Implementation of Perceptron Algorithm for NOT Logic Gate

In the field of Machine Learning, the Perceptron is a Supervised Learning Algorithm for binary classifiers. The Perceptron Model implements the following function:

    \[ \begin{array}{c} \hat{y}=\Theta\left(w_{1} x_{1}+w_{2} x_{2}+\ldots+w_{n} x_{n}+b\right) \\ =\Theta(\mathbf{w} \cdot \mathbf{x}+b) \\ \text { where } \Theta(v)=\left\{\begin{array}{cc} 1 & \text { if } v \geqslant 0 \\ 0 & \text { otherwise } \end{array}\right. \end{array} \]

For a particular choice of the weight vector $\boldsymbol{w}$ and bias parameter $\boldsymbol{b}$, the model predicts output $\boldsymbol{\hat{y}}$ for the corresponding input vector $\boldsymbol{x}$.

NOT logical function truth table is of only 1-bit binary input (0 or 1), i.e, the input vector $\boldsymbol{x}$ and the corresponding output $\boldsymbol{y}$

$\boldsymbol{x}$ $\boldsymbol{y}$
0 1
1 0

Now for the corresponding weight vector $\boldsymbol{w}$ of the input vector $\boldsymbol{x}$, the associated Perceptron Function can be defined as:

    \[$\boldsymbol{\hat{y}} = \Theta\left(w x+b\right)$\]

For the implementation, considered weight parameter is $\boldsymbol{w} = -1$ and the bias parameter is $\boldsymbol{b} = 0.5$.

Python Implementation:





# importing Python library
import numpy as np
# define Unit Step Function
def unitStep(v):
    if v >= 0:
        return 1
        return 0
# design Perceptron Model
def perceptronModel(x, w, b):
    v =, x) + b
    y = unitStep(v)
    return y
# NOT Logic Function
# w = -1, b = 0.5
def NOT_logicFunction(x):
    w = -1
    b = 0.5
    return perceptronModel(x, w, b)
# testing the Perceptron Model
test1 = np.array(1)
test2 = np.array(0)
print("NOT({}) = {}".format(1, NOT_logicFunction(test1)))
print("NOT({}) = {}".format(0, NOT_logicFunction(test2)))



NOT(1) = 0
NOT(0) = 1

Here, the model predicted output ($\boldsymbol{\hat{y}}$) for each of the test inputs are exactly matched with the NOT logic gate conventional output ($\boldsymbol{y}$) according to the truth table.
Hence, it is verified that the perceptron algorithm for NOT logic gate is correctly implemented.

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using or mail your article to See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.