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annotate learningCurve.m @ 2:882ffde0ce47

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Implement learningCurve
author Jordi Gutiérrez Hermoso <jordigh@octave.org>
date Mon, 21 Nov 2011 00:36:59 -0500 (2011-11-21)
parents 0f14514e907f
children eddd33e57f6a
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882ffde0ce47 Implement learningCurve
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1 function [error_train, error_val] = learningCurve(X, y, Xval, yval, lambda)
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2 ##LEARNINGCURVE Generates the train and cross validation set errors needed
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3 ##to plot a learning curve
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4 ## [error_train, error_val] = ...
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5 ## LEARNINGCURVE(X, y, Xval, yval, lambda) returns the train and
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6 ## cross validation set errors for a learning curve. In particular,
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7 ## it returns two vectors of the same length - error_train and
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8 ## error_val. Then, error_train(i) contains the training error for
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9 ## i examples (and similarly for error_val(i)).
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10 ##
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11 ## In this function, you will compute the train and test errors for
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12 ## dataset sizes from 1 up to m. In practice, when working with larger
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13 ## datasets, you might want to do this in larger intervals.
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14 ##
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16 ## Number of training examples
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17 m = rows (X);
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19 ## Initialise outputs
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20 error_train = zeros(m, 1);
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21 error_val = zeros(m, 1);
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882ffde0ce47 Implement learningCurve
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23 for i = 1:m
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24 Xtrain = X(1:i, :);
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25 ytrain = y(1:i);
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26 theta = trainLinearReg (Xtrain, ytrain, lambda);
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28 error_train(i) = sumsq (Xtrain*theta - ytrain)/(2*i);
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29 error_val(i) = sumsq (Xval*theta - yval)/(2*length (yval));
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30 endfor
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32 endfunction