Guys request your help!!

Instead of using the code mentioned in code section :

p=sigmoid(X*theta)>=0.5;

which is running perfectly fine. Im using my logic :

z=sigmoid(X*theta);
if z>=0.5:
p=1;
else
p=0;
endif

The above code is not running.

Please let me know the difference and is there any error in my logic.

Hello, why is the grid range defined as such?
https://github.com/everpeace/ml-class-assignments/blob/master/ex2.Logistic_Regression/mlclass-ex2/plotDecisionBoundary.m#L29-L31

J = (1/2) * sum(sum((R .* ((Theta * X')' ) - Y).^2)) + (lambda/2) * sum(sum(Theta.^2)) + (lambda/2) * sum(sum(X.^2));

X_grad = (R .* ((Theta * X')' ) - Y) * Theta + (lambda * X );
Theta_grad = (R .* ((Theta * X')' ) - Y)' * X + (lambda * Theta);

Hello! I think that line should be:

sigma3=a3-Y;

Instead of, ".-" is not supported in matlab:

sigma3 = a3.-Y;

There is no need to use "." in subtraction
It must be -->

sigme=a3-Y

Pardon me for asking a trivial question.

Can you help me on calculating the decision boundary line. How it has been "plot_y = (-1./theta(3)).*(theta(2).*plot_x + theta(1));"

Hi,
I am wondering how to derive the logic for prediction

p = sigmoid(X*theta)>=0.5;

is there any standard formula?

*[cost, grad] = costFunction(initial_theta, X, y); error: costFunction: operator : nonconformant arguments (op1 is 1x100, op2 is 1x100) error: called from costFunction at line 16 column 3

Should be
J = sum(sum((diff.^2).*(R==1)))/2;
in lieu of
J = sum((diff.^2)(R==1))/2;