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Learning curve function

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neingeist 2014-11-06 12:04:53 +01:00
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ex5/learningCurve.m
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@ -1,11 +1,11 @@
function [error_train, error_val] = ...
learningCurve(X, y, Xval, yval, lambda)
%LEARNINGCURVE Generates the train and cross validation set errors needed
%LEARNINGCURVE Generates the train and cross validation set errors needed
%to plot a learning curve
% [error_train, error_val] = ...
% LEARNINGCURVE(X, y, Xval, yval, lambda) returns the train and
% cross validation set errors for a learning curve. In particular,
% it returns two vectors of the same length - error_train and
% cross validation set errors for a learning curve. In particular,
% it returns two vectors of the same length - error_train and
% error_val. Then, error_train(i) contains the training error for
% i examples (and similarly for error_val(i)).
%
@ -22,9 +22,9 @@ error_train = zeros(m, 1);
error_val = zeros(m, 1);
% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return training errors in
% error_train and the cross validation errors in error_val.
% i.e., error_train(i) and
% Instructions: Fill in this function to return training errors in
% error_train and the cross validation errors in error_val.
% i.e., error_train(i) and
% error_val(i) should give you the errors
% obtained after training on i examples.
%
@ -35,25 +35,41 @@ error_val = zeros(m, 1);
% the _entire_ cross validation set (Xval and yval).
%
% Note: If you are using your cost function (linearRegCostFunction)
% to compute the training and cross validation error, you should
% call the function with the lambda argument set to 0.
% to compute the training and cross validation error, you should
% call the function with the lambda argument set to 0.
% Do note that you will still need to use lambda when running
% the training to obtain the theta parameters.
%
% Hint: You can loop over the examples with the following:
%
% for i = 1:m
% % Compute train/cross validation errors using training examples
% % X(1:i, :) and y(1:i), storing the result in
% % error_train(i) and error_val(i)
% ....
%
% end
for i = 1:m
% Compute train/cross validation errors using training examples
% X(1:i, :) and y(1:i), storing the result in
% error_train(i) and error_val(i)
X_ = X(1:i,:);
y_ = y(1:i);
% Train with regularization
lambda = 1;
theta = trainLinearReg(X_, y_, lambda);
% Compute the error with lambda = 0
lambda = 0;
error_train(i) = linearRegCostFunction(X_, y_, theta, lambda);
error_val(i) = linearRegCostFunction(Xval, yval, theta, lambda);
end
%
% ---------------------- Sample Solution ----------------------
% for i = 1:m
% % Compute train/cross validation errors using training examples
% % X(1:i, :) and y(1:i), storing the result in
% % error_train(i) and error_val(i)
% ....