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Perceptron.py
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Perceptron.py
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class Perceptron(object):
def __init__(self,input_num,activator):
#Initializing Perceptron with arg# and
#activator
self.activator = activator
self.weights = [0.0 for _ in range(input_num)]
self.bias = 0
def __str__(self):
return 'weights\t:%s\nbias\t:%f\n' % (self.weights,self.bias)
def predict(self,input_vec):
return self.activator(
reduce(lambda a, b: a + b,
list(map(lambda x:x[0] * x[1],list(zip(input_vec,self.weights))))) + self.bias)
def train(self, input_vec ,labels, iteration,rate):
for i in range(iteration):
self._one_iteration(input_vec,labels,rate)
def _one_iteration(self,input_vec,labels,rate):
##one iteration
samples = zip(input_vec,labels)
for (input_vec,labels) in samples:
output = self.predict(input_vec)
self._update_weights(input_vec,output,labels,rate)
def _update_weights(self,input_vec,output,labels,rate):
delta = labels - output
self.weights = list(map(lambda x:x[1] + rate * delta * x[0],list(zip(input_vec,self.weights))))
self.bias += rate * delta
def f(x):
return 1 if x > 0 else 0
def get_training_dataset():
input_vec = [[1,1],[0,0],[1,0],[0,1]]
labels = [1,0,0,0]
return input_vec,labels
def train_and_perceptron():
p = Perceptron(2,f)
input_vec,labels = get_training_dataset()
p.train(input_vec,labels,10,0.1)
return p
if __name__ == '__main__':
and_perceptron = train_and_perceptron()
print(and_perceptron)
print('1 and 1 = %d' % and_perceptron.predict([1,1]))
print('1 and 0 = %d' % and_perceptron.predict([1,0]))
print('0 and 1 = %d' % and_perceptron.predict([0,1]))
print('0 and 0 = %d' % and_perceptron.predict([0,0]))