# Implementation of Perceptron Algorithm for AND Logic with 2-bit binary input in Python

Before we start the implementation question arises **W****hat is Perceptron?**

Perceptron is an algorithm in machine learning used for binary classifiers. It is a supervised learning algorithm. To implement the perceptron algorithm, we use the function:

In this function, * W *is the weight vector and

*is bias parameter, for any choice of*

**b***, the function makes output y(unit vector ^) for the equivalent input vector*

**W**and**b**

**X.**Now, in this problem** ,** we have to implement it with the help of

**AND gate**, as we know the logical truth table for AND gate for the

**. Let’s consider input vector**

*2-bit binary variable***and output is**

*x=(x1, x2)***y**

Image:

We now consider the weight vector

W=(w1, w2) of the input vector

X=(x1, x2) Perceptron function

Image:

## Code: Perceptron Algorithm for AND Logic with 2-bit binary input in Python

For implementation in code, we consider weight **W1= 2 and W2= 2** and value of **b(bias paramter) = -1 **

import numpy as np # implementing unit Step def Steps(v): if v >= 0: return 1 else: return 0 # creating Perceptron def perceptron(x, w, b): v = np.dot(w, x) + b y = Steps(v) return y def logic_AND(x): w = np.array([2, 2]) b = -1 return perceptron(x, w, b) # testing the Perceptron Model p1 = np.array([0, 1]) p2 = np.array([1, 1]) p3 = np.array([0, 0]) p4 = np.array([1, 0]) print("AND(0, 1) = {}".format( logic_AND(p1))) print("AND(1, 1) = {}".format( logic_AND(p2))) print("AND(0, 0) = {}".format( logic_AND(p3))) print("AND(1, 0) = {}".format( logic_AND(p4)))

#### Output

AND(0, 1) = 1 AND(1, 1) = 1 AND(0, 0) = 0 AND(1, 0) = 1 [Program finished]

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