Moving course1 to course1 subdir.

machine_learning/course1/mlclass-ex5-008/mlclass-ex5/learningCurve.m

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+function [error_train, error_val] = ...
+    learningCurve(X, y, Xval, yval, lambda)
+%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 
+%       error_val. Then, error_train(i) contains the training error for
+%       i examples (and similarly for error_val(i)).
+%
+%   In this function, you will compute the train and test errors for
+%   dataset sizes from 1 up to m. In practice, when working with larger
+%   datasets, you might want to do this in larger intervals.
+%
+
+% Number of training examples
+m = size(X, 1);
+
+% You need to return these values correctly
+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 
+%               error_val(i) should give you the errors
+%               obtained after training on i examples.
+%
+% Note: You should evaluate the training error on the first i training
+%       examples (i.e., X(1:i, :) and y(1:i)).
+%
+%       For the cross-validation error, you should instead evaluate on
+%       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. 
+%       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
+%
+
+% ---------------------- Sample Solution ----------------------
+mval = size(Xval,1);
+
+for i = 1:m 
+	theta = trainLinearReg(X(1:i,:), y(1:i), lambda);
+	error_train(i) = sum( (X(1:i,:)*theta - y(1:i)) .^ 2 ) / (2*i);
+	error_val(i) = sum( (Xval*theta - yval) .^ 2 ) / (2*mval);
+end
+
+% -------------------------------------------------------------
+
+% =========================================================================
+
+end