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HamNN

This repo, as well as the associated cli app VHamNN, have been superseded by the repo VHamML .

A machine learning (ML) library for classification using a nearest neighbor algorithm based on Hamming distances. hamnn documentation

There is an associated Command Line Interface app, VHamNN , which facilitates using the hamnn library.

You can use the library with your own datasets, or with a selection of publicly available datasets that are widely used for demonstrating and testing ML classifiers, in the datasets directory. These files are either in ARFF (Attribute-Relation File Format) or in Orange file format .

Classification accuracy with datasets in the datasets directory: See this table

Do we really need another ML library? Read this!

And have a look here for a more complete description and potential use cases .

To use the HamNN library

This assumes you already have V installed on your system. If not, please refer to the VHamNN readme for instructions on installing V.

v install holder66.hamnn

And (optionally) libraries used by HamNN for generating plots and for colored output on the console:

v install vsl
v install Mewzax.chalk

In your V code:

import holder66.hamnn

Example source code:

module main

import hamnn
import os

fn main() {
    opts := hamnn.Options{
        show_flag: true
    }
    datafile_path := os.home_dir() + '/.vmodules/holder66/hamnn/datasets/iris.tab'
    println(datafile_path)

    ds := hamnn.load_file(datafile_path)
    result := hamnn.analyze_dataset(ds, opts)
    println(result)
}

Getting help:

The V lang community meets on Discord

For bug reports, feature requests, etc., please raise an issue on github:

for HamNN

for VHamNN

MNIST dataset

The mnist_train.tab file is too large to keep in the repository. If you wish to experiment with it, it can be downloaded by right-clicking on this link in a web browser, or downloaded via the command line:

wget http://henry.olders.ca/datasets/mnist_train.tab

Previous versions

The most recent version of the HamNN algorithm (2012) was written in python; one can experiment with it via a web-based interface . I’ve posted test results using this classifier with a number of publicly accessible datasets. Here are some additional test results with genomics datasets.

The process of development in its early stages is described in this essay written in 1989.