"""! @brief Unit-tests for K-Means algorithm. @authors Andrei Novikov (pyclustering@yandex.ru) @date 2014-2020 @copyright BSD-3-Clause """ import unittest import math import numpy # Generate images without having a window appear. import matplotlib matplotlib.use('Agg') from pyclustering.cluster.tests.kmeans_templates import KmeansTestTemplates from pyclustering.cluster.kmeans import kmeans from pyclustering.samples.definitions import SIMPLE_SAMPLES from pyclustering.utils import read_sample from pyclustering.utils.metric import distance_metric, type_metric class KmeansUnitTest(unittest.TestCase): def testClusterAllocationSampleSimple1(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False) def testClusterOneAllocationSampleSimple1(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[1.0, 2.5]], [10], False) def testClusterAllocationSampleSimple1Euclidean(self): metric = distance_metric(type_metric.EUCLIDEAN) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1EuclideanSquare(self): metric = distance_metric(type_metric.EUCLIDEAN_SQUARE) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1Manhattan(self): metric = distance_metric(type_metric.MANHATTAN) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1Chebyshev(self): metric = distance_metric(type_metric.CHEBYSHEV) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1Minkowski01(self): metric = distance_metric(type_metric.MINKOWSKI, degree=2) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1Minkowski02(self): metric = distance_metric(type_metric.MINKOWSKI, degree=4) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1Gower(self): metric = distance_metric(type_metric.GOWER, data=read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1UserDefined1(self): metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1UserDefined2(self): metric = distance_metric(type_metric.USER_DEFINED, func=lambda p1, p2: (p1[0] - p2[0])**2 + (p1[1] - p2[1])**2) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1UserDefined3(self): metric = distance_metric(type_metric.USER_DEFINED, func=lambda p1, p2: abs(p1[0] - p2[0]) + abs(p1[1] - p2[1])) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1UserDefinedInfitityProcessing(self): metric = distance_metric(type_metric.USER_DEFINED, func=lambda p1, p2: p1[0] + p2[0] + 2) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [10], False, metric=metric) def testClusterAllocationSampleSimple1UserDefined(self): metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1UserDefined1NumPy(self): data = numpy.array(read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)) centers = numpy.array([[3.7, 5.5], [6.7, 7.5]]) metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) KmeansTestTemplates.templateLengthProcessData(data, centers, [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1UserDefined2NumPy(self): data = numpy.array(read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)) centers = numpy.array([[3.7, 5.5], [6.7, 7.5]]) def simple_2d_euclidean_distance(point_a, point_b): return math.sqrt((point_a[0] - point_b[0]) ** 2 + (point_a[1] - point_b[1]) ** 2) metric = distance_metric(type_metric.USER_DEFINED, func=simple_2d_euclidean_distance) KmeansTestTemplates.templateLengthProcessData(data, centers, [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1Canberra(self): metric = distance_metric(type_metric.CANBERRA) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple1ChiSquare(self): metric = distance_metric(type_metric.CHI_SQUARE) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, metric=metric) def testClusterAllocationSampleSimple2(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[3.5, 4.8], [6.9, 7], [7.5, 0.5]], [10, 5, 8], False) def testClusterOneAllocationSampleSimple2(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[0.5, 0.2]], [23], False) def testClusterAllocationSampleSimple2Canberra(self): metric = distance_metric(type_metric.CANBERRA) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[3.5, 4.8], [6.9, 7], [7.5, 0.5]], [10, 5, 8], False, metric=metric) def testClusterAllocationSampleSimple2ChiSquare(self): metric = distance_metric(type_metric.CHI_SQUARE) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[3.5, 4.8], [6.9, 7], [7.5, 0.5]], [10, 5, 8], False, metric=metric) def testClusterAllocationSampleSimple2UserDefined(self): metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[3.5, 4.8], [6.9, 7], [7.5, 0.5]], [10, 5, 8], False, metric=metric) def testClusterAllocationSampleSimple2UserDefinedNumPy(self): data = numpy.array(read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2)) centers = numpy.array([[3.5, 4.8], [6.9, 7], [7.5, 0.5]]) metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) KmeansTestTemplates.templateLengthProcessData(data, centers, [10, 5, 8], False, metric=metric) def testClusterAllocationSampleSimple3(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]], [10, 10, 10, 30], False) def testClusterAllocationSampleSimple3UserDefinedInfitityProcessing(self): metric = distance_metric(type_metric.USER_DEFINED, func=lambda p1, p2: p1[0] + p2[0] + 2) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]], [60], False, metric=metric) def testClusterOneAllocationSampleSimple3(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [[0.2, 0.1]], [60], False) def testClusterAllocationSampleSimple4(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, [[1.5, 0.0], [1.5, 2.0], [1.5, 4.0], [1.5, 6.0], [1.5, 8.0]], [15, 15, 15, 15, 15], False) def testClusterOneAllocationSampleSimple4(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, [[2.0, 5.0]], [75], False) def testClusterAllocationSampleSimple5(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [[0.0, 1.0], [0.0, 0.0], [1.0, 1.0], [1.0, 0.0]], [15, 15, 15, 15], False) def testClusterOneAllocationSampleSimple5(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [[0.0, 0.0]], [60], False) def testClusterAllocationSampleSimple5Canberra(self): metric = distance_metric(type_metric.CANBERRA) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [[0.0, 1.0], [0.0, 0.0], [1.0, 1.0], [1.0, 0.0]], [30, 30], False, metric=metric) def testClusterAllocationSampleSimple5ChiSquare(self): metric = distance_metric(type_metric.CHI_SQUARE) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [[3.4, 2.6], [3.4, -3.2], [-3.4, -3.4], [-3.1, 3.3]], [15, 15, 15, 15], False, metric=metric) def testClusterOneDimensionSampleSimple7(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, [[-3.0], [2.0]], [10, 10], False) def testClusterAllocationSampleSimple7Canberra(self): metric = distance_metric(type_metric.CANBERRA) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, [[-3.0], [2.0]], [10, 10], False, metric=metric) def testClusterAllocationSampleSimple7ChiSquare(self): metric = distance_metric(type_metric.CHI_SQUARE) KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE7, [[-3.0], [2.0]], [10, 10], False, metric=metric) def testClusterOneDimensionSampleSimple8(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, [[-4.0], [3.1], [6.1], [12.0]], [15, 30, 20, 80], False) def testWrongNumberOfCentersSimpleSample1(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[2.0, 4.5], [3.3, 6.5], [5.0, 7.8]], None, False) def testWrongNumberOfCentersSimpleSample2(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[1.3, 1.5], [5.2, 8.5], [5.0, 7.8], [11.0, -3.0]], None, False) def testWrongNumberOfCentersSimpleSample3(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, [[4.5, 3.4], [-1.7, 4.3], [1.5, 1.0], [11.3, 1.2], [-4.6, -5.2]], None, False) def testWrongNumberOfCentersSimpleSample4(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, [[2.4, -1.4], [-5.2, -8.5], [-5.0, 3.1], [6.2, 1.4], [7.9, 2.4]], None, False) def testWrongNumberOfCentersSimpleSample5(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE5, [[-1.9, 3.2], [1.2, 34.5], [15.2, 34.8], [192, 234], [-32.3, -106]], None, False) def testTheSameData1(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, [ [4.0], [8.0] ], [10, 20], False) def testTheSameData2(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE12, [ [1.0, 1.0], [2.5, 2.5], [4.0, 4.0] ], [5, 5, 5], False) def testOneDimensionalData1(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, [[-2.0], [3.0], [6.0], [12.0]], [15, 30, 20, 80], False) def testOneDimensionalData2(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE9, [[4.5], [6.2]], [20, 10], False) def testThreeDimensionalData1(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, [[1.0, 0.6, 0.8], [4.1, 4.2, 4.3]], [10, 10], False) def testDifferentDimensions(self): kmeans_instance = kmeans([ [0, 1, 5], [0, 2, 3] ], [ [0, 3] ], ccore=False) self.assertRaises(ValueError, kmeans_instance.process) def testClusterAllocationOneDimensionData(self): KmeansTestTemplates.templateClusterAllocationOneDimensionData(False) def testPredictOnePoint(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, [[0.3, 0.2]], [0], False) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, [[4.1, 1.1]], [1], False) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, [[2.1, 1.9]], [2], False) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, [[2.1, 4.1]], [3], False) def testPredictOnePointUserMetric(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, [[0.3, 0.2]], [0], False, metric=metric) def testPredictTwoPoints(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, [[0.3, 0.2], [2.1, 1.9]], [0, 2], False) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, [[2.1, 4.1], [2.1, 1.9]], [3, 2], False) def testPredictTwoPointsUserMetric(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] metric = distance_metric(type_metric.USER_DEFINED, func=distance_metric(type_metric.EUCLIDEAN)) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, [[0.3, 0.2], [2.1, 1.9]], [0, 2], False, metric=metric) def testPredictFourPoints(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] to_predict = [[0.3, 0.2], [4.1, 1.1], [2.1, 1.9], [2.1, 4.1]] KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 2, 3], False) def testPredictFivePoints(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] to_predict = [[0.3, 0.2], [4.1, 1.1], [3.9, 1.1], [2.1, 1.9], [2.1, 4.1]] KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False) def testPredictFivePointsEuclideanDistance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.EUCLIDEAN) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsEuclideanSquareDistance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.EUCLIDEAN_SQUARE) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsManhattanDistance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.MANHATTAN) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsChebyshevDistance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.CHEBYSHEV) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsMinkowski2Distance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.MINKOWSKI, degree=2) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsMinkowski4Distance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.MINKOWSKI, degree=4) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsCanberraDistance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.CANBERRA) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsChiSquareDistance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.CHI_SQUARE) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsGowerDistance(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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.GOWER, data=centers+to_predict) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testPredictFivePointsUserMetric(self): centers = [[0.2, 0.1], [4.0, 1.0], [2.0, 2.0], [2.3, 3.9]] 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)) KmeansTestTemplates.templatePredict(SIMPLE_SAMPLES.SAMPLE_SIMPLE3, centers, to_predict, [0, 1, 1, 2, 3], False, metric=metric) def testObserveSampleSimple1(self): KmeansTestTemplates.templateCollectEvolution(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.5, 5.6], [6.8, 7.4]], [5, 5], False) def testObserveSampleSimple1OneCluster(self): KmeansTestTemplates.templateCollectEvolution(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.3, 5.4]], [10], False) def testObserveSampleSimple2(self): KmeansTestTemplates.templateCollectEvolution(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[3.4, 4.9], [6.8, 7.1], [7.6, 0.4]], [10, 5, 8], False) def testItermax0(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [], False, itermax=0) def testItermax1(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, itermax=1) def testItermax10Simple01(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.7, 5.5], [6.7, 7.5]], [5, 5], False, itermax=10) def testItermax10Simple02(self): KmeansTestTemplates.templateLengthProcessData(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[3.5, 4.8], [6.9, 7], [7.5, 0.5]], [10, 5, 8], False, itermax=10) def testEncoderProcedureSampleSimple4(self): KmeansTestTemplates.templateEncoderProcedures(SIMPLE_SAMPLES.SAMPLE_SIMPLE4, [[1.5, 0.0], [1.5, 2.0], [1.5, 4.0], [1.5, 6.0], [1.5, 8.0]], 5, False) def testShowResultsSampleSimple01(self): KmeansTestTemplates.templateShowClusteringResultNoFailure(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.5, 5.6], [6.8, 7.4]], False) def testShowResultsSampleSimple02(self): KmeansTestTemplates.templateShowClusteringResultNoFailure(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[3.4, 4.9], [6.8, 7.1], [7.6, 0.4]], False) def testShowResultsOneDimensionalData(self): KmeansTestTemplates.templateShowClusteringResultNoFailure(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, [[-2.0], [3.0], [6.0], [12.0]], False) def testShowResultsThreeDimensionalData(self): KmeansTestTemplates.templateShowClusteringResultNoFailure(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, [[1.0, 0.6, 0.8], [4.1, 4.2, 4.3]], False) def testAnimateResultsSampleSimple01(self): KmeansTestTemplates.templateAnimateClusteringResultNoFailure(SIMPLE_SAMPLES.SAMPLE_SIMPLE1, [[3.5, 5.6], [6.8, 7.4]], False) def testAnimateResultsSampleSimple02(self): KmeansTestTemplates.templateAnimateClusteringResultNoFailure(SIMPLE_SAMPLES.SAMPLE_SIMPLE2, [[3.4, 4.9], [6.8, 7.1], [7.6, 0.4]], False) def testAnimateResultsOneDimensionalData(self): KmeansTestTemplates.templateAnimateClusteringResultNoFailure(SIMPLE_SAMPLES.SAMPLE_SIMPLE8, [[-2.0], [3.0], [6.0], [12.0]], False) def testAnimateResultsThreeDimensionalData(self): KmeansTestTemplates.templateAnimateClusteringResultNoFailure(SIMPLE_SAMPLES.SAMPLE_SIMPLE11, [[1.0, 0.6, 0.8], [4.1, 4.2, 4.3]], False) def test_incorrect_data(self): self.assertRaises(ValueError, kmeans, [], [[1]]) def test_incorrect_centers(self): self.assertRaises(ValueError, kmeans, [[0], [1], [2]], []) def test_incorrect_tolerance(self): self.assertRaises(ValueError, kmeans, [[0], [1], [2]], [[1]], -1.0) def test_incorrect_itermax(self): self.assertRaises(ValueError, kmeans, [[0], [1], [2]], [[1]], itermax=-5)