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51 lines
1.4 KiB
51 lines
1.4 KiB
#!/usr/bin/env python
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import numpy as np
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from lib.ailib import 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, 3), ai.genRandomMatrix(3, 3), ai.genRandomMatrix(3, 3) ] # array of matrices of weights
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# 3 input neurons -> 3 hidden neurons -> 3 hidden neurons -> 3 output neurons
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# Generate the biases
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self.bias = [ ai.genRandomMatrix(1, 3), ai.genRandomMatrix(1, 3), ai.genRandomMatrix(1, 3) ]
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# This doesn't look very good, but it works so...
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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 ):
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ai.learn( 3, 0.001, self, 0.001 )
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def think( self, inp:np.array ):
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print("\n-Input-")
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print(inp)
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res = ai.think( inp, self )
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print("\n-Output-")
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print(res)
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return res
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def init():
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bot = rgb()
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bot.learn()
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#inpArr = np.asarray([1.0, 1.0, 1.0])
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#res = bot.think( inpArr )
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#err = bot.calcError( inpArr, res )
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#print(err)
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init()
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