coursera 吴恩达机器学习第二次作业提交

只贴添加的部分，时间关系有些不是最优雅的解法。。看不懂请留言

plotData.m

pos = find(y==1); neg =find(y==0);
plot(X(pos,1),X(pos,2),'k+','LineWidth',2,'MarkerSize',7);
plot(X(neg,1),X(neg,2),'ko','MarkerFaceColor','y','MarkerSize',7);

sigmoid.m

g = 1./(1+exp(-z));

costFunction.m

J = sum(-y.*log(sigmoid(X*theta)) - (1-y).*log(1-sigmoid(X*theta)))/m;
grad = (X'*(sigmoid(X*theta)-y))/m;

predict.m

p = sigmoid(X*theta);
for i=1:m,
   if p(i) >= 0.5,
      p(i) = 1;
   else
      p(i) = 0;
   end;
end;

costFunctionReg.m

J = ( log( sigmoid(theta'*X') ) * y + log( 1-sigmoid(theta'*X') ) * (1 - y) )/(-m) + (lambda / (2*m)) * ( ( theta( 2:length(theta) ) )' * theta(2:length(theta)) );
grad = ( X' * ( sigmoid(X*theta)-y ) )/m + ( lambda / m ) * ( [0; ones( length(theta) - 1 , 1 )].*theta );

coursera 吴恩达机器学习第二次作业提交

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