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"""!
@brief Examples of usage and demonstration of abilities of K-Medoids algorithm in cluster analysis.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
from pyclustering.samples.definitions import SIMPLE_SAMPLES, FCPS_SAMPLES
from pyclustering.cluster import cluster_visualizer
from pyclustering.cluster.kmedoids import kmedoids
from pyclustering.utils import read_sample, calculate_distance_matrix
from pyclustering.utils import timedcall, distance_metric, type_metric
def template_clustering(start_medoids, path, tolerance=0.25, show=True, **kwargs):
ccore = kwargs.get('ccore', True)
data_type = kwargs.get('data_type', 'points')
original_data = read_sample(path)
sample = original_data
if data_type == 'distance_matrix':
sample = calculate_distance_matrix(sample)
metric = distance_metric(type_metric.EUCLIDEAN_SQUARE, data=sample)
kmedoids_instance = kmedoids(sample, start_medoids, tolerance, metric=metric, ccore=ccore, data_type=data_type)
(ticks, result) = timedcall(kmedoids_instance.process)
clusters = kmedoids_instance.get_clusters()
print(clusters)
medoids = kmedoids_instance.get_medoids()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
if show is True:
visualizer = cluster_visualizer(1)
visualizer.append_clusters(clusters, original_data, 0)
visualizer.append_cluster([original_data[index] for index in start_medoids], marker='*', markersize=15)
visualizer.append_cluster(medoids, data=original_data, marker='*', markersize=15)
visualizer.show()
return original_data, clusters
def cluster_sample1():
template_clustering([2, 9], SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
def cluster_sample2():
template_clustering([3, 12, 20], SIMPLE_SAMPLES.SAMPLE_SIMPLE2)
def cluster_sample3():
template_clustering([4, 12, 25, 37], SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
def cluster_sample4():
template_clustering([4, 15, 30, 40, 50], SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
def cluster_sample5():
template_clustering([4, 18, 34, 55], SIMPLE_SAMPLES.SAMPLE_SIMPLE5)
def cluster_elongate():
template_clustering([8, 56], SIMPLE_SAMPLES.SAMPLE_ELONGATE)
def cluster_lsun():
#template_clustering([10, 275, 385], FCPS_SAMPLES.SAMPLE_LSUN)
template_clustering([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], FCPS_SAMPLES.SAMPLE_LSUN, data_type='distance_matrix')
def cluster_target():
template_clustering([10, 160, 310, 460, 560, 700], FCPS_SAMPLES.SAMPLE_TARGET)
def cluster_two_diamonds():
template_clustering([10, 650], FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)
def cluster_wing_nut():
template_clustering([19, 823], FCPS_SAMPLES.SAMPLE_WING_NUT)
def cluster_chainlink():
template_clustering([30, 900], FCPS_SAMPLES.SAMPLE_CHAINLINK)
def cluster_hepta():
template_clustering([0, 35, 86, 93, 125, 171, 194], FCPS_SAMPLES.SAMPLE_HEPTA)
def cluster_tetra():
template_clustering([0, 131, 214, 265], FCPS_SAMPLES.SAMPLE_TETRA)
def cluster_atom():
template_clustering([0, 650], FCPS_SAMPLES.SAMPLE_ATOM)
def cluster_engy_time():
template_clustering([10, 3000], FCPS_SAMPLES.SAMPLE_ENGY_TIME)
def display_fcps_clustering_results():
(lsun, lsun_clusters) = template_clustering([10, 275, 385], FCPS_SAMPLES.SAMPLE_LSUN, 0.1, False)
(target, target_clusters) = template_clustering([10, 160, 310, 460, 560, 700], FCPS_SAMPLES.SAMPLE_TARGET, 0.1, False)
(two_diamonds, two_diamonds_clusters) = template_clustering([10, 650], FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, 0.1, False)
(wing_nut, wing_nut_clusters) = template_clustering([19, 823], FCPS_SAMPLES.SAMPLE_WING_NUT, 0.1, False)
(chainlink, chainlink_clusters) = template_clustering([30, 900], FCPS_SAMPLES.SAMPLE_CHAINLINK, 0.1, False)
(hepta, hepta_clusters) = template_clustering([0, 35, 86, 93, 125, 171, 194], FCPS_SAMPLES.SAMPLE_HEPTA, 0.1, False)
(tetra, tetra_clusters) = template_clustering([0, 131, 214, 265], FCPS_SAMPLES.SAMPLE_TETRA, 0.1, False)
(atom, atom_clusters) = template_clustering([0, 650], FCPS_SAMPLES.SAMPLE_ATOM, 0.1, False)
visualizer = cluster_visualizer(8, 4)
visualizer.append_clusters(lsun_clusters, lsun, 0)
visualizer.append_clusters(target_clusters, target, 1)
visualizer.append_clusters(two_diamonds_clusters, two_diamonds, 2)
visualizer.append_clusters(wing_nut_clusters, wing_nut, 3)
visualizer.append_clusters(chainlink_clusters, chainlink, 4)
visualizer.append_clusters(hepta_clusters, hepta, 5)
visualizer.append_clusters(tetra_clusters, tetra, 6)
visualizer.append_clusters(atom_clusters, atom, 7)
visualizer.show()
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_atom()
cluster_engy_time()
display_fcps_clustering_results()
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