Building a Perceptron based classifier

A Perceptron is the building block of an artificial neural network. It is a single neuron that takes inputs, performs computation on them, and then produces an output. It uses a simple linear function to make the decision. Let's say we are dealing with an N-dimension input data point. A Perceptron computes the weighted summation of those N numbers and it then adds a constant to produce the output. The constant is called the bias of the neuron. It is remarkable to note that these simple Perceptrons are used to design very complex deep neural networks. Let's see how to build a Perceptron based classifier using NeuroLab.

Create a new Python file and import the following packages:

import numpy as np 
import matplotlib.pyplot as plt 
import neurolab as nl 

Load the input data from the text file data_perceptron.txt provided to you. Each line contains space separated numbers where the first two numbers are the features and the last number is the label...