cleaned up

1 files changed, 0 insertions, 60 deletions

diff --git a/src/python/nn.py b/src/python/nn.py
deleted file mode 100755
index b36b103..0000000
--- a/src/python/nn.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/usr/bin/env python
-
-# Import numpy for using matrix operations
-import numpy as np
-
-# define an actovation function
-def sigmoid(x,deriv=False):
-    if(deriv==True):
-        return x*(1-x)
-    return 1/(1+np.exp(-x))
-
-# 0 0 1 0
-# 0 1 1 0
-# 1 0 1 1
-# 1 1 1 1
-
-# define an input matrix
-X = np.array([ [0,0,1], [0,1,1], [1,0,1], [1,1,1] ])
-
-# define an output matrix
-y = np.array([[0,0,1,1]]).T
-
-# seed numpy
-np.random.seed(1)
-
-# generate some weights
-syn0 = 2*np.random.random((3,4)) - 1
-
-# define how often the calculations should be run
-
-n = 100000
-
-# loop
-for i in range(n):
-    # define the first layer
-    l0 = X
-
-    # define the second layer using the first layer and the weights
-    l1 = sigmoid(np.dot(l0,syn0))
-
-    # calculate an error
-    l1_error = y - l1
-
-    # calculate how fatal the error is
-    l1_delta = l1_error * sigmoid(l1,True)
-
-    # adjust the weights
-    syn0 += np.dot(l0.T,l1_delta)
-
-    # print some information
-    if (i % (n / 10000) == 0):
-        print("l1: " + str(l1))
-
-print("")
-print("Output After Training:")
-print(l1)
-
-l0 = np.array([0, 1, 0])
-l1 = sigmoid(np.dot(l0, syn0))
-print(l1)