simpsom 1.2.0

A lightweight Python library for Kohonen Self-Organising Maps

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

Version 1.2.0
------------

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), optional, needed only for clustering with algorithms other than Quality Threshold.

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.
net = sps.somNet(20, 20, raw_data)

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

#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 Mean Shift algorithm from sklearn.
net.cluster(raw_data, type='MeanShift')

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

- Density-Peak Clustering is now available under the ``dpeak`` command.

TO DOs:
-------

- Improve efficiency of Density-Peak Clustering
- Update the available cluster algorithms from sklearn;
- Test for Python 3