Example #1
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
Example #2
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