Finished main AI thinking. Next is to add backpropagation and learning etc

rgbAI/lib/func.py

@@ -5,13 +5,13 @@ class AIlib:
         return 1/(1 + np.exp(-x))
 
     def correctFunc(inp:np.array): # generates the correct answer for the AI 
-        return np.array( rgb[2], rgb[1], rgb[0] ) # basically invert the rgb values
+        return np.array( [inp[2], inp[1], inp[0]] ) # basically invert the rgb values
 
     def calcCost( inp:np.array, out:np.array ): # cost function, lower -> good, higher -> bad, bad bot, bad
         sumC = 0
         outLen = len(out)
 
-        correctOut = correctFunc(inp) # the "correct" output
+        correctOut = AIlib.correctFunc(inp) # the "correct" output
 
         for i in range(outLen):
             sumC += (out[i] - correctOut[i])**2 # get the difference of every value
@@ -43,7 +43,7 @@ class AIlib:
             if( layerIndex < maxLayer ):
                 return AIlib.think( layer, weights, bias, layerIndex + 1 )
             else:
-                return layer
+                return np.squeeze(np.asarray(layer))
 
         except (ValueError, IndexError) as err:
             print("\n---------")

rgbAI/main.py

@@ -14,14 +14,22 @@ class rgb(object):
             self.bias = [ ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 3) ]
             # This doesn't look very good, but it works so...
 
+            self.generation = 0
+
         else: # if we want to load our progress from before then this would do it
             self.weights = loadedWeights
             self.bias = loadedBias
 
+    def calcError( self, inp:np.array, out:np.array ):
+        cost = ai.calcCost( inp, out )
+        print(cost)
+        # Cost needs to get to 0, we can figure out this with backpropagation
+
     def learn():
         print("learn")
 
     def think(self, inp:np.array):
+        print("-----Gen " + str(self.generation) + "------")
         print("\n-Input-")
         print(inp)
         print("\n")
@@ -30,9 +38,15 @@ class rgb(object):
 
         print("\n-Output-")
         print(res)
+        print("\n----------------\n\n")
+        return res
 
-def init(): # init func
+def init(): # init
     bot = rgb()
-    bot.think( np.array([0.2, 0.4, 0.8]) )
+
+    inpArr = np.array( [0.2, 0.4, 0.8] )
+    res = bot.think( inpArr )
+
+    cost = bot.calcError(inpArr, res)
 
 init()