costFunctionJ.py

from numpy import * 

def costFunctionJ(X, y, theta):
  #COSTFUNCTION Compute cost and gradient for logistic regression
  #   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
  #   parameter for logistic regression and the gradient of the cost
  #   w.r.t. to the parameters.

  # Initialize some useful values
  m = size(y,1) # number of training examples

  # You need to return the following variables correctly 
  J = 0
  grad = zeros(size(theta,1))

  # ====================== YOUR CODE HERE ======================
  # Instructions: Compute the cost of a particular choice of theta.
  #               You should set J to the cost.
  #               Compute the partial derivatives and set grad to the partial
  #               derivatives of the cost w.r.t. each parameter in theta
  #
  # Note: grad should have the same dimensions as theta
  #
  
  
  # ===========================================
  
  return J