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def sigmoid(X):
	den = 1.0 + e ** (-1.0 * X) 
	d = 1.0 / den	 
	return d
def compute_grad(theta,x,y,lambd):
	""" Calculate the gradient of J:
	grad = 1 / m * (X' * (sigmoid(X * theta) - y))
	grad(2 : end) += lambda / m * theta(2 : end)
	"""
	m = x.shape[0]	
	x_bias = hstack((ones((m, 1)), x))	
	theta = atleast_2d(theta).transpose()
	grad = 1.0 / m * (dot(x_bias.transpose(), sigmoid(dot(x_bias, theta)) - y))
	grad[1 : :] += lambd / m * theta[1 : :]
	return grad.ravel()	
data = loadtxt('ex2data2.txt', delimiter=',')
x = data[:, 0:2]
y = data[:, 2]
lambd = 0.1
m = x.shape[0]
initial_theta = zeros(shape=(x.shape[1], 1)) 
compute_grad(initial_theta,x,y,lambd)

x_bias = hstack((ones((m, 1)), x))	

>>> import numpy
>>> data = numpy.loadtxt('ex2data2.txt', delimiter=',')
>>> x = data[:, 0: 2]
>>> y = data[:, 2]
>>> lambd = 0.1
>>> m = x.shape[0]
>>> initial_theta = numpy.zeros(shape=(x.shape[1], 1))
>>> x_bias = numpy.hstack((numpy.ones((m, 1)), x))
>>> theta = numpy.atleast_2d(initial_theta).transpose()

 
>>> x_bias.shape
(118, 3)
>>> theta.shape
(1, 2)
>>> dot(x_bias, theta)
Traceback (most recent call last):
  File "<input>", line 1, in <module>
ValueError: matrices are not aligned