Skip to main content

A vectorised implementation of the Self-Organising Map (SOM) algorithm

Project description

This is a vectorised python implementation of the Self-Organising Map (SOM) algorithm. It was created for use in the author’s master dissertation and has now been released for anyone who might want to benefit from it. Please inform me of any additional functionality that might be desired.

## Features: ##

  • Fits and plots features, Best Matching Unit (BMU) counts and training curves for SOM

  • Quantisation or topological error based stopping criteria

  • Hypercube or random uniform weight initialisation

  • Periodic Boundary Conditions (PBC) allowing the SOM to wrap like a torous

  • Semi-Supervised training mode whereby the labels for a supervised learning problem are used to update the weights of the SOM but not to find the BMU

  • Clusters SOM nodes using K-Means clustering and plots cluster maps

  • Models, clusters and images are automatically saved (this is configurable)

  • Quantisation and topological errors are automatically logged to a csv file for each run of the model (with parameter values saved for reproducibility)

  • Has been used to fit data for a masters dissertation on a dataset with an excess of 1 million records

BitBucket repo: https://bitbucket.org/GeoffreyClark/somvec

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

somvec-0.1.2.tar.gz (9.5 kB view details)

Uploaded Source

Built Distribution

somvec-0.1.2-py3-none-any.whl (9.0 kB view details)

Uploaded Python 3

File details

Details for the file somvec-0.1.2.tar.gz.

File metadata

  • Download URL: somvec-0.1.2.tar.gz
  • Upload date:
  • Size: 9.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/45.2.0 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.6.9

File hashes

Hashes for somvec-0.1.2.tar.gz
Algorithm Hash digest
SHA256 24e91a1dd557560e4f5f09e1d6a8dae2f3c21fa3b8330f93c3abf0633d4ecaa7
MD5 e3118a2a088d3d6748e547b80df716f6
BLAKE2b-256 b3ecdebd5d4fa60eaf6b456e362a5c5935f86c8838f420c6055b96302394678e

See more details on using hashes here.

File details

Details for the file somvec-0.1.2-py3-none-any.whl.

File metadata

  • Download URL: somvec-0.1.2-py3-none-any.whl
  • Upload date:
  • Size: 9.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/45.2.0 requests-toolbelt/0.9.1 tqdm/4.43.0 CPython/3.6.9

File hashes

Hashes for somvec-0.1.2-py3-none-any.whl
Algorithm Hash digest
SHA256 0811f3bfe2ea30d5da851c2f3bd72bd1c2a6a72453df2f28534ae9304595a365
MD5 ac1959646f39bf91fa024e19b446abb3
BLAKE2b-256 dafa5c1118def422d93d8ea0c323911e982b964547fe58497a973f945cf1d7ee

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page