Perceptron Algorithm for Logic Gate with 3-bit Binary Input

# importing python library
import numpy as np

# sigmoid activation function
def activationFunction(model, type ="sigmoid"):
   return {
       "sigmoid": 1 / (1 + np.exp(-model))
   }[type]

# designing perceptron model
def perceptronModel(weights, inputs, bias):
   model = np.add(np.dot(inputs, weights), bias)
   logic = activationFunction(model, type ="sigmoid")
   return np.round(logic)

# computation model
def compute(data, logicGate, weights, bias):
   weights = np.array(weights)
   output = np.array([ perceptronModel(weights,  
            datum, bias) for datum in data ])
   return output

# Print Output
def printOutput(dataset, name, data):
   print("Logic Function: {}".format(name.upper()))
   print("X1\tX2\tX3\tY")
   toPrint = ["{1}\t{2}\t{3}\t{0}".format(output, *datas)  
              for datas, output in zip(dataset, data)]
   for i in toPrint:
       print(i)

# main function
def main():
   # 3 bit binary data
   dataset = np.array([
     [0, 0, 0],
     [0, 0, 1],
     [0, 1, 0],
     [0, 1, 1],
     [1, 0, 0],
     [1, 0, 1],
     [1, 1, 0],
     [1, 1, 1]
   ])

   # Parameters of every Logic Gates
   # weight parameters: w1, w2, w3
   # bias parameter: b
   logicGate = {
       "and": compute(dataset, "and", [1, 1, 1], -2),
       "or": compute(dataset, "or", [1, 1, 1], -0.9),
       "nand": compute(dataset, "nand", [-1, -1, -1], 3),
       "nor": compute(dataset, "nor", [-1, -1, -1], 1)
   }
   for gate in logicGate:
       printOutput(dataset, gate, logicGate[gate])

if __name__ == '__main__':
   main()