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51 lines
2.0 KiB
51 lines
2.0 KiB
import numpy as np
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class AIlib:
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def sigmoid(x):
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return 1/(1 + np.exp(-x))
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def correctFunc(inp:np.array): # generates the correct answer for the AI
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return np.array( rgb[2], rgb[1], rgb[0] ) # basically invert the rgb values
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def calcCost( inp:np.array, out:np.array ): # cost function, lower -> good, higher -> bad, bad bot, bad
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sumC = 0
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outLen = len(out)
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correctOut = correctFunc(inp) # the "correct" output
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for i in range(outLen):
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sumC += (out[i] - correctOut[i])**2 # get the difference of every value
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return sumC / outLen # return the average cost of all rows
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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
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return np.random.rand(x, y)
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def think( inp:np.array, weights:list, bias:list, layerIndex: int=0 ): # recursive thinking, hehe
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# the length of weights and bias should be the same
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# if not then the neural net is flawed/incorrect
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maxLayer = len(weights) - 1
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biasLen = len(bias) - 1
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if( maxLayer != biasLen ):
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print("Neural Network Error: Length of weights and bias are not equal.")
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print( "Weights: " + str(maxLayer) + " Bias: " + str(biasLen) )
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exit()
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try:
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print("Think " + str(layerIndex))
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weightedInput = np.dot( weights[layerIndex], inp ) # dot multiply the input and the weights
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layer = AIlib.sigmoid( np.add(weightedInput, bias[layerIndex]) ) # add the biases
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print(layer)
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print("\n")
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if( layerIndex < maxLayer ):
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return AIlib.think( layer, weights, bias, layerIndex + 1 )
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else:
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return layer
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except (ValueError, IndexError) as err:
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print("\n---------")
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print( "Error: " + str(err) )
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print( "Layer index: " + str(layerIndex) )
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print( "Max layer index: " + str(maxLayer) )
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