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"""!
@brief Test templates for K-Medians clustering module.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
from pyclustering.tests.assertion import assertion
from pyclustering.cluster.kmedians import kmedians
from pyclustering.utils import read_sample, distance_metric, type_metric
from random import random
import numpy
class KmediansTestTemplates:
@staticmethod
def templateLengthProcessData(data, start_medians, expected_cluster_length, ccore, **kwargs):
tolerance = kwargs.get('tolerance', 0.01)
metric = kwargs.get('metric', None)
itermax = kwargs.get('itermax', 200)
if isinstance(data, str):
sample = read_sample(data)
else:
sample = data
kmedians_instance = kmedians(sample, start_medians, tolerance, ccore, metric=metric, itermax=itermax)
kmedians_instance.process()
clusters = kmedians_instance.get_clusters()
medians = kmedians_instance.get_medians()
wce = kmedians_instance.get_total_wce()
if itermax == 0:
assert clusters == []
assert start_medians == medians
assert 0.0 == wce
return
obtained_cluster_sizes = [len(cluster) for cluster in clusters]
assert len(sample) == sum(obtained_cluster_sizes)
assert len(medians) == len(clusters)
if expected_cluster_length is not None:
obtained_cluster_sizes.sort()
expected_cluster_length.sort()
if obtained_cluster_sizes != expected_cluster_length:
print(obtained_cluster_sizes)
assert obtained_cluster_sizes == expected_cluster_length
@staticmethod
def templateClusterAllocationOneDimensionData(ccore):
input_data = [ [random()] for i in range(10) ] + [ [random() + 3] for i in range(10) ] + [ [random() + 5] for i in range(10) ] + [ [random() + 8] for i in range(10) ]
kmedians_instance = kmedians(input_data, [ [0.0], [3.0], [5.0], [8.0] ], 0.025, ccore)
kmedians_instance.process()
clusters = kmedians_instance.get_clusters()
assert len(clusters) == 4
for cluster in clusters:
assert len(cluster) == 10
@staticmethod
def templateClusterAllocationTheSameObjects(number_objects, number_clusters, ccore_flag):
value = random()
input_data = [ [value] ] * number_objects
initial_centers = []
for i in range(number_clusters):
initial_centers.append([ random() ])
kmedians_instance = kmedians(input_data, initial_centers, ccore=ccore_flag)
kmedians_instance.process()
clusters = kmedians_instance.get_clusters()
object_mark = [False] * number_objects
allocated_number_objects = 0
for cluster in clusters:
for index_object in cluster:
assert (object_mark[index_object] is False) # one object can be in only one cluster.
object_mark[index_object] = True
allocated_number_objects += 1
assert (number_objects == allocated_number_objects) # number of allocated objects should be the same.
@staticmethod
def templatePredict(path_to_file, initial_medians, points, expected_closest_clusters, ccore, **kwargs):
sample = read_sample(path_to_file)
metric = kwargs.get('metric', distance_metric(type_metric.EUCLIDEAN_SQUARE))
itermax = kwargs.get('itermax', 200)
kmeans_instance = kmedians(sample, initial_medians, 0.001, ccore, metric=metric, itermax=itermax)
kmeans_instance.process()
closest_clusters = kmeans_instance.predict(points)
assertion.eq(len(expected_closest_clusters), len(closest_clusters))
assertion.true(numpy.array_equal(numpy.array(expected_closest_clusters), closest_clusters))
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