Class represents Genetic clustering algorithm. More...

Public Member Functions
def	__init__ (self, data, count_clusters, chromosome_count, population_count, kwargs)
	Initialize genetic clustering algorithm for cluster analysis. More...

def	process (self)
	Perform clustering procedure in line with rule of genetic clustering algorithm. More...

def	get_observer (self)
	Returns genetic algorithm observer.

def	get_clusters (self)
	Returns list of allocated clusters, each cluster contains indexes of objects from the data. More...

Detailed Description

Class represents Genetic clustering algorithm.

The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centres.

Example of clustering using genetic algorithm:

from pyclustering.cluster.ga import genetic_algorithm, ga_observer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import SIMPLE_SAMPLES
# Read input data for clustering
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
# Create instance of observer that will collect all information:
observer_instance = ga_observer(True, True, True)
# Create genetic algorithm for clustering
ga_instance = genetic_algorithm(data=sample,
                                count_clusters=4,
                                chromosome_count=100,
                                population_count=200,
                                count_mutation_gens=1)
# Start processing
ga_instance.process()
# Obtain results
clusters = ga_instance.get_clusters()
# Print cluster to console
print("Amount of clusters: '%d'. Clusters: '%s'" % (len(clusters), clusters))

There is an example of clustering results (fitness function evolution and allocated clusters) that were visualized by 'ga_visualizer':

See also: ga_visualizer; ga_observer

Definition at line 325 of file ga.py.

Constructor & Destructor Documentation

◆ init()

def pyclustering.cluster.ga.genetic_algorithm.__init__	(	self,
		data,
		count_clusters,
		chromosome_count,
		population_count,
		kwargs
	)

Initialize genetic clustering algorithm for cluster analysis.

Parameters

[in]	data	(numpy.array\|list): Input data for clustering that is represented by two dimensional array where each row is a point, for example, [[0.0, 2.1], [0.1, 2.0], [-0.2, 2.4]].
[in]	count_clusters	(uint): Amount of clusters that should be allocated in the data.
[in]	chromosome_count	(uint): Amount of chromosomes in each population.
[in]	population_count	(uint): Amount of populations.
[in]	**kwargs	Arbitrary keyword arguments (available arguments: 'count_mutation_gens', 'coeff_mutation_count', 'select_coeff', 'crossover_rate', 'observer').

Keyword Args:

count_mutation_gens (uint): Amount of genes in chromosome that is mutated on each step.
coeff_mutation_count (float): Percent of chromosomes for mutation, distributed in range (0, 1] and thus amount of chromosomes is defined as follows: 'chromosome_count' * 'coeff_mutation_count'.
select_coeff (float): Exponential coefficient for selection procedure that is used as follows: math.exp(1 + fitness(chromosome) * select_coeff).
crossover_rate (float): Crossover rate.
observer (ga_observer): Observer that is used for collecting information of about clustering process on each step.

Definition at line 371 of file ga.py.

Member Function Documentation

◆ get_clusters()

def pyclustering.cluster.ga.genetic_algorithm.get_clusters ( self )

Returns list of allocated clusters, each cluster contains indexes of objects from the data.

Returns: (list) List of allocated clusters.

See also: process()

Definition at line 493 of file ga.py.

Referenced by pyclustering.samples.answer_reader.get_cluster_lengths(), and pyclustering.cluster.optics.optics.process().

◆ process()

def pyclustering.cluster.ga.genetic_algorithm.process ( self )

Perform clustering procedure in line with rule of genetic clustering algorithm.

See also: get_clusters()

Definition at line 430 of file ga.py.

The documentation for this class was generated from the following file:

pyclustering/cluster/ga.py