Vector Quantization is a quantization technique where the input data is represented by a fixed number of representative points. It is the N-dimensional equivalent of rounding off a number. This technique is commonly used in multiple fields such as image recognition, semantic analysis, and data science. Let's see how to use artificial neural networks to build a vector quantizer.

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 file data_vector_quantization.txt. Each line in this file contains six numbers. The first two numbers form the datapoint and the last four numbers form a one-hot encoded label. There are four classes overall.

# Load input data 
text = np.loadtxt('data_vector_quantization.txt') 

Separate the text into data and labels:

# Separate it into data and labels 
data = text[:, 0:2] 
labels = text[:, 2:...