How To Implement Learning Vector Quantization (LVQ) From Scratch With Python

Last Updated on August 13, 2019

A limitation of k-Nearest Neighbors is that you must keep a large database of training examples in order to make predictions.

The Learning Vector Quantization algorithm addresses this by learning a much smaller subset of patterns that best represent the training data.

In this tutorial, you will discover how to implement the Learning Vector Quantization algorithm from scratch with Python.

After completing this tutorial, you will know:

• How to learn a set of codebook vectors from a training data set.
• How to make predictions using learned codebook vectors.
• How to apply Learning Vector Quantization to a real predictive modeling problem.

Kick-start your project with my new book Machine Learning Algorithms From Scratch, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

• Update Jan/2017: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.
• Update Aug/2018: Tested and updated to work with Python 3.6.