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"""!
@brief Unit-tests for K-Medoids algorithm.
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
"""
import unittest
# Generate images without having a window appear.
import matplotlib
matplotlib.use('Agg')
from pyclustering.cluster.kmedoids import kmedoids
from pyclustering.cluster.tests.kmedoids_templates import kmedoids_test_template
from pyclustering.samples.definitions import SIMPLE_SAMPLES, SIMPLE_ANSWERS
from pyclustering.utils import read_sample
from pyclustering.utils.metric import type_metric, distance_metric
class KmedoidsUnitTest(unittest.TestCase):
def testClusterAllocationSampleSimple1(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], False)
def testClusterAllocationSampleSimple1WrongInitials1(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [1, 2, 3, 4], [2, 2, 3, 3], False)
def testClusterAllocationSampleSimple1DistanceMatrix(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], False, data_type='distance_matrix')
def testClusterAllocationSampleSimple1DistanceMatrixNumpy(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], False, data_type='distance_matrix', input_type='numpy')
def testClusterAllocationSampleSimple1Euclidean(self):
metric = distance_metric(type_metric.EUCLIDEAN)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False)
def testClusterAllocationSampleSimple1EuclideanDistanceMatrix(self):
metric = distance_metric(type_metric.EUCLIDEAN)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False, data_type='distance_matrix')
def testClusterAllocationSampleSimple1SquareEuclidean(self):
metric = distance_metric(type_metric.EUCLIDEAN_SQUARE)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False)
def testClusterAllocationSampleSimple1SquareEuclideanDistanceMatrix(self):
metric = distance_metric(type_metric.EUCLIDEAN_SQUARE)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False, data_type='distance_matrix')
def testClusterAllocationSampleSimple1Manhattan(self):
metric = distance_metric(type_metric.MANHATTAN)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False)
def testClusterAllocationSampleSimple1ManhattanDistanceMatrix(self):
metric = distance_metric(type_metric.MANHATTAN)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False, data_type='distance_matrix')
def testClusterAllocationSampleSimple1Chebyshev(self):
metric = distance_metric(type_metric.CHEBYSHEV)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False)
def testClusterAllocationSampleSimple1ChebyshevDistanceMatrix(self):
metric = distance_metric(type_metric.CHEBYSHEV)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False, data_type='distance_matrix')
def testClusterAllocationSampleSimple1Minkowski(self):
metric = distance_metric(type_metric.MINKOWSKI, degree=2.0)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False)
def testClusterAllocationSampleSimple1MinkowskiDistanceMatrix(self):
metric = distance_metric(type_metric.MINKOWSKI, degree=2.0)
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False, data_type='distance_matrix')
def testClusterAllocationSampleSimple1Gower(self):
metric = distance_metric(type_metric.GOWER, data=read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1))
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False)
def testClusterAllocationSampleSimple1GowerDistanceMatrix(self):
metric = distance_metric(type_metric.GOWER, data=read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1))
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False, data_type='distance_matrix')
def testClusterAllocationSampleSimple1UserDefined(self):
metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN))
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False)
def testClusterAllocationSampleSimple1UserDefinedDistanceMatrix(self):
metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN))
kmedoids_test_template.templateLengthProcessWithMetric(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], metric, False, data_type='distance_matrix')
def testClusterOneAllocationSampleSimple1(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [5], [10], False)
def testClusterOneAllocationSampleSimple1DistanceMatrix(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [5], [10], False, data_type='distance_matrix')
def testClusterAllocationSampleSimple1WithMedoidsInitializer(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, None, [5, 5], False, initialize_medoids=2)
def testClusterAllocationSampleSimple2(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [3, 12, 20], [10, 5, 8], False)
def testClusterAllocationSampleSimple2DistanceMatrix(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [3, 12, 20], [10, 5, 8], False, data_type='distance_matrix')
def testClusterOneAllocationSampleSimple2(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [10], [23], False)
def testClusterOneAllocationSampleSimple2DistanceMatrix(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [10], [23], False, data_type='distance_matrix')
def testClusterAllocationSampleSimple2WithMedoidsInitializer(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, None, [10, 5, 8], False, initialize_medoids=3)
def testClusterAllocationSampleSimple3(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [4, 12, 25, 37], [10, 10, 10, 30], False)
def testClusterAllocationSampleSimple3DistanceMatrix(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [4, 12, 25, 37], [10, 10, 10, 30], False, data_type='distance_matrix')
def testClusterOneAllocationSampleSimple3(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [30], [60], False)
def testClusterAllocationSampleSimple3WithMedoidsInitializer(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, None, [10, 10, 10, 30], False, initialize_medoids=4)
def testClusterAllocationSampleSimple5(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [4, 18, 34, 55], [15, 15, 15, 15], False)
def testClusterAllocationSampleSimple5DistanceMatrix(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [4, 18, 34, 55], [15, 15, 15, 15], False, data_type='distance_matrix')
def testClusterOneAllocationSampleSimple5(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [35], [60], False)
def testClusterTheSameData1(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, [2, 20], [10, 20], False)
def testClusterTheSameData1DistanceMatrix(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, [2, 20], [10, 20], False, data_type='distance_matrix')
def testClusterTheSameData2(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, [2, 7, 12], [5, 5, 5], False)
def testClusterTheSameData2DistanceMatrix(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, [2, 7, 12], [5, 5, 5], False, data_type='distance_matrix')
def testClusterAllocationOneDimensionData(self):
kmedoids_test_template.templateClusterAllocationOneDimensionData(False)
def testClusterAllocationTheSameObjectsOneInitialMedoid(self):
kmedoids_test_template.templateClusterAllocationTheSameObjects(20, 1, False)
def testClusterAllocationTheSameObjectsTwoInitialMedoids(self):
kmedoids_test_template.templateClusterAllocationTheSameObjects(15, 2, False)
def testClusterAllocationTheSameObjectsThreeInitialMedoids(self):
kmedoids_test_template.templateClusterAllocationTheSameObjects(25, 3, False)
def testPredictOnePoint(self):
medoids = [4, 12, 25, 37]
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, [[0.3, 0.2]], [0], False)
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, [[4.1, 1.1]], [3], False)
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, [[2.1, 1.9]], [2], False)
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, [[2.1, 4.1]], [1], False)
def testPredictOnePointUserMetric(self):
medoids = [4, 12, 25, 37]
metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN))
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, [[0.3, 0.2]], [0], False, metric=metric)
def testPredictTwoPoints(self):
medoids = [4, 12, 25, 37]
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, [[0.3, 0.2], [2.1, 1.9]], [0, 2], False)
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, [[2.1, 4.1], [2.1, 1.9]], [1, 2], False)
def testPredictTwoPointsUserMetric(self):
medoids = [4, 12, 25, 37]
metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN))
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, [[0.3, 0.2], [2.1, 1.9]], [0, 2], False, metric=metric)
def testPredictFourPoints(self):
medoids = [4, 12, 25, 37]
to_predict = [[0.3, 0.2], [4.1, 1.1], [2.1, 1.9], [2.1, 4.1]]
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, to_predict, [0, 3, 2, 1], False)
def testPredictFivePoints(self):
medoids = [4, 12, 25, 37]
to_predict = [[0.3, 0.2], [4.1, 1.1], [3.9, 1.1], [2.1, 1.9], [2.1, 4.1]]
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, to_predict, [0, 3, 3, 2, 1], False)
def testPredictFivePointsUserMetric(self):
medoids = [4, 12, 25, 37]
to_predict = [[0.3, 0.2], [4.1, 1.1], [3.9, 1.1], [2.1, 1.9], [2.1, 4.1]]
metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN))
kmedoids_test_template.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, medoids, to_predict, [0, 3, 3, 2, 1], False, metric=metric)
def testAllocatedRequestedClustersSampleSimple01(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 1, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 2, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 3, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 4, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 5, None, False)
def testAllocatedRequestedClustersSampleSimple02(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 1, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 2, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 3, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 4, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 5, None, False)
def testAllocatedRequestedClustersSampleSimple03(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 2, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 5, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 8, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 10, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 15, None, False)
def testAllocatedRequestedClustersSampleSimple04(self):
sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 10, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 25, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 40, None, False)
def testAllocatedRequestedClustersWithTheSamePoints1(self):
# Bug issue #366 - Kmedoids returns incorrect number of clusters.
sample = [[0.0, 0.0], [0.1, 0.1], [0.0, 0.0], [0.1, 0.2]]
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 3, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 3, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 2, None, False)
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 1, None, False)
def testAllocatedRequestedClustersWithTheSamePoints2(self):
sample = [[0.23, 0.2], [-0.1, 0.1], [0.0, 0.9], [0.1, -0.2], [0.8, 0.1], [-0.1, 0.1], [-0.4, -0.2], [0.0, 0.9]]
answers = [1, 2, 3, 4, 5, 6, 6, 6]
for expected_amount in answers:
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, expected_amount, None, False)
def testAllocatedRequestedClustersWithTotallyTheSamePoints(self):
# Bug issue #366 - Kmedoids returns incorrect number of clusters.
sample = [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]
kmedoids_test_template.templateAllocateRequestedClusterAmount(sample, 1, None, False)
def testItermax0(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [], False, itermax=0)
def testItermax1(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], False, itermax=1)
def testItermax10Simple01(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [2, 9], [5, 5], False, itermax=10)
def testItermax10Simple02(self):
kmedoids_test_template.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [3, 12, 20], [10, 5, 8], False, itermax=10)
def testSimple01AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, SIMPLE_ANSWERS.ANSWER_SIMPLE1, False, random_state=1000)
def testSimple01AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, SIMPLE_ANSWERS.ANSWER_SIMPLE1, False, random_state=1000, data_type='distance_matrix')
def testSimple02AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, SIMPLE_ANSWERS.ANSWER_SIMPLE2, False, random_state=1000)
def testSimple02AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, SIMPLE_ANSWERS.ANSWER_SIMPLE2, False, random_state=1000, data_type='distance_matrix')
def testSimple03AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, SIMPLE_ANSWERS.ANSWER_SIMPLE3, False, random_state=1000)
def testSimple03AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, SIMPLE_ANSWERS.ANSWER_SIMPLE3, False, random_state=1000, data_type='distance_matrix')
def testSimple04AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, SIMPLE_ANSWERS.ANSWER_SIMPLE4, False, random_state=1000)
def testSimple04AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, SIMPLE_ANSWERS.ANSWER_SIMPLE4, False, random_state=1000, data_type='distance_matrix')
def testSimple05AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, SIMPLE_ANSWERS.ANSWER_SIMPLE5, False, random_state=1000)
def testSimple05AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, SIMPLE_ANSWERS.ANSWER_SIMPLE5, False, random_state=1000, data_type='distance_matrix')
def testSimple06AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE6, SIMPLE_ANSWERS.ANSWER_SIMPLE6, False, random_state=1000)
def testSimple06AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE6, SIMPLE_ANSWERS.ANSWER_SIMPLE6, False, random_state=1000, data_type='distance_matrix')
def testSimple07AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, SIMPLE_ANSWERS.ANSWER_SIMPLE7, False, random_state=1000)
def testSimple07AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, SIMPLE_ANSWERS.ANSWER_SIMPLE7, False, random_state=1000, data_type='distance_matrix')
def testSimple08AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, SIMPLE_ANSWERS.ANSWER_SIMPLE8, False, random_state=1000)
def testSimple08AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, SIMPLE_ANSWERS.ANSWER_SIMPLE8, False, random_state=1000, data_type='distance_matrix')
def testSimple09AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, SIMPLE_ANSWERS.ANSWER_SIMPLE9, False, random_state=1000)
def testSimple09AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, SIMPLE_ANSWERS.ANSWER_SIMPLE9, False, random_state=1000, data_type='distance_matrix')
def testSimple10AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, SIMPLE_ANSWERS.ANSWER_SIMPLE10, False, random_state=1000)
def testSimple10AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE10, SIMPLE_ANSWERS.ANSWER_SIMPLE10, False, random_state=1000, data_type='distance_matrix')
def testSimple11AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, SIMPLE_ANSWERS.ANSWER_SIMPLE11, False, random_state=1000)
def testSimple11AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, SIMPLE_ANSWERS.ANSWER_SIMPLE11, False, random_state=1000, data_type='distance_matrix')
def testSimple12AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, SIMPLE_ANSWERS.ANSWER_SIMPLE12, False, random_state=1000)
def testSimple12AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, SIMPLE_ANSWERS.ANSWER_SIMPLE12, False, random_state=1000, data_type='distance_matrix')
def testSimple13AnswerByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE13, SIMPLE_ANSWERS.ANSWER_SIMPLE13, False, random_state=1000)
def testSimple13AnswerDistanceMatrixByCore(self):
kmedoids_test_template.clustering_with_answer(SIMPLE_SAMPLES.SAMPLE_SIMPLE13, SIMPLE_ANSWERS.ANSWER_SIMPLE13, False, random_state=1000, data_type='distance_matrix')
def test_incorrect_data(self):
self.assertRaises(ValueError, kmedoids, [], [1])
def test_incorrect_centers(self):
self.assertRaises(ValueError, kmedoids, [[0], [1], [2]], [])
def test_incorrect_tolerance(self):
self.assertRaises(ValueError, kmedoids, [[0], [1], [2]], [1], -1.0)
def test_incorrect_itermax(self):
self.assertRaises(ValueError, kmedoids, [[0], [1], [2]], [1], itermax=-5)
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