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"""!
@brief Examples of usage and demonstration of abilities of X-Means algorithm in cluster analysis.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import ntpath
import random
from pyclustering.samples.definitions import SIMPLE_SAMPLES, FCPS_SAMPLES
from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.xmeans import xmeans, splitting_type
from pyclustering.utils import read_sample, timedcall
def template_clustering(start_centers, path, tolerance=0.025, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore=True):
sample = read_sample(path)
xmeans_instance = xmeans(sample, start_centers, 20, tolerance, criterion, ccore, repeat=5)
(ticks, _) = timedcall(xmeans_instance.process)
clusters = xmeans_instance.get_clusters()
centers = xmeans_instance.get_centers()
criterion_string = "UNKNOWN"
if (criterion == splitting_type.BAYESIAN_INFORMATION_CRITERION): criterion_string = "BAYESIAN INFORMATION CRITERION";
elif (criterion == splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH): criterion_string = "MINIMUM NOISELESS DESCRIPTION_LENGTH";
print("Sample: ", ntpath.basename(path), "\nInitial centers: '", (start_centers is not None), "', Execution time: '", ticks, "', Number of clusters:", len(clusters), ",", criterion_string, "\n")
visualizer = cluster_visualizer()
visualizer.set_canvas_title(criterion_string)
visualizer.append_clusters(clusters, sample)
visualizer.append_cluster(centers, None, marker = '*')
visualizer.show()
def cluster_sample1():
"Start with wrong number of clusters."
start_centers = [[3.7, 5.5]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample1_without_initial_centers():
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE1, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample2():
"Start with wrong number of clusters."
start_centers = [[3.5, 4.8], [2.6, 2.5]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE2, criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE2, criterion=splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample2_without_initial_centers():
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE2, criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE2, criterion=splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample3():
"Start with wrong number of clusters."
start_centers = [[0.2, 0.1], [4.0, 1.0]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE3, criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE3, criterion=splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample3_without_initial_centers():
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE3, criterion=splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE3, criterion=splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample4():
start_centers = [[1.5, 0.0], [1.5, 2.0], [1.5, 4.0], [1.5, 6.0], [1.5, 8.0]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE4, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE4, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample4_without_initial_centers():
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE4, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE4, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample5():
"Start with wrong number of clusters."
start_centers = [[0.0, 1.0], [0.0, 0.0]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_sample5_without_initial_centers():
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(None, SIMPLE_SAMPLES.SAMPLE_SIMPLE5, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_elongate():
"Not so applicable for this sample"
start_centers = [[1.0, 4.5], [3.1, 2.7]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_ELONGATE, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_ELONGATE, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_lsun():
"Not so applicable for this sample"
start_centers = [[1.0, 3.5], [2.0, 0.5], [3.0, 3.0]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_LSUN, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_LSUN, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_target():
"Not so applicable for this sample"
start_centers = [[0.2, 0.2], [0.0, -2.0], [3.0, -3.0], [3.0, 3.0], [-3.0, 3.0], [-3.0, -3.0]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TARGET, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TARGET, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_two_diamonds():
"Start with wrong number of clusters."
start_centers = [[0.8, 0.2]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_two_diamonds_without_initial_centers():
template_clustering(None, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(None, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_wing_nut():
start_centers = [[-1.5, 1.5], [1.5, 1.5]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_WING_NUT, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_WING_NUT, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_chainlink():
start_centers = [[1.1, -1.7, 1.1], [-1.4, 2.5, -1.2]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_CHAINLINK, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_CHAINLINK, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_hepta():
"Start with wrong number of clusters."
start_centers = [[0.0, 0.0, 0.0], [3.0, 0.0, 0.0], [-2.0, 0.0, 0.0], [0.0, 3.0, 0.0], [0.0, -3.0, 0.0], [0.0, 0.0, 2.5]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_HEPTA, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_HEPTA, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def cluster_tetra():
start_centers = [[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TETRA, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION)
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TETRA, criterion = splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH)
def template_clustering_performance(start_centers, path, tolerance = 0.025, criterion = splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore = False):
sample = read_sample(path)
xmeans_instance = xmeans(sample, start_centers, 20, tolerance, criterion, ccore)
(ticks, _) = timedcall(xmeans_instance.process)
criterion_string = "UNKNOWN"
if (criterion == splitting_type.BAYESIAN_INFORMATION_CRITERION): criterion_string = "BAYESIAN INFORMATION CRITERION";
elif (criterion == splitting_type.MINIMUM_NOISELESS_DESCRIPTION_LENGTH): criterion_string = "MINIMUM NOISELESS DESCRIPTION_LENGTH";
print("Sample: ", ntpath.basename(path), "', Execution time: '", ticks, "',", criterion_string)
def template_clustering_random_points_performance(cluster_length, amount_clusters, ccore_flag):
sample = [ [ random.random(), random.random() ] for _ in range(cluster_length) ]
for index in range(1, amount_clusters):
default_offset = 5
sample += [ [ random.random() + default_offset * index, random.random() + default_offset * index ] for _ in range(cluster_length) ]
initial_center = [ [ random.random(), random.random() ], [ random.random(), random.random() ] ]
ticks_array = []
amount_measures = 5
for _ in range(amount_measures):
xmeans_instance = xmeans(sample, initial_center, 20, 0.25, splitting_type.BAYESIAN_INFORMATION_CRITERION, ccore_flag)
(ticks, _) = timedcall(xmeans_instance.process)
ticks_array.append(ticks)
print("Random sample: (size:" + str(len(sample)) + ") ', Execution time: '", sum(ticks_array) / amount_measures)
def experiment_execution_time(ccore_flag = False):
template_clustering_performance([[3.7, 5.5]], SIMPLE_SAMPLES.SAMPLE_SIMPLE1, ccore = ccore_flag)
template_clustering_performance([[3.5, 4.8], [2.6, 2.5]], SIMPLE_SAMPLES.SAMPLE_SIMPLE2, ccore = ccore_flag)
template_clustering_performance([[0.2, 0.1], [4.0, 1.0]], SIMPLE_SAMPLES.SAMPLE_SIMPLE3, ccore = ccore_flag)
template_clustering_performance([[1.5, 0.0], [1.5, 2.0], [1.5, 4.0], [1.5, 6.0], [1.5, 8.0]], SIMPLE_SAMPLES.SAMPLE_SIMPLE4, ccore = ccore_flag)
template_clustering_performance([[0.0, 1.0], [0.0, 0.0]], SIMPLE_SAMPLES.SAMPLE_SIMPLE5, ccore = ccore_flag)
template_clustering_performance([[1.0, 4.5], [3.1, 2.7]], SIMPLE_SAMPLES.SAMPLE_ELONGATE, ccore = ccore_flag)
template_clustering_performance([[1.0, 3.5], [2.0, 0.5], [3.0, 3.0]], FCPS_SAMPLES.SAMPLE_LSUN, ccore = ccore_flag)
template_clustering_performance([[0.2, 0.2], [0.0, -2.0], [3.0, -3.0], [3.0, 3.0], [-3.0, 3.0], [-3.0, -3.0]], FCPS_SAMPLES.SAMPLE_TARGET, ccore = ccore_flag)
template_clustering_performance([[0.8, 0.2]], FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, ccore = ccore_flag)
template_clustering_performance([[-1.5, 1.5], [1.5, 1.5]], FCPS_SAMPLES.SAMPLE_WING_NUT, ccore = ccore_flag)
template_clustering_performance([[1.1, -1.7, 1.1], [-1.4, 2.5, -1.2]], FCPS_SAMPLES.SAMPLE_CHAINLINK, ccore = ccore_flag)
template_clustering_performance([[0.0, 0.0, 0.0], [3.0, 0.0, 0.0], [-2.0, 0.0, 0.0], [0.0, 3.0, 0.0], [0.0, -3.0, 0.0], [0.0, 0.0, 2.5]], FCPS_SAMPLES.SAMPLE_HEPTA, ccore = ccore_flag)
template_clustering_performance([[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]], FCPS_SAMPLES.SAMPLE_TETRA, ccore = ccore_flag)
template_clustering_performance([[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]], FCPS_SAMPLES.SAMPLE_ATOM)
template_clustering_random_points_performance(1000, 6, ccore_flag)
template_clustering_random_points_performance(2000, 6, ccore_flag)
template_clustering_random_points_performance(4000, 6, ccore_flag)
template_clustering_random_points_performance(6000, 6, ccore_flag)
template_clustering_random_points_performance(8000, 6, ccore_flag)
template_clustering_random_points_performance(10000, 6, ccore_flag)
template_clustering_random_points_performance(15000, 6, ccore_flag)
template_clustering_random_points_performance(30000, 6, ccore_flag)
template_clustering_random_points_performance(45000, 6, ccore_flag)
template_clustering_random_points_performance(100000, 6, ccore_flag)
template_clustering_random_points_performance(200000, 6, ccore_flag)
template_clustering_random_points_performance(300000, 6, ccore_flag)
template_clustering_random_points_performance(1000000, 6, ccore_flag)
cluster_sample1()
cluster_sample2()
cluster_sample3()
cluster_sample4()
cluster_sample5()
cluster_elongate()
cluster_lsun()
cluster_target()
cluster_two_diamonds()
cluster_wing_nut()
cluster_chainlink()
cluster_hepta()
cluster_tetra()
cluster_sample1_without_initial_centers()
cluster_sample2_without_initial_centers()
cluster_sample3_without_initial_centers()
cluster_sample4_without_initial_centers()
cluster_sample5_without_initial_centers()
cluster_two_diamonds_without_initial_centers()
experiment_execution_time(False) # Python code
experiment_execution_time(True) # C++ code + Python env.
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