fminunc alternate in numpy

There is more information about the functions of interest here: http://docs.scipy.org/doc/scipy-0.10.0/reference/tutorial/optimize.html

Also, it looks like you are doing the Coursera Machine Learning course, but in Python. You might check out http://aimotion.blogspot.com/2011/11/machine-learning-with-python-logistic.html; this guy's doing the same thing.

I was also trying to implement logistic regression as discussed in Coursera ML course, but in python. I found scipy helpful. After trying different algorithm implementations in minimize function, I found Newton Conjugate Gradient as most helpful. Also After examining its returned value, it seems that it is equivalent to that of fminunc in Octave. I have included my implementation in python below find to optimal theta.

import numpy as npimport scipy.optimize as opdef Sigmoid(z):    return 1/(1 + np.exp(-z));def Gradient(theta,x,y):    m , n = x.shape    theta = theta.reshape((n,1));    y = y.reshape((m,1))    sigmoid_x_theta = Sigmoid(x.dot(theta));    grad = ((x.T).dot(sigmoid_x_theta-y))/m;    return grad.flatten();def CostFunc(theta,x,y):    m,n = x.shape;     theta = theta.reshape((n,1));    y = y.reshape((m,1));    term1 = np.log(Sigmoid(x.dot(theta)));    term2 = np.log(1-Sigmoid(x.dot(theta)));    term1 = term1.reshape((m,1))    term2 = term2.reshape((m,1))    term = y * term1 + (1 - y) * term2;    J = -((np.sum(term))/m);    return J;# intialize X and yX = np.array([[1,2,3],[1,3,4]]);y = np.array([[1],[0]]);m , n = X.shape;initial_theta = np.zeros(n);Result = op.minimize(fun = CostFunc,                                  x0 = initial_theta,                                  args = (X, y),                                 method = 'TNC',                                 jac = Gradient);optimal_theta = Result.x;

Looks like you have to change to scipy.

There you find all basic optimization algorithms readily implemented.

http://docs.scipy.org/doc/scipy/reference/optimize.html