Sam Perry
60 lines
1.4 KiB
Matlab
60 lines
1.4 KiB
Matlab
function mllab3()
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% load input data
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X = [-0.99768; -0.69574; -0.40373; -0.10236; 0.22024; 0.47742; 0.82229];
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% load output data
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y = [2.0885; 1.1646; 0.3287; 0.46013; 0.44808; 0.10013; -0.32952];
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% insert the bias into the input data
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X = [ones(size(X, 1), 1), X];
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% perform a polynomial expansion to the fifth order
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for j = 2:5
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X = [X, X(:, 2) .^ j];
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end
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% initialise theta
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theta = [1.0, 1.0, 1.0, 1.0, 1.0];
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alpha = 0.01;
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l = 0.0;
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iterations = 1000;
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do_plot = false;
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% run gradient descent
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% you will need to modify the gradient_descent function to accept an
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% additional argument lambda (l).
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theta = gradient_descent(X, y, theta, alpha, iterations, l, do_plot)
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% plot the original data
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original_x = [-0.99768; -0.69574; -0.40373; -0.10236; 0.22024; 0.47742; 0.82229];
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figure(1);
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plot(original_x, y, 'x');
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hold on;
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x = linspace(-1, 1, 1000);
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y = [];
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for index = 1:1000
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y = [y, get_hypothesis(x(index), theta)];
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end
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plot(x, y,'-');
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disp 'Press enter to exit.';
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pause;
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close(1);
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end
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function hypothesis = get_hypothesis(x, theta)
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% helper function which we will use to calculate the
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% hypothesis for a given x and theta
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hypothesis = 0.0;
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for t = 1:length(theta)
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hypothesis = hypothesis + theta(t) * (x ^ (t - 1));
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end
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end |