Skip to main content

A lightweight Python library for Kohonen Self-Organising Maps

Project description

SimpSOM (Simple Self-Organizing Maps)
=====================================

Version 1.3.1
------------

SimpSOM is a lightweight implementation of Kohonen Self-Organising Maps (SOM) for Python 2.7,
useful for unsupervised learning, clustering and dimensionality reduction.

The package is now available on PyPI, to retrieve it just type ``pip install SimpSOM`` or download it from here
and install with ``python setup.py install``.

It allows to build and train SOM on your dataset, save/load the trained network weights, and display or print graphs
of the network with selected features.
The function ``run_colorsExample()`` will run a toy model, where a number of colors will be mapped from the 3D
RGB space to the 2D network map and clustered according to their similarity in the origin space.

Dependencies
------------

- Numpy 1.11.0 (older versions may work);
- Matplotlib 1.5.1 (older versions may work);
- Sklearn 0.15 (older versions may work);

Example of Usage
----------------

Here is a quick example on how to use the library with a ``raw_data`` dataset::

#Import the library
import SimpSOM as sps

#Build a network 20x20 with a weights format taken from the raw_data and activate Periodic Boundary Conditions.
net = sps.somNet(20, 20, raw_data, PBC=True)

#Train the network for 10000 epochs and with initial learning rate of 0.1.
net.train(0.01, 10000)

#Save the weights to file
net.save('filename_weights')

#Print a map of the network nodes and colour them according to the first feature (column number 0) of the dataset
#and then according to the distance between each node and its neighbours.
net.nodes_graph(colnum=0)
net.diff_graph()

#Project the datapoints on the new 2D network map.
net.project(raw_data, labels=labels)

#Cluster the datapoints according to the Quality Threshold algorithm.
net.cluster(raw_data, type='qthresh')

What's New
------------------------

- The SOM now works on an hexagonal grid.
- It is now possible to activate periodic boundary conditions, but only Quality Threshold and Density Peak clustering can be used with PBC;
- Fixed the random selection of weights, the random vectors now span the space of the data;
- Implemented PCA initialisations (activate with PCI=1);
- Scikit learn is now a mandatory requirement;
- KMeans clustering is now available under the command 'kmeans';
- If the number of epochs for the training is not explicitly stated, automatically choose 10*number of datapoints.


TO DOs:
-------

- Convert to Python 3

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

SimpSOM-1.3.1.tar.gz (15.4 kB view hashes)

Uploaded source

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page