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67 lines
2.3 KiB
Matlab
67 lines
2.3 KiB
Matlab
10 years ago
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function [error_train, error_val] = ...
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learningCurve(X, y, Xval, yval, lambda)
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%LEARNINGCURVE Generates the train and cross validation set errors needed
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%to plot a learning curve
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% [error_train, error_val] = ...
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% LEARNINGCURVE(X, y, Xval, yval, lambda) returns the train and
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% cross validation set errors for a learning curve. In particular,
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% it returns two vectors of the same length - error_train and
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% error_val. Then, error_train(i) contains the training error for
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% i examples (and similarly for error_val(i)).
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%
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% In this function, you will compute the train and test errors for
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% dataset sizes from 1 up to m. In practice, when working with larger
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% datasets, you might want to do this in larger intervals.
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%
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% Number of training examples
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m = size(X, 1);
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% You need to return these values correctly
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error_train = zeros(m, 1);
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error_val = zeros(m, 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 cross validation errors in error_val.
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% i.e., error_train(i) and
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% error_val(i) should give you the errors
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% obtained after training on i examples.
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%
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% Note: You should evaluate the training error on the first i training
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% examples (i.e., X(1:i, :) and y(1:i)).
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%
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% For the cross-validation error, you should instead evaluate on
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% the _entire_ cross validation set (Xval and yval).
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%
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% Note: If you are using your cost function (linearRegCostFunction)
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% to compute the training and cross validation error, you should
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% call the function with the lambda argument set to 0.
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% Do note that you will still need to use lambda when running
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% the training to obtain the theta parameters.
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%
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% Hint: You can loop over the examples with the following:
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%
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% for i = 1:m
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% % Compute train/cross validation errors using training examples
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% % X(1:i, :) and y(1:i), storing the result in
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% % error_train(i) 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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% ---------------------- Sample Solution ----------------------
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% -------------------------------------------------------------
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% =========================================================================
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end
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