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.