# -*- coding: utf-8 -*-
"""
Created on Mon Apr 16 01:08:33 2018

@author: yusuf
"""

import numpy as np

def nonlin(x,deriv=False):
	if(deriv==True):
	    return x*(1-x)

	return 1/(1+np.exp(-x))
'''
X = np.array([[0,0,1],
            [0,1,1],
            [1,0,1],
            [1,1,1]])
                
y = np.array([[0],
			[1],
			[1],
			[0]])
'''
X =  np.array([[1.81, .80, .44], [1.77, .70, .43], [1.60, .60, .38], [1.54, .54, .37], [1.66, .65, .40],
     [1.90, .90, .47], [1.75, .64, .39], [1.77, .70, .40], [1.59, .55, .37], [1.71, .75, .42],
     [1.81, .85, .43]])
y = np.array([[1],[1], [0], [0], [1], [1], [0], [0], [0], [1], [1]])   

np.random.seed(10)

# randomly initialize our weights with mean 0
syn0 = 2*np.random.random((3,4)) - 1
syn1 = 2*np.random.random((4,1)) - 1

for j in range(60000):

	# Feed forward through layers 0, 1, and 2
    l0 = X
    l1 = nonlin(np.dot(l0,syn0))
    l2 = nonlin(np.dot(l1,syn1))

    # how much did we miss the target value?
    l2_error = y - l2
    
    if (j% 10000) == 0:
        print("Error:" + str(np.mean(np.abs(l2_error))))
        
    # in what direction is the target value?
    # were we really sure? if so, don't change too much.
    l2_delta = l2_error*nonlin(l2,deriv=True)

    # how much did each l1 value contribute to the l2 error (according to the weights)?
    l1_error = l2_delta.dot(syn1.T)
    
    # in what direction is the target l1?
    # were we really sure? if so, don't change too much.
    l1_delta = l1_error * nonlin(l1,deriv=True)

    syn1 += l1.T.dot(l2_delta)
    syn0 += l0.T.dot(l1_delta)


def  yap(_X):    
    l0 = _X
    l1 = nonlin(np.dot(l0,syn0))
    l2 = nonlin(np.dot(l1,syn1))
    sonuc = l2
    if sonuc[0][0] > 0.5:
        print("erkek: ",sonuc[0][0])
    else:
        print("kadın: ",sonuc[0][0])
    