blacked all

brain.py

@@ -1,37 +1,50 @@
 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))]
+
+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, W1=None, W2=None):
-        #parameters
+        # parameters
         self.inputSize = 3
         self.outputSize = 2
         self.hiddenSize = 3
-        self.fitness = 0 
+        self.fitness = 0
 
-        #weights
+        # weights
         if W1 is not None:
-            self.W1=W1
-        else :
-            self.W1 = np.random.randn(self.inputSize, self.hiddenSize) # weights from input to hidden layer
-        
+            self.W1 = W1
+        else:
+            self.W1 = np.random.randn(
+                self.inputSize, self.hiddenSize
+            )  # weights from input to hidden layer
+
         if W2 is not None:
-            self.W2=W2
-        else :
-            self.W2 = np.random.randn(self.hiddenSize, self.outputSize) # weights from hidden to output layer
+            self.W2 = W2
+        else:
+            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
-        self.z = np.dot(X, self.W1) # dot product of X (input) and first set of 3x2 weights
-        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
+        # forward propagation through our network
+        self.z = np.dot(
+            X, self.W1
+        )  # dot product of X (input) and first set of 3x2 weights
+        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
@@ -40,4 +53,4 @@ class Neural_Network(object):
 
     def sigmoid(self, s):
         # activation function
-        return 1/(1+np.exp(-s)) -0.5
+        return 1 / (1 + np.exp(-s)) - 0.5