Creating a vector quantizer

You can use neural networks for vector quantization as well. Vector quantization is the N-dimensional version of "rounding off". This is very commonly used across multiple areas in computer vision, natural language processing, and machine learning in general.

How to do it…

1. Create a new Python file, and import the following packages:

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

2. Let's load the input data from the data_vq.txt file:

   # Define input data
   input_file = 'data_vq.txt'
   input_text = np.loadtxt(input_file)
   data = input_text[:, 0:2]
   labels = input_text[:, 2:]

3. Define a learning vector quantization (LVQ) neural network with two layers. The array in the last parameter specifies the percentage weightage to each output (they should sum up to 1):

   # Define a neural network with 2 layers:
   # 10 neurons in input layer and 4 neurons in output layer
   net = nl.net.newlvq(nl.tool.minmax(data), 10, [0.25, 0.25, 0.25, 0...