# Implementation of Perceptron Algorithm for AND Logic Gate with 2-bit Binary Input

In the field of Machine Learning, the Perceptron is a Supervised Learning Algorithm for binary classifiers. The Perceptron Model implements the following function: For a particular choice of the weight vector and bias parameter , the model predicts output for the corresponding input vector .

AND logical function truth table for 2-bit binary variables, i.e, the input vector and the corresponding output    0 0 0
0 1 0
1 0 0
1 1 1

Now for the corresponding weight vector of the input vector , the associated Perceptron Function can be defined as:  For the implementation, considered weight parameters are and the bias parameter is .

Python Implementation:

 # importing Python library  import numpy as np     # define Unit Step Function  def unitStep(v):      if v >= 0:          return 1     else:          return 0    # design Perceptron Model  def perceptronModel(x, w, b):      v = np.dot(w, x) + b      y = unitStep(v)      return y     # AND Logic Function  # w1 = 1, w2 = 1, b = -1.5  def AND_logicFunction(x):      w = np.array([1, 1])      b = -1.5     return perceptronModel(x, w, b)     # testing the Perceptron Model  test1 = np.array([0, 1])  test2 = np.array([1, 1])  test3 = np.array([0, 0])  test4 = np.array([1, 0])     print("AND({}, {}) = {}".format(0, 1, AND_logicFunction(test1)))  print("AND({}, {}) = {}".format(1, 1, AND_logicFunction(test2)))  print("AND({}, {}) = {}".format(0, 0, AND_logicFunction(test3)))  print("AND({}, {}) = {}".format(1, 0, AND_logicFunction(test4)))

Output:

AND(0, 1) = 0
AND(1, 1) = 1
AND(0, 0) = 0
AND(1, 0) = 0


Here, the model predicted output ( ) for each of the test inputs are exactly matched with the AND logic gate conventional output ( ) according to the truth table for 2-bit binary input.
Hence, it is verified that the perceptron algorithm for AND logic gate is correctly implemented.

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