56 lines
1.7 KiB
Matlab
56 lines
1.7 KiB
Matlab
function [C, sigma] = dataset3Params(X, y, Xval, yval)
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%DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise
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%where you select the optimal (C, sigma) learning parameters to use for SVM
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%with RBF kernel
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% [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and
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% sigma. You should complete this function to return the optimal C and
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% sigma based on a cross-validation set.
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%
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% You need to return the following variables correctly.
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C = 1;
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sigma = 1;
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% ====================== YOUR CODE HERE ======================
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% Instructions: Fill in this function to return the optimal C and sigma
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% learning parameters found using the cross validation set.
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% You can use svmPredict to predict the labels on the cross
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% validation set. For example,
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% predictions = svmPredict(model, Xval);
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% will return the predictions on the cross validation set.
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%
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% Note: You can compute the prediction error using
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% mean(double(predictions ~= yval))
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%
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% Code to find better C and sigma
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sampleVec = [.01 .03 .1 .3 1 3 10 30];
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model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
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predictions = svmPredict(model,Xval);
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error = mean(double(predictions ~= yval));
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for i = 1:8
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for j = 1:8
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tempC = sampleVec(i);
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tempSigma = sampleVec(j);
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tempModel= svmTrain(X, y, tempC, @(x1, x2) gaussianKernel(x1, x2, tempSigma));
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tempPredictions = svmPredict(tempModel,Xval);
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tempError = mean(double(tempPredictions ~= yval));
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if tempError < error
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error = tempError;
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C = tempC;
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sigma = tempSigma;
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endif
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endfor
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endfor
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C
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sigma
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% =========================================================================
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end
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