# # __author__ = 'Sony'
import sys
import scipy.optimize, scipy.special
from numpy import *
from matplotlib import pyplot, cm
from mpl_toolkits.mplot3d import Axes3D
 
# EX_DIRECTORY_PATH = 'C:\Users\Sony\OneDrive\#my lab\NeuralNets'
 
def plot_samples(data):
    X, y = data[:, 0], data[:, 1]
    pyplot.xlabel("X - axis")
    pyplot.ylabel("Y - axis")
    pyplot.xlim([0, 51])
    pyplot.ylim([0, 51])
 
    pyplot.scatter( X, y, c='b', marker='o', s=40, linewidths=1, label="input samples" )
    pyplot.legend()
    pyplot.show()
    pyplot.close()
 
def cost( theta, data ):
    X, y = data[:, 0], data[:, 1]
    m = shape(X)[0]
    y 	 = y.reshape(m, 1)
    X = c_[ones((m, 1)), X]
 
    J = X.dot(theta) - y
    J = J.T.dot(J) / (m)
    # print(J[0, 0])
    return J
 
def gradDesc(theta, data):
    X = data[:, 0]
    y  = data[:, 1]
    m = shape(X)[0]
    X = c_[ones((m, 1)), X]
    y 	 = y.reshape(m, 1)
    hypo = X.dot(theta)
    grad = X.T.dot(hypo - y)/m
    # print(grad)
    return grad
 
def run(theta, data ):
    result 	= scipy.optimize.fmin_cg( f = cost, fprime=gradDesc, x0=theta,  \
                                        args = (data), maxiter=50, disp=False, full_output=True )
    theta = result[0]
    minCost = result[1]
    return theta, minCost
 
def main():
    data = genfromtxt('in.txt', delimiter=',')
    theta = zeros((2, 1))
    # plot_samples(data)
    run(theta, data)
    # gradDesc(theta, data)
if __name__ == '__main__':
    main()
 
 

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5,5
10,15
44,44
24,20
2,2