def __init__(self, patternSet):
inputLayer = Layer(NetLayerType.Input, None, patternSet.inputMagnitude())
ruleLayer = Layer(NetLayerType.Rules, inputLayer, patternSet.inputMagnitude())
prodNormLayer = Layer(NetLayerType.ProdNorm, ruleLayer, patternSet.outputMagnitude())
consequentLayer = Layer(NetLayerType.Consequent, prodNormLayer, patternSet.outputMagnitude())
consequentLayer.consequences = [[randomInitialWeight() for _ in range(patternSet.inputMagnitude() + 1)] for _ in range(len(prodNormLayer.neurons))]
self.layers = [inputLayer, ruleLayer, prodNormLayer, consequentLayer]
self.patternSet = patternSet
self.absError = 100
self.buildRules()
def __init__(self, patternSet):
inputLayer = Layer(NetLayerType.Input, None, patternSet.inputMagnitude())
hiddenLayer = Layer(NetLayerType.Hidden, inputLayer, patternSet.outputMagnitude())
outputLayer = Layer(NetLayerType.Output, hiddenLayer, patternSet.outputMagnitude())
self.layers = [inputLayer, hiddenLayer, outputLayer]
self.patternSet = patternSet
self.absError = 100
self.buildCenters()