Sam Perry
25 lines
845 B
Matlab
25 lines
845 B
Matlab
function J = compute_cost_regularised(X, y, theta, l)
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%COMPUTE_COST_REGULARISED Compute cost for regularised linear regression.
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%Takes as input matrix X of training examples, a parameter vector, theta,
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%lambda (l) and an output vector y
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total_squared_error = 0.0; %cost
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m = size(y, 1); %number of training examples
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for i = 1:m
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hypothesis = calculate_hypothesis(X, theta, i);
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output = y(i);
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squared_error = (hypothesis - output) ^ 2;
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total_squared_error = total_squared_error + squared_error;
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end
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total_regularised_error = 0.0;
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for i = 2:length(theta)
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total_regularised_error = total_regularised_error + theta(i) ^ 2;
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end
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total_regularised_error = total_regularised_error * l;
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J = (1.0 / (2 * m)) * (total_squared_error + total_regularised_error);
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end |