simpsom 1.3.2

A lightweight Python library for Kohonen Self-Organising Maps

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

Version 1.3.2
------------

SimpSOM is a lightweight implementation of Kohonen Self-Organising Maps (SOM) for Python 2.7 and 3.3,
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.
- Now compatible with both Python 2.X and 3.X