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

• Last Updated : 08 Jun, 2020

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    000
010
100
111

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 libraryimport numpy as np  # define Unit Step Functiondef unitStep(v):    if v >= 0:        return 1    else:        return 0  # design Perceptron Modeldef perceptronModel(x, w, b):    v = np.dot(w, x) + b    y = unitStep(v)    return y  # AND Logic Function# w1 = 1, w2 = 1, b = -1.5def AND_logicFunction(x):    w = np.array([1, 1])    b = -1.5    return perceptronModel(x, w, b)  # testing the Perceptron Modeltest1 = 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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