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61 lines
1.9 KiB
61 lines
1.9 KiB
#!/usr/bin/env python
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import numpy as np
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from lib.func import AIlib as ai
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class rgb(object):
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def __init__(self, loadedWeights: np.matrix=None, loadedBias: np.matrix=None):
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if( not loadedWeights or not loadedBias ): # if one is null (None) then just generate new ones
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print("Generating weights and biases...")
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self.weights = [ ai.genRandomMatrix(3, 4), ai.genRandomMatrix(4, 4), ai.genRandomMatrix(4, 3) ] # array of matrices of weights
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# 3 input neurons -> 4 hidden neurons -> 4 hidden neurons -> 3 output neurons
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# Generate the biases
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self.bias = [ ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 4), ai.genRandomMatrix(1, 3) ]
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# This doesn't look very good, but it works so...
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self.generation = 0
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print( self.weights )
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print( self.bias )
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else: # if we want to load our progress from before then this would do it
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self.weights = loadedWeights
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self.bias = loadedBias
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def calcError( self, inp:np.array, out:np.array ):
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cost = ai.calcCost( inp, out )
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# Cost needs to get to 0, we can figure out this with backpropagation
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return cost
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def learn( self, inp:np.array, theta:float ):
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ai.learn( inp, self, theta )
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def think( self, inp:np.array ):
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print("-----Gen " + str(self.generation) + "------")
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print("\n-Input-")
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print(inp)
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print("\n")
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res = ai.think( inp, self.weights, self.bias )
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print("\n-Output-")
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print(res)
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print("\n----------------\n\n")
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return res
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def train( self ):
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for i in range(self.traintimes):
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inpArr = np.asarray(np.random.rand( 1, 3 ))[0]
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print("##################", inpArr)
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self.generation = i
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self.learn( inpArr, 0.1 )
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def init(): # init
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bot = rgb()
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bot.traintimes = 10
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bot.train()
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init()
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