Sam Perry
71 lines
1.8 KiB
Matlab
71 lines
1.8 KiB
Matlab
function theta = gradient_descent(X, y, theta, alpha, iterations, do_plot)
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%GRADIENT_DESCENT do Gradient Descent for a given X, y, theta, alpha
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%for a specified number of iterations
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%if less than 6 arguments was given, then set do_plot to be false
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if nargin < 6
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do_plot = false;
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end
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if(do_plot)
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plot_hypothesis(X, y, theta);
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drawnow; pause(0.1);
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end
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m = size(X, 1); %number of training examples
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cost_vector = []; %will store the results of our cost function
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for it = 1:iterations
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% gradient descent
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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theta_0 = theta(1);
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theta_1 = theta(2);
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%update theta(1) and store in temporary variable theta_0
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sigma = 0.0;
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for i = 1:m
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hypothesis = X(i, 1) * theta(1) + X(i, 2) * theta(2);
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output = y(i);
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sigma = sigma + (hypothesis - output);
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end
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theta_0 = theta_0 - ((alpha * 1.0) / m) * sigma;
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%update theta(2) and store in temporary variable theta_1
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sigma = 0.0;
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for i = 1:m
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hypothesis = X(i, 1) * theta(1) + X(i, 2) * theta(2);
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output = y(i);
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sigma = sigma + (hypothesis - output) * X(i, 2);
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end
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theta_1 = theta_1 - ((alpha * 1.0) / m) * sigma;
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%update theta
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theta = [theta_0, theta_1];
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%update cost_vector
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cost_vector = [cost_vector; compute_cost(X, y, theta)];
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if do_plot
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plot_hypothesis(X, y, theta);
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drawnow; pause(0.1);
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end
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end
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disp 'Gradient descent is finished.'
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if do_plot
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disp 'Press enter!'
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pause;
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end
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plot_cost(cost_vector);
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disp 'Press enter!';
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pause;
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end |