]: if this is set to 1, logical array elem
-%                         will use true/false rather than 1/0.
-%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-%                         numerical element will be shown without a square
-%                         bracket, unless it is the root object; if 0, square
-%                         brackets are forced for any numerical arrays.
-%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, savejson
-%                         will use the name of the passed obj variable as the 
-%                         root object name; if obj is an expression and 
-%                         does not have a name, 'root' will be used; if this 
-%                         is set to 0 and rootname is empty, the root level 
-%                         will be merged down to the lower level.
-%        opt.Inf ['"$1_Inf_"'|string]: a customized regular expression pattern
-%                         to represent +/-Inf. The matched pattern is '([-+]*)Inf'
-%                         and $1 represents the sign. For those who want to use
-%                         1e999 to represent Inf, they can set opt.Inf to '$11e999'
-%        opt.NaN ['"_NaN_"'|string]: a customized regular expression pattern
-%                         to represent NaN
-%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-%                         for example, if opt.JSONP='foo', the JSON data is
-%                         wrapped inside a function call as 'foo(...);'
-%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-%                         back to the string form
-%        opt.SaveBinary [0|1]: 1 - save the JSON file in binary mode; 0 - text mode.
-%        opt.Compact [0|1]: 1- out compact JSON format (remove all newlines and tabs)
-%
-%        opt can be replaced by a list of ('param',value) pairs. The param 
-%        string is equivallent to a field in opt and is case sensitive.
-% output:
-%      json: a string in the JSON format (see http://json.org)
-%
-% examples:
-%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-%               'SpecialData',[nan, inf, -inf]);
-%      savejson('jmesh',jsonmesh)
-%      savejson('',jsonmesh,'ArrayIndent',0,'FloatFormat','\t%.5g')
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(nargin==1)
-   varname=inputname(1);
-   obj=rootname;
-   if(isempty(varname)) 
-      varname='root';
-   end
-   rootname=varname;
-else
-   varname=inputname(2);
-end
-if(length(varargin)==1 && ischar(varargin{1}))
-   opt=struct('FileName',varargin{1});
-else
-   opt=varargin2struct(varargin{:});
-end
-opt.IsOctave=exist('OCTAVE_VERSION','builtin');
-rootisarray=0;
-rootlevel=1;
-forceroot=jsonopt('ForceRootName',0,opt);
-if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
-    rootisarray=1;
-    rootlevel=0;
-else
-    if(isempty(rootname))
-        rootname=varname;
-    end
-end
-if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
-    rootname='root';
-end
-
-whitespaces=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-if(jsonopt('Compact',0,opt)==1)
-    whitespaces=struct('tab','','newline','','sep',',');
-end
-if(~isfield(opt,'whitespaces_'))
-    opt.whitespaces_=whitespaces;
-end
-
-nl=whitespaces.newline;
-
-json=obj2json(rootname,obj,rootlevel,opt);
-if(rootisarray)
-    json=sprintf('%s%s',json,nl);
-else
-    json=sprintf('{%s%s%s}\n',nl,json,nl);
-end
-
-jsonp=jsonopt('JSONP','',opt);
-if(~isempty(jsonp))
-    json=sprintf('%s(%s);%s',jsonp,json,nl);
-end
-
-% save to a file if FileName is set, suggested by Patrick Rapin
-if(~isempty(jsonopt('FileName','',opt)))
-    if(jsonopt('SaveBinary',0,opt)==1)
-	    fid = fopen(opt.FileName, 'wb');
-	    fwrite(fid,json);
-    else
-	    fid = fopen(opt.FileName, 'wt');
-	    fwrite(fid,json,'char');
-    end
-    fclose(fid);
-end
-
-%%-------------------------------------------------------------------------
-function txt=obj2json(name,item,level,varargin)
-
-if(iscell(item))
-    txt=cell2json(name,item,level,varargin{:});
-elseif(isstruct(item))
-    txt=struct2json(name,item,level,varargin{:});
-elseif(ischar(item))
-    txt=str2json(name,item,level,varargin{:});
-else
-    txt=mat2json(name,item,level,varargin{:});
-end
-
-%%-------------------------------------------------------------------------
-function txt=cell2json(name,item,level,varargin)
-txt='';
-if(~iscell(item))
-        error('input is not a cell');
-end
-
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-ws=jsonopt('whitespaces_',struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n')),varargin{:});
-padding0=repmat(ws.tab,1,level);
-padding2=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-if(len>1)
-    if(~isempty(name))
-        txt=sprintf('%s"%s": [%s',padding0, checkname(name,varargin{:}),nl); name=''; 
-    else
-        txt=sprintf('%s[%s',padding0,nl); 
-    end
-elseif(len==0)
-    if(~isempty(name))
-        txt=sprintf('%s"%s": []',padding0, checkname(name,varargin{:})); name=''; 
-    else
-        txt=sprintf('%s[]',padding0); 
-    end
-end
-for j=1:dim(2)
-    if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
-    for i=1:dim(1)
-       txt=sprintf('%s%s',txt,obj2json(name,item{i,j},level+(dim(1)>1)+1,varargin{:}));
-       if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-    end
-    if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
-    if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-    %if(j==dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-end
-if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
-
-%%-------------------------------------------------------------------------
-function txt=struct2json(name,item,level,varargin)
-txt='';
-if(~isstruct(item))
-	error('input is not a struct');
-end
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding0=repmat(ws.tab,1,level);
-padding2=repmat(ws.tab,1,level+1);
-padding1=repmat(ws.tab,1,level+(dim(1)>1)+(len>1));
-nl=ws.newline;
-
-if(~isempty(name)) 
-    if(len>1) txt=sprintf('%s"%s": [%s',padding0,checkname(name,varargin{:}),nl); end
-else
-    if(len>1) txt=sprintf('%s[%s',padding0,nl); end
-end
-for j=1:dim(2)
-  if(dim(1)>1) txt=sprintf('%s%s[%s',txt,padding2,nl); end
-  for i=1:dim(1)
-    names = fieldnames(item(i,j));
-    if(~isempty(name) && len==1)
-        txt=sprintf('%s%s"%s": {%s',txt,padding1, checkname(name,varargin{:}),nl); 
-    else
-        txt=sprintf('%s%s{%s',txt,padding1,nl); 
-    end
-    if(~isempty(names))
-      for e=1:length(names)
-	    txt=sprintf('%s%s',txt,obj2json(names{e},getfield(item(i,j),...
-             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:}));
-        if(e<length(names)) txt=sprintf('%s%s',txt,','); end
-        txt=sprintf('%s%s',txt,nl);
-      end
-    end
-    txt=sprintf('%s%s}',txt,padding1);
-    if(i<dim(1)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-  end
-  if(dim(1)>1) txt=sprintf('%s%s%s]',txt,nl,padding2); end
-  if(j<dim(2)) txt=sprintf('%s%s',txt,sprintf(',%s',nl)); end
-end
-if(len>1) txt=sprintf('%s%s%s]',txt,nl,padding0); end
-
-%%-------------------------------------------------------------------------
-function txt=str2json(name,item,level,varargin)
-txt='';
-if(~ischar(item))
-        error('input is not a string');
-end
-item=reshape(item, max(size(item),[1 0]));
-len=size(item,1);
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding1=repmat(ws.tab,1,level);
-padding0=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-sep=ws.sep;
-
-if(~isempty(name)) 
-    if(len>1) txt=sprintf('%s"%s": [%s',padding1,checkname(name,varargin{:}),nl); end
-else
-    if(len>1) txt=sprintf('%s[%s',padding1,nl); end
-end
-isoct=jsonopt('IsOctave',0,varargin{:});
-for e=1:len
-    if(isoct)
-        val=regexprep(item(e,:),'\\','\\');
-        val=regexprep(val,'"','\"');
-        val=regexprep(val,'^"','\"');
-    else
-        val=regexprep(item(e,:),'\\','\\\\');
-        val=regexprep(val,'"','\\"');
-        val=regexprep(val,'^"','\\"');
-    end
-    val=escapejsonstring(val);
-    if(len==1)
-        obj=['"' checkname(name,varargin{:}) '": ' '"',val,'"'];
-	if(isempty(name)) obj=['"',val,'"']; end
-        txt=sprintf('%s%s%s%s',txt,padding1,obj);
-    else
-        txt=sprintf('%s%s%s%s',txt,padding0,['"',val,'"']);
-    end
-    if(e==len) sep=''; end
-    txt=sprintf('%s%s',txt,sep);
-end
-if(len>1) txt=sprintf('%s%s%s%s',txt,nl,padding1,']'); end
-
-%%-------------------------------------------------------------------------
-function txt=mat2json(name,item,level,varargin)
-if(~isnumeric(item) && ~islogical(item))
-        error('input is not an array');
-end
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-padding1=repmat(ws.tab,1,level);
-padding0=repmat(ws.tab,1,level+1);
-nl=ws.newline;
-sep=ws.sep;
-
-if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
-   isempty(item) ||jsonopt('ArrayToStruct',0,varargin{:}))
-    if(isempty(name))
-    	txt=sprintf('%s{%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
-              padding1,nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
-    else
-    	txt=sprintf('%s"%s": {%s%s"_ArrayType_": "%s",%s%s"_ArraySize_": %s,%s',...
-              padding1,checkname(name,varargin{:}),nl,padding0,class(item),nl,padding0,regexprep(mat2str(size(item)),'\s+',','),nl);
-    end
-else
-    if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1 && level>0)
-        numtxt=regexprep(regexprep(matdata2json(item,level+1,varargin{:}),'^\[',''),']','');
-    else
-        numtxt=matdata2json(item,level+1,varargin{:});
-    end
-    if(isempty(name))
-    	txt=sprintf('%s%s',padding1,numtxt);
-    else
-        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
-           	txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
-        else
-    	    txt=sprintf('%s"%s": %s',padding1,checkname(name,varargin{:}),numtxt);
-        end
-    end
-    return;
-end
-dataformat='%s%s%s%s%s';
-
-if(issparse(item))
-    [ix,iy]=find(item);
-    data=full(item(find(item)));
-    if(~isreal(item))
-       data=[real(data(:)),imag(data(:))];
-       if(size(item,1)==1)
-           % Kludge to have data's 'transposedness' match item's.
-           % (Necessary for complex row vector handling below.)
-           data=data';
-       end
-       txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
-    end
-    txt=sprintf(dataformat,txt,padding0,'"_ArrayIsSparse_": ','1', sep);
-    if(size(item,1)==1)
-        % Row vector, store only column indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([iy(:),data'],level+2,varargin{:}), nl);
-    elseif(size(item,2)==1)
-        % Column vector, store only row indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([ix,data],level+2,varargin{:}), nl);
-    else
-        % General case, store row and column indices.
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-           matdata2json([ix,iy,data],level+2,varargin{:}), nl);
-    end
-else
-    if(isreal(item))
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-            matdata2json(item(:)',level+2,varargin{:}), nl);
-    else
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayIsComplex_": ','1', sep);
-        txt=sprintf(dataformat,txt,padding0,'"_ArrayData_": ',...
-            matdata2json([real(item(:)) imag(item(:))],level+2,varargin{:}), nl);
-    end
-end
-txt=sprintf('%s%s%s',txt,padding1,'}');
-
-%%-------------------------------------------------------------------------
-function txt=matdata2json(mat,level,varargin)
-
-ws=struct('tab',sprintf('\t'),'newline',sprintf('\n'),'sep',sprintf(',\n'));
-ws=jsonopt('whitespaces_',ws,varargin{:});
-tab=ws.tab;
-nl=ws.newline;
-
-if(size(mat,1)==1)
-    pre='';
-    post='';
-    level=level-1;
-else
-    pre=sprintf('[%s',nl);
-    post=sprintf('%s%s]',nl,repmat(tab,1,level-1));
-end
-
-if(isempty(mat))
-    txt='null';
-    return;
-end
-floatformat=jsonopt('FloatFormat','%.10g',varargin{:});
-%if(numel(mat)>1)
-    formatstr=['[' repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf('],%s',nl)]];
-%else
-%    formatstr=[repmat([floatformat ','],1,size(mat,2)-1) [floatformat sprintf(',\n')]];
-%end
-
-if(nargin>=2 && size(mat,1)>1 && jsonopt('ArrayIndent',1,varargin{:})==1)
-    formatstr=[repmat(tab,1,level) formatstr];
-end
-
-txt=sprintf(formatstr,mat');
-txt(end-length(nl):end)=[];
-if(islogical(mat) && jsonopt('ParseLogical',0,varargin{:})==1)
-   txt=regexprep(txt,'1','true');
-   txt=regexprep(txt,'0','false');
-end
-%txt=regexprep(mat2str(mat),'\s+',',');
-%txt=regexprep(txt,';',sprintf('],\n['));
-% if(nargin>=2 && size(mat,1)>1)
-%     txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
-% end
-txt=[pre txt post];
-if(any(isinf(mat(:))))
-    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
-end
-if(any(isnan(mat(:))))
-    txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
-end
-
-%%-------------------------------------------------------------------------
-function newname=checkname(name,varargin)
-isunpack=jsonopt('UnpackHex',1,varargin{:});
-newname=name;
-if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
-    return
-end
-if(isunpack)
-    isoct=jsonopt('IsOctave',0,varargin{:});
-    if(~isoct)
-        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
-    else
-        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
-        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
-        if(isempty(pos)) return; end
-        str0=name;
-        pos0=[0 pend(:)' length(name)];
-        newname='';
-        for i=1:length(pos)
-            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
-        end
-        if(pos(end)~=length(name))
-            newname=[newname str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-end
-
-%%-------------------------------------------------------------------------
-function newstr=escapejsonstring(str)
-newstr=str;
-isoct=exist('OCTAVE_VERSION','builtin');
-if(isoct)
-   vv=sscanf(OCTAVE_VERSION,'%f');
-   if(vv(1)>=3.8) isoct=0; end
-end
-if(isoct)
-  escapechars={'\a','\f','\n','\r','\t','\v'};
-  for i=1:length(escapechars);
-    newstr=regexprep(newstr,escapechars{i},escapechars{i});
-  end
-else
-  escapechars={'\a','\b','\f','\n','\r','\t','\v'};
-  for i=1:length(escapechars);
-    newstr=regexprep(newstr,escapechars{i},regexprep(escapechars{i},'\\','\\\\'));
-  end
-end

assignments/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m

@@ -1,504 +0,0 @@
-function json=saveubjson(rootname,obj,varargin)
-%
-% json=saveubjson(rootname,obj,filename)
-%    or
-% json=saveubjson(rootname,obj,opt)
-% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
-%
-% convert a MATLAB object (cell, struct or array) into a Universal 
-% Binary JSON (UBJSON) binary string
-%
-% author: Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% created on 2013/08/17
-%
-% $Id: saveubjson.m 460 2015-01-03 00:30:45Z fangq $
-%
-% input:
-%      rootname: the name of the root-object, when set to '', the root name
-%        is ignored, however, when opt.ForceRootName is set to 1 (see below),
-%        the MATLAB variable name will be used as the root name.
-%      obj: a MATLAB object (array, cell, cell array, struct, struct array)
-%      filename: a string for the file name to save the output UBJSON data
-%      opt: a struct for additional options, ignore to use default values.
-%        opt can have the following fields (first in [.|.] is the default)
-%
-%        opt.FileName [''|string]: a file name to save the output JSON data
-%        opt.ArrayToStruct[0|1]: when set to 0, saveubjson outputs 1D/2D
-%                         array in JSON array format; if sets to 1, an
-%                         array will be shown as a struct with fields
-%                         "_ArrayType_", "_ArraySize_" and "_ArrayData_"; for
-%                         sparse arrays, the non-zero elements will be
-%                         saved to _ArrayData_ field in triplet-format i.e.
-%                         (ix,iy,val) and "_ArrayIsSparse_" will be added
-%                         with a value of 1; for a complex array, the 
-%                         _ArrayData_ array will include two columns 
-%                         (4 for sparse) to record the real and imaginary 
-%                         parts, and also "_ArrayIsComplex_":1 is added. 
-%        opt.ParseLogical [1|0]: if this is set to 1, logical array elem
-%                         will use true/false rather than 1/0.
-%        opt.NoRowBracket [1|0]: if this is set to 1, arrays with a single
-%                         numerical element will be shown without a square
-%                         bracket, unless it is the root object; if 0, square
-%                         brackets are forced for any numerical arrays.
-%        opt.ForceRootName [0|1]: when set to 1 and rootname is empty, saveubjson
-%                         will use the name of the passed obj variable as the 
-%                         root object name; if obj is an expression and 
-%                         does not have a name, 'root' will be used; if this 
-%                         is set to 0 and rootname is empty, the root level 
-%                         will be merged down to the lower level.
-%        opt.JSONP [''|string]: to generate a JSONP output (JSON with padding),
-%                         for example, if opt.JSON='foo', the JSON data is
-%                         wrapped inside a function call as 'foo(...);'
-%        opt.UnpackHex [1|0]: conver the 0x[hex code] output by loadjson 
-%                         back to the string form
-%
-%        opt can be replaced by a list of ('param',value) pairs. The param 
-%        string is equivallent to a field in opt and is case sensitive.
-% output:
-%      json: a binary string in the UBJSON format (see http://ubjson.org)
-%
-% examples:
-%      jsonmesh=struct('MeshNode',[0 0 0;1 0 0;0 1 0;1 1 0;0 0 1;1 0 1;0 1 1;1 1 1],... 
-%               'MeshTetra',[1 2 4 8;1 3 4 8;1 2 6 8;1 5 6 8;1 5 7 8;1 3 7 8],...
-%               'MeshTri',[1 2 4;1 2 6;1 3 4;1 3 7;1 5 6;1 5 7;...
-%                          2 8 4;2 8 6;3 8 4;3 8 7;5 8 6;5 8 7],...
-%               'MeshCreator','FangQ','MeshTitle','T6 Cube',...
-%               'SpecialData',[nan, inf, -inf]);
-%      saveubjson('jsonmesh',jsonmesh)
-%      saveubjson('jsonmesh',jsonmesh,'meshdata.ubj')
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details
-%
-% -- this function is part of JSONLab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-if(nargin==1)
-   varname=inputname(1);
-   obj=rootname;
-   if(isempty(varname)) 
-      varname='root';
-   end
-   rootname=varname;
-else
-   varname=inputname(2);
-end
-if(length(varargin)==1 && ischar(varargin{1}))
-   opt=struct('FileName',varargin{1});
-else
-   opt=varargin2struct(varargin{:});
-end
-opt.IsOctave=exist('OCTAVE_VERSION','builtin');
-rootisarray=0;
-rootlevel=1;
-forceroot=jsonopt('ForceRootName',0,opt);
-if((isnumeric(obj) || islogical(obj) || ischar(obj) || isstruct(obj) || iscell(obj)) && isempty(rootname) && forceroot==0)
-    rootisarray=1;
-    rootlevel=0;
-else
-    if(isempty(rootname))
-        rootname=varname;
-    end
-end
-if((isstruct(obj) || iscell(obj))&& isempty(rootname) && forceroot)
-    rootname='root';
-end
-json=obj2ubjson(rootname,obj,rootlevel,opt);
-if(~rootisarray)
-    json=['{' json '}'];
-end
-
-jsonp=jsonopt('JSONP','',opt);
-if(~isempty(jsonp))
-    json=[jsonp '(' json ')'];
-end
-
-% save to a file if FileName is set, suggested by Patrick Rapin
-if(~isempty(jsonopt('FileName','',opt)))
-    fid = fopen(opt.FileName, 'wb');
-    fwrite(fid,json);
-    fclose(fid);
-end
-
-%%-------------------------------------------------------------------------
-function txt=obj2ubjson(name,item,level,varargin)
-
-if(iscell(item))
-    txt=cell2ubjson(name,item,level,varargin{:});
-elseif(isstruct(item))
-    txt=struct2ubjson(name,item,level,varargin{:});
-elseif(ischar(item))
-    txt=str2ubjson(name,item,level,varargin{:});
-else
-    txt=mat2ubjson(name,item,level,varargin{:});
-end
-
-%%-------------------------------------------------------------------------
-function txt=cell2ubjson(name,item,level,varargin)
-txt='';
-if(~iscell(item))
-        error('input is not a cell');
-end
-
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item); % let's handle 1D cell first
-if(len>1) 
-    if(~isempty(name))
-        txt=[S_(checkname(name,varargin{:})) '[']; name=''; 
-    else
-        txt='['; 
-    end
-elseif(len==0)
-    if(~isempty(name))
-        txt=[S_(checkname(name,varargin{:})) 'Z']; name=''; 
-    else
-        txt='Z'; 
-    end
-end
-for j=1:dim(2)
-    if(dim(1)>1) txt=[txt '[']; end
-    for i=1:dim(1)
-       txt=[txt obj2ubjson(name,item{i,j},level+(len>1),varargin{:})];
-    end
-    if(dim(1)>1) txt=[txt ']']; end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=struct2ubjson(name,item,level,varargin)
-txt='';
-if(~isstruct(item))
-	error('input is not a struct');
-end
-dim=size(item);
-if(ndims(squeeze(item))>2) % for 3D or higher dimensions, flatten to 2D for now
-    item=reshape(item,dim(1),numel(item)/dim(1));
-    dim=size(item);
-end
-len=numel(item);
-
-if(~isempty(name)) 
-    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
-else
-    if(len>1) txt='['; end
-end
-for j=1:dim(2)
-  if(dim(1)>1) txt=[txt '[']; end
-  for i=1:dim(1)
-     names = fieldnames(item(i,j));
-     if(~isempty(name) && len==1)
-        txt=[txt S_(checkname(name,varargin{:})) '{']; 
-     else
-        txt=[txt '{']; 
-     end
-     if(~isempty(names))
-       for e=1:length(names)
-	     txt=[txt obj2ubjson(names{e},getfield(item(i,j),...
-             names{e}),level+(dim(1)>1)+1+(len>1),varargin{:})];
-       end
-     end
-     txt=[txt '}'];
-  end
-  if(dim(1)>1) txt=[txt ']']; end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=str2ubjson(name,item,level,varargin)
-txt='';
-if(~ischar(item))
-        error('input is not a string');
-end
-item=reshape(item, max(size(item),[1 0]));
-len=size(item,1);
-
-if(~isempty(name)) 
-    if(len>1) txt=[S_(checkname(name,varargin{:})) '[']; end
-else
-    if(len>1) txt='['; end
-end
-isoct=jsonopt('IsOctave',0,varargin{:});
-for e=1:len
-    val=item(e,:);
-    if(len==1)
-        obj=['' S_(checkname(name,varargin{:})) '' '',S_(val),''];
-	if(isempty(name)) obj=['',S_(val),'']; end
-        txt=[txt,'',obj];
-    else
-        txt=[txt,'',['',S_(val),'']];
-    end
-end
-if(len>1) txt=[txt ']']; end
-
-%%-------------------------------------------------------------------------
-function txt=mat2ubjson(name,item,level,varargin)
-if(~isnumeric(item) && ~islogical(item))
-        error('input is not an array');
-end
-
-if(length(size(item))>2 || issparse(item) || ~isreal(item) || ...
-   isempty(item) || jsonopt('ArrayToStruct',0,varargin{:}))
-      cid=I_(uint32(max(size(item))));
-      if(isempty(name))
-    	txt=['{' S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1)) ];
-      else
-          if(isempty(item))
-              txt=[S_(checkname(name,varargin{:})),'Z'];
-              return;
-          else
-    	      txt=[S_(checkname(name,varargin{:})),'{',S_('_ArrayType_'),S_(class(item)),S_('_ArraySize_'),I_a(size(item),cid(1))];
-          end
-      end
-else
-    if(isempty(name))
-    	txt=matdata2ubjson(item,level+1,varargin{:});
-    else
-        if(numel(item)==1 && jsonopt('NoRowBracket',1,varargin{:})==1)
-            numtxt=regexprep(regexprep(matdata2ubjson(item,level+1,varargin{:}),'^\[',''),']','');
-           	txt=[S_(checkname(name,varargin{:})) numtxt];
-        else
-    	    txt=[S_(checkname(name,varargin{:})),matdata2ubjson(item,level+1,varargin{:})];
-        end
-    end
-    return;
-end
-if(issparse(item))
-    [ix,iy]=find(item);
-    data=full(item(find(item)));
-    if(~isreal(item))
-       data=[real(data(:)),imag(data(:))];
-       if(size(item,1)==1)
-           % Kludge to have data's 'transposedness' match item's.
-           % (Necessary for complex row vector handling below.)
-           data=data';
-       end
-       txt=[txt,S_('_ArrayIsComplex_'),'T'];
-    end
-    txt=[txt,S_('_ArrayIsSparse_'),'T'];
-    if(size(item,1)==1)
-        % Row vector, store only column indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([iy(:),data'],level+2,varargin{:})];
-    elseif(size(item,2)==1)
-        % Column vector, store only row indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([ix,data],level+2,varargin{:})];
-    else
-        % General case, store row and column indices.
-        txt=[txt,S_('_ArrayData_'),...
-           matdata2ubjson([ix,iy,data],level+2,varargin{:})];
-    end
-else
-    if(isreal(item))
-        txt=[txt,S_('_ArrayData_'),...
-            matdata2ubjson(item(:)',level+2,varargin{:})];
-    else
-        txt=[txt,S_('_ArrayIsComplex_'),'T'];
-        txt=[txt,S_('_ArrayData_'),...
-            matdata2ubjson([real(item(:)) imag(item(:))],level+2,varargin{:})];
-    end
-end
-txt=[txt,'}'];
-
-%%-------------------------------------------------------------------------
-function txt=matdata2ubjson(mat,level,varargin)
-if(isempty(mat))
-    txt='Z';
-    return;
-end
-if(size(mat,1)==1)
-    level=level-1;
-end
-type='';
-hasnegtive=(mat<0);
-if(isa(mat,'integer') || isinteger(mat) || (isfloat(mat) && all(mod(mat(:),1) == 0)))
-    if(isempty(hasnegtive))
-       if(max(mat(:))<=2^8)
-           type='U';
-       end
-    end
-    if(isempty(type))
-        % todo - need to consider negative ones separately
-        id= histc(abs(max(mat(:))),[0 2^7 2^15 2^31 2^63]);
-        if(isempty(find(id)))
-            error('high-precision data is not yet supported');
-        end
-        key='iIlL';
-	type=key(find(id));
-    end
-    txt=[I_a(mat(:),type,size(mat))];
-elseif(islogical(mat))
-    logicalval='FT';
-    if(numel(mat)==1)
-        txt=logicalval(mat+1);
-    else
-        txt=['[$U#' I_a(size(mat),'l') typecast(swapbytes(uint8(mat(:)')),'uint8')];
-    end
-else
-    if(numel(mat)==1)
-        txt=['[' D_(mat) ']'];
-    else
-        txt=D_a(mat(:),'D',size(mat));
-    end
-end
-
-%txt=regexprep(mat2str(mat),'\s+',',');
-%txt=regexprep(txt,';',sprintf('],['));
-% if(nargin>=2 && size(mat,1)>1)
-%     txt=regexprep(txt,'\[',[repmat(sprintf('\t'),1,level) '[']);
-% end
-if(any(isinf(mat(:))))
-    txt=regexprep(txt,'([-+]*)Inf',jsonopt('Inf','"$1_Inf_"',varargin{:}));
-end
-if(any(isnan(mat(:))))
-    txt=regexprep(txt,'NaN',jsonopt('NaN','"_NaN_"',varargin{:}));
-end
-
-%%-------------------------------------------------------------------------
-function newname=checkname(name,varargin)
-isunpack=jsonopt('UnpackHex',1,varargin{:});
-newname=name;
-if(isempty(regexp(name,'0x([0-9a-fA-F]+)_','once')))
-    return
-end
-if(isunpack)
-    isoct=jsonopt('IsOctave',0,varargin{:});
-    if(~isoct)
-        newname=regexprep(name,'(^x|_){1}0x([0-9a-fA-F]+)_','${native2unicode(hex2dec($2))}');
-    else
-        pos=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','start');
-        pend=regexp(name,'(^x|_){1}0x([0-9a-fA-F]+)_','end');
-        if(isempty(pos)) return; end
-        str0=name;
-        pos0=[0 pend(:)' length(name)];
-        newname='';
-        for i=1:length(pos)
-            newname=[newname str0(pos0(i)+1:pos(i)-1) char(hex2dec(str0(pos(i)+3:pend(i)-1)))];
-        end
-        if(pos(end)~=length(name))
-            newname=[newname str0(pos0(end-1)+1:pos0(end))];
-        end
-    end
-end
-%%-------------------------------------------------------------------------
-function val=S_(str)
-if(length(str)==1)
-  val=['C' str];
-else
-  val=['S' I_(int32(length(str))) str];
-end
-%%-------------------------------------------------------------------------
-function val=I_(num)
-if(~isinteger(num))
-    error('input is not an integer');
-end
-if(num>=0 && num<255)
-   val=['U' data2byte(swapbytes(cast(num,'uint8')),'uint8')];
-   return;
-end
-key='iIlL';
-cid={'int8','int16','int32','int64'};
-for i=1:4
-  if((num>0 && num<2^(i*8-1)) || (num<0 && num>=-2^(i*8-1)))
-    val=[key(i) data2byte(swapbytes(cast(num,cid{i})),'uint8')];
-    return;
-  end
-end
-error('unsupported integer');
-
-%%-------------------------------------------------------------------------
-function val=D_(num)
-if(~isfloat(num))
-    error('input is not a float');
-end
-
-if(isa(num,'single'))
-  val=['d' data2byte(num,'uint8')];
-else
-  val=['D' data2byte(num,'uint8')];
-end
-%%-------------------------------------------------------------------------
-function data=I_a(num,type,dim,format)
-id=find(ismember('iUIlL',type));
-
-if(id==0)
-  error('unsupported integer array');
-end
-
-% based on UBJSON specs, all integer types are stored in big endian format
-
-if(id==1)
-  data=data2byte(swapbytes(int8(num)),'uint8');
-  blen=1;
-elseif(id==2)
-  data=data2byte(swapbytes(uint8(num)),'uint8');
-  blen=1;
-elseif(id==3)
-  data=data2byte(swapbytes(int16(num)),'uint8');
-  blen=2;
-elseif(id==4)
-  data=data2byte(swapbytes(int32(num)),'uint8');
-  blen=4;
-elseif(id==5)
-  data=data2byte(swapbytes(int64(num)),'uint8');
-  blen=8;
-end
-
-if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
-  format='opt';
-end
-if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
-  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
-      cid=I_(uint32(max(dim)));
-      data=['$' type '#' I_a(dim,cid(1)) data(:)'];
-  else
-      data=['$' type '#' I_(int32(numel(data)/blen)) data(:)'];
-  end
-  data=['[' data(:)'];
-else
-  data=reshape(data,blen,numel(data)/blen);
-  data(2:blen+1,:)=data;
-  data(1,:)=type;
-  data=data(:)';
-  data=['[' data(:)' ']'];
-end
-%%-------------------------------------------------------------------------
-function data=D_a(num,type,dim,format)
-id=find(ismember('dD',type));
-
-if(id==0)
-  error('unsupported float array');
-end
-
-if(id==1)
-  data=data2byte(single(num),'uint8');
-elseif(id==2)
-  data=data2byte(double(num),'uint8');
-end
-
-if(nargin>=3 && length(dim)>=2 && prod(dim)~=dim(2))
-  format='opt';
-end
-if((nargin<4 || strcmp(format,'opt')) && numel(num)>1)
-  if(nargin>=3 && (length(dim)==1 || (length(dim)>=2 && prod(dim)~=dim(2))))
-      cid=I_(uint32(max(dim)));
-      data=['$' type '#' I_a(dim,cid(1)) data(:)'];
-  else
-      data=['$' type '#' I_(int32(numel(data)/(id*4))) data(:)'];
-  end
-  data=['[' data];
-else
-  data=reshape(data,(id*4),length(data)/(id*4));
-  data(2:(id*4+1),:)=data;
-  data(1,:)=type;
-  data=data(:)';
-  data=['[' data(:)' ']'];
-end
-%%-------------------------------------------------------------------------
-function bytes=data2byte(varargin)
-bytes=typecast(varargin{:});
-bytes=bytes(:)';

assignments/machine-learning-ex3/ex3/lib/jsonlab/varargin2struct.m

@@ -1,40 +0,0 @@
-function opt=varargin2struct(varargin)
-%
-% opt=varargin2struct('param1',value1,'param2',value2,...)
-%   or
-% opt=varargin2struct(...,optstruct,...)
-%
-% convert a series of input parameters into a structure
-%
-% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
-% date: 2012/12/22
-%
-% input:
-%      'param', value: the input parameters should be pairs of a string and a value
-%       optstruct: if a parameter is a struct, the fields will be merged to the output struct
-%
-% output:
-%      opt: a struct where opt.param1=value1, opt.param2=value2 ...
-%
-% license:
-%     BSD, see LICENSE_BSD.txt files for details 
-%
-% -- this function is part of jsonlab toolbox (http://iso2mesh.sf.net/cgi-bin/index.cgi?jsonlab)
-%
-
-len=length(varargin);
-opt=struct;
-if(len==0) return; end
-i=1;
-while(i<=len)
-    if(isstruct(varargin{i}))
-        opt=mergestruct(opt,varargin{i});
-    elseif(ischar(varargin{i}) && i<len)
-        opt=setfield(opt,varargin{i},varargin{i+1});
-        i=i+1;
-    else
-        error('input must be in the form of ...,''name'',value,... pairs or structs');
-    end
-    i=i+1;
-end
-

assignments/machine-learning-ex3/ex3/lib/makeValidFieldName.m

@@ -1,30 +0,0 @@
-function str = makeValidFieldName(str)
-% From MATLAB doc: field names must begin with a letter, which may be
-% followed by any combination of letters, digits, and underscores.
-% Invalid characters will be converted to underscores, and the prefix
-% "x0x[Hex code]_" will be added if the first character is not a letter.
-    isoct=exist('OCTAVE_VERSION','builtin');
-    pos=regexp(str,'^[^A-Za-z]','once');
-    if(~isempty(pos))
-        if(~isoct)
-            str=regexprep(str,'^([^A-Za-z])','x0x${sprintf(''%X'',unicode2native($1))}_','once');
-        else
-            str=sprintf('x0x%X_%s',char(str(1)),str(2:end));
-        end
-    end
-    if(isempty(regexp(str,'[^0-9A-Za-z_]', 'once' ))) return;  end
-    if(~isoct)
-        str=regexprep(str,'([^0-9A-Za-z_])','_0x${sprintf(''%X'',unicode2native($1))}_');
-    else
-        pos=regexp(str,'[^0-9A-Za-z_]');
-        if(isempty(pos)) return; end
-        str0=str;
-        pos0=[0 pos(:)' length(str)];
-        str='';
-        for i=1:length(pos)
-            str=[str str0(pos0(i)+1:pos(i)-1) sprintf('_0x%X_',str0(pos(i)))];
-        end
-        if(pos(end)~=length(str))
-            str=[str str0(pos0(end-1)+1:pos0(end))];
-        end
-    end

assignments/machine-learning-ex3/ex3/lib/submitWithConfiguration.m

@@ -1,125 +0,0 @@
-function submitWithConfiguration(conf)
-  addpath('./lib/jsonlab');
-
-  parts = parts(conf);
-
-  fprintf('== Submitting solutions | %s...\n', conf.itemName);
-
-  tokenFile = 'token.mat';
-  if exist(tokenFile, 'file')
-    load(tokenFile);
-    [email token] = promptToken(email, token, tokenFile);
-  else
-    [email token] = promptToken('', '', tokenFile);
-  end
-
-  if isempty(token)
-    fprintf('!! Submission Cancelled\n');
-    return
-  end
-
-  try
-    response = submitParts(conf, email, token, parts);
-  catch
-    e = lasterror();
-    fprintf( ...
-      '!! Submission failed: unexpected error: %s\n', ...
-      e.message);
-    fprintf('!! Please try again later.\n');
-    return
-  end
-
-  if isfield(response, 'errorMessage')
-    fprintf('!! Submission failed: %s\n', response.errorMessage);
-  else
-    showFeedback(parts, response);
-    save(tokenFile, 'email', 'token');
-  end
-end
-
-function [email token] = promptToken(email, existingToken, tokenFile)
-  if (~isempty(email) && ~isempty(existingToken))
-    prompt = sprintf( ...
-      'Use token from last successful submission (%s)? (Y/n): ', ...
-      email);
-    reenter = input(prompt, 's');
-
-    if (isempty(reenter) || reenter(1) == 'Y' || reenter(1) == 'y')
-      token = existingToken;
-      return;
-    else
-      delete(tokenFile);
-    end
-  end
-  email = input('Login (email address): ', 's');
-  token = input('Token: ', 's');
-end
-
-function isValid = isValidPartOptionIndex(partOptions, i)
-  isValid = (~isempty(i)) && (1 <= i) && (i <= numel(partOptions));
-end
-
-function response = submitParts(conf, email, token, parts)
-  body = makePostBody(conf, email, token, parts);
-  submissionUrl = submissionUrl();
-  params = {'jsonBody', body};
-[code, responseBody] = system(sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, submissionUrl));
-  response = loadjson(responseBody);
-end
-
-function body = makePostBody(conf, email, token, parts)
-  bodyStruct.assignmentSlug = conf.assignmentSlug;
-  bodyStruct.submitterEmail = email;
-  bodyStruct.secret = token;
-  bodyStruct.parts = makePartsStruct(conf, parts);
-
-  opt.Compact = 1;
-  body = savejson('', bodyStruct, opt);
-end
-
-function partsStruct = makePartsStruct(conf, parts)
-  for part = parts
-    partId = part{:}.id;
-    fieldName = makeValidFieldName(partId);
-    outputStruct.output = conf.output(partId);
-    partsStruct.(fieldName) = outputStruct;
-  end
-end
-
-function [parts] = parts(conf)
-  parts = {};
-  for partArray = conf.partArrays
-    part.id = partArray{:}{1};
-    part.sourceFiles = partArray{:}{2};
-    part.name = partArray{:}{3};
-    parts{end + 1} = part;
-  end
-end
-
-function showFeedback(parts, response)
-  fprintf('== \n');
-  fprintf('== %43s | %9s | %-s\n', 'Part Name', 'Score', 'Feedback');
-  fprintf('== %43s | %9s | %-s\n', '---------', '-----', '--------');
-  for part = parts
-    score = '';
-    partFeedback = '';
-    partFeedback = response.partFeedbacks.(makeValidFieldName(part{:}.id));
-    partEvaluation = response.partEvaluations.(makeValidFieldName(part{:}.id));
-    score = sprintf('%d / %3d', partEvaluation.score, partEvaluation.maxScore);
-    fprintf('== %43s | %9s | %-s\n', part{:}.name, score, partFeedback);
-  end
-  evaluation = response.evaluation;
-  totalScore = sprintf('%d / %d', evaluation.score, evaluation.maxScore);
-  fprintf('==                                   --------------------------------\n');
-  fprintf('== %43s | %9s | %-s\n', '', totalScore, '');
-  fprintf('== \n');
-end
-
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-%
-% Service configuration
-%
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-function submissionUrl = submissionUrl()
-  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
-end

assignments/machine-learning-ex3/ex3/lrCostFunction.m

@@ -1,58 +0,0 @@
-function [J, grad] = lrCostFunction(theta, X, y, lambda)
-%LRCOSTFUNCTION Compute cost and gradient for logistic regression with 
-%regularization
-%   J = LRCOSTFUNCTION(theta, X, y, lambda) computes the cost of using
-%   theta as the parameter for regularized logistic regression and the
-%   gradient of the cost w.r.t. to the parameters. 
-
-% Initialize some useful values
-m = length(y); % number of training examples
-
-% You need to return the following variables correctly 
-J = 0;
-grad = zeros(size(theta));
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Compute the cost of a particular choice of theta.
-%               You should set J to the cost.
-%               Compute the partial derivatives and set grad to the partial
-%               derivatives of the cost w.r.t. each parameter in theta
-%
-
-hx = sigmoid(X * theta); %hypothesis, m * 1
-% Hint: The computation of the cost function and gradients can be
-%       efficiently vectorized. For example, consider the computation
-%
-%           sigmoid(X * theta)
-%
-%       Each row of the resulting matrix will contain the value of the
-%       prediction for that example. You can make use of this to vectorize
-%       the cost function and gradient computations. 
-%
-J = 1 / m * sum(-y' * log(hx) - (1 - y)' * log(1 - hx)) + lambda / (2 * m) * theta(2:end)' * theta(2:end);
-
-% Hint: When computing the gradient of the regularized cost function, 
-%       there're many possible vectorized solutions, but one solution
-%       looks like:
-%           grad = (unregularized gradient for logistic regression)
-%           temp = theta; 
-%           temp(1) = 0;   % because we don't add anything for j = 0  
-%           grad = grad + YOUR_CODE_HERE (using the temp variable)
-%
-gradf = (1 / m) * (X(:, 1)' * (hx - y));
-gradb = (1 / m) * (X(:, 2:end)' * (hx - y)) + lambda * theta(2:end) / m;
-
-grad = [gradf;gradb];
-size(grad)
-
-
-
-
-
-
-
-% =============================================================
-
-grad = grad(:);
-
-end

assignments/machine-learning-ex3/ex3/oneVsAll.m

@@ -1,74 +0,0 @@
-function [all_theta] = oneVsAll(X, y, num_labels, lambda)
-%ONEVSALL trains multiple logistic regression classifiers and returns all
-%the classifiers in a matrix all_theta, where the i-th row of all_theta 
-%corresponds to the classifier for label i
-%   [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
-%   logisitc regression classifiers and returns each of these classifiers
-%   in a matrix all_theta, where the i-th row of all_theta corresponds 
-%   to the classifier for label i
-%   num_labels --> k
-
-% Some useful variables
-m = size(X, 1);
-n = size(X, 2);
-
-% You need to return the following variables correctly 
-all_theta = zeros(num_labels, n + 1);
-
-% Add ones to the X data matrix
-X = [ones(m, 1) X];
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: You should complete the following code to train num_labels
-%               logistic regression classifiers with regularization
-%               parameter lambda. 
-%
-% Hint: theta(:) will return a column vector.
-%
-% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use 
-%       whether the ground truth is true/false for this class.
-%
-% Note: For this assignment, we recommend using fmincg to optimize the cost
-%       function. It is okay to use a for-loop (for c = 1:num_labels) to
-%       loop over the different classes.
-%
-%       fmincg works similarly to fminunc, but is more efficient when we
-%       are dealing with large number of parameters.
-%
-% Example Code for fmincg:
-%
-%     % Set Initial theta
-%     initial_theta = zeros(n + 1, 1);
-%     
-%     % Set options for fminunc
-%     options = optimset('GradObj', 'on', 'MaxIter', 50);
-% 
-%     % Run fmincg to obtain the optimal theta
-%     % This function will return theta and the cost 
-%     [theta] = ...
-%         fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
-%                 initial_theta, options);
-%
-
-options = optimset('GradObj', 'on', 'MaxIter', 50);
-for c = 1 : num_labels
-initial_theta = zeros(n + 1, 1);
-all_theta(c,:)= fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
-                   initial_theta, options);
-size(all_theta(c,:))
-end
-%size(all_theta)
-
-%initial_theta = zeros(n + 1, num_labels);
-%all_theta= fmincg (@(t)(lrCostFunction(t, X, y, lambda)), ...
-   %               initial_theta, options);
-
-
-
-
-
-
-% =========================================================================
-
-
-end

assignments/machine-learning-ex3/ex3/predict.m

@@ -1,37 +0,0 @@
-function p = predict(Theta1, Theta2, X)
-%PREDICT Predict the label of an input given a trained neural network
-%   p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the
-%   trained weights of a neural network (Theta1, Theta2)
-
-% Useful values
-m = size(X, 1);
-num_labels = size(Theta2, 1);
-
-% You need to return the following variables correctly 
-p = zeros(size(X, 1), 1);
-X = [ones(m,1), X];
-% ====================== YOUR CODE HERE ======================
-% Instructions: Complete the following code to make predictions using
-%               your learned neural network. You should set p to a 
-%               vector containing labels between 1 to num_labels.
-%
-% Hint: The max function might come in useful. In particular, the max
-%       function can also return the index of the max element, for more
-%       information see 'help max'. If your examples are in rows, then, you
-%       can use max(A, [], 2) to obtain the max for each row.
-%
-
-
-
-a2 = sigmoid(X * Theta1');
-a2 = [ones(size(a2,1), 1), a2];
-p = sigmoid(a2 *Theta2');
-[temp_p,p] = max(p, [], 2);
-
-
-
-
-% =========================================================================
-
-
-end

assignments/machine-learning-ex3/ex3/predictOneVsAll.m

@@ -1,44 +0,0 @@
-function p = predictOneVsAll(all_theta, X)
-%PREDICT Predict the label for a trained one-vs-all classifier. The labels 
-%are in the range 1..K, where K = size(all_theta, 1). 
-%  p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions
-%  for each example in the matrix X. Note that X contains the examples in
-%  rows. all_theta is a matrix where the i-th row is a trained logistic
-%  regression theta vector for the i-th class. You should set p to a vector
-%  of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2
-%  for 4 examples) 
-
-m = size(X, 1);
-num_labels = size(all_theta, 1);
-
-% You need to return the following variables correctly 
-p = zeros(size(X, 1), 1);
-
-% Add ones to the X data matrix
-X = [ones(m, 1) X];
-
-% ====================== YOUR CODE HERE ======================
-% Instructions: Complete the following code to make predictions using
-%               your learned logistic regression parameters (one-vs-all).
-%               You should set p to a vector of predictions (from 1 to
-%               num_labels).
-%
-% Hint: This code can be done all vectorized using the max function.
-%       In particular, the max function can also return the index of the 
-%       max element, for more information see 'help max'. If your examples 
-%       are in rows, then, you can use max(A, [], 2) to obtain the max 
-%       for each row.
-%       
-p = X * all_theta';
-[temp_p,p] = max(p, [], 2);
-%p(find(p == 10)) = 0;
-
-
-
-
-
-
-% =========================================================================
-
-
-end

assignments/machine-learning-ex3/ex3/sigmoid.m

@@ -1,6 +0,0 @@
-function g = sigmoid(z)
-%SIGMOID Compute sigmoid functoon
-%   J = SIGMOID(z) computes the sigmoid of z.
-
-g = 1.0 ./ (1.0 + exp(-z));
-end

assignments/machine-learning-ex3/ex3/submit.m

@@ -1,56 +0,0 @@
-function submit()
-  addpath('./lib');
-
-  conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
-  conf.itemName = 'Multi-class Classification and Neural Networks';
-  conf.partArrays = { ...
-    { ...
-      '1', ...
-      { 'lrCostFunction.m' }, ...
-      'Regularized Logistic Regression', ...
-    }, ...
-    { ...
-      '2', ...
-      { 'oneVsAll.m' }, ...
-      'One-vs-All Classifier Training', ...
-    }, ...
-    { ...
-      '3', ...
-      { 'predictOneVsAll.m' }, ...
-      'One-vs-All Classifier Prediction', ...
-    }, ...
-    { ...
-      '4', ...
-      { 'predict.m' }, ...
-      'Neural Network Prediction Function' ...
-    }, ...
-  };
-  conf.output = @output;
-
-  submitWithConfiguration(conf);
-end
-
-function out = output(partId, auxdata)
-  % Random Test Cases
-  X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
-  y = sin(X(:,1) + X(:,2)) > 0;
-  Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ...
-          1 1 ;  1 2 ;  2 1 ; 2 2 ; ...
-         -1 1 ;  -1 2 ;  -2 1 ; -2 2 ; ...
-          1 -1 ; 1 -2 ;  -2 -1 ; -2 -2 ];
-  ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]';
-  t1 = sin(reshape(1:2:24, 4, 3));
-  t2 = cos(reshape(1:2:40, 4, 5));
-
-  if partId == '1'
-    [J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1);
-    out = sprintf('%0.5f ', J);
-    out = [out sprintf('%0.5f ', grad)];
-  elseif partId == '2'
-    out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1));
-  elseif partId == '3'
-    out = sprintf('%0.5f ', predictOneVsAll(t1, Xm));
-  elseif partId == '4'
-    out = sprintf('%0.5f ', predict(t1, t2, Xm));
-  end 
-end

assignments/machine-learning-ex3/ex3/token.mat

@@ -1,15 +0,0 @@
-# Created by Octave 4.2.0, Sun Nov 27 12:41:13 2016 GMT <unknown@unknown>
-# name: email
-# type: sq_string
-# elements: 1
-# length: 17
-scruel@vip.qq.com
-
-
-# name: token
-# type: sq_string
-# elements: 1
-# length: 16
-CPfDADNFz0Hd0Qgk
-
-

assignments/machine-learning-ex4/ex4/checkNNGradients.m

@@ -1,50 +0,0 @@
-function checkNNGradients(lambda)
-%CHECKNNGRADIENTS Creates a small neural network to check the
-%backpropagation gradients
-%   CHECKNNGRADIENTS(lambda) Creates a small neural network to check the
-%   backpropagation gradients, it will output the analytical gradients
-%   produced by your backprop code and the numerical gradients (computed
-%   using computeNumericalGradient). These two gradient computations should
-%   result in very similar values.
-%
-
-if ~exist('lambda', 'var') || isempty(lambda)
-    lambda = 0;
-end
-
-input_layer_size = 3;
-hidden_layer_size = 5;
-num_labels = 3;
-m = 5;
-
-% We generate some 'random' test data
-Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size);
-Theta2 = debugInitializeWeights(num_labels, hidden_layer_size);
-% Reusing debugInitializeWeights to generate X
-X  = debugInitializeWeights(m, input_layer_size - 1);
-y  = 1 + mod(1:m, num_labels)';
-
-% Unroll parameters
-nn_params = [Theta1(:) ; Theta2(:)];
-% Short hand for cost function
-costFunc = @(p) nnCostFunction(p, input_layer_size, hidden_layer_size, ...
-                               num_labels, X, y, lambda);
-[cost, grad] = costFunc(nn_params);
-numgrad = computeNumericalGradient(costFunc, nn_params);
-
-% Visually examine the two gradient computations.  The two columns
-% you get should be very similar. 
-disp([numgrad grad]);
-fprintf(['The above two columns you get should be very similar.\n' ...
-         '(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']);
-
-% Evaluate the norm of the difference between two solutions.  
-% If you have a correct implementation, and assuming you used EPSILON = 0.0001 
-% in computeNumericalGradient.m, then diff below should be less than 1e-9
-diff = norm(numgrad-grad)/norm(numgrad+grad);
-
-fprintf(['If your backpropagation implementation is correct, then \n' ...
-         'the relative difference will be small (less than 1e-9). \n' ...
-         '\nRelative Difference: %g\n'], diff);
-
-end

assignments/machine-learning-ex4/ex4/computeNumericalGradient.m

@@ -1,29 +0,0 @@
-function numgrad = computeNumericalGradient(J, theta)
-%COMPUTENUMERICALGRADIENT Computes the gradient using "finite differences"
-%and gives us a numerical estimate of the gradient.
-%   numgrad = COMPUTENUMERICALGRADIENT(J, theta) computes the numerical
-%   gradient of the function J around theta. Calling y = J(theta) should
-%   return the function value at theta.
-
-% Notes: The following code implements numerical gradient checking, and 
-%        returns the numerical gradient.It sets numgrad(i) to (a numerical 
-%        approximation of) the partial derivative of J with respect to the 
-%        i-th input argument, evaluated at theta. (i.e., numgrad(i) should 
-%        be the (approximately) the partial derivative of J with respect 
-%        to theta(i).)
-%                
-
-numgrad = zeros(size(theta));
-perturb = zeros(size(theta));
-e = 1e-4;
-for p = 1:numel(theta)
-    % Set perturbation vector
-    perturb(p) = e;
-    loss1 = J(theta - perturb);
-    loss2 = J(theta + perturb);
-    % Compute Numerical Gradient
-    numgrad(p) = (loss2 - loss1) / (2*e);
-    perturb(p) = 0;
-end
-
-end

assignments/machine-learning-ex4/ex4/debugInitializeWeights.m

@@ -1,22 +0,0 @@
-function W = debugInitializeWeights(fan_out, fan_in)
-%DEBUGINITIALIZEWEIGHTS Initialize the weights of a layer with fan_in
-%incoming connections and fan_out outgoing connections using a fixed
-%strategy, this will help you later in debugging
-%   W = DEBUGINITIALIZEWEIGHTS(fan_in, fan_out) initializes the weights 
-%   of a layer with fan_in incoming connections and fan_out outgoing 
-%   connections using a fix set of values
-%
-%   Note that W should be set to a matrix of size(1 + fan_in, fan_out) as
-%   the first row of W handles the "bias" terms
-%
-
-% Set W to zeros
-W = zeros(fan_out, 1 + fan_in);
-
-% Initialize W using "sin", this ensures that W is always of the same
-% values and will be useful for debugging
-W = reshape(sin(1:numel(W)), size(W)) / 10;
-
-% =========================================================================
-
-end

assignments/machine-learning-ex4/ex4/displayData.m

@@ -1,59 +0,0 @@
-function [h, display_array] = displayData(X, example_width)
-%DISPLAYDATA Display 2D data in a nice grid
-%   [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
-%   stored in X in a nice grid. It returns the figure handle h and the 
-%   displayed array if requested.
-
-% Set example_width automatically if not passed in
-if ~exist('example_width', 'var') || isempty(example_width) 
-	example_width = round(sqrt(size(X, 2)));
-end
-
-% Gray Image
-colormap(gray);
-
-% Compute rows, cols
-[m n] = size(X);
-example_height = (n / example_width);
-
-% Compute number of items to display
-display_rows = floor(sqrt(m));
-display_cols = ceil(m / display_rows);
-
-% Between images padding
-pad = 1;
-
-% Setup blank display
-display_array = - ones(pad + display_rows * (example_height + pad), ...
-                       pad + display_cols * (example_width + pad));
-
-% Copy each example into a patch on the display array
-curr_ex = 1;
-for j = 1:display_rows
-	for i = 1:display_cols
-		if curr_ex > m, 
-			break; 
-		end
-		% Copy the patch
-		
-		% Get the max value of the patch
-		max_val = max(abs(X(curr_ex, :)));
-		display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ...
-		              pad + (i - 1) * (example_width + pad) + (1:example_width)) = ...
-						reshape(X(curr_ex, :), example_height, example_width) / max_val;
-		curr_ex = curr_ex + 1;
-	end
-	if curr_ex > m, 
-		break; 
-	end
-end
-
-% Display Image
-h = imagesc(display_array, [-1 1]);
-
-% Do not show axis
-axis image off
-
-drawnow;
-
-end

assignments/machine-learning-ex4/ex4/ex4.m

@@ -1,250 +0,0 @@
-%% Machine Learning Online Class - Exercise 4 Neural Network Learning
-
-%  Instructions
-%  ------------
-% 
-%  This file contains code that helps you get started on the
-%  linear exercise. You will need to complete the following functions 
-%  in this exericse:
-%
-%     sigmoidGradient.m
-%     randInitializeWeights.m
-%     nnCostFunction.m
-%
-%  For this exercise, you will not need to change any code in this file,
-%  or any other files other than those mentioned above.
-%
-
-%% Initialization
-clear ; close all; clc
-
-%% Setup the parameters you will use for this exercise
-input_layer_size  = 400;  % 20x20 Input Images of Digits
-hidden_layer_size = 25;   % 25 hidden units
-num_labels = 10;          % 10 labels, from 1 to 10   
-                          % (note that we have mapped "0" to label 10)
-
-%% =========== Part 1: Loading and Visualizing Data =============
-%  We start the exercise by first loading and visualizing the dataset. 
-%  You will be working with a dataset that contains handwritten digits.
-%
-
-% Load Training Data
-fprintf('Loading and Visualizing Data ...\n')
-
-load('ex4data1.mat');
-m = size(X, 1);
-
-% Randomly select 100 data points to display
-sel = randperm(size(X, 1));
-sel = sel(1:100);
-
-displayData(X(sel, :));
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-
-%% ================ Part 2: Loading Parameters ================
-% In this part of the exercise, we load some pre-initialized 
-% neural network parameters.
-
-fprintf('\nLoading Saved Neural Network Parameters ...\n')
-
-% Load the weights into variables Theta1 and Theta2
-load('ex4weights.mat');
-
-% Unroll parameters 
-nn_params = [Theta1(:) ; Theta2(:)];
-
-%% ================ Part 3: Compute Cost (Feedforward) ================
-%  To the neural network, you should first start by implementing the
-%  feedforward part of the neural network that returns the cost only. You
-%  should complete the code in nnCostFunction.m to return cost. After
-%  implementing the feedforward to compute the cost, you can verify that
-%  your implementation is correct by verifying that you get the same cost
-%  as us for the fixed debugging parameters.
-%
-%  We suggest implementing the feedforward cost *without* regularization
-%  first so that it will be easier for you to debug. Later, in part 4, you
-%  will get to implement the regularized cost.
-%
-fprintf('\nFeedforward Using Neural Network ...\n')
-
-% Weight regularization parameter (we set this to 0 here).
-lambda = 0;
-
-J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...
-                   num_labels, X, y, lambda);
-
-fprintf(['Cost at parameters (loaded from ex4weights): %f '...
-         '\n(this value should be about 0.287629)\n'], J);
-
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
-
-%% =============== Part 4: Implement Regularization ===============
-%  Once your cost function implementation is correct, you should now
-%  continue to implement the regularization with the cost.
-%
-
-fprintf('\nChecking Cost Function (w/ Regularization) ... \n')
-
-% Weight regularization parameter (we set this to 1 here).
-lambda = 1;
-
-J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...
-                   num_labels, X, y, lambda);
-
-fprintf(['Cost at parameters (loaded from ex4weights): %f '...
-         '\n(this value should be about 0.383770)\n'], J);
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-
-%% ================ Part 5: Sigmoid Gradient  ================
-%  Before you start implementing the neural network, you will first
-%  implement the gradient for the sigmoid function. You should complete the
-%  code in the sigmoidGradient.m file.
-%
-
-fprintf('\nEvaluating sigmoid gradient...\n')
-
-g = sigmoidGradient([1 -0.5 0 0.5 1]);
-fprintf('Sigmoid gradient evaluated at [1 -0.5 0 0.5 1]:\n  ');
-fprintf('%f ', g);
-fprintf('\n\n');
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-
-%% ================ Part 6: Initializing Pameters ================
-%  In this part of the exercise, you will be starting to implment a two
-%  layer neural network that classifies digits. You will start by
-%  implementing a function to initialize the weights of the neural network
-%  (randInitializeWeights.m)
-
-fprintf('\nInitializing Neural Network Parameters ...\n')
-
-initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
-initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);
-
-% Unroll parameters
-initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];
-
-
-%% =============== Part 7: Implement Backpropagation ===============
-%  Once your cost matches up with ours, you should proceed to implement the
-%  backpropagation algorithm for the neural network. You should add to the
-%  code you've written in nnCostFunction.m to return the partial
-%  derivatives of the parameters.
-%
-fprintf('\nChecking Backpropagation... \n');
-
-%  Check gradients by running checkNNGradients
-checkNNGradients;
-
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
-
-
-%% =============== Part 8: Implement Regularization ===============
-%  Once your backpropagation implementation is correct, you should now
-%  continue to implement the regularization with the cost and gradient.
-%
-
-fprintf('\nChecking Backpropagation (w/ Regularization) ... \n')
-
-%  Check gradients by running checkNNGradients
-lambda = 3;
-checkNNGradients(lambda);
-
-% Also output the costFunction debugging values
-debug_J  = nnCostFunction(nn_params, input_layer_size, ...
-                          hidden_layer_size, num_labels, X, y, lambda);
-
-fprintf(['\n\nCost at (fixed) debugging parameters (w/ lambda = 10): %f ' ...
-         '\n(this value should be about 0.576051)\n\n'], debug_J);
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-
-%% =================== Part 8: Training NN ===================
-%  You have now implemented all the code necessary to train a neural 
-%  network. To train your neural network, we will now use "fmincg", which
-%  is a function which works similarly to "fminunc". Recall that these
-%  advanced optimizers are able to train our cost functions efficiently as
-%  long as we provide them with the gradient computations.
-%
-fprintf('\nTraining Neural Network... \n')
-
-%  After you have completed the assignment, change the MaxIter to a larger
-%  value to see how more training helps.
-options = optimset('MaxIter', 400);
-
-%  You should also try different values of lambda
-lambda = 1;
-
-% Create "short hand" for the cost function to be minimized
-costFunction = @(p) nnCostFunction(p, ...
-                                   input_layer_size, ...
-                                   hidden_layer_size, ...
-                                   num_labels, X, y, lambda);
-
-% Now, costFunction is a function that takes in only one argument (the
-% neural network parameters)
-
-[nn_params, cost] = fmincg(costFunction, initial_nn_params, options);
-
-% Obtain Theta1 and Theta2 back from nn_params
-disp("test")
-Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
-                 hidden_layer_size, (input_layer_size + 1));
-
-Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
-                 num_labels, (hidden_layer_size + 1));
-
-fprintf('Program paused. Press enter to continue.\n');
-pause;
-
-
-%% ================= Part 9: Visualize Weights =================
-%  You can now "visualize" what the neural network is learning by 
-%  displaying the hidden units to see what features they are capturing in 
-%  the data.
-
-fprintf('\nVisualizing Neural Network... \n')
-
-displayData(Theta1(:, 2:end));
-
-fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
-
-%% ================= Part 10: Implement Predict =================
-%  After training the neural network, we would like to use it to predict
-%  the labels. You will now implement the "predict" function to use the
-%  neural network to predict the labels of the training set. This lets
-%  you compute the training set accuracy.
-
-pred = predict(Theta1, Theta2, X);
-
-fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
-
-%  Randomly permute examples
-rp = randperm(m);
-
-for i = 1:m
-    % Display 
-    fprintf('\nDisplaying Example Image\n');
-    displayData(X(rp(i), :));
-
-    pred = predict(Theta1, Theta2, X(rp(i),:));
-    fprintf('\nNeural Network Prediction: %d (digit %d)\n', pred, mod(pred, 10));
-    
-    % Pause
-    fprintf('Program paused. Press enter to continue.\n');
-    pause;
-end