Imporvements to the AIs init function

lib/func.py

@@ -1,10 +1,11 @@
 import numpy as np
 
-def sigmoid(x):
+class AIlib:
+    def sigmoid(x):
         return 1/(1 + np.exp(-x))
 
-def correctFunc(rgb): # generates the correct answer for the AI 
+    def correctFunc(rgb): # generates the correct answer for the AI 
         return ( rgb[2], rgb[1], rgb[0] ) # basically invert the rgb values
 
-def genRandomMatrix( x:int, y:int ):
+    def genRandomMatrix( x:int, y:int ): # generate a matrix with x, y dimensions with random values from 0-1 in it
         return np.random.rand(x, y)

main.py

@@ -1,14 +1,26 @@
 #!/usr/bin/env python
 
-from lib.func import *
+from lib.func import AIlib as ai
 
 class rgb(object):
-    def __init__(self):
-        self.weights = 1
+    def __init__(self, loadedWeights = None, loadedBias = None):
 
-    def think(self, inputMatrix):
-        print("thonk: " + str( sigmoid(self.weights) ))
+        if( not loadedWeights or not loadedBias ):
+            self.weights = [ ai.genRandomMatrix(3, 4), ai.genRandomMatrix(4, 4), ai.genRandomMatrix(4, 3) ] # array of matrices of weights
+            # 3 input neurons -> 4 hidden neurons -> 4 hidden neurons -> 3 output neurons
+
+            # Will be needing biases too
+            self.bias = [ ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 3) ]
+            # This doesn't look very good, but it works so...
+            # This is all we need 
+        else: # if we want to load our progress from before then this would do it
+            print("Loading neural net...")
+            self.weights = loadedWeights
+            self.bias = loadedBias
 
+    def think(self, inputMatrix):
+        print(self.weights)
+        print(self.bias)
 def init(): # init func
     bot = rgb()