from neuron import Neuron myNeuron = Neuron(3,'step') myNeuron.setWeights([0,0,0]) # the first weight is of Bias myNeuron.setMaxInteractions(100) # learning the AND function bias = 1 trainingMatrix = [[bias,0,0],[bias,1,0],[bias,1,1]] desiredArray = [0,0,1] converged = myNeuron.train(trainingMatrix,desiredArray) if converged: print 'Training OK (%d interactions)' %(myNeuron.getTrainingInteractions()) else: print 'Training FAILED (+%d interactions)' %(myNeuron.getMaxInteractions()) for i in myNeuron.rangeWeights(): print 'W'+str(i)+': '+str(myNeuron.getWeight(i)) myNeuron.setInputs([bias,0,1]) print myNeuron.getTrainingInteractions() print 'Output: '+str(myNeuron.think()) #output desired is 0
from neuron import Neuron myNeuron = Neuron(3, 'step') myNeuron.setWeights([0, 0, 0]) # the first weight is of Bias myNeuron.setMaxInteractions(100) # learning the AND function bias = 1 trainingMatrix = [[bias, 0, 0], [bias, 1, 0], [bias, 1, 1]] desiredArray = [0, 0, 1] converged = myNeuron.train(trainingMatrix, desiredArray) if converged: print 'Training OK (%d interactions)' % ( myNeuron.getTrainingInteractions()) else: print 'Training FAILED (+%d interactions)' % ( myNeuron.getMaxInteractions()) for i in myNeuron.rangeWeights(): print 'W' + str(i) + ': ' + str(myNeuron.getWeight(i)) myNeuron.setInputs([bias, 0, 1]) print myNeuron.getTrainingInteractions() print 'Output: ' + str(myNeuron.think()) #output desired is 0