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Build Status

PyClustering

Dependencies

Performance

# As by default - C/C++ part of the library is usedxmeans_instance_1=xmeans(data_points,start_centers,20,ccore=True);# The same - C/C++ part of the library is used by defaultxmeans_instance_2=xmeans(data_points,start_centers,20);# Switch off core - Python is usedxmeans_instance_3=xmeans(data_points,start_centers,20,ccore=False);

Installation

$ pip3 install pyclustering

# 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.pytest

# 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.pytest

# install pyclustering library$ python3 setup.py install# optionally - test the library$ python3 setup.pytest

Proposals, Questions, Bugs

PyClustering Status

Cite the Library

Brief Overview of the Library Content

Examples in the Library

Code Examples

frompyclustering.clusterimportcluster_visualizer;frompyclustering.cluster.cureimportcure;frompyclustering.utilsimportread_sample;frompyclustering.samples.definitionsimportFCPS_SAMPLES;# Input data in following format [ [0.1, 0.5], [0.3, 0.1], ... ].input_data=read_sample(FCPS_SAMPLES.SAMPLE_LSUN);# Allocate three clusters.cure_instance=cure(input_data,3);cure_instance.process();clusters=cure_instance.get_clusters();# Visualize allocated clusters.visualizer=cluster_visualizer();visualizer.append_clusters(clusters,input_data);visualizer.show();

frompyclustering.cluster.kmeansimportkmeans,kmeans_visualizerfrompyclustering.cluster.center_initializerimportkmeans_plusplus_initializerfrompyclustering.samples.definitionsimportFCPS_SAMPLESfrompyclustering.utilsimportread_sample# Load list of points for cluster analysis.sample=read_sample(FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)# Prepare initial centers using K-Means++ method.initial_centers=kmeans_plusplus_initializer(sample,2).initialize()# Create instance of K-Means algorithm with prepared centers.kmeans_instance=kmeans(sample,initial_centers)# Run cluster analysis and obtain results.kmeans_instance.process()clusters=kmeans_instance.get_clusters()final_centers=kmeans_instance.get_centers()# Visualize obtained resultskmeans_visualizer.show_clusters(sample,clusters,final_centers)

frompyclustering.clusterimportcluster_visualizerfrompyclustering.cluster.opticsimportoptics,ordering_analyser,ordering_visualizerfrompyclustering.samples.definitionsimportFCPS_SAMPLESfrompyclustering.utilsimportread_sample# Read sample for clustering from some filesample=read_sample(FCPS_SAMPLES.SAMPLE_LSUN)# Run cluster analysis where connectivity radius is bigger than realradius=2.0neighbors=3amount_of_clusters=3optics_instance=optics(sample,radius,neighbors,amount_of_clusters)# Performs cluster analysisoptics_instance.process()# Obtain results of clusteringclusters=optics_instance.get_clusters()noise=optics_instance.get_noise()ordering=optics_instance.get_ordering()# Visualize ordering diagramanalyser=ordering_analyser(ordering)ordering_visualizer.show_ordering_diagram(analyser,amount_of_clusters)# Visualize clustering resultsvisualizer=cluster_visualizer()visualizer.append_clusters(clusters,sample)visualizer.show()

frompyclustering.nnet.pcnnimportpcnn_network,pcnn_visualizer# 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.allocate_sync_ensembles()# Show output dynamic.pcnn_visualizer.show_output_dynamic(dynamic,ensembles)

frompyclustering.clusterimportcluster_visualizerfrompyclustering.samples.definitionsimportSIMPLE_SAMPLESfrompyclustering.utilsimportread_samplefrompyclustering.nnet.cnnimportcnn_network,cnn_visualizer# Load stimulus from file.stimulus=read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)# Create chaotic neural network, amount of neurons should be equal to amount of stimulus.network_instance=cnn_network(len(stimulus))# Perform simulation during 100 steps.steps=100output_dynamic=network_instance.simulate(steps,stimulus)# Display output dynamic of the network.cnn_visualizer.show_output_dynamic(output_dynamic)# Display dynamic matrix and observation matrix to show clustering phenomenon.cnn_visualizer.show_dynamic_matrix(output_dynamic)cnn_visualizer.show_observation_matrix(output_dynamic)# Visualize clustering results.clusters=output_dynamic.allocate_sync_ensembles(10)visualizer=cluster_visualizer()visualizer.append_clusters(clusters,stimulus)visualizer.show()

Illustrations