Add exercise 3
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ml_login_data.mat
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function [h, display_array] = displayData(X, example_width)
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%DISPLAYDATA Display 2D data in a nice grid
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% [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
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% stored in X in a nice grid. It returns the figure handle h and the
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% displayed array if requested.
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% Set example_width automatically if not passed in
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if ~exist('example_width', 'var') || isempty(example_width)
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example_width = round(sqrt(size(X, 2)));
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end
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% Gray Image
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colormap(gray);
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% Compute rows, cols
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[m n] = size(X);
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example_height = (n / example_width);
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% Compute number of items to display
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display_rows = floor(sqrt(m));
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display_cols = ceil(m / display_rows);
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% Between images padding
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pad = 1;
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% Setup blank display
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display_array = - ones(pad + display_rows * (example_height + pad), ...
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pad + display_cols * (example_width + pad));
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% Copy each example into a patch on the display array
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curr_ex = 1;
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for j = 1:display_rows
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for i = 1:display_cols
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if curr_ex > m,
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break;
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end
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% Copy the patch
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% Get the max value of the patch
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max_val = max(abs(X(curr_ex, :)));
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display_array(pad + (j - 1) * (example_height + pad) + (1:example_height), ...
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pad + (i - 1) * (example_width + pad) + (1:example_width)) = ...
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reshape(X(curr_ex, :), example_height, example_width) / max_val;
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curr_ex = curr_ex + 1;
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end
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if curr_ex > m,
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break;
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end
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end
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% Display Image
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h = imagesc(display_array, [-1 1]);
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% Do not show axis
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axis image off
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drawnow;
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end
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%% Machine Learning Online Class - Exercise 3 | Part 1: One-vs-all
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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
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% linear exercise. You will need to complete the following functions
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% in this exericse:
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%
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% lrCostFunction.m (logistic regression cost function)
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% oneVsAll.m
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% predictOneVsAll.m
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% predict.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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%% Setup the parameters you will use for this part of the exercise
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input_layer_size = 400; % 20x20 Input Images of Digits
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num_labels = 10; % 10 labels, from 1 to 10
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% (note that we have mapped "0" to label 10)
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%% =========== Part 1: Loading and Visualizing Data =============
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% We start the exercise by first loading and visualizing the dataset.
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% You will be working with a dataset that contains handwritten digits.
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%
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% Load Training Data
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fprintf('Loading and Visualizing Data ...\n')
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load('ex3data1.mat'); % training data stored in arrays X, y
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m = size(X, 1);
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% Randomly select 100 data points to display
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rand_indices = randperm(m);
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sel = X(rand_indices(1:100), :);
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displayData(sel);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ============ Part 2: Vectorize Logistic Regression ============
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% In this part of the exercise, you will reuse your logistic regression
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% code from the last exercise. You task here is to make sure that your
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% regularized logistic regression implementation is vectorized. After
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% that, you will implement one-vs-all classification for the handwritten
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% digit dataset.
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%
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fprintf('\nTraining One-vs-All Logistic Regression...\n')
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lambda = 0.1;
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[all_theta] = oneVsAll(X, y, num_labels, lambda);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 3: Predict for One-Vs-All ================
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% After ...
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pred = predictOneVsAll(all_theta, X);
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fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
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%% Machine Learning Online Class - Exercise 3 | Part 2: Neural Networks
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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
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% linear exercise. You will need to complete the following functions
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% in this exericse:
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%
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% lrCostFunction.m (logistic regression cost function)
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% oneVsAll.m
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% predictOneVsAll.m
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% predict.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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%% Setup the parameters you will use for this exercise
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input_layer_size = 400; % 20x20 Input Images of Digits
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hidden_layer_size = 25; % 25 hidden units
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num_labels = 10; % 10 labels, from 1 to 10
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% (note that we have mapped "0" to label 10)
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%% =========== Part 1: Loading and Visualizing Data =============
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% We start the exercise by first loading and visualizing the dataset.
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% You will be working with a dataset that contains handwritten digits.
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%
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% Load Training Data
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fprintf('Loading and Visualizing Data ...\n')
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load('ex3data1.mat');
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m = size(X, 1);
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% Randomly select 100 data points to display
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sel = randperm(size(X, 1));
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sel = sel(1:100);
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displayData(X(sel, :));
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 2: Loading Pameters ================
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% In this part of the exercise, we load some pre-initialized
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% neural network parameters.
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fprintf('\nLoading Saved Neural Network Parameters ...\n')
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% Load the weights into variables Theta1 and Theta2
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load('ex3weights.mat');
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%% ================= Part 3: Implement Predict =================
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% After training the neural network, we would like to use it to predict
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% the labels. You will now implement the "predict" function to use the
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% neural network to predict the labels of the training set. This lets
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% you compute the training set accuracy.
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pred = predict(Theta1, Theta2, X);
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fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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% To give you an idea of the network's output, you can also run
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% through the examples one at the a time to see what it is predicting.
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% Randomly permute examples
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rp = randperm(m);
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for i = 1:m
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% Display
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fprintf('\nDisplaying Example Image\n');
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displayData(X(rp(i), :));
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pred = predict(Theta1, Theta2, X(rp(i),:));
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fprintf('\nNeural Network Prediction: %d (digit %d)\n', pred, mod(pred, 10));
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% Pause
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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end
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function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
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% Minimize a continuous differentialble multivariate function. Starting point
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% is given by "X" (D by 1), and the function named in the string "f", must
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% return a function value and a vector of partial derivatives. The Polack-
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% Ribiere flavour of conjugate gradients is used to compute search directions,
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% and a line search using quadratic and cubic polynomial approximations and the
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% Wolfe-Powell stopping criteria is used together with the slope ratio method
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% for guessing initial step sizes. Additionally a bunch of checks are made to
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% make sure that exploration is taking place and that extrapolation will not
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% be unboundedly large. The "length" gives the length of the run: if it is
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% positive, it gives the maximum number of line searches, if negative its
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% absolute gives the maximum allowed number of function evaluations. You can
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% (optionally) give "length" a second component, which will indicate the
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% reduction in function value to be expected in the first line-search (defaults
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% to 1.0). The function returns when either its length is up, or if no further
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% progress can be made (ie, we are at a minimum, or so close that due to
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% numerical problems, we cannot get any closer). If the function terminates
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% within a few iterations, it could be an indication that the function value
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% and derivatives are not consistent (ie, there may be a bug in the
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% implementation of your "f" function). The function returns the found
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% solution "X", a vector of function values "fX" indicating the progress made
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% and "i" the number of iterations (line searches or function evaluations,
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% depending on the sign of "length") used.
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%
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% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
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%
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% See also: checkgrad
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%
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% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13
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%
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%
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% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen
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%
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% Permission is granted for anyone to copy, use, or modify these
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% programs and accompanying documents for purposes of research or
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% education, provided this copyright notice is retained, and note is
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% made of any changes that have been made.
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%
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% These programs and documents are distributed without any warranty,
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% express or implied. As the programs were written for research
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% purposes only, they have not been tested to the degree that would be
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% advisable in any important application. All use of these programs is
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% entirely at the user's own risk.
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%
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% [ml-class] Changes Made:
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% 1) Function name and argument specifications
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% 2) Output display
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%
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% Read options
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if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter')
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length = options.MaxIter;
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else
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length = 100;
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end
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RHO = 0.01; % a bunch of constants for line searches
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SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions
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INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket
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EXT = 3.0; % extrapolate maximum 3 times the current bracket
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MAX = 20; % max 20 function evaluations per line search
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RATIO = 100; % maximum allowed slope ratio
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argstr = ['feval(f, X']; % compose string used to call function
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for i = 1:(nargin - 3)
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argstr = [argstr, ',P', int2str(i)];
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end
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argstr = [argstr, ')'];
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if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end
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S=['Iteration '];
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i = 0; % zero the run length counter
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ls_failed = 0; % no previous line search has failed
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fX = [];
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[f1 df1] = eval(argstr); % get function value and gradient
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i = i + (length<0); % count epochs?!
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s = -df1; % search direction is steepest
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d1 = -s'*s; % this is the slope
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z1 = red/(1-d1); % initial step is red/(|s|+1)
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while i < abs(length) % while not finished
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i = i + (length>0); % count iterations?!
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X0 = X; f0 = f1; df0 = df1; % make a copy of current values
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X = X + z1*s; % begin line search
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[f2 df2] = eval(argstr);
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i = i + (length<0); % count epochs?!
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d2 = df2'*s;
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f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1
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if length>0, M = MAX; else M = min(MAX, -length-i); end
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success = 0; limit = -1; % initialize quanteties
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while 1
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while ((f2 > f1+z1*RHO*d1) | (d2 > -SIG*d1)) & (M > 0)
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limit = z1; % tighten the bracket
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if f2 > f1
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z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit
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else
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A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit
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B = 3*(f3-f2)-z3*(d3+2*d2);
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z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok!
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end
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if isnan(z2) | isinf(z2)
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z2 = z3/2; % if we had a numerical problem then bisect
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end
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z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits
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z1 = z1 + z2; % update the step
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X = X + z2*s;
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[f2 df2] = eval(argstr);
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M = M - 1; i = i + (length<0); % count epochs?!
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d2 = df2'*s;
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z3 = z3-z2; % z3 is now relative to the location of z2
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end
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if f2 > f1+z1*RHO*d1 | d2 > -SIG*d1
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break; % this is a failure
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elseif d2 > SIG*d1
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success = 1; break; % success
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elseif M == 0
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break; % failure
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end
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A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation
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B = 3*(f3-f2)-z3*(d3+2*d2);
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z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok!
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if ~isreal(z2) | isnan(z2) | isinf(z2) | z2 < 0 % num prob or wrong sign?
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if limit < -0.5 % if we have no upper limit
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z2 = z1 * (EXT-1); % the extrapolate the maximum amount
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else
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z2 = (limit-z1)/2; % otherwise bisect
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end
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elseif (limit > -0.5) & (z2+z1 > limit) % extraplation beyond max?
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z2 = (limit-z1)/2; % bisect
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elseif (limit < -0.5) & (z2+z1 > z1*EXT) % extrapolation beyond limit
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z2 = z1*(EXT-1.0); % set to extrapolation limit
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elseif z2 < -z3*INT
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z2 = -z3*INT;
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elseif (limit > -0.5) & (z2 < (limit-z1)*(1.0-INT)) % too close to limit?
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z2 = (limit-z1)*(1.0-INT);
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end
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f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2
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z1 = z1 + z2; X = X + z2*s; % update current estimates
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[f2 df2] = eval(argstr);
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M = M - 1; i = i + (length<0); % count epochs?!
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d2 = df2'*s;
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end % end of line search
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if success % if line search succeeded
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f1 = f2; fX = [fX' f1]';
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fprintf('%s %4i | Cost: %4.6e\r', S, i, f1);
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s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction
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tmp = df1; df1 = df2; df2 = tmp; % swap derivatives
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d2 = df1'*s;
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if d2 > 0 % new slope must be negative
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s = -df1; % otherwise use steepest direction
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d2 = -s'*s;
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end
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z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO
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d1 = d2;
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ls_failed = 0; % this line search did not fail
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else
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X = X0; f1 = f0; df1 = df0; % restore point from before failed line search
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if ls_failed | i > abs(length) % line search failed twice in a row
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break; % or we ran out of time, so we give up
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end
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tmp = df1; df1 = df2; df2 = tmp; % swap derivatives
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s = -df1; % try steepest
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d1 = -s'*s;
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z1 = 1/(1-d1);
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ls_failed = 1; % this line search failed
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end
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if exist('OCTAVE_VERSION')
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fflush(stdout);
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end
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end
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fprintf('\n');
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function [J, grad] = lrCostFunction(theta, X, y, lambda)
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%LRCOSTFUNCTION Compute cost and gradient for logistic regression with
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%regularization
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% J = LRCOSTFUNCTION(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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% Hint: The computation of the cost function and gradients can be
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% efficiently vectorized. For example, consider the computation
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%
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% sigmoid(X * theta)
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%
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% Each row of the resulting matrix will contain the value of the
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% prediction for that example. You can make use of this to vectorize
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% the cost function and gradient computations.
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%
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% Hint: When computing the gradient of the regularized cost function,
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% there're many possible vectorized solutions, but one solution
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% looks like:
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% grad = (unregularized gradient for logistic regression)
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% temp = theta;
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% temp(1) = 0; % because we don't add anything for j = 0
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% grad = grad + YOUR_CODE_HERE (using the temp variable)
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%
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% =============================================================
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grad = grad(:);
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end
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|
||||
function [all_theta] = oneVsAll(X, y, num_labels, lambda)
|
||||
%ONEVSALL trains multiple logistic regression classifiers and returns all
|
||||
%the classifiers in a matrix all_theta, where the i-th row of all_theta
|
||||
%corresponds to the classifier for label i
|
||||
% [all_theta] = ONEVSALL(X, y, num_labels, lambda) trains num_labels
|
||||
% logisitc regression classifiers and returns each of these classifiers
|
||||
% in a matrix all_theta, where the i-th row of all_theta corresponds
|
||||
% to the classifier for label i
|
||||
|
||||
% Some useful variables
|
||||
m = size(X, 1);
|
||||
n = size(X, 2);
|
||||
|
||||
% You need to return the following variables correctly
|
||||
all_theta = zeros(num_labels, n + 1);
|
||||
|
||||
% Add ones to the X data matrix
|
||||
X = [ones(m, 1) X];
|
||||
|
||||
% ====================== YOUR CODE HERE ======================
|
||||
% Instructions: You should complete the following code to train num_labels
|
||||
% logistic regression classifiers with regularization
|
||||
% parameter lambda.
|
||||
%
|
||||
% Hint: theta(:) will return a column vector.
|
||||
%
|
||||
% Hint: You can use y == c to obtain a vector of 1's and 0's that tell use
|
||||
% whether the ground truth is true/false for this class.
|
||||
%
|
||||
% Note: For this assignment, we recommend using fmincg to optimize the cost
|
||||
% function. It is okay to use a for-loop (for c = 1:num_labels) to
|
||||
% loop over the different classes.
|
||||
%
|
||||
% fmincg works similarly to fminunc, but is more efficient when we
|
||||
% are dealing with large number of parameters.
|
||||
%
|
||||
% Example Code for fmincg:
|
||||
%
|
||||
% % Set Initial theta
|
||||
% initial_theta = zeros(n + 1, 1);
|
||||
%
|
||||
% % Set options for fminunc
|
||||
% options = optimset('GradObj', 'on', 'MaxIter', 50);
|
||||
%
|
||||
% % Run fmincg to obtain the optimal theta
|
||||
% % This function will return theta and the cost
|
||||
% [theta] = ...
|
||||
% fmincg (@(t)(lrCostFunction(t, X, (y == c), lambda)), ...
|
||||
% initial_theta, options);
|
||||
%
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
% =========================================================================
|
||||
|
||||
|
||||
end
|
||||
@ -0,0 +1,35 @@
|
||||
function p = predict(Theta1, Theta2, X)
|
||||
%PREDICT Predict the label of an input given a trained neural network
|
||||
% p = PREDICT(Theta1, Theta2, X) outputs the predicted label of X given the
|
||||
% trained weights of a neural network (Theta1, Theta2)
|
||||
|
||||
% Useful values
|
||||
m = size(X, 1);
|
||||
num_labels = size(Theta2, 1);
|
||||
|
||||
% You need to return the following variables correctly
|
||||
p = zeros(size(X, 1), 1);
|
||||
|
||||
% ====================== YOUR CODE HERE ======================
|
||||
% Instructions: Complete the following code to make predictions using
|
||||
% your learned neural network. You should set p to a
|
||||
% vector containing labels between 1 to num_labels.
|
||||
%
|
||||
% Hint: The max function might come in useful. In particular, the max
|
||||
% function can also return the index of the max element, for more
|
||||
% information see 'help max'. If your examples are in rows, then, you
|
||||
% can use max(A, [], 2) to obtain the max for each row.
|
||||
%
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
% =========================================================================
|
||||
|
||||
|
||||
end
|
||||
@ -0,0 +1,42 @@
|
||||
function p = predictOneVsAll(all_theta, X)
|
||||
%PREDICT Predict the label for a trained one-vs-all classifier. The labels
|
||||
%are in the range 1..K, where K = size(all_theta, 1).
|
||||
% p = PREDICTONEVSALL(all_theta, X) will return a vector of predictions
|
||||
% for each example in the matrix X. Note that X contains the examples in
|
||||
% rows. all_theta is a matrix where the i-th row is a trained logistic
|
||||
% regression theta vector for the i-th class. You should set p to a vector
|
||||
% of values from 1..K (e.g., p = [1; 3; 1; 2] predicts classes 1, 3, 1, 2
|
||||
% for 4 examples)
|
||||
|
||||
m = size(X, 1);
|
||||
num_labels = size(all_theta, 1);
|
||||
|
||||
% You need to return the following variables correctly
|
||||
p = zeros(size(X, 1), 1);
|
||||
|
||||
% Add ones to the X data matrix
|
||||
X = [ones(m, 1) X];
|
||||
|
||||
% ====================== YOUR CODE HERE ======================
|
||||
% Instructions: Complete the following code to make predictions using
|
||||
% your learned logistic regression parameters (one-vs-all).
|
||||
% You should set p to a vector of predictions (from 1 to
|
||||
% num_labels).
|
||||
%
|
||||
% Hint: This code can be done all vectorized using the max function.
|
||||
% In particular, the max function can also return the index of the
|
||||
% max element, for more information see 'help max'. If your examples
|
||||
% are in rows, then, you can use max(A, [], 2) to obtain the max
|
||||
% for each row.
|
||||
%
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
% =========================================================================
|
||||
|
||||
|
||||
end
|
||||
@ -0,0 +1,6 @@
|
||||
function g = sigmoid(z)
|
||||
%SIGMOID Compute sigmoid functoon
|
||||
% J = SIGMOID(z) computes the sigmoid of z.
|
||||
|
||||
g = 1.0 ./ (1.0 + exp(-z));
|
||||
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 = '3';
|
||||
end
|
||||
|
||||
function [partNames] = validParts()
|
||||
partNames = { 'Vectorized Logistic Regression ', ...
|
||||
'One-vs-all classifier training', ...
|
||||
'One-vs-all classifier prediction', ...
|
||||
'Neural network prediction function' ...
|
||||
};
|
||||
end
|
||||
|
||||
function srcs = sources()
|
||||
% Separated by part
|
||||
srcs = { { 'lrCostFunction.m' }, ...
|
||||
{ 'oneVsAll.m' }, ...
|
||||
{ 'predictOneVsAll.m' }, ...
|
||||
{ 'predict.m' } };
|
||||
end
|
||||
|
||||
function out = output(partId, auxdata)
|
||||
% Random Test Cases
|
||||
X = [ones(20,1) (exp(1) * sin(1:1:20))' (exp(0.5) * cos(1:1:20))'];
|
||||
y = sin(X(:,1) + X(:,2)) > 0;
|
||||
Xm = [ -1 -1 ; -1 -2 ; -2 -1 ; -2 -2 ; ...
|
||||
1 1 ; 1 2 ; 2 1 ; 2 2 ; ...
|
||||
-1 1 ; -1 2 ; -2 1 ; -2 2 ; ...
|
||||
1 -1 ; 1 -2 ; -2 -1 ; -2 -2 ];
|
||||
ym = [ 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 ]';
|
||||
t1 = sin(reshape(1:2:24, 4, 3));
|
||||
t2 = cos(reshape(1:2:40, 4, 5));
|
||||
|
||||
if partId == 1
|
||||
[J, grad] = lrCostFunction([0.25 0.5 -0.5]', X, y, 0.1);
|
||||
out = sprintf('%0.5f ', J);
|
||||
out = [out sprintf('%0.5f ', grad)];
|
||||
elseif partId == 2
|
||||
out = sprintf('%0.5f ', oneVsAll(Xm, ym, 4, 0.1));
|
||||
elseif partId == 3
|
||||
out = sprintf('%0.5f ', predictOneVsAll(t1, Xm));
|
||||
elseif partId == 4
|
||||
out = sprintf('%0.5f ', predict(t1, t2, Xm));
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
% ====================== 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