Wu Endaの機械学習プログラミングジョブ13:dataset3Paramsが最適なパラメーターを選択します



Wu Endas Machine Learning Programming Job 13



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))