From 2cf3a89e7096eecf6a78c3b80de827e65b459582 Mon Sep 17 00:00:00 2001
From: "E. Almqvist" <h1g.server@gmail.com>
Date: Mon, 31 Aug 2020 12:29:46 +0000
Subject: [PATCH] Small fixes

---
 rgbAI/lib/func.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/rgbAI/lib/func.py b/rgbAI/lib/func.py
index f079756..4336679 100644
--- a/rgbAI/lib/func.py
+++ b/rgbAI/lib/func.py
@@ -16,26 +16,25 @@ class AIlib:
         for i in range(outLen):
             sumC += (out[i] - correctOut[i])**2 # get the difference of every value
 
-        return sumC / outLen # return the average cost of all rows
+        return sumC # return the cost
 
     def genRandomMatrix( x:int, y:int, min: float=0.0, max: float=1.0 ): # generate a matrix with x, y dimensions with random values from min-max in it
-        return np.random.rand(x, y)
+        # apply ranger with * and -
+        mat = np.random.rand(x, y) - 0.25
+        return mat
 
     def think( inp:np.array, weights:list, bias:list, layerIndex: int=0 ): # recursive thinking, hehe
         # the length of weights and bias should be the same
         # if not then the neural net is flawed/incorrect
         maxLayer = len(weights) - 1
-        biasLen = len(bias) - 1
-        if( maxLayer != biasLen ):
-            print("Neural Network Error: Length of weights and bias are not equal.")
-            print( "Weights: " + str(maxLayer) + " Bias: " + str(biasLen) )
-            exit()
 
         try:
             weightedInput = np.dot( inp, weights[layerIndex] ) # dot multiply the input and the weights
             layer = AIlib.sigmoid( np.add(weightedInput, bias[layerIndex]) ) # add the biases
 
             if( layerIndex < maxLayer ):
+                print(weights[layerIndex])
+                print("\n")
                 print("Layer " + str(layerIndex))
                 print(layer)
                 print("\n")