A Implementation of a single perceptron that predicts the output of a linear function

perceptron.py

import numpy as np 

inputs  =  np.array([[1],[100],[3],[30],[40]])
testSet =  np.array([[10],[13],[19],[33],[1]])
outputs =  np.array([[5],[203],[9],[63],[83]])
bias    =  1
biasWeight = 1
#initWeights 
weights = np.random.random()

print inputs.shape


def sigmoid(x):
	return 1/(1+np.exp(-x))

for j in xrange(1, 70000):
	for k in xrange(len(inputs)):
		x = (inputs[k] * weights) + (1 * biasWeight)
		x = sigmoid(x)
		biasWeight = biasWeight + sigmoid(outputs[k]) - x
		weights = weights + ((sigmoid(outputs[k]) - x) * inputs[k])
		if j%10000 == 0:
			print weights
	
print (testSet * weights) + (bias * biasWeight)