Implementation of Perceptron Algorithm for AND Logic with 2-bit binary input in Python
Before we start the implementation question arises What 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 b is bias parameter, for any choice of W and b, the function makes output y(unit vector ^) for the equivalent input vector 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 2-bit binary variable. Let’s consider input vector x=(x1, x2) and output is y
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We now consider the weight vector
W=(w1, w2) of the input vector
X=(x1, x2) Perceptron function
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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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