function [C, sigma] = dataset3Params(X, y, Xval, yval)
%DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise
%where you select the optimal (C, sigma) learning parameters to use for SVM
%with RBF kernel
% [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and
% sigma. You should complete this function to return the optimal C and
% sigma based on a cross-validation set.
%
% You need to return the following variables correctly.
C = 1;
sigma = 0.3;
% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return the optimal C and sigma
% learning parameters found using the cross validation set.
% You can use svmPredict to predict the labels on the cross
% validation set. For example,
% predictions = svmPredict(model, Xval);
% will return the predictions on the cross validation set.
%
% Note: You can compute the prediction error using
% mean(double(predictions ~= yval))
%
arry = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30];
cTemp = 1;
sTemp = 0.3;
error = Inf;
length(arry)
for i = 1:length(arry)
for j = 1:length(arry)
cTemp = arry(i);
sTemp = arry(j);
model = svmTrain(X, y,cTemp, @(x1, x2) gaussianKernel(x1, x2, sTemp));
predictions = svmPredict(model, Xval);
if(mean(double(predictions ~= yval)) < error)
C = cTemp;
sigma = sTemp;
error = mean(double(predictions ~= yval));
end
end
end
% =========================================================================
end
吴恩达的机器学习编程作业13:dataset3Params 选择最优参数
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转载自blog.csdn.net/melon__/article/details/82150107
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