genetics code (untested)

brain.py

@@ -1,17 +1,25 @@
 import numpy as np
+import random
 
+def mat_mult(A,B):
+    return [[sum([A[i][m]*B[m][j] for m in range(len(A[0]))]) for j in range(len(B[0]))] for i in range(len(A))]
 
 class Neural_Network(object):
     # inspired from https://enlight.nyc/projects/neural-network/
-    def __init__(self):
+    def __init__(self, W1=None, W2=None):
         #parameters
         self.inputSize = 3
         self.outputSize = 2
         self.hiddenSize = 3
+        self.fitness = 0 
 
         #weights
-        self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # weights from input to hidden layer
-        self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # weights from hidden to output layer
+        if not W1 :
+            self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # weights from input to hidden layer
+        if not W2 :
+            self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # weights from hidden to output layer
+        # self.w1 = [[random.random() for i in range(self.hiddenSize)] for i in range(self.inputSize)]
+        # self.w2 = [[random.random() for i in range(self.outputSize)] for i in range(self.hiddenSize)]
 
     def predict(self, X):
         #forward propagation through our network
@@ -19,6 +27,10 @@ class Neural_Network(object):
         self.z2 = self.sigmoid(self.z) # activation function
         self.z3 = np.dot(self.z2, self.W2) # dot product of hidden layer (z2) and second set of 3x1 weights
         o = self.sigmoid(self.z3) # final activation function
+        # self.z = mat_mult(X, self.w1) # dot product of X (input) and first set of 3x2 weights
+        # self.z2 = self.sigmoid(self.z) # activation function
+        # self.z3 = mat_mult(self.z2, self.w2) # dot product of hidden layer (z2) and second set of 3x1 weights
+        # o = self.sigmoid(self.z3) # final activation function
         return o
 
     def sigmoid(self, s):