Coursera-MachineLearning-Week7编程题目整理

gaussianKernel.m

sim = exp(-sum((x1-x2).^2)/(sigma.^2*2));  % similarity(x,l^(1)) = exp(-(||x-l^(1)||^(2))/2Sigma^2)

dataset3Params.m

steps = [0.01,0.03,0.1,0.3,1,3,10,30];
minError = Inf;
minC = Inf;
minSigma = Inf;for i = 1:length(steps)  %遍历所有的选项for j = 1:length(steps)  %遍历所有的选项currC = steps(i);  %选择CcurrSigma = steps(j);  %选择Sigma%训练模型model = svmTrain(X, y, currC, @(x1, x2) gaussianKernel(x1, x2, currSigma));predictions = svmPredict(model, Xval);  %进行预测error = mean(double(predictions ~= yval));  %计算误差%选择最小误差的参数if(error < minError)minError = error;minC = currC;minSigma = currSigma;endend
endC = minC;
sigma = minSigma;

processEmail.m

    %遍历词汇表for i = 1:length(vocabList)if(strcmp(vocabList(i), str))  %如果相等word_indices = [word_indices; i];  %添加索引break;endend

emailFeatures.m

for i = 1:length(word_indices)  %遍历单词向量表x(word_indices(i)) = 1  %将对应的值置为1
end