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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 ======================
- % ?????
- for i = 1:m
- X_train = X(1:i,:);
- y_train = y(1:i,:);
- theta = trainLinearReg(X_train, y_train, lambda);
- [error_train(i,:), grad] = linearRegCostFunction(X_train, y_train, theta, 0);
- [error_val(i,:), grad] = linearRegCostFunction(Xval, yval, theta, 0);
- endfor
- % -------------------------------------------------------------
- % =========================================================================
- end
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