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41 lines
1.1 KiB
Python
41 lines
1.1 KiB
Python
import numpy as np
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class NN():
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score = 0
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def __init__(self, *args):
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self.layers = args
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self.synapses = []
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for i in range(len(self.layers)-1):
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self.synapses.append(np.random.normal(scale=1,size=(self.layers[i], self.layers[i+1])))
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def calculateOutput(self, input):
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layerInput = input
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for synapseLayer in self.synapses:
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layerInput = np.dot(layerInput, synapseLayer)
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layerInput = self.sigmoid(layerInput)
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return layerInput
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def sigmoid(self, s):
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#print("Arr:", arr)
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#s = np.array(arr)
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# activation function
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res = 1 / (1 + np.exp(-s))
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#print("Res:", res)
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return res
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def getSynapses(self):
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return self.synapses
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def setSynapses(self, synapses):
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self.synapses = synapses
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def mutate(self, val):
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mut_arr = []
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for i in range(len(self.layers)-1):
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mut_arr.append(np.random.normal(scale=val,size=(self.layers[i], self.layers[i+1])))
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self.synapses = np.add(self.synapses, mut_arr)
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return mut_arr
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