Initial commit of ex2
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function [J, grad] = costFunction(theta, X, y)
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%COSTFUNCTION Compute cost and gradient for logistic regression
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% J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
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% parameter for logistic regression and the gradient of the cost
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% w.r.t. to the parameters.
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% Initialize some useful values
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m = length(y); % number of training examples
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% You need to return the following variables correctly
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J = 0;
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grad = zeros(size(theta));
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% ====================== YOUR CODE HERE ======================
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% Instructions: Compute the cost of a particular choice of theta.
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% You should set J to the cost.
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% Compute the partial derivatives and set grad to the partial
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% derivatives of the cost w.r.t. each parameter in theta
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%
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% Note: grad should have the same dimensions as theta
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%
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% =============================================================
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end
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function [J, grad] = costFunctionReg(theta, X, y, lambda)
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%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
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% J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
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% theta as the parameter for regularized logistic regression and the
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% gradient of the cost w.r.t. to the parameters.
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% Initialize some useful values
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m = length(y); % number of training examples
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% You need to return the following variables correctly
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J = 0;
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grad = zeros(size(theta));
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% ====================== YOUR CODE HERE ======================
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% Instructions: Compute the cost of a particular choice of theta.
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% You should set J to the cost.
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% Compute the partial derivatives and set grad to the partial
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% derivatives of the cost w.r.t. each parameter in theta
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% =============================================================
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end
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@ -0,0 +1,135 @@
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%% Machine Learning Online Class - Exercise 2: Logistic Regression
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%
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the logistic
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% regression exercise. You will need to complete the following functions
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% in this exericse:
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%
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% sigmoid.m
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% costFunction.m
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% predict.m
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% costFunctionReg.m
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%
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% For this exercise, you will not need to change any code in this file,
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% or any other files other than those mentioned above.
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%
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%% Initialization
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clear ; close all; clc
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%% Load Data
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% The first two columns contains the exam scores and the third column
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% contains the label.
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data = load('ex2data1.txt');
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X = data(:, [1, 2]); y = data(:, 3);
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%% ==================== Part 1: Plotting ====================
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% We start the exercise by first plotting the data to understand the
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% the problem we are working with.
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fprintf(['Plotting data with + indicating (y = 1) examples and o ' ...
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'indicating (y = 0) examples.\n']);
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plotData(X, y);
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% Put some labels
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hold on;
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% Labels and Legend
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xlabel('Exam 1 score')
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ylabel('Exam 2 score')
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% Specified in plot order
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legend('Admitted', 'Not admitted')
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hold off;
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============ Part 2: Compute Cost and Gradient ============
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% In this part of the exercise, you will implement the cost and gradient
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% for logistic regression. You neeed to complete the code in
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% costFunction.m
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% Setup the data matrix appropriately, and add ones for the intercept term
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[m, n] = size(X);
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% Add intercept term to x and X_test
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X = [ones(m, 1) X];
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% Initialize fitting parameters
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initial_theta = zeros(n + 1, 1);
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% Compute and display initial cost and gradient
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[cost, grad] = costFunction(initial_theta, X, y);
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fprintf('Cost at initial theta (zeros): %f\n', cost);
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fprintf('Gradient at initial theta (zeros): \n');
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fprintf(' %f \n', grad);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============= Part 3: Optimizing using fminunc =============
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% In this exercise, you will use a built-in function (fminunc) to find the
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% optimal parameters theta.
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% Set options for fminunc
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options = optimset('GradObj', 'on', 'MaxIter', 400);
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% Run fminunc to obtain the optimal theta
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% This function will return theta and the cost
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[theta, cost] = ...
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fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
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% Print theta to screen
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fprintf('Cost at theta found by fminunc: %f\n', cost);
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fprintf('theta: \n');
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fprintf(' %f \n', theta);
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% Plot Boundary
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plotDecisionBoundary(theta, X, y);
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% Put some labels
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hold on;
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% Labels and Legend
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xlabel('Exam 1 score')
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ylabel('Exam 2 score')
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% Specified in plot order
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legend('Admitted', 'Not admitted')
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hold off;
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============== Part 4: Predict and Accuracies ==============
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% After learning the parameters, you'll like to use it to predict the outcomes
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% on unseen data. In this part, you will use the logistic regression model
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% to predict the probability that a student with score 45 on exam 1 and
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% score 85 on exam 2 will be admitted.
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%
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% Furthermore, you will compute the training and test set accuracies of
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% our model.
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%
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% Your task is to complete the code in predict.m
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% Predict probability for a student with score 45 on exam 1
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% and score 85 on exam 2
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prob = sigmoid([1 45 85] * theta);
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fprintf(['For a student with scores 45 and 85, we predict an admission ' ...
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'probability of %f\n\n'], prob);
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% Compute accuracy on our training set
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p = predict(theta, X);
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fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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Binary file not shown.
@ -0,0 +1,116 @@
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%% Machine Learning Online Class - Exercise 2: Logistic Regression
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%
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% Instructions
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% ------------
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%
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% This file contains code that helps you get started on the second part
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% of the exercise which covers regularization with logistic regression.
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%
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% You will need to complete the following functions in this exericse:
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%
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% sigmoid.m
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% costFunction.m
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% predict.m
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% costFunctionReg.m
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%
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% For this exercise, you will not need to change any code in this file,
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% or any other files other than those mentioned above.
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%
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%% Initialization
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clear ; close all; clc
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%% Load Data
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% The first two columns contains the X values and the third column
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% contains the label (y).
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data = load('ex2data2.txt');
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X = data(:, [1, 2]); y = data(:, 3);
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plotData(X, y);
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% Put some labels
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hold on;
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% Labels and Legend
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xlabel('Microchip Test 1')
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ylabel('Microchip Test 2')
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% Specified in plot order
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legend('y = 1', 'y = 0')
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hold off;
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%% =========== Part 1: Regularized Logistic Regression ============
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% In this part, you are given a dataset with data points that are not
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% linearly separable. However, you would still like to use logistic
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% regression to classify the data points.
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%
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% To do so, you introduce more features to use -- in particular, you add
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% polynomial features to our data matrix (similar to polynomial
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% regression).
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%
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% Add Polynomial Features
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% Note that mapFeature also adds a column of ones for us, so the intercept
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% term is handled
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X = mapFeature(X(:,1), X(:,2));
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% Initialize fitting parameters
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initial_theta = zeros(size(X, 2), 1);
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% Set regularization parameter lambda to 1
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lambda = 1;
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% Compute and display initial cost and gradient for regularized logistic
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% regression
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[cost, grad] = costFunctionReg(initial_theta, X, y, lambda);
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fprintf('Cost at initial theta (zeros): %f\n', cost);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% ============= Part 2: Regularization and Accuracies =============
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% Optional Exercise:
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% In this part, you will get to try different values of lambda and
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% see how regularization affects the decision coundart
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%
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% Try the following values of lambda (0, 1, 10, 100).
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%
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% How does the decision boundary change when you vary lambda? How does
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% the training set accuracy vary?
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%
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% Initialize fitting parameters
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initial_theta = zeros(size(X, 2), 1);
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% Set regularization parameter lambda to 1 (you should vary this)
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lambda = 1;
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% Set Options
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options = optimset('GradObj', 'on', 'MaxIter', 400);
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% Optimize
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[theta, J, exit_flag] = ...
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fminunc(@(t)(costFunctionReg(t, X, y, lambda)), initial_theta, options);
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% Plot Boundary
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plotDecisionBoundary(theta, X, y);
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hold on;
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title(sprintf('lambda = %g', lambda))
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% Labels and Legend
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xlabel('Microchip Test 1')
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ylabel('Microchip Test 2')
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legend('y = 1', 'y = 0', 'Decision boundary')
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hold off;
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% Compute accuracy on our training set
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p = predict(theta, X);
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fprintf('Train Accuracy: %f\n', mean(double(p == y)) * 100);
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@ -0,0 +1,100 @@
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@ -0,0 +1,118 @@
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||||
-0.13882,0.54605,1
|
||||
-0.29435,0.77997,1
|
||||
-0.26555,0.96272,1
|
||||
-0.16187,0.8019,1
|
||||
-0.17339,0.64839,1
|
||||
-0.28283,0.47295,1
|
||||
-0.36348,0.31213,1
|
||||
-0.30012,0.027047,1
|
||||
-0.23675,-0.21418,1
|
||||
-0.06394,-0.18494,1
|
||||
0.062788,-0.16301,1
|
||||
0.22984,-0.41155,1
|
||||
0.2932,-0.2288,1
|
||||
0.48329,-0.18494,1
|
||||
0.64459,-0.14108,1
|
||||
0.46025,0.012427,1
|
||||
0.6273,0.15863,1
|
||||
0.57546,0.26827,1
|
||||
0.72523,0.44371,1
|
||||
0.22408,0.52412,1
|
||||
0.44297,0.67032,1
|
||||
0.322,0.69225,1
|
||||
0.13767,0.57529,1
|
||||
-0.0063364,0.39985,1
|
||||
-0.092742,0.55336,1
|
||||
-0.20795,0.35599,1
|
||||
-0.20795,0.17325,1
|
||||
-0.43836,0.21711,1
|
||||
-0.21947,-0.016813,1
|
||||
-0.13882,-0.27266,1
|
||||
0.18376,0.93348,0
|
||||
0.22408,0.77997,0
|
||||
0.29896,0.61915,0
|
||||
0.50634,0.75804,0
|
||||
0.61578,0.7288,0
|
||||
0.60426,0.59722,0
|
||||
0.76555,0.50219,0
|
||||
0.92684,0.3633,0
|
||||
0.82316,0.27558,0
|
||||
0.96141,0.085526,0
|
||||
0.93836,0.012427,0
|
||||
0.86348,-0.082602,0
|
||||
0.89804,-0.20687,0
|
||||
0.85196,-0.36769,0
|
||||
0.82892,-0.5212,0
|
||||
0.79435,-0.55775,0
|
||||
0.59274,-0.7405,0
|
||||
0.51786,-0.5943,0
|
||||
0.46601,-0.41886,0
|
||||
0.35081,-0.57968,0
|
||||
0.28744,-0.76974,0
|
||||
0.085829,-0.75512,0
|
||||
0.14919,-0.57968,0
|
||||
-0.13306,-0.4481,0
|
||||
-0.40956,-0.41155,0
|
||||
-0.39228,-0.25804,0
|
||||
-0.74366,-0.25804,0
|
||||
-0.69758,0.041667,0
|
||||
-0.75518,0.2902,0
|
||||
-0.69758,0.68494,0
|
||||
-0.4038,0.70687,0
|
||||
-0.38076,0.91886,0
|
||||
-0.50749,0.90424,0
|
||||
-0.54781,0.70687,0
|
||||
0.10311,0.77997,0
|
||||
0.057028,0.91886,0
|
||||
-0.10426,0.99196,0
|
||||
-0.081221,1.1089,0
|
||||
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
|
||||
@ -0,0 +1,21 @@
|
||||
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
|
||||
@ -0,0 +1,29 @@
|
||||
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
|
||||
@ -0,0 +1,48 @@
|
||||
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
|
||||
@ -0,0 +1,27 @@
|
||||
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
|
||||
@ -0,0 +1,18 @@
|
||||
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
|
||||
@ -0,0 +1,574 @@
|
||||
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://class.coursera.org/ml-007';
|
||||
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
|
||||
@ -0,0 +1,20 @@
|
||||
% 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
|
||||
|
||||
Reference in New Issue