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"""!
@brief Examples of usage and demonstration of abilities of K-Medians 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.kmedians import kmedians
from pyclustering.utils import draw_clusters
from pyclustering.utils import read_sample
from pyclustering.utils import timedcall
def template_clustering(start_medians, path, tolerance = 0.25):
sample = read_sample(path)
kmedians_instance = kmedians(sample, start_medians, tolerance)
(ticks, result) = timedcall(kmedians_instance.process)
clusters = kmedians_instance.get_clusters()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
draw_clusters(sample, clusters)
def cluster_sample1():
start_medians = [[3.7, 5.5], [6.7, 7.5]]
template_clustering(start_medians, SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
def cluster_sample2():
start_medians = [[3.5, 4.8], [6.9, 7], [7.5, 0.5]]
template_clustering(start_medians, SIMPLE_SAMPLES.SAMPLE_SIMPLE2)
def cluster_sample3():
start_medians = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]]
template_clustering(start_medians, SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
def cluster_sample4():
start_medians = [[1.5, 0.0], [1.5, 2.0], [1.5, 4.0], [1.5, 6.0], [1.5, 8.0]]
template_clustering(start_medians, SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
def cluster_sample5():
start_medians = [[0.0, 1.0], [0.0, 0.0], [1.0, 1.0], [1.0, 0.0]]
template_clustering(start_medians, SIMPLE_SAMPLES.SAMPLE_SIMPLE5)
def cluster_elongate():
start_medians = [[1.0, 4.5], [3.1, 2.7]]
template_clustering(start_medians, SIMPLE_SAMPLES.SAMPLE_ELONGATE)
def cluster_lsun():
start_medians = [[1.0, 3.5], [2.0, 0.5], [3.0, 3.0]]
template_clustering(start_medians, FCPS_SAMPLES.SAMPLE_LSUN)
def cluster_target():
start_medians = [[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_medians, FCPS_SAMPLES.SAMPLE_TARGET)
def cluster_two_diamonds():
start_medians = [[0.8, 0.2], [3.0, 0.0]]
template_clustering(start_medians, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)
def cluster_wing_nut():
start_medians = [[-1.5, 1.5], [1.5, 1.5]]
template_clustering(start_medians, FCPS_SAMPLES.SAMPLE_WING_NUT)
def cluster_chainlink():
start_medians = [[1.1, -1.7, 1.1], [-1.4, 2.5, -1.2]]
template_clustering(start_medians, FCPS_SAMPLES.SAMPLE_CHAINLINK)
def cluster_hepta():
start_medians = [[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], [0.0, 0.0, -2.5]]
template_clustering(start_medians, FCPS_SAMPLES.SAMPLE_HEPTA)
def cluster_tetra():
start_medians = [[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]]
template_clustering(start_medians, FCPS_SAMPLES.SAMPLE_TETRA)
def cluster_engy_time():
start_medians = [[0.5, 0.5], [2.3, 2.9]]
template_clustering(start_medians, FCPS_SAMPLES.SAMPLE_ENGY_TIME)
def cluster_atom():
start_medians = [[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]]
template_clustering(start_medians, FCPS_SAMPLES.SAMPLE_ATOM)
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()
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