ex7

machine learning/machine-learning-ex7/ex7/computeCentroids.m

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+function centroids = computeCentroids(X, idx, K)
+%COMPUTECENTROIDS returns the new centroids by computing the means of the 
+%data points assigned to each centroid.
+%   centroids = COMPUTECENTROIDS(X, idx, K) returns the new centroids by 
+%   computing the means of the data points assigned to each centroid. It is
+%   given a dataset X where each row is a single data point, a vector
+%   idx of centroid assignments (i.e. each entry in range [1..K]) for each
+%   example, and K, the number of centroids. You should return a matrix
+%   centroids, where each row of centroids is the mean of the data points
+%   assigned to it.
+%
+
+% Useful variables
+[m n] = size(X);
+
+% You need to return the following variables correctly.
+centroids = zeros(K, n);
+
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Go over every centroid and compute mean of all points that
+%               belong to it. Concretely, the row vector centroids(i, :)
+%               should contain the mean of the data points assigned to
+%               centroid i.
+%
+% Note: You can use a for-loop over the centroids to compute this.
+%
+
+for k = 1:K
+  % obtain a vector that contains only k in the idx vector
+  idx_konly = (idx == k);
+  % count the number of examples that are assigned centroid k
+  num_ex_assigned_k = sum(idx_konly);
+  % compute the mean value for centroid k
+  % multiplying idx_konly with each element in X results in a matrix that have all the
+  % unwanted values zeroed out, leaving the ones that are assigned k
+  centroids(k,:) = 1 / abs(num_ex_assigned_k) * sum(idx_konly .* X);
+endfor
+
+
+
+
+
+% =============================================================
+
+
+end
+

machine learning/machine-learning-ex7/ex7/displayData.m

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+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

machine learning/machine-learning-ex7/ex7/drawLine.m

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+function drawLine(p1, p2, varargin)
+%DRAWLINE Draws a line from point p1 to point p2
+%   DRAWLINE(p1, p2) Draws a line from point p1 to point p2 and holds the
+%   current figure
+
+plot([p1(1) p2(1)], [p1(2) p2(2)], varargin{:});
+
+end

machine learning/machine-learning-ex7/ex7/ex7.m

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+%% Machine Learning Online Class
+%  Exercise 7 | Principle Component Analysis and K-Means Clustering
+%
+%  Instructions
+%  ------------
+%
+%  This file contains code that helps you get started on the
+%  exercise. You will need to complete the following functions:
+%
+%     pca.m
+%     projectData.m
+%     recoverData.m
+%     computeCentroids.m
+%     findClosestCentroids.m
+%     kMeansInitCentroids.m
+%
+%  For this exercise, you will not need to change any code in this file,
+%  or any other files other than those mentioned above.
+%
+
+%% Initialization
+clear ; close all; clc
+
+%% ================= Part 1: Find Closest Centroids ====================
+%  To help you implement K-Means, we have divided the learning algorithm 
+%  into two functions -- findClosestCentroids and computeCentroids. In this
+%  part, you should complete the code in the findClosestCentroids function. 
+%
+fprintf('Finding closest centroids.\n\n');
+
+% Load an example dataset that we will be using
+load('ex7data2.mat');
+
+% Select an initial set of centroids
+K = 3; % 3 Centroids
+initial_centroids = [3 3; 6 2; 8 5];
+
+% Find the closest centroids for the examples using the
+% initial_centroids
+idx = findClosestCentroids(X, initial_centroids);
+
+fprintf('Closest centroids for the first 3 examples: \n')
+fprintf(' %d', idx(1:3));
+fprintf('\n(the closest centroids should be 1, 3, 2 respectively)\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% ===================== Part 2: Compute Means =========================
+%  After implementing the closest centroids function, you should now
+%  complete the computeCentroids function.
+%
+fprintf('\nComputing centroids means.\n\n');
+
+%  Compute means based on the closest centroids found in the previous part.
+centroids = computeCentroids(X, idx, K);
+
+fprintf('Centroids computed after initial finding of closest centroids: \n')
+fprintf(' %f %f \n' , centroids');
+fprintf('\n(the centroids should be\n');
+fprintf('   [ 2.428301 3.157924 ]\n');
+fprintf('   [ 5.813503 2.633656 ]\n');
+fprintf('   [ 7.119387 3.616684 ]\n\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% =================== Part 3: K-Means Clustering ======================
+%  After you have completed the two functions computeCentroids and
+%  findClosestCentroids, you have all the necessary pieces to run the
+%  kMeans algorithm. In this part, you will run the K-Means algorithm on
+%  the example dataset we have provided. 
+%
+fprintf('\nRunning K-Means clustering on example dataset.\n\n');
+
+% Load an example dataset
+load('ex7data2.mat');
+
+% Settings for running K-Means
+K = 3;
+max_iters = 10;
+
+% For consistency, here we set centroids to specific values
+% but in practice you want to generate them automatically, such as by
+% settings them to be random examples (as can be seen in
+% kMeansInitCentroids).
+initial_centroids = [3 3; 6 2; 8 5];
+
+% Run K-Means algorithm. The 'true' at the end tells our function to plot
+% the progress of K-Means
+[centroids, idx] = runkMeans(X, initial_centroids, max_iters, true);
+fprintf('\nK-Means Done.\n\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% ============= Part 4: K-Means Clustering on Pixels ===============
+%  In this exercise, you will use K-Means to compress an image. To do this,
+%  you will first run K-Means on the colors of the pixels in the image and
+%  then you will map each pixel onto its closest centroid.
+%  
+%  You should now complete the code in kMeansInitCentroids.m
+%
+
+fprintf('\nRunning K-Means clustering on pixels from an image.\n\n');
+
+%  Load an image of a bird
+A = double(imread('bird_small.png'));
+
+% If imread does not work for you, you can try instead
+%   load ('bird_small.mat');
+
+A = A / 255; % Divide by 255 so that all values are in the range 0 - 1
+
+% Size of the image
+img_size = size(A);
+
+% Reshape the image into an Nx3 matrix where N = number of pixels.
+% Each row will contain the Red, Green and Blue pixel values
+% This gives us our dataset matrix X that we will use K-Means on.
+X = reshape(A, img_size(1) * img_size(2), 3);
+
+% Run your K-Means algorithm on this data
+% You should try different values of K and max_iters here
+K = 16; 
+max_iters = 10;
+
+% When using K-Means, it is important the initialize the centroids
+% randomly. 
+% You should complete the code in kMeansInitCentroids.m before proceeding
+initial_centroids = kMeansInitCentroids(X, K);
+
+% Run K-Means
+[centroids, idx] = runkMeans(X, initial_centroids, max_iters);
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% ================= Part 5: Image Compression ======================
+%  In this part of the exercise, you will use the clusters of K-Means to
+%  compress an image. To do this, we first find the closest clusters for
+%  each example. After that, we 
+
+fprintf('\nApplying K-Means to compress an image.\n\n');
+
+% Find closest cluster members
+idx = findClosestCentroids(X, centroids);
+
+% Essentially, now we have represented the image X as in terms of the
+% indices in idx. 
+
+% We can now recover the image from the indices (idx) by mapping each pixel
+% (specified by its index in idx) to the centroid value
+X_recovered = centroids(idx,:);
+
+% Reshape the recovered image into proper dimensions
+X_recovered = reshape(X_recovered, img_size(1), img_size(2), 3);
+
+% Display the original image 
+subplot(1, 2, 1);
+imagesc(A); 
+title('Original');
+
+% Display compressed image side by side
+subplot(1, 2, 2);
+imagesc(X_recovered)
+title(sprintf('Compressed, with %d colors.', K));
+
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+

machine learning/machine-learning-ex7/ex7/ex7_pca.m

@@ -0,0 +1,235 @@
+%% Machine Learning Online Class
+%  Exercise 7 | Principle Component Analysis and K-Means Clustering
+%
+%  Instructions
+%  ------------
+%
+%  This file contains code that helps you get started on the
+%  exercise. You will need to complete the following functions:
+%
+%     pca.m
+%     projectData.m
+%     recoverData.m
+%     computeCentroids.m
+%     findClosestCentroids.m
+%     kMeansInitCentroids.m
+%
+%  For this exercise, you will not need to change any code in this file,
+%  or any other files other than those mentioned above.
+%
+
+%% Initialization
+clear ; close all; clc
+
+%% ================== Part 1: Load Example Dataset  ===================
+%  We start this exercise by using a small dataset that is easily to
+%  visualize
+%
+fprintf('Visualizing example dataset for PCA.\n\n');
+
+%  The following command loads the dataset. You should now have the 
+%  variable X in your environment
+load ('ex7data1.mat');
+
+%  Visualize the example dataset
+plot(X(:, 1), X(:, 2), 'bo');
+axis([0.5 6.5 2 8]); axis square;
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% =============== Part 2: Principal Component Analysis ===============
+%  You should now implement PCA, a dimension reduction technique. You
+%  should complete the code in pca.m
+%
+fprintf('\nRunning PCA on example dataset.\n\n');
+
+%  Before running PCA, it is important to first normalize X
+[X_norm, mu, sigma] = featureNormalize(X);
+
+%  Run PCA
+[U, S] = pca(X_norm);
+
+%  Compute mu, the mean of the each feature
+
+%  Draw the eigenvectors centered at mean of data. These lines show the
+%  directions of maximum variations in the dataset.
+hold on;
+drawLine(mu, mu + 1.5 * S(1,1) * U(:,1)', '-k', 'LineWidth', 2);
+drawLine(mu, mu + 1.5 * S(2,2) * U(:,2)', '-k', 'LineWidth', 2);
+hold off;
+
+fprintf('Top eigenvector: \n');
+fprintf(' U(:,1) = %f %f \n', U(1,1), U(2,1));
+fprintf('\n(you should expect to see -0.707107 -0.707107)\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% =================== Part 3: Dimension Reduction ===================
+%  You should now implement the projection step to map the data onto the 
+%  first k eigenvectors. The code will then plot the data in this reduced 
+%  dimensional space.  This will show you what the data looks like when 
+%  using only the corresponding eigenvectors to reconstruct it.
+%
+%  You should complete the code in projectData.m
+%
+fprintf('\nDimension reduction on example dataset.\n\n');
+
+%  Plot the normalized dataset (returned from pca)
+plot(X_norm(:, 1), X_norm(:, 2), 'bo');
+axis([-4 3 -4 3]); axis square
+
+%  Project the data onto K = 1 dimension
+K = 1;
+Z = projectData(X_norm, U, K);
+fprintf('Projection of the first example: %f\n', Z(1));
+fprintf('\n(this value should be about 1.481274)\n\n');
+
+X_rec  = recoverData(Z, U, K);
+fprintf('Approximation of the first example: %f %f\n', X_rec(1, 1), X_rec(1, 2));
+fprintf('\n(this value should be about  -1.047419 -1.047419)\n\n');
+
+%  Draw lines connecting the projected points to the original points
+hold on;
+plot(X_rec(:, 1), X_rec(:, 2), 'ro');
+for i = 1:size(X_norm, 1)
+    drawLine(X_norm(i,:), X_rec(i,:), '--k', 'LineWidth', 1);
+end
+hold off
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% =============== Part 4: Loading and Visualizing Face Data =============
+%  We start the exercise by first loading and visualizing the dataset.
+%  The following code will load the dataset into your environment
+%
+fprintf('\nLoading face dataset.\n\n');
+
+%  Load Face dataset
+load ('ex7faces.mat')
+
+%  Display the first 100 faces in the dataset
+displayData(X(1:100, :));
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% =========== Part 5: PCA on Face Data: Eigenfaces  ===================
+%  Run PCA and visualize the eigenvectors which are in this case eigenfaces
+%  We display the first 36 eigenfaces.
+%
+fprintf(['\nRunning PCA on face dataset.\n' ...
+         '(this might take a minute or two ...)\n\n']);
+
+%  Before running PCA, it is important to first normalize X by subtracting 
+%  the mean value from each feature
+[X_norm, mu, sigma] = featureNormalize(X);
+
+%  Run PCA
+[U, S] = pca(X_norm);
+
+%  Visualize the top 36 eigenvectors found
+displayData(U(:, 1:36)');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% ============= Part 6: Dimension Reduction for Faces =================
+%  Project images to the eigen space using the top k eigenvectors 
+%  If you are applying a machine learning algorithm 
+fprintf('\nDimension reduction for face dataset.\n\n');
+
+K = 100;
+Z = projectData(X_norm, U, K);
+
+fprintf('The projected data Z has a size of: ')
+fprintf('%d ', size(Z));
+
+fprintf('\n\nProgram paused. Press enter to continue.\n');
+pause;
+
+%% ==== Part 7: Visualization of Faces after PCA Dimension Reduction ====
+%  Project images to the eigen space using the top K eigen vectors and 
+%  visualize only using those K dimensions
+%  Compare to the original input, which is also displayed
+
+fprintf('\nVisualizing the projected (reduced dimension) faces.\n\n');
+
+K = 100;
+X_rec  = recoverData(Z, U, K);
+
+% Display normalized data
+subplot(1, 2, 1);
+displayData(X_norm(1:100,:));
+title('Original faces');
+axis square;
+
+% Display reconstructed data from only k eigenfaces
+subplot(1, 2, 2);
+displayData(X_rec(1:100,:));
+title('Recovered faces');
+axis square;
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+%% === Part 8(a): Optional (ungraded) Exercise: PCA for Visualization ===
+%  One useful application of PCA is to use it to visualize high-dimensional
+%  data. In the last K-Means exercise you ran K-Means on 3-dimensional 
+%  pixel colors of an image. We first visualize this output in 3D, and then
+%  apply PCA to obtain a visualization in 2D.
+
+close all; close all; clc
+
+% Reload the image from the previous exercise and run K-Means on it
+% For this to work, you need to complete the K-Means assignment first
+A = double(imread('bird_small.png'));
+
+% If imread does not work for you, you can try instead
+%   load ('bird_small.mat');
+
+A = A / 255;
+img_size = size(A);
+X = reshape(A, img_size(1) * img_size(2), 3);
+K = 16; 
+max_iters = 10;
+initial_centroids = kMeansInitCentroids(X, K);
+[centroids, idx] = runkMeans(X, initial_centroids, max_iters);
+
+%  Sample 1000 random indexes (since working with all the data is
+%  too expensive. If you have a fast computer, you may increase this.
+sel = floor(rand(1000, 1) * size(X, 1)) + 1;
+
+%  Setup Color Palette
+palette = hsv(K);
+colors = palette(idx(sel), :);
+
+%  Visualize the data and centroid memberships in 3D
+figure;
+scatter3(X(sel, 1), X(sel, 2), X(sel, 3), 10, colors);
+title('Pixel dataset plotted in 3D. Color shows centroid memberships');
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% === Part 8(b): Optional (ungraded) Exercise: PCA for Visualization ===
+% Use PCA to project this cloud to 2D for visualization
+
+% Subtract the mean to use PCA
+[X_norm, mu, sigma] = featureNormalize(X);
+
+% PCA and project the data to 2D
+[U, S] = pca(X_norm);
+Z = projectData(X_norm, U, 2);
+
+% Plot in 2D
+figure;
+plotDataPoints(Z(sel, :), idx(sel), K);
+title('Pixel dataset plotted in 2D, using PCA for dimensionality reduction');
+fprintf('Program paused. Press enter to continue.\n');
+pause;

machine learning/machine-learning-ex7/ex7/featureNormalize.m

@@ -0,0 +1,17 @@
+function [X_norm, mu, sigma] = featureNormalize(X)
+%FEATURENORMALIZE Normalizes the features in X 
+%   FEATURENORMALIZE(X) returns a normalized version of X where
+%   the mean value of each feature is 0 and the standard deviation
+%   is 1. This is often a good preprocessing step to do when
+%   working with learning algorithms.
+
+mu = mean(X);
+X_norm = bsxfun(@minus, X, mu);
+
+sigma = std(X_norm);
+X_norm = bsxfun(@rdivide, X_norm, sigma);
+
+
+% ============================================================
+
+end

machine learning/machine-learning-ex7/ex7/findClosestCentroids.m

@@ -0,0 +1,44 @@
+function idx = findClosestCentroids(X, centroids)
+%FINDCLOSESTCENTROIDS computes the centroid memberships for every example
+%   idx = FINDCLOSESTCENTROIDS (X, centroids) returns the closest centroids
+%   in idx for a dataset X where each row is a single example. idx = m x 1 
+%   vector of centroid assignments (i.e. each entry in range [1..K])
+%
+
+% Set K
+K = size(centroids, 1);
+
+% You need to return the following variables correctly.
+idx = zeros(size(X,1), 1);
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Go over every example, find its closest centroid, and store
+%               the index inside idx at the appropriate location.
+%               Concretely, idx(i) should contain the index of the centroid
+%               closest to example i. Hence, it should be a value in the 
+%               range 1..K
+%
+% Note: You can use a for-loop over the examples to compute this.
+%
+m = rows(X);
+for i = 1:m               % for each example
+  min_xi = zeros(K,1);    
+  for j = 1:K
+    cj = centroids(j,:);    % extract the centroid at ith row
+    % distance = sqrt(u1-v1)^2 + (u2-v2)^)
+    % http://mathonline.wikidot.com/the-distance-between-two-vectors
+    min_xi(j) = sum((X(i,:) - cj) .^ 2) ^(1/2);
+    
+  endfor
+  %min_xi;
+  [v, idx(i)] = min(min_xi);
+endfor
+
+
+
+
+
+% =============================================================
+
+end
+

machine learning/machine-learning-ex7/ex7/kMeansInitCentroids.m

@@ -0,0 +1,30 @@
+function centroids = kMeansInitCentroids(X, K)
+%KMEANSINITCENTROIDS This function initializes K centroids that are to be 
+%used in K-Means on the dataset X
+%   centroids = KMEANSINITCENTROIDS(X, K) returns K initial centroids to be
+%   used with the K-Means on the dataset X
+%
+
+% You should return this values correctly
+centroids = zeros(K, size(X, 2));
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: You should set centroids to randomly chosen examples from
+%               the dataset X
+%
+
+
+% Initialize the centroids to be random examples
+% Randomly reorder the indices of examples
+randidx = randperm(size(X, 1));
+% Take the first K examples as centroids
+centroids = X(randidx(1:K), :);
+
+
+
+
+
+% =============================================================
+
+end
+

machine learning/machine-learning-ex7/ex7/lib/jsonlab/AUTHORS.txt

@@ -0,0 +1,41 @@
+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

machine learning/machine-learning-ex7/ex7/lib/jsonlab/ChangeLog.txt

@@ -0,0 +1,74 @@
+============================================================================
+
+   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

machine learning/machine-learning-ex7/ex7/lib/jsonlab/LICENSE_BSD.txt

@@ -0,0 +1,25 @@
+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.

machine learning/machine-learning-ex7/ex7/lib/jsonlab/README.txt

@@ -0,0 +1,394 @@
+===============================================================================
+=                                 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).

machine learning/machine-learning-ex7/ex7/lib/jsonlab/jsonopt.m

@@ -0,0 +1,32 @@
+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
+

machine learning/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m

@@ -0,0 +1,566 @@
+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
+

machine learning/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m

@@ -0,0 +1,528 @@
+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
+

machine learning/machine-learning-ex7/ex7/lib/jsonlab/mergestruct.m

@@ -0,0 +1,33 @@
+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
+

machine learning/machine-learning-ex7/ex7/lib/jsonlab/savejson.m

@@ -0,0 +1,475 @@
+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

machine learning/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m

@@ -0,0 +1,504 @@
+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(:)';

machine learning/machine-learning-ex7/ex7/lib/jsonlab/varargin2struct.m

@@ -0,0 +1,40 @@
+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
+

machine learning/machine-learning-ex7/ex7/lib/makeValidFieldName.m

@@ -0,0 +1,30 @@
+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

machine learning/machine-learning-ex7/ex7/lib/submitWithConfiguration.m

@@ -0,0 +1,179 @@
+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('\n!! Submission failed: %s\n', e.message);
+    fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
+      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
+    fprintf('\nPlease correct your code and resubmit.\n');
+    return
+  end
+
+  if isfield(response, 'errorMessage')
+    fprintf('!! Submission failed: %s\n', response.errorMessage);
+  elseif isfield(response, 'errorCode')
+    fprintf('!! Submission failed: %s\n', response.message);
+  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();
+
+  responseBody = getResponse(submissionUrl, body);
+  jsonResponse = validateResponse(responseBody);
+  response = loadjson(jsonResponse);
+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
+
+% use urlread or curl to send submit results to the grader and get a response
+function response = getResponse(url, body)
+% try using urlread() and a secure connection
+  params = {'jsonBody', body};
+  [response, success] = urlread(url, 'post', params);
+
+  if (success == 0)
+    % urlread didn't work, try curl & the peer certificate patch
+    if ispc
+      % testing note: use 'jsonBody =' for a test case
+      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
+    else
+      % it's linux/OS X, so use the other form
+      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
+    end
+    % get the response body for the peer certificate patch method
+    [code, response] = system(json_command);
+    % test the success code
+    if (code ~= 0)
+      fprintf('[error] submission with curl() was not successful\n');
+    end
+  end
+end
+
+% validate the grader's response
+function response = validateResponse(resp)
+  % test if the response is json or an HTML page
+  isJson = length(resp) > 0 && resp(1) == '{';
+  isHtml = findstr(lower(resp), '<html');
+
+  if (isJson)
+    response = resp;
+  elseif (isHtml)
+    % the response is html, so it's probably an error message
+    printHTMLContents(resp);
+    error('Grader response is an HTML message');
+  else
+    error('Grader sent no response');
+  end
+end
+
+% parse a HTML response and print it's contents
+function printHTMLContents(response)
+  strippedResponse = regexprep(response, '<[^>]+>', ' ');
+  strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
+  fprintf(strippedResponse);
+end
+
+
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% Service configuration
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+function submissionUrl = submissionUrl()
+  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
+end

machine learning/machine-learning-ex7/ex7/myex7.m

@@ -0,0 +1,178 @@
+%% Machine Learning Online Class
+%  Exercise 7 | Principle Component Analysis and K-Means Clustering
+%
+%  Instructions
+%  ------------
+%
+%  This file contains code that helps you get started on the
+%  exercise. You will need to complete the following functions:
+%
+%     pca.m
+%     projectData.m
+%     recoverData.m
+%     computeCentroids.m
+%     findClosestCentroids.m
+%     kMeansInitCentroids.m
+%
+%  For this exercise, you will not need to change any code in this file,
+%  or any other files other than those mentioned above.
+%
+
+%% Initialization
+clear ; close all; clc
+
+%% ================= Part 1: Find Closest Centroids ====================
+%  To help you implement K-Means, we have divided the learning algorithm 
+%  into two functions -- findClosestCentroids and computeCentroids. In this
+%  part, you should complete the code in the findClosestCentroids function. 
+%
+fprintf('Finding closest centroids.\n\n');
+
+% Load an example dataset that we will be using
+load('ex7data2.mat');
+
+% Select an initial set of centroids
+K = 3; % 3 Centroids
+initial_centroids = [3 3; 6 2; 8 5];
+
+% Find the closest centroids for the examples using the
+% initial_centroids
+idx = findClosestCentroids(X, initial_centroids);
+
+fprintf('Closest centroids for the first 3 examples: \n')
+fprintf(' %d', idx(1:3));
+fprintf('\n(the closest centroids should be 1, 3, 2 respectively)\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+
+%% ===================== Part 2: Compute Means =========================
+%  After implementing the closest centroids function, you should now
+%  complete the computeCentroids function.
+%
+fprintf('\nComputing centroids means.\n\n');
+
+%  Compute means based on the closest centroids found in the previous part.
+centroids = computeCentroids(X, idx, K);
+
+fprintf('Centroids computed after initial finding of closest centroids: \n')
+fprintf(' %f %f \n' , centroids');
+fprintf('\n(the centroids should be\n');
+fprintf('   [ 2.428301 3.157924 ]\n');
+fprintf('   [ 5.813503 2.633656 ]\n');
+fprintf('   [ 7.119387 3.616684 ]\n\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+
+
+
+
+%% =================== Part 3: K-Means Clustering ======================
+%  After you have completed the two functions computeCentroids and
+%  findClosestCentroids, you have all the necessary pieces to run the
+%  kMeans algorithm. In this part, you will run the K-Means algorithm on
+%  the example dataset we have provided. 
+%
+fprintf('\nRunning K-Means clustering on example dataset.\n\n');
+
+% Load an example dataset
+load('ex7data2.mat');
+
+% Settings for running K-Means
+K = 3;
+max_iters = 10;
+
+% For consistency, here we set centroids to specific values
+% but in practice you want to generate them automatically, such as by
+% settings them to be random examples (as can be seen in
+% kMeansInitCentroids).
+initial_centroids = [3 3; 6 2; 8 5];
+
+% Run K-Means algorithm. The 'true' at the end tells our function to plot
+% the progress of K-Means
+
+
+
+%[centroids, idx] = runkMeans(X, initial_centroids, max_iters, true);
+fprintf('\nK-Means Done.\n\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+
+
+
+
+%% ============= Part 4: K-Means Clustering on Pixels ===============
+%  In this exercise, you will use K-Means to compress an image. To do this,
+%  you will first run K-Means on the colors of the pixels in the image and
+%  then you will map each pixel onto its closest centroid.
+%  
+%  You should now complete the code in kMeansInitCentroids.m
+%
+
+fprintf('\nRunning K-Means clustering on pixels from an image.\n\n');
+
+%  Load an image of a bird
+A = double(imread('bird_small.png'));
+
+% If imread does not work for you, you can try instead
+%   load ('bird_small.mat');
+
+A = A / 255; % Divide by 255 so that all values are in the range 0 - 1
+
+% Size of the image
+img_size = size(A);
+
+% Reshape the image into an Nx3 matrix where N = number of pixels.
+% Each row will contain the Red, Green and Blue pixel values
+% This gives us our dataset matrix X that we will use K-Means on.
+X = reshape(A, img_size(1) * img_size(2), 3);
+
+% Run your K-Means algorithm on this data
+% You should try different values of K and max_iters here
+K = 16; 
+max_iters = 10;
+
+% When using K-Means, it is important the initialize the centroids
+% randomly. 
+% You should complete the code in kMeansInitCentroids.m before proceeding
+initial_centroids = kMeansInitCentroids(X, K);
+
+% Run K-Means
+[centroids, idx] = runkMeans(X, initial_centroids, max_iters,false);
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+
+
+%% ================= Part 5: Image Compression ======================
+%  In this part of the exercise, you will use the clusters of K-Means to
+%  compress an image. To do this, we first find the closest clusters for
+%  each example. After that, we 
+
+fprintf('\nApplying K-Means to compress an image.\n\n');
+
+% Find closest cluster members
+idx = findClosestCentroids(X, centroids);
+
+% Essentially, now we have represented the image X as in terms of the
+% indices in idx. 
+
+% We can now recover the image from the indices (idx) by mapping each pixel
+% (specified by its index in idx) to the centroid value
+X_recovered = centroids(idx,:);
+
+% Reshape the recovered image into proper dimensions
+X_recovered = reshape(X_recovered, img_size(1), img_size(2), 3);
+
+% Display the original image 
+subplot(1, 2, 1);
+imagesc(A); 
+title('Original');
+
+% Display compressed image side by side
+subplot(1, 2, 2);
+imagesc(X_recovered)
+title(sprintf('Compressed, with %d colors.', K));
+
+
+fprintf('Program paused. Press enter to continue.\n');

machine learning/machine-learning-ex7/ex7/mypca.m

@@ -0,0 +1,105 @@
+%% Machine Learning Online Class
+%  Exercise 7 | Principle Component Analysis and K-Means Clustering
+%
+%  Instructions
+%  ------------
+%
+%  This file contains code that helps you get started on the
+%  exercise. You will need to complete the following functions:
+%
+%     pca.m
+%     projectData.m
+%     recoverData.m
+%     computeCentroids.m
+%     findClosestCentroids.m
+%     kMeansInitCentroids.m
+%
+%  For this exercise, you will not need to change any code in this file,
+%  or any other files other than those mentioned above.
+%
+
+%% Initialization
+clear ; close all; clc
+
+%% ================== Part 1: Load Example Dataset  ===================
+%  We start this exercise by using a small dataset that is easily to
+%  visualize
+%
+fprintf('Visualizing example dataset for PCA.\n\n');
+
+%  The following command loads the dataset. You should now have the 
+%  variable X in your environment
+load ('ex7data1.mat');
+
+%  Visualize the example dataset
+plot(X(:, 1), X(:, 2), 'bo');
+axis([0.5 6.5 2 8]); axis square;
+
+fprintf('Program paused. Press enter to continue.\n');
+
+
+
+%% =============== Part 2: Principal Component Analysis ===============
+%  You should now implement PCA, a dimension reduction technique. You
+%  should complete the code in pca.m
+%
+fprintf('\nRunning PCA on example dataset.\n\n');
+
+%  Before running PCA, it is important to first normalize X
+[X_norm, mu, sigma] = featureNormalize(X);
+
+%  Run PCA
+[U, S] = pca(X_norm);
+
+%  Compute mu, the mean of the each feature
+
+%  Draw the eigenvectors centered at mean of data. These lines show the
+%  directions of maximum variations in the dataset.
+hold on;
+drawLine(mu, mu + 1.5 * S(1,1) * U(:,1)', '-k', 'LineWidth', 2);
+drawLine(mu, mu + 1.5 * S(2,2) * U(:,2)', '-k', 'LineWidth', 2);
+hold off;
+
+fprintf('Top eigenvector: \n');
+fprintf(' U(:,1) = %f %f \n', U(1,1), U(2,1));
+fprintf('\n(you should expect to see -0.707107 -0.707107)\n');
+
+fprintf('Program paused. Press enter to continue.\n');
+
+
+
+
+%% =================== Part 3: Dimension Reduction ===================
+%  You should now implement the projection step to map the data onto the 
+%  first k eigenvectors. The code will then plot the data in this reduced 
+%  dimensional space.  This will show you what the data looks like when 
+%  using only the corresponding eigenvectors to reconstruct it.
+%
+%  You should complete the code in projectData.m
+%
+fprintf('\nDimension reduction on example dataset.\n\n');
+
+%  Plot the normalized dataset (returned from pca)
+plot(X_norm(:, 1), X_norm(:, 2), 'bo');
+axis([-4 3 -4 3]); axis square
+
+%  Project the data onto K = 1 dimension
+K = 1;
+Z = projectData(X_norm, U, K);
+fprintf('Projection of the first example: %f\n', Z(1));
+fprintf('\n(this value should be about 1.481274)\n\n');
+
+X_rec  = recoverData(Z, U, K);
+fprintf('Approximation of the first example: %f %f\n', X_rec(1, 1), X_rec(1, 2));
+fprintf('\n(this value should be about  -1.047419 -1.047419)\n\n');
+
+%  Draw lines connecting the projected points to the original points
+hold on;
+plot(X_rec(:, 1), X_rec(:, 2), 'ro');
+for i = 1:size(X_norm, 1)
+    drawLine(X_norm(i,:), X_rec(i,:), '--k', 'LineWidth', 1);
+end
+hold off
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;

machine learning/machine-learning-ex7/ex7/pca.m

@@ -0,0 +1,31 @@
+function [U, S] = pca(X)
+%PCA Run principal component analysis on the dataset X
+%   [U, S, X] = pca(X) computes eigenvectors of the covariance matrix of X
+%   Returns the eigenvectors U, the eigenvalues (on diagonal) in S
+%
+
+% Useful values
+[m, n] = size(X);
+
+% You need to return the following variables correctly.
+U = zeros(n);
+S = zeros(n);
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: You should first compute the covariance matrix. Then, you
+%               should use the "svd" function to compute the eigenvectors
+%               and eigenvalues of the covariance matrix. 
+%
+% Note: When computing the covariance matrix, remember to divide by m (the
+%       number of examples).
+%
+
+sigma = 1 / m * X' * X;
+[U, S, V] = svd(sigma);
+
+
+
+
+% =========================================================================
+
+end

machine learning/machine-learning-ex7/ex7/plotDataPoints.m

@@ -0,0 +1,14 @@
+function plotDataPoints(X, idx, K)
+%PLOTDATAPOINTS plots data points in X, coloring them so that those with the same
+%index assignments in idx have the same color
+%   PLOTDATAPOINTS(X, idx, K) plots data points in X, coloring them so that those 
+%   with the same index assignments in idx have the same color
+
+% Create palette
+palette = hsv(K + 1);
+colors = palette(idx, :);
+
+% Plot the data
+scatter(X(:,1), X(:,2), 15, colors);
+
+end

machine learning/machine-learning-ex7/ex7/plotProgresskMeans.m

@@ -0,0 +1,27 @@
+function plotProgresskMeans(X, centroids, previous, idx, K, i)
+%PLOTPROGRESSKMEANS is a helper function that displays the progress of 
+%k-Means as it is running. It is intended for use only with 2D data.
+%   PLOTPROGRESSKMEANS(X, centroids, previous, idx, K, i) plots the data
+%   points with colors assigned to each centroid. With the previous
+%   centroids, it also plots a line between the previous locations and
+%   current locations of the centroids.
+%
+
+% Plot the examples
+plotDataPoints(X, idx, K);
+
+% Plot the centroids as black x's
+plot(centroids(:,1), centroids(:,2), 'x', ...
+     'MarkerEdgeColor','k', ...
+     'MarkerSize', 10, 'LineWidth', 3);
+
+% Plot the history of the centroids with lines
+for j=1:size(centroids,1)
+    drawLine(centroids(j, :), previous(j, :));
+end
+
+% Title
+title(sprintf('Iteration number %d', i))
+
+end
+

machine learning/machine-learning-ex7/ex7/projectData.m

@@ -0,0 +1,27 @@
+function Z = projectData(X, U, K)
+%PROJECTDATA Computes the reduced data representation when projecting only 
+%on to the top k eigenvectors
+%   Z = projectData(X, U, K) computes the projection of 
+%   the normalized inputs X into the reduced dimensional space spanned by
+%   the first K columns of U. It returns the projected examples in Z.
+%
+
+% You need to return the following variables correctly.
+Z = zeros(size(X, 1), K);
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the projection of the data using only the top K 
+%               eigenvectors in U (first K columns). 
+%               For the i-th example X(i,:), the projection on to the k-th 
+%               eigenvector is given as follows:
+%                    x = X(i, :)';
+%                    projection_k = x' * U(:, k);
+%
+
+U_reduce = U(:,1: K);
+Z = X * U_reduce;
+
+
+% =============================================================
+
+end

machine learning/machine-learning-ex7/ex7/recoverData.m

@@ -0,0 +1,31 @@
+function X_rec = recoverData(Z, U, K)
+%RECOVERDATA Recovers an approximation of the original data when using the 
+%projected data
+%   X_rec = RECOVERDATA(Z, U, K) recovers an approximation the 
+%   original data that has been reduced to K dimensions. It returns the
+%   approximate reconstruction in X_rec.
+%
+
+% You need to return the following variables correctly.
+X_rec = zeros(size(Z, 1), size(U, 1));
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the approximation of the data by projecting back
+%               onto the original space using the top K eigenvectors in U.
+%
+%               For the i-th example Z(i,:), the (approximate)
+%               recovered data for dimension j is given as follows:
+%                    v = Z(i, :)';
+%                    recovered_j = v' * U(j, 1:K)';
+%
+%               Notice that U(j, 1:K) is a row vector.
+%               
+
+U_reduce = U(:, 1: K);
+% U_reduce (nxk), Z(mxk)
+% 
+X_rec= (U_reduce * Z')';
+
+% =============================================================
+
+end

machine learning/machine-learning-ex7/ex7/runkMeans.m

@@ -0,0 +1,64 @@
+function [centroids, idx] = runkMeans(X, initial_centroids, ...
+                                      max_iters, plot_progress)
+%RUNKMEANS runs the K-Means algorithm on data matrix X, where each row of X
+%is a single example
+%   [centroids, idx] = RUNKMEANS(X, initial_centroids, max_iters, ...
+%   plot_progress) runs the K-Means algorithm on data matrix X, where each 
+%   row of X is a single example. It uses initial_centroids used as the
+%   initial centroids. max_iters specifies the total number of interactions 
+%   of K-Means to execute. plot_progress is a true/false flag that 
+%   indicates if the function should also plot its progress as the 
+%   learning happens. This is set to false by default. runkMeans returns 
+%   centroids, a Kxn matrix of the computed centroids and idx, a m x 1 
+%   vector of centroid assignments (i.e. each entry in range [1..K])
+%
+
+% Set default value for plot progress
+if ~exist('plot_progress', 'var') || isempty(plot_progress)
+    plot_progress = false;
+end
+
+% Plot the data if we are plotting progress
+if plot_progress
+    figure;
+    hold on;
+end
+
+% Initialize values
+[m n] = size(X);
+K = size(initial_centroids, 1);
+centroids = initial_centroids;
+previous_centroids = centroids;
+idx = zeros(m, 1);
+
+% Run K-Means
+for i=1:max_iters
+    
+    % Output progress
+    fprintf('K-Means iteration %d/%d...\n', i, max_iters);
+    if exist('OCTAVE_VERSION')
+        fflush(stdout);
+    end
+    
+    % For each example in X, assign it to the closest centroid
+    idx = findClosestCentroids(X, centroids);
+    
+    % Optionally, plot progress here
+    if plot_progress
+        plotProgresskMeans(X, centroids, previous_centroids, idx, K, i);
+        previous_centroids = centroids;
+        fprintf('Press enter to continue.\n');
+        pause;
+    end
+    
+    % Given the memberships, compute new centroids
+    centroids = computeCentroids(X, idx, K);
+end
+
+% Hold off if we are plotting progress
+if plot_progress
+    hold off;
+end
+
+end
+

machine learning/machine-learning-ex7/ex7/submit.m

@@ -0,0 +1,60 @@
+function submit()
+  addpath('./lib');
+
+  conf.assignmentSlug = 'k-means-clustering-and-pca';
+  conf.itemName = 'K-Means Clustering and PCA';
+  conf.partArrays = { ...
+    { ...
+      '1', ...
+      { 'findClosestCentroids.m' }, ...
+      'Find Closest Centroids (k-Means)', ...
+    }, ...
+    { ...
+      '2', ...
+      { 'computeCentroids.m' }, ...
+      'Compute Centroid Means (k-Means)', ...
+    }, ...
+    { ...
+      '3', ...
+      { 'pca.m' }, ...
+      'PCA', ...
+    }, ...
+    { ...
+      '4', ...
+      { 'projectData.m' }, ...
+      'Project Data (PCA)', ...
+    }, ...
+    { ...
+      '5', ...
+      { 'recoverData.m' }, ...
+      'Recover Data (PCA)', ...
+    }, ...
+  };
+  conf.output = @output;
+
+  submitWithConfiguration(conf);
+end
+
+function out = output(partId, auxstring)
+  % Random Test Cases
+  X = reshape(sin(1:165), 15, 11);
+  Z = reshape(cos(1:121), 11, 11);
+  C = Z(1:5, :);
+  idx = (1 + mod(1:15, 3))';
+  if partId == '1'
+    idx = findClosestCentroids(X, C);
+    out = sprintf('%0.5f ', idx(:));
+  elseif partId == '2'
+    centroids = computeCentroids(X, idx, 3);
+    out = sprintf('%0.5f ', centroids(:));
+  elseif partId == '3'
+    [U, S] = pca(X);
+    out = sprintf('%0.5f ', abs([U(:); S(:)]));
+  elseif partId == '4'
+    X_proj = projectData(X, Z, 5);
+    out = sprintf('%0.5f ', X_proj(:));
+  elseif partId == '5'
+    X_rec = recoverData(X(:,1:5), Z, 5);
+    out = sprintf('%0.5f ', X_rec(:));
+  end 
+end

machine learning/machine-learning-ex7/ex7/token.mat

@@ -0,0 +1,15 @@
+# Created by Octave 4.2.2, Tue Nov 27 10:23:40 2018 HKT <astron@astron>
+# name: email
+# type: sq_string
+# elements: 1
+# length: 20
+larry1chan@gmail.com
+
+
+# name: token
+# type: sq_string
+# elements: 1
+# length: 16
+r9wHEMhkU18F54yN
+
+

machine learning/machine-learning-ex8/ex8/checkCostFunction.m

@@ -0,0 +1,48 @@
+function checkCostFunction(lambda)
+%CHECKCOSTFUNCTION Creates a collaborative filering problem 
+%to check your cost function and gradients
+%   CHECKCOSTFUNCTION(lambda) Creates a collaborative filering problem 
+%   to check your cost function and gradients, it will output the 
+%   analytical gradients produced by your code and the numerical gradients 
+%   (computed using computeNumericalGradient). These two gradient 
+%   computations should result in very similar values.
+
+% Set lambda
+if ~exist('lambda', 'var') || isempty(lambda)
+    lambda = 0;
+end
+
+%% Create small problem
+X_t = rand(4, 3);
+Theta_t = rand(5, 3);
+
+% Zap out most entries
+Y = X_t * Theta_t';
+Y(rand(size(Y)) > 0.5) = 0;
+R = zeros(size(Y));
+R(Y ~= 0) = 1;
+
+%% Run Gradient Checking
+X = randn(size(X_t));
+Theta = randn(size(Theta_t));
+num_users = size(Y, 2);
+num_movies = size(Y, 1);
+num_features = size(Theta_t, 2);
+
+numgrad = computeNumericalGradient( ...
+                @(t) cofiCostFunc(t, Y, R, num_users, num_movies, ...
+                                num_features, lambda), [X(:); Theta(:)]);
+
+[cost, grad] = cofiCostFunc([X(:); Theta(:)],  Y, R, num_users, ...
+                          num_movies, num_features, lambda);
+
+disp([numgrad grad]);
+fprintf(['The above two columns you get should be very similar.\n' ...
+         '(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']);
+
+diff = norm(numgrad-grad)/norm(numgrad+grad);
+fprintf(['If your cost function implementation is correct, then \n' ...
+         'the relative difference will be small (less than 1e-9). \n' ...
+         '\nRelative Difference: %g\n'], diff);
+
+end

machine learning/machine-learning-ex8/ex8/cofiCostFunc.m

@@ -0,0 +1,62 @@
+function [J, grad] = cofiCostFunc(params, Y, R, num_users, num_movies, ...
+                                  num_features, lambda)
+%COFICOSTFUNC Collaborative filtering cost function
+%   [J, grad] = COFICOSTFUNC(params, Y, R, num_users, num_movies, ...
+%   num_features, lambda) returns the cost and gradient for the
+%   collaborative filtering problem.
+%
+
+% Unfold the U and W matrices from params
+X = reshape(params(1:num_movies*num_features), num_movies, num_features);
+Theta = reshape(params(num_movies*num_features+1:end), ...
+                num_users, num_features);
+
+            
+% You need to return the following values correctly
+J = 0;
+X_grad = zeros(size(X));
+Theta_grad = zeros(size(Theta));
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the cost function and gradient for collaborative
+%               filtering. Concretely, you should first implement the cost
+%               function (without regularization) and make sure it is
+%               matches our costs. After that, you should implement the 
+%               gradient and use the checkCostFunction routine to check
+%               that the gradient is correct. Finally, you should implement
+%               regularization.
+%
+% Notes: X - num_movies  x num_features matrix of movie features
+%        Theta - num_users  x num_features matrix of user features
+%        Y - num_movies x num_users matrix of user ratings of movies
+%        R - num_movies x num_users matrix, where R(i, j) = 1 if the 
+%            i-th movie was rated by the j-th user
+%
+% You should set the following variables correctly:
+%
+%        X_grad - num_movies x num_features matrix, containing the 
+%                 partial derivatives w.r.t. to each element of X
+%        Theta_grad - num_users x num_features matrix, containing the 
+%                     partial derivatives w.r.t. to each element of Theta
+%
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+% =============================================================
+
+grad = [X_grad(:); Theta_grad(:)];
+
+end

machine learning/machine-learning-ex8/ex8/computeNumericalGradient.m

@@ -0,0 +1,29 @@
+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

machine learning/machine-learning-ex8/ex8/estimateGaussian.m

@@ -0,0 +1,36 @@
+function [mu sigma2] = estimateGaussian(X)
+%ESTIMATEGAUSSIAN This function estimates the parameters of a 
+%Gaussian distribution using the data in X
+%   [mu sigma2] = estimateGaussian(X), 
+%   The input X is the dataset with each n-dimensional data point in one row
+%   The output is an n-dimensional vector mu, the mean of the data set
+%   and the variances sigma^2, an n x 1 vector
+% 
+
+% Useful variables
+[m, n] = size(X);
+
+% You should return these values correctly
+mu = zeros(n, 1);
+sigma2 = zeros(n, 1);
+
+% ====================== YOUR CODE HERE ======================
+% Instructions: Compute the mean of the data and the variances
+%               In particular, mu(i) should contain the mean of
+%               the data for the i-th feature and sigma2(i)
+%               should contain variance of the i-th feature.
+%
+
+
+
+
+
+
+
+
+
+
+% =============================================================
+
+
+end

machine learning/machine-learning-ex8/ex8/ex8.m

@@ -0,0 +1,121 @@
+%% Machine Learning Online Class
+%  Exercise 8 | Anomaly Detection and Collaborative Filtering
+%
+%  Instructions
+%  ------------
+%
+%  This file contains code that helps you get started on the
+%  exercise. You will need to complete the following functions:
+%
+%     estimateGaussian.m
+%     selectThreshold.m
+%     cofiCostFunc.m
+%
+%  For this exercise, you will not need to change any code in this file,
+%  or any other files other than those mentioned above.
+%
+
+%% Initialization
+clear ; close all; clc
+
+%% ================== Part 1: Load Example Dataset  ===================
+%  We start this exercise by using a small dataset that is easy to
+%  visualize.
+%
+%  Our example case consists of 2 network server statistics across
+%  several machines: the latency and throughput of each machine.
+%  This exercise will help us find possibly faulty (or very fast) machines.
+%
+
+fprintf('Visualizing example dataset for outlier detection.\n\n');
+
+%  The following command loads the dataset. You should now have the
+%  variables X, Xval, yval in your environment
+load('ex8data1.mat');
+
+%  Visualize the example dataset
+plot(X(:, 1), X(:, 2), 'bx');
+axis([0 30 0 30]);
+xlabel('Latency (ms)');
+ylabel('Throughput (mb/s)');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause
+
+
+%% ================== Part 2: Estimate the dataset statistics ===================
+%  For this exercise, we assume a Gaussian distribution for the dataset.
+%
+%  We first estimate the parameters of our assumed Gaussian distribution, 
+%  then compute the probabilities for each of the points and then visualize 
+%  both the overall distribution and where each of the points falls in 
+%  terms of that distribution.
+%
+fprintf('Visualizing Gaussian fit.\n\n');
+
+%  Estimate my and sigma2
+[mu sigma2] = estimateGaussian(X);
+
+%  Returns the density of the multivariate normal at each data point (row) 
+%  of X
+p = multivariateGaussian(X, mu, sigma2);
+
+%  Visualize the fit
+visualizeFit(X,  mu, sigma2);
+xlabel('Latency (ms)');
+ylabel('Throughput (mb/s)');
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% ================== Part 3: Find Outliers ===================
+%  Now you will find a good epsilon threshold using a cross-validation set
+%  probabilities given the estimated Gaussian distribution
+% 
+
+pval = multivariateGaussian(Xval, mu, sigma2);
+
+[epsilon F1] = selectThreshold(yval, pval);
+fprintf('Best epsilon found using cross-validation: %e\n', epsilon);
+fprintf('Best F1 on Cross Validation Set:  %f\n', F1);
+fprintf('   (you should see a value epsilon of about 8.99e-05)\n');
+fprintf('   (you should see a Best F1 value of  0.875000)\n\n');
+
+%  Find the outliers in the training set and plot the
+outliers = find(p < epsilon);
+
+%  Draw a red circle around those outliers
+hold on
+plot(X(outliers, 1), X(outliers, 2), 'ro', 'LineWidth', 2, 'MarkerSize', 10);
+hold off
+
+fprintf('Program paused. Press enter to continue.\n');
+pause;
+
+%% ================== Part 4: Multidimensional Outliers ===================
+%  We will now use the code from the previous part and apply it to a 
+%  harder problem in which more features describe each datapoint and only 
+%  some features indicate whether a point is an outlier.
+%
+
+%  Loads the second dataset. You should now have the
+%  variables X, Xval, yval in your environment
+load('ex8data2.mat');
+
+%  Apply the same steps to the larger dataset
+[mu sigma2] = estimateGaussian(X);
+
+%  Training set 
+p = multivariateGaussian(X, mu, sigma2);
+
+%  Cross-validation set
+pval = multivariateGaussian(Xval, mu, sigma2);
+
+%  Find the best threshold
+[epsilon F1] = selectThreshold(yval, pval);
+
+fprintf('Best epsilon found using cross-validation: %e\n', epsilon);
+fprintf('Best F1 on Cross Validation Set:  %f\n', F1);
+fprintf('   (you should see a value epsilon of about 1.38e-18)\n');
+fprintf('   (you should see a Best F1 value of 0.615385)\n');
+fprintf('# Outliers found: %d\n\n', sum(p < epsilon));

machine learning/machine-learning-ex8/ex8/ex8_cofi.m

@@ -0,0 +1,237 @@
+%% Machine Learning Online Class
+%  Exercise 8 | Anomaly Detection and Collaborative Filtering
+%
+%  Instructions
+%  ------------
+%
+%  This file contains code that helps you get started on the
+%  exercise. You will need to complete the following functions:
+%
+%     estimateGaussian.m
+%     selectThreshold.m
+%     cofiCostFunc.m
+%
+%  For this exercise, you will not need to change any code in this file,
+%  or any other files other than those mentioned above.
+%
+
+%% =============== Part 1: Loading movie ratings dataset ================
+%  You will start by loading the movie ratings dataset to understand the
+%  structure of the data.
+%  
+fprintf('Loading movie ratings dataset.\n\n');
+
+%  Load data
+load ('ex8_movies.mat');
+
+%  Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies on 
+%  943 users
+%
+%  R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
+%  rating to movie i
+
+%  From the matrix, we can compute statistics like average rating.
+fprintf('Average rating for movie 1 (Toy Story): %f / 5\n\n', ...
+        mean(Y(1, R(1, :))));
+
+%  We can "visualize" the ratings matrix by plotting it with imagesc
+imagesc(Y);
+ylabel('Movies');
+xlabel('Users');
+
+fprintf('\nProgram paused. Press enter to continue.\n');
+pause;
+
+%% ============ Part 2: Collaborative Filtering Cost Function ===========
+%  You will now implement the cost function for collaborative filtering.
+%  To help you debug your cost function, we have included set of weights
+%  that we trained on that. Specifically, you should complete the code in 
+%  cofiCostFunc.m to return J.
+
+%  Load pre-trained weights (X, Theta, num_users, num_movies, num_features)
+load ('ex8_movieParams.mat');
+
+%  Reduce the data set size so that this runs faster
+num_users = 4; num_movies = 5; num_features = 3;
+X = X(1:num_movies, 1:num_features);
+Theta = Theta(1:num_users, 1:num_features);
+Y = Y(1:num_movies, 1:num_users);
+R = R(1:num_movies, 1:num_users);
+
+%  Evaluate cost function
+J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...
+               num_features, 0);
+           
+fprintf(['Cost at loaded parameters: %f '...
+         '\n(this value should be about 22.22)\n'], J);
+
+fprintf('\nProgram paused. Press enter to continue.\n');
+pause;
+
+
+%% ============== Part 3: Collaborative Filtering Gradient ==============
+%  Once your cost function matches up with ours, you should now implement 
+%  the collaborative filtering gradient function. Specifically, you should 
+%  complete the code in cofiCostFunc.m to return the grad argument.
+%  
+fprintf('\nChecking Gradients (without regularization) ... \n');
+
+%  Check gradients by running checkNNGradients
+checkCostFunction;
+
+fprintf('\nProgram paused. Press enter to continue.\n');
+pause;
+
+
+%% ========= Part 4: Collaborative Filtering Cost Regularization ========
+%  Now, you should implement regularization for the cost function for 
+%  collaborative filtering. You can implement it by adding the cost of
+%  regularization to the original cost computation.
+%  
+
+%  Evaluate cost function
+J = cofiCostFunc([X(:) ; Theta(:)], Y, R, num_users, num_movies, ...
+               num_features, 1.5);
+           
+fprintf(['Cost at loaded parameters (lambda = 1.5): %f '...
+         '\n(this value should be about 31.34)\n'], J);
+
+fprintf('\nProgram paused. Press enter to continue.\n');
+pause;
+
+
+%% ======= Part 5: Collaborative Filtering Gradient Regularization ======
+%  Once your cost matches up with ours, you should proceed to implement 
+%  regularization for the gradient. 
+%
+
+%  
+fprintf('\nChecking Gradients (with regularization) ... \n');
+
+%  Check gradients by running checkNNGradients
+checkCostFunction(1.5);
+
+fprintf('\nProgram paused. Press enter to continue.\n');
+pause;
+
+
+%% ============== Part 6: Entering ratings for a new user ===============
+%  Before we will train the collaborative filtering model, we will first
+%  add ratings that correspond to a new user that we just observed. This
+%  part of the code will also allow you to put in your own ratings for the
+%  movies in our dataset!
+%
+movieList = loadMovieList();
+
+%  Initialize my ratings
+my_ratings = zeros(1682, 1);
+
+% Check the file movie_idx.txt for id of each movie in our dataset
+% For example, Toy Story (1995) has ID 1, so to rate it "4", you can set
+my_ratings(1) = 4;
+
+% Or suppose did not enjoy Silence of the Lambs (1991), you can set
+my_ratings(98) = 2;
+
+% We have selected a few movies we liked / did not like and the ratings we
+% gave are as follows:
+my_ratings(7) = 3;
+my_ratings(12)= 5;
+my_ratings(54) = 4;
+my_ratings(64)= 5;
+my_ratings(66)= 3;
+my_ratings(69) = 5;
+my_ratings(183) = 4;
+my_ratings(226) = 5;
+my_ratings(355)= 5;
+
+fprintf('\n\nNew user ratings:\n');
+for i = 1:length(my_ratings)
+    if my_ratings(i) > 0 
+        fprintf('Rated %d for %s\n', my_ratings(i), ...
+                 movieList{i});
+    end
+end
+
+fprintf('\nProgram paused. Press enter to continue.\n');
+pause;
+
+
+%% ================== Part 7: Learning Movie Ratings ====================
+%  Now, you will train the collaborative filtering model on a movie rating 
+%  dataset of 1682 movies and 943 users
+%
+
+fprintf('\nTraining collaborative filtering...\n');
+
+%  Load data
+load('ex8_movies.mat');
+
+%  Y is a 1682x943 matrix, containing ratings (1-5) of 1682 movies by 
+%  943 users
+%
+%  R is a 1682x943 matrix, where R(i,j) = 1 if and only if user j gave a
+%  rating to movie i
+
+%  Add our own ratings to the data matrix
+Y = [my_ratings Y];
+R = [(my_ratings ~= 0) R];
+
+%  Normalize Ratings
+[Ynorm, Ymean] = normalizeRatings(Y, R);
+
+%  Useful Values
+num_users = size(Y, 2);
+num_movies = size(Y, 1);
+num_features = 10;
+
+% Set Initial Parameters (Theta, X)
+X = randn(num_movies, num_features);
+Theta = randn(num_users, num_features);
+
+initial_parameters = [X(:); Theta(:)];
+
+% Set options for fmincg
+options = optimset('GradObj', 'on', 'MaxIter', 100);
+
+% Set Regularization
+lambda = 10;
+theta = fmincg (@(t)(cofiCostFunc(t, Ynorm, R, num_users, num_movies, ...
+                                num_features, lambda)), ...
+                initial_parameters, options);
+
+% Unfold the returned theta back into U and W
+X = reshape(theta(1:num_movies*num_features), num_movies, num_features);
+Theta = reshape(theta(num_movies*num_features+1:end), ...
+                num_users, num_features);
+
+fprintf('Recommender system learning completed.\n');
+
+fprintf('\nProgram paused. Press enter to continue.\n');
+pause;
+
+%% ================== Part 8: Recommendation for you ====================
+%  After training the model, you can now make recommendations by computing
+%  the predictions matrix.
+%
+
+p = X * Theta';
+my_predictions = p(:,1) + Ymean;
+
+movieList = loadMovieList();
+
+[r, ix] = sort(my_predictions, 'descend');
+fprintf('\nTop recommendations for you:\n');
+for i=1:10
+    j = ix(i);
+    fprintf('Predicting rating %.1f for movie %s\n', my_predictions(j), ...
+            movieList{j});
+end
+
+fprintf('\n\nOriginal ratings provided:\n');
+for i = 1:length(my_ratings)
+    if my_ratings(i) > 0 
+        fprintf('Rated %d for %s\n', my_ratings(i), ...
+                 movieList{i});
+    end
+end

machine learning/machine-learning-ex8/ex8/fmincg.m

@@ -0,0 +1,175 @@
+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');

machine learning/machine-learning-ex8/ex8/lib/jsonlab/AUTHORS.txt

@@ -0,0 +1,41 @@
+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

machine learning/machine-learning-ex8/ex8/lib/jsonlab/ChangeLog.txt

@@ -0,0 +1,74 @@
+============================================================================
+
+   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

machine learning/machine-learning-ex8/ex8/lib/jsonlab/LICENSE_BSD.txt

@@ -0,0 +1,25 @@
+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.

machine learning/machine-learning-ex8/ex8/lib/jsonlab/README.txt

@@ -0,0 +1,394 @@
+===============================================================================
+=                                 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).

machine learning/machine-learning-ex8/ex8/lib/jsonlab/jsonopt.m

@@ -0,0 +1,32 @@
+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
+

machine learning/machine-learning-ex8/ex8/lib/jsonlab/loadjson.m

@@ -0,0 +1,566 @@
+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
+

machine learning/machine-learning-ex8/ex8/lib/jsonlab/loadubjson.m

@@ -0,0 +1,528 @@
+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
+

machine learning/machine-learning-ex8/ex8/lib/jsonlab/mergestruct.m

@@ -0,0 +1,33 @@
+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
+

machine learning/machine-learning-ex8/ex8/lib/jsonlab/savejson.m

@@ -0,0 +1,475 @@
+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

machine learning/machine-learning-ex8/ex8/lib/jsonlab/saveubjson.m

@@ -0,0 +1,504 @@
+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(:)';

machine learning/machine-learning-ex8/ex8/lib/jsonlab/varargin2struct.m

@@ -0,0 +1,40 @@
+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
+

machine learning/machine-learning-ex8/ex8/lib/makeValidFieldName.m

@@ -0,0 +1,30 @@
+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

machine learning/machine-learning-ex8/ex8/lib/submitWithConfiguration.m

@@ -0,0 +1,179 @@
+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('\n!! Submission failed: %s\n', e.message);
+    fprintf('\n\nFunction: %s\nFileName: %s\nLineNumber: %d\n', ...
+      e.stack(1,1).name, e.stack(1,1).file, e.stack(1,1).line);
+    fprintf('\nPlease correct your code and resubmit.\n');
+    return
+  end
+
+  if isfield(response, 'errorMessage')
+    fprintf('!! Submission failed: %s\n', response.errorMessage);
+  elseif isfield(response, 'errorCode')
+    fprintf('!! Submission failed: %s\n', response.message);
+  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();
+
+  responseBody = getResponse(submissionUrl, body);
+  jsonResponse = validateResponse(responseBody);
+  response = loadjson(jsonResponse);
+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
+
+% use urlread or curl to send submit results to the grader and get a response
+function response = getResponse(url, body)
+% try using urlread() and a secure connection
+  params = {'jsonBody', body};
+  [response, success] = urlread(url, 'post', params);
+
+  if (success == 0)
+    % urlread didn't work, try curl & the peer certificate patch
+    if ispc
+      % testing note: use 'jsonBody =' for a test case
+      json_command = sprintf('echo jsonBody=%s | curl -k -X POST -d @- %s', body, url);
+    else
+      % it's linux/OS X, so use the other form
+      json_command = sprintf('echo ''jsonBody=%s'' | curl -k -X POST -d @- %s', body, url);
+    end
+    % get the response body for the peer certificate patch method
+    [code, response] = system(json_command);
+    % test the success code
+    if (code ~= 0)
+      fprintf('[error] submission with curl() was not successful\n');
+    end
+  end
+end
+
+% validate the grader's response
+function response = validateResponse(resp)
+  % test if the response is json or an HTML page
+  isJson = length(resp) > 0 && resp(1) == '{';
+  isHtml = findstr(lower(resp), '<html');
+
+  if (isJson)
+    response = resp;
+  elseif (isHtml)
+    % the response is html, so it's probably an error message
+    printHTMLContents(resp);
+    error('Grader response is an HTML message');
+  else
+    error('Grader sent no response');
+  end
+end
+
+% parse a HTML response and print it's contents
+function printHTMLContents(response)
+  strippedResponse = regexprep(response, '<[^>]+>', ' ');
+  strippedResponse = regexprep(strippedResponse, '[\t ]+', ' ');
+  fprintf(strippedResponse);
+end
+
+
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% Service configuration
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+function submissionUrl = submissionUrl()
+  submissionUrl = 'https://www-origin.coursera.org/api/onDemandProgrammingImmediateFormSubmissions.v1';
+end

machine learning/machine-learning-ex8/ex8/loadMovieList.m

@@ -0,0 +1,25 @@
+function movieList = loadMovieList()
+%GETMOVIELIST reads the fixed movie list in movie.txt and returns a
+%cell array of the words
+%   movieList = GETMOVIELIST() reads the fixed movie list in movie.txt 
+%   and returns a cell array of the words in movieList.
+
+
+%% Read the fixed movieulary list
+fid = fopen('movie_ids.txt');
+
+% Store all movies in cell array movie{}
+n = 1682;  % Total number of movies 
+
+movieList = cell(n, 1);
+for i = 1:n
+    % Read line
+    line = fgets(fid);
+    % Word Index (can ignore since it will be = i)
+    [idx, movieName] = strtok(line, ' ');
+    % Actual Word
+    movieList{i} = strtrim(movieName);
+end
+fclose(fid);
+
+end

machine learning/machine-learning-ex8/ex8/movie_ids.txt

@@ -0,0 +1,1682 @@
+1 Toy Story (1995)
+2 GoldenEye (1995)
+3 Four Rooms (1995)
+4 Get Shorty (1995)
+5 Copycat (1995)
+6 Shanghai Triad (Yao a yao yao dao waipo qiao) (1995)
+7 Twelve Monkeys (1995)
+8 Babe (1995)
+9 Dead Man Walking (1995)
+10 Richard III (1995)
+11 Seven (Se7en) (1995)
+12 Usual Suspects, The (1995)
+13 Mighty Aphrodite (1995)
+14 Postino, Il (1994)
+15 Mr. Holland's Opus (1995)
+16 French Twist (Gazon maudit) (1995)
+17 From Dusk Till Dawn (1996)
+18 White Balloon, The (1995)
+19 Antonia's Line (1995)
+20 Angels and Insects (1995)
+21 Muppet Treasure Island (1996)
+22 Braveheart (1995)
+23 Taxi Driver (1976)
+24 Rumble in the Bronx (1995)
+25 Birdcage, The (1996)
+26 Brothers McMullen, The (1995)
+27 Bad Boys (1995)
+28 Apollo 13 (1995)
+29 Batman Forever (1995)
+30 Belle de jour (1967)
+31 Crimson Tide (1995)
+32 Crumb (1994)
+33 Desperado (1995)
+34 Doom Generation, The (1995)
+35 Free Willy 2: The Adventure Home (1995)
+36 Mad Love (1995)
+37 Nadja (1994)
+38 Net, The (1995)
+39 Strange Days (1995)
+40 To Wong Foo, Thanks for Everything! Julie Newmar (1995)
+41 Billy Madison (1995)
+42 Clerks (1994)
+43 Disclosure (1994)
+44 Dolores Claiborne (1994)
+45 Eat Drink Man Woman (1994)
+46 Exotica (1994)
+47 Ed Wood (1994)
+48 Hoop Dreams (1994)
+49 I.Q. (1994)
+50 Star Wars (1977)
+51 Legends of the Fall (1994)
+52 Madness of King George, The (1994)
+53 Natural Born Killers (1994)
+54 Outbreak (1995)
+55 Professional, The (1994)
+56 Pulp Fiction (1994)
+57 Priest (1994)
+58 Quiz Show (1994)
+59 Three Colors: Red (1994)
+60 Three Colors: Blue (1993)
+61 Three Colors: White (1994)
+62 Stargate (1994)
+63 Santa Clause, The (1994)
+64 Shawshank Redemption, The (1994)
+65 What's Eating Gilbert Grape (1993)
+66 While You Were Sleeping (1995)
+67 Ace Ventura: Pet Detective (1994)
+68 Crow, The (1994)
+69 Forrest Gump (1994)
+70 Four Weddings and a Funeral (1994)
+71 Lion King, The (1994)
+72 Mask, The (1994)
+73 Maverick (1994)
+74 Faster Pussycat! Kill! Kill! (1965)
+75 Brother Minister: The Assassination of Malcolm X (1994)
+76 Carlito's Way (1993)
+77 Firm, The (1993)
+78 Free Willy (1993)
+79 Fugitive, The (1993)
+80 Hot Shots! Part Deux (1993)
+81 Hudsucker Proxy, The (1994)
+82 Jurassic Park (1993)
+83 Much Ado About Nothing (1993)
+84 Robert A. Heinlein's The Puppet Masters (1994)
+85 Ref, The (1994)
+86 Remains of the Day, The (1993)
+87 Searching for Bobby Fischer (1993)
+88 Sleepless in Seattle (1993)
+89 Blade Runner (1982)
+90 So I Married an Axe Murderer (1993)
+91 Nightmare Before Christmas, The (1993)
+92 True Romance (1993)
+93 Welcome to the Dollhouse (1995)
+94 Home Alone (1990)
+95 Aladdin (1992)
+96 Terminator 2: Judgment Day (1991)
+97 Dances with Wolves (1990)
+98 Silence of the Lambs, The (1991)
+99 Snow White and the Seven Dwarfs (1937)
+100 Fargo (1996)
+101 Heavy Metal (1981)
+102 Aristocats, The (1970)
+103 All Dogs Go to Heaven 2 (1996)
+104 Theodore Rex (1995)
+105 Sgt. Bilko (1996)
+106 Diabolique (1996)
+107 Moll Flanders (1996)
+108 Kids in the Hall: Brain Candy (1996)
+109 Mystery Science Theater 3000: The Movie (1996)
+110 Operation Dumbo Drop (1995)
+111 Truth About Cats & Dogs, The (1996)
+112 Flipper (1996)
+113 Horseman on the Roof, The (Hussard sur le toit, Le) (1995)
+114 Wallace & Gromit: The Best of Aardman Animation (1996)
+115 Haunted World of Edward D. Wood Jr., The (1995)
+116 Cold Comfort Farm (1995)
+117 Rock, The (1996)
+118 Twister (1996)
+119 Maya Lin: A Strong Clear Vision (1994)
+120 Striptease (1996)
+121 Independence Day (ID4) (1996)
+122 Cable Guy, The (1996)
+123 Frighteners, The (1996)
+124 Lone Star (1996)
+125 Phenomenon (1996)
+126 Spitfire Grill, The (1996)
+127 Godfather, The (1972)
+128 Supercop (1992)
+129 Bound (1996)
+130 Kansas City (1996)
+131 Breakfast at Tiffany's (1961)
+132 Wizard of Oz, The (1939)
+133 Gone with the Wind (1939)
+134 Citizen Kane (1941)
+135 2001: A Space Odyssey (1968)
+136 Mr. Smith Goes to Washington (1939)
+137 Big Night (1996)
+138 D3: The Mighty Ducks (1996)
+139 Love Bug, The (1969)
+140 Homeward Bound: The Incredible Journey (1993)
+141 20,000 Leagues Under the Sea (1954)
+142 Bedknobs and Broomsticks (1971)
+143 Sound of Music, The (1965)
+144 Die Hard (1988)
+145 Lawnmower Man, The (1992)
+146 Unhook the Stars (1996)
+147 Long Kiss Goodnight, The (1996)
+148 Ghost and the Darkness, The (1996)
+149 Jude (1996)
+150 Swingers (1996)
+151 Willy Wonka and the Chocolate Factory (1971)
+152 Sleeper (1973)
+153 Fish Called Wanda, A (1988)
+154 Monty Python's Life of Brian (1979)
+155 Dirty Dancing (1987)
+156 Reservoir Dogs (1992)
+157 Platoon (1986)
+158 Weekend at Bernie's (1989)
+159 Basic Instinct (1992)
+160 Glengarry Glen Ross (1992)
+161 Top Gun (1986)
+162 On Golden Pond (1981)
+163 Return of the Pink Panther, The (1974)
+164 Abyss, The (1989)
+165 Jean de Florette (1986)
+166 Manon of the Spring (Manon des sources) (1986)
+167 Private Benjamin (1980)
+168 Monty Python and the Holy Grail (1974)
+169 Wrong Trousers, The (1993)
+170 Cinema Paradiso (1988)
+171 Delicatessen (1991)
+172 Empire Strikes Back, The (1980)
+173 Princess Bride, The (1987)
+174 Raiders of the Lost Ark (1981)
+175 Brazil (1985)
+176 Aliens (1986)
+177 Good, The Bad and The Ugly, The (1966)
+178 12 Angry Men (1957)
+179 Clockwork Orange, A (1971)
+180 Apocalypse Now (1979)
+181 Return of the Jedi (1983)
+182 GoodFellas (1990)
+183 Alien (1979)
+184 Army of Darkness (1993)
+185 Psycho (1960)
+186 Blues Brothers, The (1980)
+187 Godfather: Part II, The (1974)
+188 Full Metal Jacket (1987)
+189 Grand Day Out, A (1992)
+190 Henry V (1989)
+191 Amadeus (1984)
+192 Raging Bull (1980)
+193 Right Stuff, The (1983)
+194 Sting, The (1973)
+195 Terminator, The (1984)
+196 Dead Poets Society (1989)
+197 Graduate, The (1967)
+198 Nikita (La Femme Nikita) (1990)
+199 Bridge on the River Kwai, The (1957)
+200 Shining, The (1980)
+201 Evil Dead II (1987)
+202 Groundhog Day (1993)
+203 Unforgiven (1992)
+204 Back to the Future (1985)
+205 Patton (1970)
+206 Akira (1988)
+207 Cyrano de Bergerac (1990)
+208 Young Frankenstein (1974)
+209 This Is Spinal Tap (1984)
+210 Indiana Jones and the Last Crusade (1989)
+211 M*A*S*H (1970)
+212 Unbearable Lightness of Being, The (1988)
+213 Room with a View, A (1986)
+214 Pink Floyd - The Wall (1982)
+215 Field of Dreams (1989)
+216 When Harry Met Sally... (1989)
+217 Bram Stoker's Dracula (1992)
+218 Cape Fear (1991)
+219 Nightmare on Elm Street, A (1984)
+220 Mirror Has Two Faces, The (1996)
+221 Breaking the Waves (1996)
+222 Star Trek: First Contact (1996)
+223 Sling Blade (1996)
+224 Ridicule (1996)
+225 101 Dalmatians (1996)
+226 Die Hard 2 (1990)
+227 Star Trek VI: The Undiscovered Country (1991)
+228 Star Trek: The Wrath of Khan (1982)
+229 Star Trek III: The Search for Spock (1984)
+230 Star Trek IV: The Voyage Home (1986)
+231 Batman Returns (1992)
+232 Young Guns (1988)
+233 Under Siege (1992)
+234 Jaws (1975)
+235 Mars Attacks! (1996)
+236 Citizen Ruth (1996)
+237 Jerry Maguire (1996)
+238 Raising Arizona (1987)
+239 Sneakers (1992)
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+1585 American Dream (1990)
+1586 Lashou shentan (1992)
+1587 Terror in a Texas Town (1958)
+1588 Salut cousin! (1996)
+1589 Schizopolis (1996)
+1590 To Have, or Not (1995)
+1591 Duoluo tianshi (1995)
+1592 Magic Hour, The (1998)
+1593 Death in Brunswick (1991)
+1594 Everest (1998)
+1595 Shopping (1994)
+1596 Nemesis 2: Nebula (1995)
+1597 Romper Stomper (1992)
+1598 City of Industry (1997)
+1599 Someone Else's America (1995)
+1600 Guantanamera (1994)
+1601 Office Killer (1997)
+1602 Price Above Rubies, A (1998)
+1603 Angela (1995)
+1604 He Walked by Night (1948)
+1605 Love Serenade (1996)
+1606 Deceiver (1997)
+1607 Hurricane Streets (1998)
+1608 Buddy (1997)
+1609 B*A*P*S (1997)
+1610 Truth or Consequences, N.M. (1997)
+1611 Intimate Relations (1996)
+1612 Leading Man, The (1996)
+1613 Tokyo Fist (1995)
+1614 Reluctant Debutante, The (1958)
+1615 Warriors of Virtue (1997)
+1616 Desert Winds (1995)
+1617 Hugo Pool (1997)
+1618 King of New York (1990)
+1619 All Things Fair (1996)
+1620 Sixth Man, The (1997)
+1621 Butterfly Kiss (1995)
+1622 Paris, France (1993)
+1623 Cérémonie, La (1995)
+1624 Hush (1998)
+1625 Nightwatch (1997)
+1626 Nobody Loves Me (Keiner liebt mich) (1994)
+1627 Wife, The (1995)
+1628 Lamerica (1994)
+1629 Nico Icon (1995)
+1630 Silence of the Palace, The (Saimt el Qusur) (1994)
+1631 Slingshot, The (1993)
+1632 Land and Freedom (Tierra y libertad) (1995)
+1633 Á köldum klaka (Cold Fever) (1994)
+1634 Etz Hadomim Tafus (Under the Domin Tree) (1994)
+1635 Two Friends (1986) 
+1636 Brothers in Trouble (1995)
+1637 Girls Town (1996)
+1638 Normal Life (1996)
+1639 Bitter Sugar (Azucar Amargo) (1996)
+1640 Eighth Day, The (1996)
+1641 Dadetown (1995)
+1642 Some Mother's Son (1996)
+1643 Angel Baby (1995)
+1644 Sudden Manhattan (1996)
+1645 Butcher Boy, The (1998)
+1646 Men With Guns (1997)
+1647 Hana-bi (1997)
+1648 Niagara, Niagara (1997)
+1649 Big One, The (1997)
+1650 Butcher Boy, The (1998)
+1651 Spanish Prisoner, The (1997)
+1652 Temptress Moon (Feng Yue) (1996)
+1653 Entertaining Angels: The Dorothy Day Story (1996)
+1654 Chairman of the Board (1998)
+1655 Favor, The (1994)
+1656 Little City (1998)
+1657 Target (1995)
+1658 Substance of Fire, The (1996)
+1659 Getting Away With Murder (1996)
+1660 Small Faces (1995)
+1661 New Age, The (1994)
+1662 Rough Magic (1995)
+1663 Nothing Personal (1995)
+1664 8 Heads in a Duffel Bag (1997)
+1665 Brother's Kiss, A (1997)
+1666 Ripe (1996)
+1667 Next Step, The (1995)
+1668 Wedding Bell Blues (1996)
+1669 MURDER and murder (1996)
+1670 Tainted (1998)
+1671 Further Gesture, A (1996)
+1672 Kika (1993)
+1673 Mirage (1995)
+1674 Mamma Roma (1962)
+1675 Sunchaser, The (1996)
+1676 War at Home, The (1996)
+1677 Sweet Nothing (1995)
+1678 Mat' i syn (1997)
+1679 B. Monkey (1998)
+1680 Sliding Doors (1998)
+1681 You So Crazy (1994)
+1682 Scream of Stone (Schrei aus Stein) (1991)

machine learning/machine-learning-ex8/ex8/multivariateGaussian.m

@@ -0,0 +1,22 @@
+function p = multivariateGaussian(X, mu, Sigma2)
+%MULTIVARIATEGAUSSIAN Computes the probability density function of the
+%multivariate gaussian distribution.
+%    p = MULTIVARIATEGAUSSIAN(X, mu, Sigma2) Computes the probability 
+%    density function of the examples X under the multivariate gaussian 
+%    distribution with parameters mu and Sigma2. If Sigma2 is a matrix, it is
+%    treated as the covariance matrix. If Sigma2 is a vector, it is treated
+%    as the \sigma^2 values of the variances in each dimension (a diagonal
+%    covariance matrix)
+%
+
+k = length(mu);
+
+if (size(Sigma2, 2) == 1) || (size(Sigma2, 1) == 1)
+    Sigma2 = diag(Sigma2);
+end
+
+X = bsxfun(@minus, X, mu(:)');
+p = (2 * pi) ^ (- k / 2) * det(Sigma2) ^ (-0.5) * ...
+    exp(-0.5 * sum(bsxfun(@times, X * pinv(Sigma2), X), 2));
+
+end

machine learning/machine-learning-ex8/ex8/normalizeRatings.m

@@ -0,0 +1,17 @@
+function [Ynorm, Ymean] = normalizeRatings(Y, R)
+%NORMALIZERATINGS Preprocess data by subtracting mean rating for every 
+%movie (every row)
+%   [Ynorm, Ymean] = NORMALIZERATINGS(Y, R) normalized Y so that each movie
+%   has a rating of 0 on average, and returns the mean rating in Ymean.
+%
+
+[m, n] = size(Y);
+Ymean = zeros(m, 1);
+Ynorm = zeros(size(Y));
+for i = 1:m
+    idx = find(R(i, :) == 1);
+    Ymean(i) = mean(Y(i, idx));
+    Ynorm(i, idx) = Y(i, idx) - Ymean(i);
+end
+
+end

machine learning/machine-learning-ex8/ex8/selectThreshold.m

@@ -0,0 +1,46 @@
+function [bestEpsilon bestF1] = selectThreshold(yval, pval)
+%SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting
+%outliers
+%   [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best
+%   threshold to use for selecting outliers based on the results from a
+%   validation set (pval) and the ground truth (yval).
+%
+
+bestEpsilon = 0;
+bestF1 = 0;
+F1 = 0;
+
+stepsize = (max(pval) - min(pval)) / 1000;
+for epsilon = min(pval):stepsize:max(pval)
+    
+    % ====================== YOUR CODE HERE ======================
+    % Instructions: Compute the F1 score of choosing epsilon as the
+    %               threshold and place the value in F1. The code at the
+    %               end of the loop will compare the F1 score for this
+    %               choice of epsilon and set it to be the best epsilon if
+    %               it is better than the current choice of epsilon.
+    %               
+    % Note: You can use predictions = (pval < epsilon) to get a binary vector
+    %       of 0's and 1's of the outlier predictions
+
+
+
+
+
+
+
+
+
+
+
+
+
+    % =============================================================
+
+    if F1 > bestF1
+       bestF1 = F1;
+       bestEpsilon = epsilon;
+    end
+end
+
+end

machine learning/machine-learning-ex8/ex8/submit.m

@@ -0,0 +1,77 @@
+function submit()
+  addpath('./lib');
+
+  conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
+  conf.itemName = 'Anomaly Detection and Recommender Systems';
+  conf.partArrays = { ...
+    { ...
+      '1', ...
+      { 'estimateGaussian.m' }, ...
+      'Estimate Gaussian Parameters', ...
+    }, ...
+    { ...
+      '2', ...
+      { 'selectThreshold.m' }, ...
+      'Select Threshold', ...
+    }, ...
+    { ...
+      '3', ...
+      { 'cofiCostFunc.m' }, ...
+      'Collaborative Filtering Cost', ...
+    }, ...
+    { ...
+      '4', ...
+      { 'cofiCostFunc.m' }, ...
+      'Collaborative Filtering Gradient', ...
+    }, ...
+    { ...
+      '5', ...
+      { 'cofiCostFunc.m' }, ...
+      'Regularized Cost', ...
+    }, ...
+    { ...
+      '6', ...
+      { 'cofiCostFunc.m' }, ...
+      'Regularized Gradient', ...
+    }, ...
+  };
+  conf.output = @output;
+
+  submitWithConfiguration(conf);
+end
+
+function out = output(partId, auxstring)
+  % Random Test Cases
+  n_u = 3; n_m = 4; n = 5;
+  X = reshape(sin(1:n_m*n), n_m, n);
+  Theta = reshape(cos(1:n_u*n), n_u, n);
+  Y = reshape(sin(1:2:2*n_m*n_u), n_m, n_u);
+  R = Y > 0.5;
+  pval = [abs(Y(:)) ; 0.001; 1];
+  Y = (Y .* double(R));  % set 'Y' values to 0 for movies not reviewed
+  yval = [R(:) ; 1; 0];
+  params = [X(:); Theta(:)];
+  if partId == '1'
+    [mu sigma2] = estimateGaussian(X);
+    out = sprintf('%0.5f ', [mu(:); sigma2(:)]);
+  elseif partId == '2'
+    [bestEpsilon bestF1] = selectThreshold(yval, pval);
+    out = sprintf('%0.5f ', [bestEpsilon(:); bestF1(:)]);
+  elseif partId == '3'
+    [J] = cofiCostFunc(params, Y, R, n_u, n_m, ...
+                       n, 0);
+    out = sprintf('%0.5f ', J(:));
+  elseif partId == '4'
+    [J, grad] = cofiCostFunc(params, Y, R, n_u, n_m, ...
+                             n, 0);
+    out = sprintf('%0.5f ', grad(:));
+  elseif partId == '5'
+    [J] = cofiCostFunc(params, Y, R, n_u, n_m, ...
+                       n, 1.5);
+    out = sprintf('%0.5f ', J(:));
+  elseif partId == '6'
+    [J, grad] = cofiCostFunc(params, Y, R, n_u, n_m, ...
+                             n, 1.5);
+    out = sprintf('%0.5f ', grad(:));
+  end 
+end

machine learning/machine-learning-ex8/ex8/visualizeFit.m

@@ -0,0 +1,20 @@
+function visualizeFit(X, mu, sigma2)
+%VISUALIZEFIT Visualize the dataset and its estimated distribution.
+%   VISUALIZEFIT(X, p, mu, sigma2) This visualization shows you the 
+%   probability density function of the Gaussian distribution. Each example
+%   has a location (x1, x2) that depends on its feature values.
+%
+
+[X1,X2] = meshgrid(0:.5:35); 
+Z = multivariateGaussian([X1(:) X2(:)],mu,sigma2);
+Z = reshape(Z,size(X1));
+
+plot(X(:, 1), X(:, 2),'bx');
+hold on;
+% Do not plot if there are infinities
+if (sum(isinf(Z)) == 0)
+    contour(X1, X2, Z, 10.^(-20:3:0)');
+end
+hold off;
+
+end