Coursera机器学习 week3 assignment

plotData.m:

pos = find(y==1); neg = find(y==0);

plot(X(pos, 1), X(pos, 2), 'k+', 'LineWidth', 2, 'Markersize', 8)
plot(X(neg, 1), X(neg, 2), 'ko', 'MarkerFaceColor', 'y', 'Markersize', 8)

sigmoid.m:

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

costFunction.m:

b = sigmoid(X*theta);

J = -(1/m)*( (log(b'))*y + log(1-b')*(1-y) );

grad = (1/m) * ( X'*(b-y) );

predict.m:

b = sigmoid(X*theta);

for i = 1:m
   if  b(i)>=0.5
        p(i) = 1;
   else
        p(i) = 0;
        
   end
end

costFunctionReg.m:

b = sigmoid(X*theta);

J = -(1/m)*( (log(b'))*y + log(1-b')*(1-y) ) + (lambda/(2*m))*(sum(theta.^2)-theta(1)^2);


k = X'*(b-y);

grad(1) = (1/m)*k(1);

long = length(k);
k2 = (1/m) * (k(2:long,:)) + (lambda/m)*(theta(2:long,:));
grad(2:long,:) = k2;