Warning - Attention Users =========================

Please be aware that the pyclustering library is no longer supported as of 2021 due to personal reasons. There will be no further maintenance, issue addressing, or feature development for this repository.

For continued usage, I recommend seeking alternative solutions.

Thank you for your understanding.

Build Status ============

|Build Status Linux MacOS| |Build Status Win| |Coverage Status| |PyPi| |Download Counter| |JOSS|

PyClustering ============

pyclustering is a Python, C++ data mining library (clustering algorithm, oscillatory networks, neural networks). The library provides Python and C++ implementations (C++ pyclustering library) of each algorithm or model. C++ pyclustering library is a part of pyclustering and supported for Linux, Windows and MacOS operating systems.

Version: 0.11.dev

License: The 3-Clause BSD License

E-Mail: pyclustering@yandex.ru

Documentation: https://pyclustering.github.io/docs/0.10.1/html/

Homepage: https://pyclustering.github.io/

PyClustering Wiki: https://github.com/annoviko/pyclustering/wiki

Dependencies ============

Required packages: scipy, matplotlib, numpy, Pillow

Python version: >=3.6 (32-bit, 64-bit)

C++ version: >= 14 (32-bit, 64-bit)

Performance ===========

Each algorithm is implemented using Python and C/C++ language, if your platform is not supported then Python implementation is used, otherwise C/C++. Implementation can be chosen by ccore flag (by default it is always 'True' and it means that C/C++ is used), for example:

.. code:: python

# As by default - C/C++ part of the library is used xmeansinstance1 = xmeans(datapoints, startcenters, 20, ccore=True);

# The same - C/C++ part of the library is used by default xmeansinstance2 = xmeans(datapoints, startcenters, 20);

# Switch off core - Python is used xmeansinstance3 = xmeans(datapoints, startcenters, 20, ccore=False);

Installation ============

Installation using pip3 tool:

.. code:: bash

$ pip3 install pyclustering

Manual installation from official repository using Makefile:

.. code:: bash

# get sources of the pyclustering library, for example, from repository $ mkdir pyclustering $ cd pyclustering/ $ git clone https://github.com/annoviko/pyclustering.git .

# compile CCORE library (core of the pyclustering library). $ cd ccore/ $ make ccore_64bit # build for 64-bit OS

# $ make ccore_32bit # build for 32-bit OS

# return to parent folder of the pyclustering library $ cd ../

# install pyclustering library $ python3 setup.py install

# optionally - test the library $ python3 setup.py test

Manual installation using CMake:

.. code:: bash

# get sources of the pyclustering library, for example, from repository $ mkdir pyclustering $ cd pyclustering/ $ git clone https://github.com/annoviko/pyclustering.git .

# generate build files. $ mkdir build $ cmake ..

# build pyclustering-shared target depending on what was generated (Makefile or MSVC solution) # if Makefile has been generated then $ make pyclustering-shared

# return to parent folder of the pyclustering library $ cd ../

# install pyclustering library $ python3 setup.py install

# optionally - test the library $ python3 setup.py test

Manual installation using Microsoft Visual Studio solution:

• Clone repository from: https://github.com/annoviko/pyclustering.git
• Open folder pyclustering/ccore
• Open Visual Studio project ccore.sln
• Select solution platform: x86 or x64
• Build pyclustering-shared project.
• Add pyclustering folder to python path or install it using setup.py

# install pyclustering library $ python3 setup.py install

# optionally - test the library $ python3 setup.py test

Proposals, Questions, Bugs ==========================

In case of any questions, proposals or bugs related to the pyclustering please contact to pyclustering@yandex.ru or create an issue here.

PyClustering Status ===================

+----------------------+------------------------------+-------------------------------------+---------------------------------+ | Branch | master | 0.10.dev | 0.10.1.rel | +======================+==============================+=====================================+=================================+ | Build (Linux, MacOS) | |Build Status Linux MacOS| | |Build Status Linux MacOS 0.10.dev| | |Build Status Linux 0.10.1.rel| | +----------------------+------------------------------+-------------------------------------+---------------------------------+ | Build (Win) | |Build Status Win| | |Build Status Win 0.10.dev| | |Build Status Win 0.10.1.rel| | +----------------------+------------------------------+-------------------------------------+---------------------------------+ | Code Coverage | |Coverage Status| | |Coverage Status 0.10.dev| | |Coverage Status 0.10.1.rel| | +----------------------+------------------------------+-------------------------------------+---------------------------------+

Cite the Library ================

If you are using pyclustering library in a scientific paper, please, cite the library:

Novikov, A., 2019. PyClustering: Data Mining Library. Journal of Open Source Software, 4(36), p.1230. Available at: http://dx.doi.org/10.21105/joss.01230.

BibTeX entry:

.. code::

@article{Novikov2019, doi = {10.21105/joss.01230}, url = {https://doi.org/10.21105/joss.01230}, year = 2019, month = {apr}, publisher = {The Open Journal}, volume = {4}, number = {36}, pages = {1230}, author = {Andrei Novikov}, title = {{PyClustering}: Data Mining Library}, journal = {Journal of Open Source Software} }

Brief Overview of the Library Content =====================================

Clustering algorithms and methods (module pyclustering.cluster):

+------------------------+---------+-----+ | Algorithm | Python | C++ | +========================+=========+=====+ | Agglomerative | ✓ | ✓ | +------------------------+---------+-----+ | BANG | ✓ | | +------------------------+---------+-----+ | BIRCH | ✓ | | +------------------------+---------+-----+ | BSAS | ✓ | ✓ | +------------------------+---------+-----+ | CLARANS | ✓ | | +------------------------+---------+-----+ | CLIQUE | ✓ | ✓ | +------------------------+---------+-----+ | CURE | ✓ | ✓ | +------------------------+---------+-----+ | DBSCAN | ✓ | ✓ | +------------------------+---------+-----+ | Elbow | ✓ | ✓ | +------------------------+---------+-----+ | EMA | ✓ | | +------------------------+---------+-----+ | Fuzzy C-Means | ✓ | ✓ | +------------------------+---------+-----+ | GA (Genetic Algorithm) | ✓ | ✓ | +------------------------+---------+-----+ | G-Means | ✓ | ✓ | +------------------------+---------+-----+ | HSyncNet | ✓ | ✓ | +------------------------+---------+-----+ | K-Means | ✓ | ✓ | +------------------------+---------+-----+ | K-Means++ | ✓ | ✓ | +------------------------+---------+-----+ | K-Medians | ✓ | ✓ | +------------------------+---------+-----+ | K-Medoids | ✓ | ✓ | +------------------------+---------+-----+ | MBSAS | ✓ | ✓ | +------------------------+---------+-----+ | OPTICS | ✓ | ✓ | +------------------------+---------+-----+ | ROCK | ✓ | ✓ | +------------------------+---------+-----+ | Silhouette | ✓ | ✓ | +------------------------+---------+-----+ | SOM-SC | ✓ | ✓ | +------------------------+---------+-----+ | SyncNet | ✓ | ✓ | +------------------------+---------+-----+ | Sync-SOM | ✓ | | +------------------------+---------+-----+ | TTSAS | ✓ | ✓ | +------------------------+---------+-----+ | X-Means | ✓ | ✓ | +------------------------+---------+-----+

Oscillatory networks and neural networks (module pyclustering.nnet):

+--------------------------------------------------------------------------------+---------+-----+ | Model | Python | C++ | +================================================================================+=========+=====+ | CNN (Chaotic Neural Network) | ✓ | | +--------------------------------------------------------------------------------+---------+-----+ | fSync (Oscillatory network based on Landau-Stuart equation and Kuramoto model) | ✓ | | +--------------------------------------------------------------------------------+---------+-----+ | HHN (Oscillatory network based on Hodgkin-Huxley model) | ✓ | ✓ | +--------------------------------------------------------------------------------+---------+-----+ | Hysteresis Oscillatory Network | ✓ | | +--------------------------------------------------------------------------------+---------+-----+ | LEGION (Local Excitatory Global Inhibitory Oscillatory Network) | ✓ | ✓ | +--------------------------------------------------------------------------------+---------+-----+ | PCNN (Pulse-Coupled Neural Network) | ✓ | ✓ | +--------------------------------------------------------------------------------+---------+-----+ | SOM (Self-Organized Map) | ✓ | ✓ | +--------------------------------------------------------------------------------+---------+-----+ | Sync (Oscillatory network based on Kuramoto model) | ✓ | ✓ | +--------------------------------------------------------------------------------+---------+-----+ | SyncPR (Oscillatory network for pattern recognition) | ✓ | ✓ | +--------------------------------------------------------------------------------+---------+-----+ | SyncSegm (Oscillatory network for image segmentation) | ✓ | ✓ | +--------------------------------------------------------------------------------+---------+-----+

Graph Coloring Algorithms (module pyclustering.gcolor):

+------------------------+---------+-----+ | Algorithm | Python | C++ | +========================+=========+=====+ | DSatur | ✓ | | +------------------------+---------+-----+ | Hysteresis | ✓ | | +------------------------+---------+-----+ | GColorSync | ✓ | | +------------------------+---------+-----+

Containers (module pyclustering.container):

+------------------------+---------+-----+ | Algorithm | Python | C++ | +========================+=========+=====+ | KD Tree | ✓ | ✓ | +------------------------+---------+-----+ | CF Tree | ✓ | | +------------------------+---------+-----+

Examples in the Library =======================

The library contains examples for each algorithm and oscillatory network model:

Clustering examples: `pyclustering/cluster/examples

Graph coloring examples: pyclustering/gcolor/examples

Oscillatory network examples: pyclustering/nnet/examples`

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/exampleclusterplace.png :alt: Where are examples?

Code Examples =============

Data clustering by CURE algorithm

.. code:: python

from pyclustering.cluster import cluster_visualizer; from pyclustering.cluster.cure import cure; from pyclustering.utils import read_sample; from pyclustering.samples.definitions import FCPS_SAMPLES;

# Input data in following format [ [0.1, 0.5], [0.3, 0.1], ... ]. inputdata = readsample(FCPSSAMPLES.SAMPLELSUN);

# Allocate three clusters. cureinstance = cure(inputdata, 3); cure_instance.process(); clusters = cureinstance.getclusters();

# Visualize allocated clusters. visualizer = cluster_visualizer(); visualizer.appendclusters(clusters, inputdata); visualizer.show();

Data clustering by K-Means algorithm

.. code:: python

from pyclustering.cluster.kmeans import kmeans, kmeans_visualizer from pyclustering.cluster.centerinitializer import kmeansplusplus_initializer from pyclustering.samples.definitions import FCPS_SAMPLES from pyclustering.utils import read_sample

# Load list of points for cluster analysis. sample = readsample(FCPSSAMPLES.SAMPLETWODIAMONDS)

# Prepare initial centers using K-Means++ method. initialcenters = kmeansplusplus_initializer(sample, 2).initialize()

# Create instance of K-Means algorithm with prepared centers. kmeansinstance = kmeans(sample, initialcenters)

# Run cluster analysis and obtain results. kmeans_instance.process() clusters = kmeansinstance.getclusters() finalcenters = kmeansinstance.get_centers()

# Visualize obtained results kmeansvisualizer.showclusters(sample, clusters, final_centers)

Data clustering by OPTICS algorithm

.. code:: python

from pyclustering.cluster import cluster_visualizer from pyclustering.cluster.optics import optics, orderinganalyser, orderingvisualizer from pyclustering.samples.definitions import FCPS_SAMPLES from pyclustering.utils import read_sample

# Read sample for clustering from some file sample = readsample(FCPSSAMPLES.SAMPLE_LSUN)

# Run cluster analysis where connectivity radius is bigger than real radius = 2.0 neighbors = 3 amountofclusters = 3 opticsinstance = optics(sample, radius, neighbors, amountof_clusters)

# Performs cluster analysis optics_instance.process()

# Obtain results of clustering clusters = opticsinstance.getclusters() noise = opticsinstance.getnoise() ordering = opticsinstance.getordering()

# Visualize ordering diagram analyser = ordering_analyser(ordering) orderingvisualizer.showorderingdiagram(analyser, amountof_clusters)

# Visualize clustering results visualizer = cluster_visualizer() visualizer.append_clusters(clusters, sample) visualizer.show()

Simulation of oscillatory network PCNN

.. code:: python

from pyclustering.nnet.pcnn import pcnnnetwork, pcnnvisualizer

# Create Pulse-Coupled neural network with 10 oscillators. net = pcnn_network(10)

# Perform simulation during 100 steps using binary external stimulus. dynamic = net.simulate(50, [1, 1, 1, 0, 0, 0, 0, 1, 1, 1])

# Allocate synchronous ensembles from the output dynamic. ensembles = dynamic.allocatesyncensembles()

# Show output dynamic. pcnnvisualizer.showoutput_dynamic(dynamic, ensembles)

Simulation of chaotic neural network CNN

.. code:: python

from pyclustering.cluster import cluster_visualizer from pyclustering.samples.definitions import SIMPLE_SAMPLES from pyclustering.utils import read_sample from pyclustering.nnet.cnn import cnnnetwork, cnnvisualizer

# Load stimulus from file. stimulus = readsample(SIMPLESAMPLES.SAMPLE_SIMPLE3)

# Create chaotic neural network, amount of neurons should be equal to amount of stimulus. networkinstance = cnnnetwork(len(stimulus))

# Perform simulation during 100 steps. steps = 100 outputdynamic = networkinstance.simulate(steps, stimulus)

# Display output dynamic of the network. cnnvisualizer.showoutputdynamic(outputdynamic)

# Display dynamic matrix and observation matrix to show clustering phenomenon. cnnvisualizer.showdynamicmatrix(outputdynamic) cnnvisualizer.showobservationmatrix(outputdynamic)

# Visualize clustering results. clusters = outputdynamic.allocatesync_ensembles(10) visualizer = cluster_visualizer() visualizer.append_clusters(clusters, stimulus) visualizer.show()

Illustrations =============

Cluster allocation on FCPS dataset collection by DBSCAN:

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/fcpsclusteranalysis.png :alt: Clustering by DBSCAN

Cluster allocation by OPTICS using cluster-ordering diagram:

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/opticsexampleclustering.png :alt: Clustering by OPTICS

Partial synchronization (clustering) in Sync oscillatory network:

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/syncpartialsynchronization.png :alt: Partial synchronization in Sync oscillatory network

Cluster visualization by SOM (Self-Organized Feature Map)

.. image:: https://github.com/annoviko/pyclustering/blob/master/docs/img/targetsomprocessing.png :alt: Cluster visualization by SOM

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