59 lines
1.9 KiB
Matlab
59 lines
1.9 KiB
Matlab
function [lambda_vec, error_train, error_val] = ...
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validationCurve(X, y, Xval, yval)
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%VALIDATIONCURVE Generate the train and validation errors needed to
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%plot a validation curve that we can use to select lambda
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% [lambda_vec, error_train, error_val] = ...
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% VALIDATIONCURVE(X, y, Xval, yval) returns the train
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% and validation errors (in error_train, error_val)
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% for different values of lambda. You are given the training set (X,
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% y) and validation set (Xval, yval).
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%
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% Selected values of lambda (you should not change this)
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lambda_vec = [0 0.001 0.003 0.01 0.03 0.1 0.3 1 3 10]';
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% You need to return these variables correctly.
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error_train = zeros(length(lambda_vec), 1);
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error_val = zeros(length(lambda_vec), 1);
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% ====================== YOUR CODE HERE ======================
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% Instructions: Fill in this function to return training errors in
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% error_train and the validation errors in error_val. The
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% vector lambda_vec contains the different lambda parameters
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% to use for each calculation of the errors, i.e,
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% error_train(i), and error_val(i) should give
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% you the errors obtained after training with
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% lambda = lambda_vec(i)
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%
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% Note: You can loop over lambda_vec with the following:
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%
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% for i = 1:length(lambda_vec)
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% lambda = lambda_vec(i);
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% % Compute train / val errors when training linear
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% % regression with regularization parameter lambda
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% % You should store the result in error_train(i)
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% % and error_val(i)
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% ....
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%
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% end
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%
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%
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for i = 1:length(lambda_vec)
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lambda = lambda_vec(i);
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Theta = trainLinearReg(X,y,lambda);
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error_train(i) = linearRegCostFunction(X,y,Theta,0);
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error_val(i) = linearRegCostFunction(Xval,yval,Theta,0);
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endfor
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% =========================================================================
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end
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