Add programming exercise 4
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ml_login_data.mat
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function checkNNGradients(lambda)
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%CHECKNNGRADIENTS Creates a small neural network to check the
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%backpropagation gradients
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% CHECKNNGRADIENTS(lambda) Creates a small neural network to check the
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% backpropagation gradients, it will output the analytical gradients
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% produced by your backprop code and the numerical gradients (computed
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% using computeNumericalGradient). These two gradient computations should
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% result in very similar values.
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%
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if ~exist('lambda', 'var') || isempty(lambda)
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lambda = 0;
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end
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input_layer_size = 3;
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hidden_layer_size = 5;
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num_labels = 3;
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m = 5;
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% We generate some 'random' test data
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Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size);
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Theta2 = debugInitializeWeights(num_labels, hidden_layer_size);
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% Reusing debugInitializeWeights to generate X
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X = debugInitializeWeights(m, input_layer_size - 1);
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y = 1 + mod(1:m, num_labels)';
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% Unroll parameters
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nn_params = [Theta1(:) ; Theta2(:)];
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% Short hand for cost function
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costFunc = @(p) nnCostFunction(p, input_layer_size, hidden_layer_size, ...
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num_labels, X, y, lambda);
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[cost, grad] = costFunc(nn_params);
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numgrad = computeNumericalGradient(costFunc, nn_params);
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% Visually examine the two gradient computations. The two columns
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% you get should be very similar.
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disp([numgrad grad]);
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fprintf(['The above two columns you get should be very similar.\n' ...
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'(Left-Your Numerical Gradient, Right-Analytical Gradient)\n\n']);
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% Evaluate the norm of the difference between two solutions.
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% If you have a correct implementation, and assuming you used EPSILON = 0.0001
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% in computeNumericalGradient.m, then diff below should be less than 1e-9
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diff = norm(numgrad-grad)/norm(numgrad+grad);
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fprintf(['If your backpropagation implementation is correct, then \n' ...
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'the relative difference will be small (less than 1e-9). \n' ...
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'\nRelative Difference: %g\n'], diff);
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end
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function numgrad = computeNumericalGradient(J, theta)
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%COMPUTENUMERICALGRADIENT Computes the gradient using "finite differences"
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%and gives us a numerical estimate of the gradient.
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% numgrad = COMPUTENUMERICALGRADIENT(J, theta) computes the numerical
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% gradient of the function J around theta. Calling y = J(theta) should
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% return the function value at theta.
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% Notes: The following code implements numerical gradient checking, and
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% returns the numerical gradient.It sets numgrad(i) to (a numerical
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% approximation of) the partial derivative of J with respect to the
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% i-th input argument, evaluated at theta. (i.e., numgrad(i) should
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% be the (approximately) the partial derivative of J with respect
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% to theta(i).)
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%
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numgrad = zeros(size(theta));
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perturb = zeros(size(theta));
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e = 1e-4;
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for p = 1:numel(theta)
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% Set perturbation vector
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perturb(p) = e;
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loss1 = J(theta - perturb);
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loss2 = J(theta + perturb);
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% Compute Numerical Gradient
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numgrad(p) = (loss2 - loss1) / (2*e);
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perturb(p) = 0;
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end
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end
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function W = debugInitializeWeights(fan_out, fan_in)
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%DEBUGINITIALIZEWEIGHTS Initialize the weights of a layer with fan_in
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%incoming connections and fan_out outgoing connections using a fixed
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%strategy, this will help you later in debugging
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% W = DEBUGINITIALIZEWEIGHTS(fan_in, fan_out) initializes the weights
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% of a layer with fan_in incoming connections and fan_out outgoing
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% connections using a fix set of values
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%
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% Note that W should be set to a matrix of size(1 + fan_in, fan_out) as
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% the first row of W handles the "bias" terms
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%
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% Set W to zeros
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W = zeros(fan_out, 1 + fan_in);
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% Initialize W using "sin", this ensures that W is always of the same
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% values and will be useful for debugging
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W = reshape(sin(1:numel(W)), size(W)) / 10;
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% =========================================================================
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end
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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 4 Neural Network Learning
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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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% sigmoidGradient.m
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% randInitializeWeights.m
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% nnCostFunction.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('ex4data1.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 Parameters ================
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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('ex4weights.mat');
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% Unroll parameters
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nn_params = [Theta1(:) ; Theta2(:)];
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%% ================ Part 3: Compute Cost (Feedforward) ================
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% To the neural network, you should first start by implementing the
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% feedforward part of the neural network that returns the cost only. You
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% should complete the code in nnCostFunction.m to return cost. After
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% implementing the feedforward to compute the cost, you can verify that
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% your implementation is correct by verifying that you get the same cost
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% as us for the fixed debugging parameters.
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%
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% We suggest implementing the feedforward cost *without* regularization
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% first so that it will be easier for you to debug. Later, in part 4, you
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% will get to implement the regularized cost.
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%
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fprintf('\nFeedforward Using Neural Network ...\n')
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% Weight regularization parameter (we set this to 0 here).
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lambda = 0;
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J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...
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num_labels, X, y, lambda);
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fprintf(['Cost at parameters (loaded from ex4weights): %f '...
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'\n(this value should be about 0.287629)\n'], J);
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% =============== Part 4: Implement Regularization ===============
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% Once your cost function implementation is correct, you should now
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% continue to implement the regularization with the cost.
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%
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fprintf('\nChecking Cost Function (w/ Regularization) ... \n')
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% Weight regularization parameter (we set this to 1 here).
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lambda = 1;
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J = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, ...
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num_labels, X, y, lambda);
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fprintf(['Cost at parameters (loaded from ex4weights): %f '...
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'\n(this value should be about 0.383770)\n'], J);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 5: Sigmoid Gradient ================
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% Before you start implementing the neural network, you will first
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% implement the gradient for the sigmoid function. You should complete the
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% code in the sigmoidGradient.m file.
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%
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fprintf('\nEvaluating sigmoid gradient...\n')
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g = sigmoidGradient([1 -0.5 0 0.5 1]);
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fprintf('Sigmoid gradient evaluated at [1 -0.5 0 0.5 1]:\n ');
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fprintf('%f ', g);
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fprintf('\n\n');
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================ Part 6: Initializing Pameters ================
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% In this part of the exercise, you will be starting to implment a two
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% layer neural network that classifies digits. You will start by
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% implementing a function to initialize the weights of the neural network
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% (randInitializeWeights.m)
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fprintf('\nInitializing Neural Network Parameters ...\n')
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initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
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initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels);
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% Unroll parameters
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initial_nn_params = [initial_Theta1(:) ; initial_Theta2(:)];
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%% =============== Part 7: Implement Backpropagation ===============
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% Once your cost matches up with ours, you should proceed to implement the
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% backpropagation algorithm for the neural network. You should add to the
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% code you've written in nnCostFunction.m to return the partial
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% derivatives of the parameters.
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%
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fprintf('\nChecking Backpropagation... \n');
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% Check gradients by running checkNNGradients
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checkNNGradients;
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fprintf('\nProgram paused. Press enter to continue.\n');
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pause;
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%% =============== Part 8: Implement Regularization ===============
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% Once your backpropagation implementation is correct, you should now
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% continue to implement the regularization with the cost and gradient.
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%
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fprintf('\nChecking Backpropagation (w/ Regularization) ... \n')
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% Check gradients by running checkNNGradients
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lambda = 3;
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checkNNGradients(lambda);
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% Also output the costFunction debugging values
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debug_J = nnCostFunction(nn_params, input_layer_size, ...
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hidden_layer_size, num_labels, X, y, lambda);
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fprintf(['\n\nCost at (fixed) debugging parameters (w/ lambda = 10): %f ' ...
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'\n(this value should be about 0.576051)\n\n'], debug_J);
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% =================== Part 8: Training NN ===================
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% You have now implemented all the code necessary to train a neural
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% network. To train your neural network, we will now use "fmincg", which
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% is a function which works similarly to "fminunc". Recall that these
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% advanced optimizers are able to train our cost functions efficiently as
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% long as we provide them with the gradient computations.
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%
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fprintf('\nTraining Neural Network... \n')
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% After you have completed the assignment, change the MaxIter to a larger
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% value to see how more training helps.
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options = optimset('MaxIter', 50);
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% You should also try different values of lambda
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lambda = 1;
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% Create "short hand" for the cost function to be minimized
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costFunction = @(p) nnCostFunction(p, ...
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input_layer_size, ...
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hidden_layer_size, ...
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num_labels, X, y, lambda);
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% Now, costFunction is a function that takes in only one argument (the
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% neural network parameters)
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[nn_params, cost] = fmincg(costFunction, initial_nn_params, options);
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% Obtain Theta1 and Theta2 back from nn_params
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Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
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hidden_layer_size, (input_layer_size + 1));
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Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
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num_labels, (hidden_layer_size + 1));
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fprintf('Program paused. Press enter to continue.\n');
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pause;
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%% ================= Part 9: Visualize Weights =================
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% You can now "visualize" what the neural network is learning by
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% displaying the hidden units to see what features they are capturing in
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% the data.
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fprintf('\nVisualizing Neural Network... \n')
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displayData(Theta1(:, 2:end));
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||||||
|
fprintf('\nProgram paused. Press enter to continue.\n');
|
||||||
|
pause;
|
||||||
|
|
||||||
|
%% ================= Part 10: Implement Predict =================
|
||||||
|
% After training the neural network, we would like to use it to predict
|
||||||
|
% the labels. You will now implement the "predict" function to use the
|
||||||
|
% neural network to predict the labels of the training set. This lets
|
||||||
|
% you compute the training set accuracy.
|
||||||
|
|
||||||
|
pred = predict(Theta1, Theta2, X);
|
||||||
|
|
||||||
|
fprintf('\nTraining Set Accuracy: %f\n', mean(double(pred == y)) * 100);
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -0,0 +1,175 @@
|
|||||||
|
function [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
|
||||||
|
% Minimize a continuous differentialble multivariate function. Starting point
|
||||||
|
% is given by "X" (D by 1), and the function named in the string "f", must
|
||||||
|
% return a function value and a vector of partial derivatives. The Polack-
|
||||||
|
% Ribiere flavour of conjugate gradients is used to compute search directions,
|
||||||
|
% and a line search using quadratic and cubic polynomial approximations and the
|
||||||
|
% Wolfe-Powell stopping criteria is used together with the slope ratio method
|
||||||
|
% for guessing initial step sizes. Additionally a bunch of checks are made to
|
||||||
|
% make sure that exploration is taking place and that extrapolation will not
|
||||||
|
% be unboundedly large. The "length" gives the length of the run: if it is
|
||||||
|
% positive, it gives the maximum number of line searches, if negative its
|
||||||
|
% absolute gives the maximum allowed number of function evaluations. You can
|
||||||
|
% (optionally) give "length" a second component, which will indicate the
|
||||||
|
% reduction in function value to be expected in the first line-search (defaults
|
||||||
|
% to 1.0). The function returns when either its length is up, or if no further
|
||||||
|
% progress can be made (ie, we are at a minimum, or so close that due to
|
||||||
|
% numerical problems, we cannot get any closer). If the function terminates
|
||||||
|
% within a few iterations, it could be an indication that the function value
|
||||||
|
% and derivatives are not consistent (ie, there may be a bug in the
|
||||||
|
% implementation of your "f" function). The function returns the found
|
||||||
|
% solution "X", a vector of function values "fX" indicating the progress made
|
||||||
|
% and "i" the number of iterations (line searches or function evaluations,
|
||||||
|
% depending on the sign of "length") used.
|
||||||
|
%
|
||||||
|
% Usage: [X, fX, i] = fmincg(f, X, options, P1, P2, P3, P4, P5)
|
||||||
|
%
|
||||||
|
% See also: checkgrad
|
||||||
|
%
|
||||||
|
% Copyright (C) 2001 and 2002 by Carl Edward Rasmussen. Date 2002-02-13
|
||||||
|
%
|
||||||
|
%
|
||||||
|
% (C) Copyright 1999, 2000 & 2001, Carl Edward Rasmussen
|
||||||
|
%
|
||||||
|
% Permission is granted for anyone to copy, use, or modify these
|
||||||
|
% programs and accompanying documents for purposes of research or
|
||||||
|
% education, provided this copyright notice is retained, and note is
|
||||||
|
% made of any changes that have been made.
|
||||||
|
%
|
||||||
|
% These programs and documents are distributed without any warranty,
|
||||||
|
% express or implied. As the programs were written for research
|
||||||
|
% purposes only, they have not been tested to the degree that would be
|
||||||
|
% advisable in any important application. All use of these programs is
|
||||||
|
% entirely at the user's own risk.
|
||||||
|
%
|
||||||
|
% [ml-class] Changes Made:
|
||||||
|
% 1) Function name and argument specifications
|
||||||
|
% 2) Output display
|
||||||
|
%
|
||||||
|
|
||||||
|
% Read options
|
||||||
|
if exist('options', 'var') && ~isempty(options) && isfield(options, 'MaxIter')
|
||||||
|
length = options.MaxIter;
|
||||||
|
else
|
||||||
|
length = 100;
|
||||||
|
end
|
||||||
|
|
||||||
|
|
||||||
|
RHO = 0.01; % a bunch of constants for line searches
|
||||||
|
SIG = 0.5; % RHO and SIG are the constants in the Wolfe-Powell conditions
|
||||||
|
INT = 0.1; % don't reevaluate within 0.1 of the limit of the current bracket
|
||||||
|
EXT = 3.0; % extrapolate maximum 3 times the current bracket
|
||||||
|
MAX = 20; % max 20 function evaluations per line search
|
||||||
|
RATIO = 100; % maximum allowed slope ratio
|
||||||
|
|
||||||
|
argstr = ['feval(f, X']; % compose string used to call function
|
||||||
|
for i = 1:(nargin - 3)
|
||||||
|
argstr = [argstr, ',P', int2str(i)];
|
||||||
|
end
|
||||||
|
argstr = [argstr, ')'];
|
||||||
|
|
||||||
|
if max(size(length)) == 2, red=length(2); length=length(1); else red=1; end
|
||||||
|
S=['Iteration '];
|
||||||
|
|
||||||
|
i = 0; % zero the run length counter
|
||||||
|
ls_failed = 0; % no previous line search has failed
|
||||||
|
fX = [];
|
||||||
|
[f1 df1] = eval(argstr); % get function value and gradient
|
||||||
|
i = i + (length<0); % count epochs?!
|
||||||
|
s = -df1; % search direction is steepest
|
||||||
|
d1 = -s'*s; % this is the slope
|
||||||
|
z1 = red/(1-d1); % initial step is red/(|s|+1)
|
||||||
|
|
||||||
|
while i < abs(length) % while not finished
|
||||||
|
i = i + (length>0); % count iterations?!
|
||||||
|
|
||||||
|
X0 = X; f0 = f1; df0 = df1; % make a copy of current values
|
||||||
|
X = X + z1*s; % begin line search
|
||||||
|
[f2 df2] = eval(argstr);
|
||||||
|
i = i + (length<0); % count epochs?!
|
||||||
|
d2 = df2'*s;
|
||||||
|
f3 = f1; d3 = d1; z3 = -z1; % initialize point 3 equal to point 1
|
||||||
|
if length>0, M = MAX; else M = min(MAX, -length-i); end
|
||||||
|
success = 0; limit = -1; % initialize quanteties
|
||||||
|
while 1
|
||||||
|
while ((f2 > f1+z1*RHO*d1) | (d2 > -SIG*d1)) & (M > 0)
|
||||||
|
limit = z1; % tighten the bracket
|
||||||
|
if f2 > f1
|
||||||
|
z2 = z3 - (0.5*d3*z3*z3)/(d3*z3+f2-f3); % quadratic fit
|
||||||
|
else
|
||||||
|
A = 6*(f2-f3)/z3+3*(d2+d3); % cubic fit
|
||||||
|
B = 3*(f3-f2)-z3*(d3+2*d2);
|
||||||
|
z2 = (sqrt(B*B-A*d2*z3*z3)-B)/A; % numerical error possible - ok!
|
||||||
|
end
|
||||||
|
if isnan(z2) | isinf(z2)
|
||||||
|
z2 = z3/2; % if we had a numerical problem then bisect
|
||||||
|
end
|
||||||
|
z2 = max(min(z2, INT*z3),(1-INT)*z3); % don't accept too close to limits
|
||||||
|
z1 = z1 + z2; % update the step
|
||||||
|
X = X + z2*s;
|
||||||
|
[f2 df2] = eval(argstr);
|
||||||
|
M = M - 1; i = i + (length<0); % count epochs?!
|
||||||
|
d2 = df2'*s;
|
||||||
|
z3 = z3-z2; % z3 is now relative to the location of z2
|
||||||
|
end
|
||||||
|
if f2 > f1+z1*RHO*d1 | d2 > -SIG*d1
|
||||||
|
break; % this is a failure
|
||||||
|
elseif d2 > SIG*d1
|
||||||
|
success = 1; break; % success
|
||||||
|
elseif M == 0
|
||||||
|
break; % failure
|
||||||
|
end
|
||||||
|
A = 6*(f2-f3)/z3+3*(d2+d3); % make cubic extrapolation
|
||||||
|
B = 3*(f3-f2)-z3*(d3+2*d2);
|
||||||
|
z2 = -d2*z3*z3/(B+sqrt(B*B-A*d2*z3*z3)); % num. error possible - ok!
|
||||||
|
if ~isreal(z2) | isnan(z2) | isinf(z2) | z2 < 0 % num prob or wrong sign?
|
||||||
|
if limit < -0.5 % if we have no upper limit
|
||||||
|
z2 = z1 * (EXT-1); % the extrapolate the maximum amount
|
||||||
|
else
|
||||||
|
z2 = (limit-z1)/2; % otherwise bisect
|
||||||
|
end
|
||||||
|
elseif (limit > -0.5) & (z2+z1 > limit) % extraplation beyond max?
|
||||||
|
z2 = (limit-z1)/2; % bisect
|
||||||
|
elseif (limit < -0.5) & (z2+z1 > z1*EXT) % extrapolation beyond limit
|
||||||
|
z2 = z1*(EXT-1.0); % set to extrapolation limit
|
||||||
|
elseif z2 < -z3*INT
|
||||||
|
z2 = -z3*INT;
|
||||||
|
elseif (limit > -0.5) & (z2 < (limit-z1)*(1.0-INT)) % too close to limit?
|
||||||
|
z2 = (limit-z1)*(1.0-INT);
|
||||||
|
end
|
||||||
|
f3 = f2; d3 = d2; z3 = -z2; % set point 3 equal to point 2
|
||||||
|
z1 = z1 + z2; X = X + z2*s; % update current estimates
|
||||||
|
[f2 df2] = eval(argstr);
|
||||||
|
M = M - 1; i = i + (length<0); % count epochs?!
|
||||||
|
d2 = df2'*s;
|
||||||
|
end % end of line search
|
||||||
|
|
||||||
|
if success % if line search succeeded
|
||||||
|
f1 = f2; fX = [fX' f1]';
|
||||||
|
fprintf('%s %4i | Cost: %4.6e\r', S, i, f1);
|
||||||
|
s = (df2'*df2-df1'*df2)/(df1'*df1)*s - df2; % Polack-Ribiere direction
|
||||||
|
tmp = df1; df1 = df2; df2 = tmp; % swap derivatives
|
||||||
|
d2 = df1'*s;
|
||||||
|
if d2 > 0 % new slope must be negative
|
||||||
|
s = -df1; % otherwise use steepest direction
|
||||||
|
d2 = -s'*s;
|
||||||
|
end
|
||||||
|
z1 = z1 * min(RATIO, d1/(d2-realmin)); % slope ratio but max RATIO
|
||||||
|
d1 = d2;
|
||||||
|
ls_failed = 0; % this line search did not fail
|
||||||
|
else
|
||||||
|
X = X0; f1 = f0; df1 = df0; % restore point from before failed line search
|
||||||
|
if ls_failed | i > abs(length) % line search failed twice in a row
|
||||||
|
break; % or we ran out of time, so we give up
|
||||||
|
end
|
||||||
|
tmp = df1; df1 = df2; df2 = tmp; % swap derivatives
|
||||||
|
s = -df1; % try steepest
|
||||||
|
d1 = -s'*s;
|
||||||
|
z1 = 1/(1-d1);
|
||||||
|
ls_failed = 1; % this line search failed
|
||||||
|
end
|
||||||
|
if exist('OCTAVE_VERSION')
|
||||||
|
fflush(stdout);
|
||||||
|
end
|
||||||
|
end
|
||||||
|
fprintf('\n');
|
||||||
@ -0,0 +1,91 @@
|
|||||||
|
function [J grad] = nnCostFunction(nn_params, ...
|
||||||
|
input_layer_size, ...
|
||||||
|
hidden_layer_size, ...
|
||||||
|
num_labels, ...
|
||||||
|
X, y, lambda)
|
||||||
|
%NNCOSTFUNCTION Implements the neural network cost function for a two layer
|
||||||
|
%neural network which performs classification
|
||||||
|
% [J grad] = NNCOSTFUNCTON(nn_params, hidden_layer_size, num_labels, ...
|
||||||
|
% X, y, lambda) computes the cost and gradient of the neural network. The
|
||||||
|
% parameters for the neural network are "unrolled" into the vector
|
||||||
|
% nn_params and need to be converted back into the weight matrices.
|
||||||
|
%
|
||||||
|
% The returned parameter grad should be a "unrolled" vector of the
|
||||||
|
% partial derivatives of the neural network.
|
||||||
|
%
|
||||||
|
|
||||||
|
% Reshape nn_params back into the parameters Theta1 and Theta2, the weight matrices
|
||||||
|
% for our 2 layer neural network
|
||||||
|
Theta1 = reshape(nn_params(1:hidden_layer_size * (input_layer_size + 1)), ...
|
||||||
|
hidden_layer_size, (input_layer_size + 1));
|
||||||
|
|
||||||
|
Theta2 = reshape(nn_params((1 + (hidden_layer_size * (input_layer_size + 1))):end), ...
|
||||||
|
num_labels, (hidden_layer_size + 1));
|
||||||
|
|
||||||
|
% Setup some useful variables
|
||||||
|
m = size(X, 1);
|
||||||
|
|
||||||
|
% You need to return the following variables correctly
|
||||||
|
J = 0;
|
||||||
|
Theta1_grad = zeros(size(Theta1));
|
||||||
|
Theta2_grad = zeros(size(Theta2));
|
||||||
|
|
||||||
|
% ====================== YOUR CODE HERE ======================
|
||||||
|
% Instructions: You should complete the code by working through the
|
||||||
|
% following parts.
|
||||||
|
%
|
||||||
|
% Part 1: Feedforward the neural network and return the cost in the
|
||||||
|
% variable J. After implementing Part 1, you can verify that your
|
||||||
|
% cost function computation is correct by verifying the cost
|
||||||
|
% computed in ex4.m
|
||||||
|
%
|
||||||
|
% Part 2: Implement the backpropagation algorithm to compute the gradients
|
||||||
|
% Theta1_grad and Theta2_grad. You should return the partial derivatives of
|
||||||
|
% the cost function with respect to Theta1 and Theta2 in Theta1_grad and
|
||||||
|
% Theta2_grad, respectively. After implementing Part 2, you can check
|
||||||
|
% that your implementation is correct by running checkNNGradients
|
||||||
|
%
|
||||||
|
% Note: The vector y passed into the function is a vector of labels
|
||||||
|
% containing values from 1..K. You need to map this vector into a
|
||||||
|
% binary vector of 1's and 0's to be used with the neural network
|
||||||
|
% cost function.
|
||||||
|
%
|
||||||
|
% Hint: We recommend implementing backpropagation using a for-loop
|
||||||
|
% over the training examples if you are implementing it for the
|
||||||
|
% first time.
|
||||||
|
%
|
||||||
|
% Part 3: Implement regularization with the cost function and gradients.
|
||||||
|
%
|
||||||
|
% Hint: You can implement this around the code for
|
||||||
|
% backpropagation. That is, you can compute the gradients for
|
||||||
|
% the regularization separately and then add them to Theta1_grad
|
||||||
|
% and Theta2_grad from Part 2.
|
||||||
|
%
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
% -------------------------------------------------------------
|
||||||
|
|
||||||
|
% =========================================================================
|
||||||
|
|
||||||
|
% Unroll gradients
|
||||||
|
grad = [Theta1_grad(:) ; Theta2_grad(:)];
|
||||||
|
|
||||||
|
|
||||||
|
end
|
||||||
@ -0,0 +1,20 @@
|
|||||||
|
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);
|
||||||
|
|
||||||
|
h1 = sigmoid([ones(m, 1) X] * Theta1');
|
||||||
|
h2 = sigmoid([ones(m, 1) h1] * Theta2');
|
||||||
|
[dummy, p] = max(h2, [], 2);
|
||||||
|
|
||||||
|
% =========================================================================
|
||||||
|
|
||||||
|
|
||||||
|
end
|
||||||
@ -0,0 +1,32 @@
|
|||||||
|
function W = randInitializeWeights(L_in, L_out)
|
||||||
|
%RANDINITIALIZEWEIGHTS Randomly initialize the weights of a layer with L_in
|
||||||
|
%incoming connections and L_out outgoing connections
|
||||||
|
% W = RANDINITIALIZEWEIGHTS(L_in, L_out) randomly initializes the weights
|
||||||
|
% of a layer with L_in incoming connections and L_out outgoing
|
||||||
|
% connections.
|
||||||
|
%
|
||||||
|
% Note that W should be set to a matrix of size(L_out, 1 + L_in) as
|
||||||
|
% the column row of W handles the "bias" terms
|
||||||
|
%
|
||||||
|
|
||||||
|
% You need to return the following variables correctly
|
||||||
|
W = zeros(L_out, 1 + L_in);
|
||||||
|
|
||||||
|
% ====================== YOUR CODE HERE ======================
|
||||||
|
% Instructions: Initialize W randomly so that we break the symmetry while
|
||||||
|
% training the neural network.
|
||||||
|
%
|
||||||
|
% Note: The first row of W corresponds to the parameters for the bias units
|
||||||
|
%
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
% =========================================================================
|
||||||
|
|
||||||
|
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 +1,33 @@
|
|||||||
|
function g = sigmoidGradient(z)
|
||||||
|
%SIGMOIDGRADIENT returns the gradient of the sigmoid function
|
||||||
|
%evaluated at z
|
||||||
|
% g = SIGMOIDGRADIENT(z) computes the gradient of the sigmoid function
|
||||||
|
% evaluated at z. This should work regardless if z is a matrix or a
|
||||||
|
% vector. In particular, if z is a vector or matrix, you should return
|
||||||
|
% the gradient for each element.
|
||||||
|
|
||||||
|
g = zeros(size(z));
|
||||||
|
|
||||||
|
% ====================== YOUR CODE HERE ======================
|
||||||
|
% Instructions: Compute the gradient of the sigmoid function evaluated at
|
||||||
|
% each value of z (z can be a matrix, vector or scalar).
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
% =============================================================
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
end
|
||||||
@ -0,0 +1,578 @@
|
|||||||
|
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 = '4';
|
||||||
|
end
|
||||||
|
|
||||||
|
function [partNames] = validParts()
|
||||||
|
partNames = { 'Feedforward and Cost Function', ...
|
||||||
|
'Regularized Cost Function', ...
|
||||||
|
'Sigmoid Gradient', ...
|
||||||
|
'Neural Network Gradient (Backpropagation)' ...
|
||||||
|
'Regularized Gradient' ...
|
||||||
|
};
|
||||||
|
end
|
||||||
|
|
||||||
|
function srcs = sources()
|
||||||
|
% Separated by part
|
||||||
|
srcs = { { 'nnCostFunction.m' }, ...
|
||||||
|
{ 'nnCostFunction.m' }, ...
|
||||||
|
{ 'sigmoidGradient.m' }, ...
|
||||||
|
{ 'nnCostFunction.m' }, ...
|
||||||
|
{ 'nnCostFunction.m' } };
|
||||||
|
end
|
||||||
|
|
||||||
|
function out = output(partId, auxstring)
|
||||||
|
% Random Test Cases
|
||||||
|
X = reshape(3 * sin(1:1:30), 3, 10);
|
||||||
|
Xm = reshape(sin(1:32), 16, 2) / 5;
|
||||||
|
ym = 1 + mod(1:16,4)';
|
||||||
|
t1 = sin(reshape(1:2:24, 4, 3));
|
||||||
|
t2 = cos(reshape(1:2:40, 4, 5));
|
||||||
|
t = [t1(:) ; t2(:)];
|
||||||
|
if partId == 1
|
||||||
|
[J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);
|
||||||
|
out = sprintf('%0.5f ', J);
|
||||||
|
elseif partId == 2
|
||||||
|
[J] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);
|
||||||
|
out = sprintf('%0.5f ', J);
|
||||||
|
elseif partId == 3
|
||||||
|
out = sprintf('%0.5f ', sigmoidGradient(X));
|
||||||
|
elseif partId == 4
|
||||||
|
[J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 0);
|
||||||
|
out = sprintf('%0.5f ', J);
|
||||||
|
out = [out sprintf('%0.5f ', grad)];
|
||||||
|
elseif partId == 5
|
||||||
|
[J, grad] = nnCostFunction(t, 2, 4, 4, Xm, ym, 1.5);
|
||||||
|
out = sprintf('%0.5f ', J);
|
||||||
|
out = [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