# Commits

committed e9464a9

Start of ex2

• Participants
• Parent commits acabc1b

# File ex2.py

`+import matplotlib.pylab as plt`
`+import numpy as np`
`+`
`+`
`+def parse_line(line):`
`+    return map(float, line.split(','))`
`+`
`+`
`+def load():`
`+    with open('ex2/ex2data1.txt') as fo:`
`+        return np.array([parse_line(line) for line in fo])`
`+`
`+`
`+def plot(data):`
`+    passed = data[data[:,2]==1]`
`+    failed = data[data[:,2]==0]`
`+`
`+    fig = plt.figure()`
`+    ax = fig.add_subplot(111)`
`+    ax.scatter(passed[:,0], passed[:,1], color='black', marker='+',`
`+               label='Admitted')`
`+    ax.scatter(failed[:,0], failed[:,1], color='orange', marker='o',`
`+              label='Not admitted')`
`+    ax.set_xlim(min(data[:,0]), max(data[:,0]))`
`+    ax.set_ylim(min(data[:,1]), max(data[:,1]))`
`+    ax.set_xlabel('Exam 1 score')`
`+    ax.set_ylabel('Exam 2 score')`
`+    plt.legend()`
`+    plt.show()`
`+`
`+`

# File ex2/costFunction.m

`+function [J, grad] = costFunction(theta, X, y)`
`+%COSTFUNCTION Compute cost and gradient for logistic regression`
`+%   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the`
`+%   parameter for logistic regression and the gradient of the cost`
`+%   w.r.t. to the parameters.`
`+`
`+% Initialize some useful values`
`+m = length(y); % number of training examples`
`+`
`+% You need to return the following variables correctly `
`+J = 0;`
`+grad = zeros(size(theta));`
`+`
`+% ====================== YOUR CODE HERE ======================`
`+% Instructions: Compute the cost of a particular choice of theta.`
`+%               You should set J to the cost.`
`+%               Compute the partial derivatives and set grad to the partial`
`+%               derivatives of the cost w.r.t. each parameter in theta`
`+%`
`+% Note: grad should have the same dimensions as theta`
`+%`
`+`
`+`
`+`
`+`
`+`
`+`
`+`
`+`
`+% =============================================================`
`+`
`+end`

# File ex2/costFunctionReg.m

`+function [J, grad] = costFunctionReg(theta, X, y, lambda)`
`+%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization`
`+%   J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using`
`+%   theta as the parameter for regularized logistic regression and the`
`+%   gradient of the cost w.r.t. to the parameters. `
`+`
`+% Initialize some useful values`
`+m = length(y); % number of training examples`
`+`
`+% You need to return the following variables correctly `
`+J = 0;`
`+grad = zeros(size(theta));`
`+`
`+% ====================== YOUR CODE HERE ======================`
`+% Instructions: Compute the cost of a particular choice of theta.`
`+%               You should set J to the cost.`
`+%               Compute the partial derivatives and set grad to the partial`
`+%               derivatives of the cost w.r.t. each parameter in theta`
`+`
`+`
`+`
`+`
`+`
`+`
`+% =============================================================`
`+`
`+end`

# File ex2/ex2.m

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

# File ex2/ex2_reg.m

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

# File ex2/ex2data1.txt

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# File ex2/ex2data2.txt

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`+0.28744,1.087,0`
`+0.39689,0.82383,0`
`+0.63882,0.88962,0`
`+0.82316,0.66301,0`
`+0.67339,0.64108,0`
`+1.0709,0.10015,0`
`+-0.046659,-0.57968,0`
`+-0.23675,-0.63816,0`
`+-0.15035,-0.36769,0`
`+-0.49021,-0.3019,0`
`+-0.46717,-0.13377,0`
`+-0.28859,-0.060673,0`
`+-0.61118,-0.067982,0`
`+-0.66302,-0.21418,0`
`+-0.59965,-0.41886,0`
`+-0.72638,-0.082602,0`
`+-0.83007,0.31213,0`
`+-0.72062,0.53874,0`
`+-0.59389,0.49488,0`
`+-0.48445,0.99927,0`
`+-0.0063364,0.99927,0`
`+0.63265,-0.030612,0`

# File ex2/mapFeature.m

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

# File ex2/plotData.m

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

# File ex2/plotDecisionBoundary.m

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

# File ex2/predict.m

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

# File ex2/sigmoid.m

`+function g = sigmoid(z)`
`+%SIGMOID Compute sigmoid functoon`
`+%   J = SIGMOID(z) computes the sigmoid of z.`
`+`
`+% You need to return the following variables correctly `
`+g = zeros(size(z));`
`+`
`+% ====================== YOUR CODE HERE ======================`
`+% Instructions: Compute the sigmoid of each value of z (z can be a matrix,`
`+%               vector or scalar).`
`+`
`+`
`+`
`+`
`+`
`+% =============================================================`
`+`
`+end`

# File ex2/submit.m

`+function submit(partId, webSubmit)`
`+%SUBMIT Submit your code and output to the ml-class servers`
`+%   SUBMIT() will connect to the ml-class server and submit your solution`
`+`
`+  fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...`
`+          homework_id());`
`+  if ~exist('partId', 'var') || isempty(partId)`
`+    partId = promptPart();`
`+  end`
`+`
`+  if ~exist('webSubmit', 'var') || isempty(webSubmit)`
`+    webSubmit = 0; % submit directly by default `
`+  end`
`+`
`+  % Check valid partId`
`+  partNames = validParts();`
`+  if ~isValidPartId(partId)`
`+    fprintf('!! Invalid homework part selected.\n');`
`+    fprintf('!! Expected an integer from 1 to %d.\n', numel(partNames) + 1);`
`+    fprintf('!! Submission Cancelled\n');`
`+    return`
`+  end`
`+`
`+  if ~exist('ml_login_data.mat','file')`
`+    [login password] = loginPrompt();`
`+    save('ml_login_data.mat','login','password');`
`+  else  `
`+    load('ml_login_data.mat');`
`+    [login password] = quickLogin(login, password);`
`+    save('ml_login_data.mat','login','password');`
`+  end`
`+`
`+  if isempty(login)`
`+    fprintf('!! Submission Cancelled\n');`
`+    return`
`+  end`
`+`
`+  fprintf('\n== Connecting to ml-class ... '); `
`+  if exist('OCTAVE_VERSION') `
`+    fflush(stdout);`
`+  end`
`+`
`+  % Setup submit list`
`+  if partId == numel(partNames) + 1`
`+    submitParts = 1:numel(partNames);`
`+  else`
`+    submitParts = [partId];`
`+  end`
`+`
`+  for s = 1:numel(submitParts)`
`+    thisPartId = submitParts(s);`
`+    if (~webSubmit) % submit directly to server`
`+      [login, ch, signature, auxstring] = getChallenge(login, thisPartId);`
`+      if isempty(login) || isempty(ch) || isempty(signature)`
`+        % Some error occured, error string in first return element.`
`+        fprintf('\n!! Error: %s\n\n', login);`
`+        return`
`+      end`
`+`
`+      % Attempt Submission with Challenge`
`+      ch_resp = challengeResponse(login, password, ch);`
`+`
`+      [result, str] = submitSolution(login, ch_resp, thisPartId, ...`
`+             output(thisPartId, auxstring), source(thisPartId), signature);`
`+`
`+      partName = partNames{thisPartId};`
`+`
`+      fprintf('\n== [ml-class] Submitted Assignment %s - Part %d - %s\n', ...`
`+        homework_id(), thisPartId, partName);`
`+      fprintf('== %s\n', strtrim(str));`
`+`
`+      if exist('OCTAVE_VERSION')`
`+        fflush(stdout);`
`+      end`
`+    else`
`+      [result] = submitSolutionWeb(login, thisPartId, output(thisPartId), ...`
`+                            source(thisPartId));`
`+      result = base64encode(result);`
`+`
`+      fprintf('\nSave as submission file [submit_ex%s_part%d.txt (enter to accept default)]:', ...`
`+        homework_id(), thisPartId);`
`+      saveAsFile = input('', 's');`
`+      if (isempty(saveAsFile))`
`+        saveAsFile = sprintf('submit_ex%s_part%d.txt', homework_id(), thisPartId);`
`+      end`
`+`
`+      fid = fopen(saveAsFile, 'w');`
`+      if (fid)`
`+        fwrite(fid, result);`
`+        fclose(fid);`
`+        fprintf('\nSaved your solutions to %s.\n\n', saveAsFile);`
`+        fprintf(['You can now submit your solutions through the web \n' ...`
`+                 'form in the programming exercises. Select the corresponding \n' ...`
`+                 'programming exercise to access the form.\n']);`
`+`
`+      else`
`+        fprintf('Unable to save to %s\n\n', saveAsFile);`
`+        fprintf(['You can create a submission file by saving the \n' ...`
`+                 'following text in a file: (press enter to continue)\n\n']);`
`+        pause;`
`+        fprintf(result);`
`+      end`
`+    end`
`+  end`
`+end`
`+`
`+% ================== CONFIGURABLES FOR EACH HOMEWORK ==================`
`+`
`+function id = homework_id() `
`+  id = '2';`
`+end`
`+`
`+function [partNames] = validParts()`
`+  partNames = { 'Sigmoid Function ', ...`
`+                'Logistic Regression Cost', ...`
`+                'Logistic Regression Gradient', ...`
`+                'Predict', ...`
`+                'Regularized Logistic Regression Cost' ...`
`+                'Regularized Logistic Regression Gradient' ...`
`+                };`
`+end`
`+`
`+function srcs = sources()`
`+  % Separated by part`
`+  srcs = { { 'sigmoid.m' }, ...`
`+           { 'costFunction.m' }, ...`
`+           { 'costFunction.m' }, ...`
`+           { 'predict.m' }, ...`
`+           { 'costFunctionReg.m' }, ...`
`+           { 'costFunctionReg.m' } };`
`+end`
`+`
`+function out = output(partId, auxstring)`
`+  % Random Test Cases`
`+  X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];`
`+  y = sin(X(:,1) + X(:,2)) > 0;`
`+  if partId == 1`
`+    out = sprintf('%0.5f ', sigmoid(X));`
`+  elseif partId == 2`
`+    out = sprintf('%0.5f ', costFunction([0.25 0.5 -0.5]', X, y));`
`+  elseif partId == 3`
`+    [cost, grad] = costFunction([0.25 0.5 -0.5]', X, y);`
`+    out = sprintf('%0.5f ', grad);`
`+  elseif partId == 4`
`+    out = sprintf('%0.5f ', predict([0.25 0.5 -0.5]', X));`
`+  elseif partId == 5`
`+    out = sprintf('%0.5f ', costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1));`
`+  elseif partId == 6`
`+    [cost, grad] = costFunctionReg([0.25 0.5 -0.5]', X, y, 0.1);`
`+    out = sprintf('%0.5f ', grad);`
`+  end `
`+end`
`+`
`+`
`+% ====================== SERVER CONFIGURATION ===========================`
`+`
`+% ***************** REMOVE -staging WHEN YOU DEPLOY *********************`
`+function url = site_url()`
`+  url = 'http://www.coursera.org/ml';`
`+end`
`+`
`+function url = challenge_url()`
`+  url = [site_url() '/assignment/challenge'];`
`+end`
`+`
`+function url = submit_url()`
`+  url = [site_url() '/assignment/submit'];`
`+end`
`+`
`+% ========================= CHALLENGE HELPERS =========================`
`+`
`+function src = source(partId)`
`+  src = '';`
`+  src_files = sources();`
`+  if partId <= numel(src_files)`
`+      flist = src_files{partId};`
`+      for i = 1:numel(flist)`
`+          fid = fopen(flist{i});`
`+          if (fid == -1) `
`+            error('Error opening %s (is it missing?)', flist{i});`
`+          end`
`+          line = fgets(fid);`
`+          while ischar(line)`
`+            src = [src line];            `
`+            line = fgets(fid);`
`+          end`
`+          fclose(fid);`
`+          src = [src '||||||||'];`
`+      end`
`+  end`
`+end`
`+`
`+function ret = isValidPartId(partId)`
`+  partNames = validParts();`
`+  ret = (~isempty(partId)) && (partId >= 1) && (partId <= numel(partNames) + 1);`
`+end`
`+`
`+function partId = promptPart()`
`+  fprintf('== Select which part(s) to submit:\n');`
`+  partNames = validParts();`
`+  srcFiles = sources();`
`+  for i = 1:numel(partNames)`
`+    fprintf('==   %d) %s [', i, partNames{i});`
`+    fprintf(' %s ', srcFiles{i}{:});`
`+    fprintf(']\n');`
`+  end`
`+  fprintf('==   %d) All of the above \n==\nEnter your choice [1-%d]: ', ...`
`+          numel(partNames) + 1, numel(partNames) + 1);`
`+  selPart = input('', 's');`
`+  partId = str2num(selPart);`
`+  if ~isValidPartId(partId)`
`+    partId = -1;`
`+  end`
`+end`
`+`
`+function [email,ch,signature,auxstring] = getChallenge(email, part)`
`+  str = urlread(challenge_url(), 'post', {'email_address', email, 'assignment_part_sid', [homework_id() '-' num2str(part)], 'response_encoding', 'delim'});`
`+`
`+  str = strtrim(str);`
`+  r = struct;`
`+  while(numel(str) > 0)`
`+    [f, str] = strtok (str, '|');`
`+    [v, str] = strtok (str, '|');`
`+    r = setfield(r, f, v);`
`+  end`
`+`
`+  email = getfield(r, 'email_address');`
`+  ch = getfield(r, 'challenge_key');`
`+  signature = getfield(r, 'state');`
`+  auxstring = getfield(r, 'challenge_aux_data');`
`+end`
`+`
`+function [result, str] = submitSolutionWeb(email, part, output, source)`
`+`
`+  result = ['{"assignment_part_sid":"' base64encode([homework_id() '-' num2str(part)], '') '",' ...`
`+            '"email_address":"' base64encode(email, '') '",' ...`
`+            '"submission":"' base64encode(output, '') '",' ...`
`+            '"submission_aux":"' base64encode(source, '') '"' ...`
`+            '}'];`
`+  str = 'Web-submission';`
`+end`
`+`
`+function [result, str] = submitSolution(email, ch_resp, part, output, ...`
`+                                        source, signature)`
`+`
`+  params = {'assignment_part_sid', [homework_id() '-' num2str(part)], ...`
`+            'email_address', email, ...`
`+            'submission', base64encode(output, ''), ...`
`+            'submission_aux', base64encode(source, ''), ...`
`+            'challenge_response', ch_resp, ...`
`+            'state', signature};`
`+`
`+  str = urlread(submit_url(), 'post', params);`
`+`
`+  % Parse str to read for success / failure`
`+  result = 0;`
`+`
`+end`
`+`
`+% =========================== LOGIN HELPERS ===========================`
`+`
`+function [login password] = loginPrompt()`
`+  % Prompt for password`
`+  [login password] = basicPrompt();`
`+  `
`+  if isempty(login) || isempty(password)`
`+    login = []; password = [];`
`+  end`
`+end`
`+`
`+`
`+function [login password] = basicPrompt()`
`+  login = input('Login (Email address): ', 's');`
`+  password = input('Password: ', 's');`
`+end`
`+`
`+function [login password] = quickLogin(login,password)`
`+  disp(['You are currently logged in as ' login '.']);`
`+  cont_token = input('Is this you? (y/n - type n to reenter password)','s');`
`+  if(isempty(cont_token) || cont_token(1)=='Y'||cont_token(1)=='y')`
`+    return;`
`+  else`
`+    [login password] = loginPrompt();`
`+  end`
`+end`
`+`
`+function [str] = challengeResponse(email, passwd, challenge)`
`+  str = sha1([challenge passwd]);`
`+end`
`+`
`+% =============================== SHA-1 ================================`
`+`
`+function hash = sha1(str)`
`+  `
`+  % Initialize variables`
`+  h0 = uint32(1732584193);`
`+  h1 = uint32(4023233417);`
`+  h2 = uint32(2562383102);`
`+  h3 = uint32(271733878);`
`+  h4 = uint32(3285377520);`
`+  `
`+  % Convert to word array`
`+  strlen = numel(str);`
`+`
`+  % Break string into chars and append the bit 1 to the message`
`+  mC = [double(str) 128];`
`+  mC = [mC zeros(1, 4-mod(numel(mC), 4), 'uint8')];`
`+  `
`+  numB = strlen * 8;`
`+  if exist('idivide')`
`+    numC = idivide(uint32(numB + 65), 512, 'ceil');`
`+  else`
`+    numC = ceil(double(numB + 65)/512);`
`+  end`
`+  numW = numC * 16;`
`+  mW = zeros(numW, 1, 'uint32');`
`+  `
`+  idx = 1;`
`+  for i = 1:4:strlen + 1`
`+    mW(idx) = bitor(bitor(bitor( ...`
`+                  bitshift(uint32(mC(i)), 24), ...`
`+                  bitshift(uint32(mC(i+1)), 16)), ...`
`+                  bitshift(uint32(mC(i+2)), 8)), ...`
`+                  uint32(mC(i+3)));`
`+    idx = idx + 1;`
`+  end`
`+  `
`+  % Append length of message`
`+  mW(numW - 1) = uint32(bitshift(uint64(numB), -32));`
`+  mW(numW) = uint32(bitshift(bitshift(uint64(numB), 32), -32));`
`+`
`+  % Process the message in successive 512-bit chs`
`+  for cId = 1 : double(numC)`
`+    cSt = (cId - 1) * 16 + 1;`
`+    cEnd = cId * 16;`
`+    ch = mW(cSt : cEnd);`
`+    `
`+    % Extend the sixteen 32-bit words into eighty 32-bit words`
`+    for j = 17 : 80`
`+      ch(j) = ch(j - 3);`
`+      ch(j) = bitxor(ch(j), ch(j - 8));`
`+      ch(j) = bitxor(ch(j), ch(j - 14));`
`+      ch(j) = bitxor(ch(j), ch(j - 16));`
`+      ch(j) = bitrotate(ch(j), 1);`
`+    end`
`+  `
`+    % Initialize hash value for this ch`
`+    a = h0;`
`+    b = h1;`
`+    c = h2;`
`+    d = h3;`
`+    e = h4;`
`+    `
`+    % Main loop`
`+    for i = 1 : 80`
`+      if(i >= 1 && i <= 20)`
`+        f = bitor(bitand(b, c), bitand(bitcmp(b), d));`
`+        k = uint32(1518500249);`
`+      elseif(i >= 21 && i <= 40)`
`+        f = bitxor(bitxor(b, c), d);`
`+        k = uint32(1859775393);`
`+      elseif(i >= 41 && i <= 60)`
`+        f = bitor(bitor(bitand(b, c), bitand(b, d)), bitand(c, d));`
`+        k = uint32(2400959708);`
`+      elseif(i >= 61 && i <= 80)`
`+        f = bitxor(bitxor(b, c), d);`
`+        k = uint32(3395469782);`
`+      end`
`+      `
`+      t = bitrotate(a, 5);`
`+      t = bitadd(t, f);`
`+      t = bitadd(t, e);`
`+      t = bitadd(t, k);`
`+      t = bitadd(t, ch(i));`
`+      e = d;`
`+      d = c;`
`+      c = bitrotate(b, 30);`
`+      b = a;`
`+      a = t;`
`+      `
`+    end`
`+    h0 = bitadd(h0, a);`
`+    h1 = bitadd(h1, b);`
`+    h2 = bitadd(h2, c);`
`+    h3 = bitadd(h3, d);`
`+    h4 = bitadd(h4, e);`
`+`
`+  end`
`+`
`+  hash = reshape(dec2hex(double([h0 h1 h2 h3 h4]), 8)', [1 40]);`
`+  `
`+  hash = lower(hash);`
`+`
`+end`
`+`
`+function ret = bitadd(iA, iB)`
`+  ret = double(iA) + double(iB);`
`+  ret = bitset(ret, 33, 0);`
`+  ret = uint32(ret);`
`+end`
`+`
`+function ret = bitrotate(iA, places)`
`+  t = bitshift(iA, places - 32);`
`+  ret = bitshift(iA, places);`
`+  ret = bitor(ret, t);`
`+end`
`+`
`+% =========================== Base64 Encoder ============================`
`+% Thanks to Peter John Acklam`
`+%`
`+`
`+function y = base64encode(x, eol)`
`+%BASE64ENCODE Perform base64 encoding on a string.`
`+%`
`+%   BASE64ENCODE(STR, EOL) encode the given string STR.  EOL is the line ending`
`+%   sequence to use; it is optional and defaults to '\n' (ASCII decimal 10).`
`+%   The returned encoded string is broken into lines of no more than 76`
`+%   characters each, and each line will end with EOL unless it is empty.  Let`
`+%   EOL be empty if you do not want the encoded string broken into lines.`
`+%`
`+%   STR and EOL don't have to be strings (i.e., char arrays).  The only`
`+%   requirement is that they are vectors containing values in the range 0-255.`
`+%`
`+%   This function may be used to encode strings into the Base64 encoding`
`+%   specified in RFC 2045 - MIME (Multipurpose Internet Mail Extensions).  The`
`+%   Base64 encoding is designed to represent arbitrary sequences of octets in a`
`+%   form that need not be humanly readable.  A 65-character subset`
`+%   ([A-Za-z0-9+/=]) of US-ASCII is used, enabling 6 bits to be represented per`
`+%   printable character.`
`+%`
`+%   Examples`
`+%   --------`
`+%`
`+%   If you want to encode a large file, you should encode it in chunks that are`
`+%   a multiple of 57 bytes.  This ensures that the base64 lines line up and`
`+%   that you do not end up with padding in the middle.  57 bytes of data fills`
`+%   one complete base64 line (76 == 57*4/3):`
`+%`
`+%   If ifid and ofid are two file identifiers opened for reading and writing,`
`+%   respectively, then you can base64 encode the data with`
`+%`
`+%      while ~feof(ifid)`
`+%         fwrite(ofid, base64encode(fread(ifid, 60*57)));`
`+%      end`
`+%`
`+%   or, if you have enough memory,`
`+%`
`+%      fwrite(ofid, base64encode(fread(ifid)));`
`+%`
`+%   See also BASE64DECODE.`
`+`
`+%   Author:      Peter John Acklam`
`+%   Time-stamp:  2004-02-03 21:36:56 +0100`
`+%   E-mail:      pjacklam@online.no`
`+%   URL:         http://home.online.no/~pjacklam`
`+`
`+   if isnumeric(x)`
`+      x = num2str(x);`
`+   end`
`+`
`+   % make sure we have the EOL value`
`+   if nargin < 2`
`+      eol = sprintf('\n');`
`+   else`
`+      if sum(size(eol) > 1) > 1`
`+         error('EOL must be a vector.');`
`+      end`
`+      if any(eol(:) > 255)`
`+         error('EOL can not contain values larger than 255.');`
`+      end`
`+   end`
`+`
`+   if sum(size(x) > 1) > 1`
`+      error('STR must be a vector.');`
`+   end`
`+`
`+   x   = uint8(x);`
`+   eol = uint8(eol);`
`+`
`+   ndbytes = length(x);                 % number of decoded bytes`
`+   nchunks = ceil(ndbytes / 3);         % number of chunks/groups`
`+   nebytes = 4 * nchunks;               % number of encoded bytes`
`+`
`+   % add padding if necessary, to make the length of x a multiple of 3`
`+   if rem(ndbytes, 3)`
`+      x(end+1 : 3*nchunks) = 0;`
`+   end`
`+`
`+   x = reshape(x, [3, nchunks]);        % reshape the data`
`+   y = repmat(uint8(0), 4, nchunks);    % for the encoded data`
`+`
`+   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%`
`+   % Split up every 3 bytes into 4 pieces`
`+   %`
`+   %    aaaaaabb bbbbcccc ccdddddd`
`+   %`
`+   % to form`
`+   %`
`+   %    00aaaaaa 00bbbbbb 00cccccc 00dddddd`
`+   %`
`+   y(1,:) = bitshift(x(1,:), -2);                  % 6 highest bits of x(1,:)`
`+`
`+   y(2,:) = bitshift(bitand(x(1,:), 3), 4);        % 2 lowest bits of x(1,:)`
`+   y(2,:) = bitor(y(2,:), bitshift(x(2,:), -4));   % 4 highest bits of x(2,:)`
`+`
`+   y(3,:) = bitshift(bitand(x(2,:), 15), 2);       % 4 lowest bits of x(2,:)`
`+   y(3,:) = bitor(y(3,:), bitshift(x(3,:), -6));   % 2 highest bits of x(3,:)`
`+`
`+   y(4,:) = bitand(x(3,:), 63);                    % 6 lowest bits of x(3,:)`
`+`
`+   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%`
`+   % Now perform the following mapping`
`+   %`
`+   %   0  - 25  ->  A-Z`
`+   %   26 - 51  ->  a-z`
`+   %   52 - 61  ->  0-9`
`+   %   62       ->  +`
`+   %   63       ->  /`
`+   %`
`+   % We could use a mapping vector like`
`+   %`
`+   %   ['A':'Z', 'a':'z', '0':'9', '+/']`
`+   %`
`+   % but that would require an index vector of class double.`
`+   %`
`+   z = repmat(uint8(0), size(y));`
`+   i =           y <= 25;  z(i) = 'A'      + double(y(i));`
`+   i = 26 <= y & y <= 51;  z(i) = 'a' - 26 + double(y(i));`
`+   i = 52 <= y & y <= 61;  z(i) = '0' - 52 + double(y(i));`
`+   i =           y == 62;  z(i) = '+';`
`+   i =           y == 63;  z(i) = '/';`
`+   y = z;`
`+`
`+   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%`
`+   % Add padding if necessary.`
`+   %`
`+   npbytes = 3 * nchunks - ndbytes;     % number of padding bytes`
`+   if npbytes`
`+      y(end-npbytes+1 : end) = '=';     % '=' is used for padding`
`+   end`
`+`
`+   if isempty(eol)`
`+`
`+      % reshape to a row vector`
`+      y = reshape(y, [1, nebytes]);`
`+`
`+   else`
`+`
`+      nlines = ceil(nebytes / 76);      % number of lines`
`+      neolbytes = length(eol);          % number of bytes in eol string`
`+`
`+      % pad data so it becomes a multiple of 76 elements`
`+      y = [y(:) ; zeros(76 * nlines - numel(y), 1)];`
`+      y(nebytes + 1 : 76 * nlines) = 0;`
`+      y = reshape(y, 76, nlines);`
`+`
`+      % insert eol strings`
`+      eol = eol(:);`
`+      y(end + 1 : end + neolbytes, :) = eol(:, ones(1, nlines));`
`+`
`+      % remove padding, but keep the last eol string`
`+      m = nebytes + neolbytes * (nlines - 1);`
`+      n = (76+neolbytes)*nlines - neolbytes;`
`+      y(m+1 : n) = '';`
`+`
`+      % extract and reshape to row vector`
`+      y = reshape(y, 1, m+neolbytes);`
`+`
`+   end`
`+`
`+   % output is a character array`
`+   y = char(y);`
`+`
`+end`

# File ex2/submitWeb.m

`+% submitWeb Creates files from your code and output for web submission.`
`+%`
`+%   If the submit function does not work for you, use the web-submission mechanism.`
`+%   Call this function to produce a file for the part you wish to submit. Then,`
`+%   submit the file to the class servers using the "Web Submission" button on the `
`+%   Programming Exercises page on the course website.`
`+%`
`+%   You should call this function without arguments (submitWeb), to receive`
`+%   an interactive prompt for submission; optionally you can call it with the partID`
`+%   if you so wish. Make sure your working directory is set to the directory `
`+%   containing the submitWeb.m file and your assignment files.`
`+`
`+function submitWeb(partId)`
`+  if ~exist('partId', 'var') || isempty(partId)`
`+    partId = [];`
`+  end`
`+  `
`+  submit(partId, 1);`
`+end`
`+`