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"""!
@brief Test templates for K-Means clustering module.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
from pyclustering.tests.assertion import assertion
from pyclustering.cluster.encoder import type_encoding, cluster_encoder
from pyclustering.cluster.kmeans import kmeans, kmeans_observer, kmeans_visualizer
from pyclustering.utils import read_sample
from pyclustering.utils.metric import distance_metric, type_metric
from random import random
import numpy
class KmeansTestTemplates:
@staticmethod
def templateLengthProcessData(data, start_centers, expected_cluster_length, ccore, **kwargs):
if isinstance(data, str):
sample = read_sample(data)
else:
sample = data
metric = kwargs.get('metric', distance_metric(type_metric.EUCLIDEAN_SQUARE))
itermax = kwargs.get('itermax', 200)
kmeans_instance = kmeans(sample, start_centers, 0.001, ccore, metric=metric, itermax=itermax)
kmeans_instance.process()
clusters = kmeans_instance.get_clusters()
centers = kmeans_instance.get_centers()
wce = kmeans_instance.get_total_wce()
if itermax == 0:
assertion.eq(start_centers, centers)
assertion.eq([], clusters)
assertion.eq(0.0, wce)
return
expected_wce = 0.0
for index_cluster in range(len(clusters)):
for index_point in clusters[index_cluster]:
expected_wce += metric(sample[index_point], centers[index_cluster])
assertion.eq(expected_wce, wce)
obtained_cluster_sizes = [len(cluster) for cluster in clusters]
assertion.eq(len(sample), sum(obtained_cluster_sizes))
assertion.eq(len(clusters), len(centers))
for center in centers:
assertion.eq(len(sample[0]), len(center))
if expected_cluster_length is not None:
obtained_cluster_sizes.sort()
expected_cluster_length.sort()
assertion.eq(obtained_cluster_sizes, expected_cluster_length)
@staticmethod
def templatePredict(path_to_file, initial_centers, 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 = kmeans(sample, initial_centers, 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))
@staticmethod
def templateClusterAllocationOneDimensionData(ccore_flag):
input_data = [ [random()] for _ in range(10) ] + [ [random() + 3] for _ in range(10) ] + [ [random() + 5] for _ in range(10) ] + [ [random() + 8] for _ in range(10) ]
kmeans_instance = kmeans(input_data, [ [0.0], [3.0], [5.0], [8.0] ], 0.025, ccore_flag)
kmeans_instance.process()
clusters = kmeans_instance.get_clusters()
assertion.eq(4, len(clusters))
for cluster in clusters:
assertion.eq(10, len(cluster))
@staticmethod
def templateEncoderProcedures(filename, initial_centers, number_clusters, ccore_flag):
sample = read_sample(filename)
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag)
kmeans_instance.process()
clusters = kmeans_instance.get_clusters()
encoding = kmeans_instance.get_cluster_encoding()
encoder = cluster_encoder(encoding, clusters, sample)
encoder.set_encoding(type_encoding.CLUSTER_INDEX_LABELING)
encoder.set_encoding(type_encoding.CLUSTER_OBJECT_LIST_SEPARATION)
encoder.set_encoding(type_encoding.CLUSTER_INDEX_LIST_SEPARATION)
assertion.eq(number_clusters, len(clusters))
@staticmethod
def templateCollectEvolution(filename, initial_centers, number_clusters, ccore_flag):
sample = read_sample(filename)
observer = kmeans_observer()
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag, observer=observer)
kmeans_instance.process()
assertion.le(1, len(observer))
for i in range(len(observer)):
assertion.le(1, len(observer.get_centers(i)))
for center in observer.get_centers(i):
assertion.eq(len(sample[0]), len(center))
assertion.le(1, len(observer.get_clusters(i)))
@staticmethod
def templateShowClusteringResultNoFailure(filename, initial_centers, ccore_flag):
sample = read_sample(filename)
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag)
kmeans_instance.process()
clusters = kmeans_instance.get_clusters()
centers = kmeans_instance.get_centers()
kmeans_visualizer.show_clusters(sample, clusters, centers, initial_centers)
@staticmethod
def templateAnimateClusteringResultNoFailure(filename, initial_centers, ccore_flag):
sample = read_sample(filename)
observer = kmeans_observer()
kmeans_instance = kmeans(sample, initial_centers, 0.025, ccore_flag, observer=observer)
kmeans_instance.process()
kmeans_visualizer.animate_cluster_allocation(sample, observer)
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