1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
|
"""!
@brief Examples of usage and demonstration of abilities of K-Means algorithm in cluster analysis.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import random
from pyclustering.samples.definitions import SIMPLE_SAMPLES, FCPS_SAMPLES, FAMOUS_SAMPLES
from pyclustering.cluster import cluster_visualizer_multidim
from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
from pyclustering.cluster.kmeans import kmeans, kmeans_observer, kmeans_visualizer
from pyclustering.utils import read_sample
from pyclustering.utils import timedcall
from pyclustering.utils.metric import distance_metric, type_metric
def template_clustering(start_centers, path, tolerance = 0.25, ccore = False):
sample = read_sample(path)
dimension = len(sample[0])
metric = distance_metric(type_metric.MANHATTAN)
observer = kmeans_observer()
kmeans_instance = kmeans(sample, start_centers, tolerance, ccore, observer=observer, metric=metric)
(ticks, _) = timedcall(kmeans_instance.process)
clusters = kmeans_instance.get_clusters()
centers = kmeans_instance.get_centers()
print("Sample: ", path, "\t\tExecution time: ", ticks, "\n")
visualizer = cluster_visualizer_multidim()
visualizer.append_clusters(clusters, sample)
visualizer.show()
if dimension > 3:
kmeans_visualizer.show_clusters(sample, clusters, centers, start_centers)
kmeans_visualizer.animate_cluster_allocation(sample, observer)
def cluster_sample1():
start_centers = [[4.7, 5.9], [5.7, 6.5]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
def cluster_sample2():
start_centers = [[3.5, 4.8], [6.9, 7], [7.5, 0.5]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE2)
def cluster_sample3():
start_centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
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)
def cluster_sample5():
start_centers = [[0.0, 1.0], [0.0, 0.0], [1.0, 1.0], [1.0, 0.0]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE5)
def cluster_sample7():
start_centers = [[-3.0], [2.5]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE7)
def cluster_sample8():
start_centers = [[-4.0], [3.1], [6.1], [12.0]]
template_clustering(start_centers, SIMPLE_SAMPLES.SAMPLE_SIMPLE8)
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)
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)
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)
def cluster_two_diamonds():
start_centers = [[0.8, 0.2], [3.0, 0.0]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS)
def cluster_wing_nut():
"Almost good!"
start_centers = [[-1.5, 1.5], [1.5, 1.5]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_WING_NUT)
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)
def cluster_hepta():
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], [0.0, 0.0, -2.5]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_HEPTA)
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)
def cluster_engy_time():
start_centers = [[0.5, 0.5], [2.3, 2.9]]
template_clustering(start_centers, FCPS_SAMPLES.SAMPLE_ENGY_TIME)
def cluster_iris():
start_centers = kmeans_plusplus_initializer(read_sample(FAMOUS_SAMPLES.SAMPLE_IRIS), 4).initialize()
template_clustering(start_centers, FAMOUS_SAMPLES.SAMPLE_IRIS)
def experiment_execution_time(ccore = False):
template_clustering([[3.7, 5.5], [6.7, 7.5]], SIMPLE_SAMPLES.SAMPLE_SIMPLE1, ccore)
template_clustering([[3.5, 4.8], [6.9, 7], [7.5, 0.5]], SIMPLE_SAMPLES.SAMPLE_SIMPLE2, ccore)
template_clustering([[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]], SIMPLE_SAMPLES.SAMPLE_SIMPLE3, ccore)
template_clustering([[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)
template_clustering([[0.0, 1.0], [0.0, 0.0], [1.0, 1.0], [1.0, 0.0]], SIMPLE_SAMPLES.SAMPLE_SIMPLE5, ccore)
template_clustering([[1.0, 4.5], [3.1, 2.7]], SIMPLE_SAMPLES.SAMPLE_ELONGATE, ccore)
template_clustering([[1.0, 3.5], [2.0, 0.5], [3.0, 3.0]], FCPS_SAMPLES.SAMPLE_LSUN, ccore)
template_clustering([[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)
template_clustering([[0.8, 0.2], [3.0, 0.0]], FCPS_SAMPLES.SAMPLE_TWO_DIAMONDS, ccore)
template_clustering([[-1.5, 1.5], [1.5, 1.5]], FCPS_SAMPLES.SAMPLE_WING_NUT, ccore)
template_clustering([[1.1, -1.7, 1.1], [-1.4, 2.5, -1.2]], FCPS_SAMPLES.SAMPLE_CHAINLINK, ccore)
template_clustering([[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]], FCPS_SAMPLES.SAMPLE_HEPTA, ccore)
template_clustering([[1, 0, 0], [0, 1, 0], [0, -1, 0], [-1, 0, 0]], FCPS_SAMPLES.SAMPLE_TETRA, ccore)
template_clustering([[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]], FCPS_SAMPLES.SAMPLE_ATOM, ccore)
template_clustering([[0.5, 0.5], [2.3, 2.9]], FCPS_SAMPLES.SAMPLE_ENGY_TIME, ccore)
def clustering_random_points(amount_points, amount_centers, ccore):
sample = [ [ random.random(), random.random() ] for _ in range(amount_points) ]
centers = [ [ random.random(), random.random() ] for _ in range(amount_centers) ]
kmeans_instance = kmeans(sample, centers, 0.0001, ccore)
(ticks, _) = timedcall(kmeans_instance.process)
print("Execution time ("+ str(amount_points) +" 2D-points):", ticks)
def performance_measure_random_points(ccore):
clustering_random_points(1000, 5, ccore)
clustering_random_points(2000, 5, ccore)
clustering_random_points(3000, 5, ccore)
clustering_random_points(4000, 5, ccore)
clustering_random_points(5000, 5, ccore)
clustering_random_points(10000, 5, ccore)
clustering_random_points(20000, 5, ccore)
cluster_sample1()
cluster_sample2()
cluster_sample3()
cluster_sample4()
cluster_sample5()
cluster_sample7()
cluster_sample8()
cluster_elongate()
cluster_lsun()
cluster_target()
cluster_two_diamonds()
cluster_wing_nut()
cluster_chainlink()
cluster_hepta()
cluster_tetra()
cluster_engy_time()
cluster_iris()
experiment_execution_time(False) # Python code
experiment_execution_time(True) # C++ code + Python env.
performance_measure_random_points(False)
|
