/dports/biology/ncbi-cxx-toolkit/ncbi_cxx--25_2_0/src/algo/cobalt/unit_test/ |
H A D | clusterer_unit_test.cpp | 83 CClusterer clusterer; in BOOST_AUTO_TEST_CASE() local 351 CClusterer clusterer; in BOOST_AUTO_TEST_CASE() local 357 clusterer.Run(); in BOOST_AUTO_TEST_CASE() 364 CClusterer clusterer; in BOOST_AUTO_TEST_CASE() local 373 clusterer.Run(); in BOOST_AUTO_TEST_CASE() 391 clusterer.Run(); in BOOST_AUTO_TEST_CASE() 408 clusterer.Run(); in BOOST_AUTO_TEST_CASE() 440 clusterer.Run(); in BOOST_AUTO_TEST_CASE() 468 clusterer.Run(); in BOOST_AUTO_TEST_CASE() 510 clusterer->Run(); in BOOST_AUTO_TEST_CASE() [all …]
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/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/tests/ |
H A D | test_flat.py | 51 clusterer = HDBSCAN(cluster_selection_method='eom').fit(X) 52 n_clusters = n_clusters_from_labels(clusterer.labels_) 61 clusterer.probabilities_) 65 n_clusters = n_clusters_from_labels(clusterer.labels_) 74 clusterer.probabilities_) 91 clusterer = HDBSCAN(cluster_selection_method='eom', 97 clusterer.probabilities_) 107 clusterer = HDBSCAN(cluster_selection_method='leaf', 113 clusterer.probabilities_) 124 clusterer = HDBSCAN(cluster_selection_method='eom', [all …]
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H A D | test_hdbscan.py | 359 if_matplotlib(clusterer.condensed_tree_.plot)( 403 clusterer.single_linkage_tree_.to_numpy() 404 clusterer.condensed_tree_.to_numpy() 405 clusterer.minimum_spanning_tree_.to_numpy() 411 if_pandas(clusterer.condensed_tree_.to_pandas)() 426 scores = clusterer.outlier_scores_ 461 clusterer = HDBSCAN(prediction_data=True).fit(X) 471 clusterer = HDBSCAN(min_cluster_size=200).fit(X) 474 clusterer.generate_prediction_data() 608 clusterer = HDBSCAN().fit(H) [all …]
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/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/ |
H A D | prediction.py | 370 if clusterer.prediction_data_ is None: 391 min_samples = clusterer.min_samples or clusterer.min_cluster_size 398 clusterer.condensed_tree_, 450 clusterer.prediction_data_ 470 min_samples = clusterer.min_samples or clusterer.min_cluster_size 475 tree = clusterer.condensed_tree_._raw_tree 555 min_samples = clusterer.min_samples or clusterer.min_cluster_size 603 def all_points_membership_vectors(clusterer): argument 638 clusterer.prediction_data_.exemplars, 642 clusterer.condensed_tree_._raw_tree, [all …]
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H A D | flat.py | 112 new_clusterer = clusterer 124 if not isinstance(clusterer, HDBSCAN): 135 new_clusterer = clusterer 197 def approximate_predict_flat(clusterer, argument 279 condensed_tree = clusterer.condensed_tree_ 292 if clusterer.prediction_data_ is None: 337 min_samples = clusterer.min_samples or clusterer.min_cluster_size 363 clusterer, points_to_predict, argument 414 condensed_tree = clusterer.condensed_tree_ 452 min_samples = clusterer.min_samples or clusterer.min_cluster_size [all …]
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/dports/math/apache-commons-math/commons-math3-3.6.1-src/src/main/java/org/apache/commons/math3/ml/clustering/ |
H A D | MultiKMeansPlusPlusClusterer.java | 37 private final KMeansPlusPlusClusterer<T> clusterer; field in MultiKMeansPlusPlusClusterer 49 public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer, in MultiKMeansPlusPlusClusterer() argument 51 this(clusterer, numTrials, new SumOfClusterVariances<T>(clusterer.getDistanceMeasure())); in MultiKMeansPlusPlusClusterer() 60 public MultiKMeansPlusPlusClusterer(final KMeansPlusPlusClusterer<T> clusterer, in MultiKMeansPlusPlusClusterer() argument 63 super(clusterer.getDistanceMeasure()); in MultiKMeansPlusPlusClusterer() 64 this.clusterer = clusterer; in MultiKMeansPlusPlusClusterer() 74 return clusterer; in getClusterer() 117 List<CentroidCluster<T>> clusters = clusterer.cluster(points); in cluster()
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/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/test/recognition/ |
H A D | test_recognition_cg.cpp | 124 clusterer.setInputCloud (model_downsampled_); in TEST() 125 clusterer.setInputRf (model_rf); in TEST() 126 clusterer.setSceneCloud (scene_downsampled_); in TEST() 127 clusterer.setSceneRf (scene_rf); in TEST() 129 clusterer.setHoughBinSize (0.03); in TEST() 130 clusterer.setHoughThreshold (10); in TEST() 131 EXPECT_TRUE (clusterer.recognize (rototranslations)); in TEST() 149 clusterer.setInputCloud (model_downsampled_); in TEST() 150 clusterer.setSceneCloud (scene_downsampled_); in TEST() 152 clusterer.setGCSize (0.015); in TEST() [all …]
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/dports/biology/ncbi-cxx-toolkit/ncbi_cxx--25_2_0/src/algo/cobalt/demo/ |
H A D | clusterer_app.cpp | 194 CClusterer clusterer; in x_RunBinary() local 195 clusterer.SetLinks(links); in x_RunBinary() 196 clusterer.SetClustMethod(clust_method); in x_RunBinary() 197 clusterer.SetMakeTrees(false); in x_RunBinary() 198 clusterer.Run(); in x_RunBinary() 210 << clusterer.GetClusterId(i) << NcbiEndl; in x_RunBinary() 279 CClusterer clusterer; in x_RunSparse() local 280 clusterer.SetLinks(links); in x_RunSparse() 281 clusterer.SetClustMethod(clust_method); in x_RunSparse() 282 clusterer.SetMakeTrees(false); in x_RunSparse() [all …]
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/dports/math/apache-commons-math/commons-math3-3.6.1-src/src/test/java/org/apache/commons/math3/ml/clustering/ |
H A D | FuzzyKMeansClustererTest.java | 89 … final FuzzyKMeansClusterer<DoublePoint> clusterer = new FuzzyKMeansClusterer<DoublePoint>(3, 2.0); in testNullDataset() local 90 clusterer.cluster(null); in testNullDataset() 97 final FuzzyKMeansClusterer<DoublePoint> clusterer = in testGetters() local 100 Assert.assertEquals(3, clusterer.getK()); in testGetters() 101 Assert.assertEquals(2.0, clusterer.getFuzziness(), 1e-6); in testGetters() 102 Assert.assertEquals(100, clusterer.getMaxIterations()); in testGetters() 103 Assert.assertEquals(1e-6, clusterer.getEpsilon(), 1e-12); in testGetters() 104 Assert.assertThat(clusterer.getDistanceMeasure(), CoreMatchers.is(measure)); in testGetters() 105 Assert.assertThat(clusterer.getRandomGenerator(), CoreMatchers.is(random)); in testGetters()
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H A D | DBSCANClustererTest.java | 152 final DBSCANClusterer<DoublePoint> clusterer = new DBSCANClusterer<DoublePoint>(3, 3); in testSingleLink() local 153 List<Cluster<DoublePoint>> clusters = clusterer.cluster(Arrays.asList(points)); in testSingleLink() 186 DBSCANClusterer<DoublePoint> clusterer = new DBSCANClusterer<DoublePoint>(2.0, 5); in testNullDataset() local 187 clusterer.cluster(null); in testNullDataset()
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/clustering/ |
H A D | KMeansOutlierDetection.java | 83 KMeans<O, ?> clusterer; field in KMeansOutlierDetection 90 public KMeansOutlierDetection(KMeans<O, ?> clusterer) { in KMeansOutlierDetection() argument 92 this.clusterer = clusterer; in KMeansOutlierDetection() 103 DistanceFunction<? super O> df = clusterer.getDistanceFunction(); in run() 107 Clustering<?> c = clusterer.run(database, relation); in run() 133 return TypeUtil.array(clusterer.getDistanceFunction().getInputTypeRestriction()); in getInputTypeRestriction() 160 KMeans<O, ?> clusterer; field in KMeansOutlierDetection.Parameterizer 168 clusterer = clusterP.instantiateClass(config); in makeOptions() 174 return new KMeansOutlierDetection<>(clusterer); in makeInstance()
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H A D | SilhouetteOutlierDetection.java | 89 ClusteringAlgorithm<?> clusterer; field in SilhouetteOutlierDetection 103 …on(DistanceFunction<? super O> distanceFunction, ClusteringAlgorithm<?> clusterer, NoiseHandling n… in SilhouetteOutlierDetection() argument 105 this.clusterer = clusterer; in SilhouetteOutlierDetection() 115 Clustering<?> c = clusterer.run(database); in run() 197 TypeInformation[] t = clusterer.getInputTypeRestriction(); in getInputTypeRestriction() 234 ClusteringAlgorithm<?> clusterer; field in SilhouetteOutlierDetection.Parameterizer 247 clusterer = clusterP.instantiateClass(config); in makeOptions() 258 return new SilhouetteOutlierDetection<>(distanceFunction, clusterer, noiseOption); in makeInstance()
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/cluster/ |
H A D | plot_inductive_clustering.py | 54 def __init__(self, clusterer, classifier): 55 self.clusterer = clusterer 59 self.clusterer_ = clone(self.clusterer) 90 clusterer = AgglomerativeClustering(n_clusters=3) 91 cluster_labels = clusterer.fit_predict(X) 114 inductive_learner = InductiveClusterer(clusterer, classifier).fit(X)
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H A D | plot_kmeans_silhouette_analysis.py | 72 clusterer = KMeans(n_clusters=n_clusters, random_state=10) variable 73 cluster_labels = clusterer.fit_predict(X) 133 centers = clusterer.cluster_centers_
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/dports/textproc/py-nltk/nltk-3.4.1/nltk/cluster/ |
H A D | kmeans.py | 206 clusterer = KMeansClusterer(2, euclidean_distance, initial_means=means) 207 clusters = clusterer.cluster(vectors, True, trace=True) 211 print('Means:', clusterer.means()) 219 clusterer = KMeansClusterer(2, euclidean_distance, repeats=10) 220 clusters = clusterer.cluster(vectors, True) 223 print('Means:', clusterer.means()) 229 print(clusterer.classify(vector))
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H A D | em.py | 194 clusterer = cluster.EMClusterer(means, bias=0.1) 195 clusters = clusterer.cluster(vectors, True, trace=True) 203 print('Prior: ', clusterer._priors[c]) 204 print('Mean: ', clusterer._means[c]) 205 print('Covar: ', clusterer._covariance_matrices[c]) 211 print(clusterer.classify(vector)) 216 pdist = clusterer.classification_probdist(vector)
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H A D | gaac.py | 154 clusterer = GAAClusterer(4) 155 clusters = clusterer.cluster(vectors, True) 157 print('Clusterer:', clusterer) 163 clusterer.dendrogram().show() 168 print(clusterer.classify(vector))
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/dports/math/mlpack/mlpack-3.4.2/src/mlpack/methods/gmm/ |
H A D | em_fit.hpp | 66 InitialClusteringType clusterer = InitialClusteringType(), 111 const InitialClusteringType& Clusterer() const { return clusterer; } in Clusterer() 113 InitialClusteringType& Clusterer() { return clusterer; } in Clusterer() 186 InitialClusteringType clusterer; member in mlpack::gmm::EMFit
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/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/doc/tutorials/content/sources/correspondence_grouping/ |
H A D | correspondence_grouping.cpp | 309 pcl::Hough3DGrouping<PointType, PointType, RFType, RFType> clusterer; in main() local 310 clusterer.setHoughBinSize (cg_size_); in main() 311 clusterer.setHoughThreshold (cg_thresh_); in main() 312 clusterer.setUseInterpolation (true); in main() 313 clusterer.setUseDistanceWeight (false); in main() 315 clusterer.setInputCloud (model_keypoints); in main() 316 clusterer.setInputRf (model_rf); in main() 317 clusterer.setSceneCloud (scene_keypoints); in main() 318 clusterer.setSceneRf (scene_rf); in main() 319 clusterer.setModelSceneCorrespondences (model_scene_corrs); in main() [all …]
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/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/doc/tutorials/content/sources/global_hypothesis_verification/ |
H A D | global_hypothesis_verification.cpp | 347 pcl::Hough3DGrouping<PointType, PointType, RFType, RFType> clusterer; in main() local 348 clusterer.setHoughBinSize (cg_size_); in main() 349 clusterer.setHoughThreshold (cg_thresh_); in main() 350 clusterer.setUseInterpolation (true); in main() 351 clusterer.setUseDistanceWeight (false); in main() 353 clusterer.setInputCloud (model_keypoints); in main() 354 clusterer.setInputRf (model_rf); in main() 355 clusterer.setSceneCloud (scene_keypoints); in main() 356 clusterer.setSceneRf (scene_rf); in main() 357 clusterer.setModelSceneCorrespondences (model_scene_corrs); in main() [all …]
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/dports/graphics/tesseract/tesseract-5.0.0/src/training/ |
H A D | mftraining.cpp | 93 CLUSTERER *clusterer = in ClusterOneConfig() local 96 LIST proto_list = ClusterSamples(clusterer, &Config); in ClusterOneConfig() 101 MergeInsignificantProtos(proto_list, class_label, clusterer, &Config); in ClusterOneConfig() 108 proto_list = RemoveInsignificantProtos(proto_list, true, false, clusterer->SampleSize); in ClusterOneConfig() 109 FreeClusterer(clusterer); in ClusterOneConfig()
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/dports/biology/mummer/mummer-4.0.0beta2-2-g277dac5/src/tigr/ |
H A D | mgaps_main.cc | 133 …ClusterMatches clusterer(Fixed_Separation, Max_Separation, Min_Output_Score, Separation_Factor, Us… in main() local 149 clusterer.Cluster_each(A.data(), UF, A.size() - 1, [&](const cluster_type&& cl) { in main() 150 clusterer.Print_Cluster(cl, label, std::cout); in main()
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/dports/math/apache-commons-math/commons-math3-3.6.1-src/src/test/java/org/apache/commons/math3/stat/clustering/ |
H A D | DBSCANClustererTest.java | 153 …final DBSCANClusterer<EuclideanIntegerPoint> clusterer = new DBSCANClusterer<EuclideanIntegerPoint… in testSingleLink() local 154 List<Cluster<EuclideanIntegerPoint>> clusters = clusterer.cluster(Arrays.asList(points)); in testSingleLink() 187 …DBSCANClusterer<EuclideanDoublePoint> clusterer = new DBSCANClusterer<EuclideanDoublePoint>(2.0, 5… in testNullDataset() local 188 clusterer.cluster(null); in testNullDataset()
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/ |
H A D | _birch.py | 695 clusterer = self.n_clusters 701 if isinstance(clusterer, numbers.Integral): 702 clusterer = AgglomerativeClustering(n_clusters=self.n_clusters) 706 elif clusterer is not None and not hasattr(clusterer, "fit_predict"): 714 if clusterer is None or not_enough_centroids: 727 self.subcluster_labels_ = clusterer.fit_predict(self.subcluster_centers_)
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/dports/math/py-hdbscan/hdbscan-0.8.27/ |
H A D | README.rst | 80 clusterer = hdbscan.HDBSCAN(min_cluster_size=10) 81 cluster_labels = clusterer.fit_predict(data) 100 understand your clustering results. After fitting data the clusterer 111 The clusterer objects also have an attribute providing cluster membership 121 The HDBSCAN clusterer objects also support the GLOSH outlier detection algorithm. 122 After fitting the clusterer to data the outlier scores can be accessed via the 141 the robust single linkage clusterer, again with the ability to plot 154 clusterer = hdbscan.RobustSingleLinkage(cut=0.125, k=7) 155 cluster_labels = clusterer.fit_predict(data) 156 hierarchy = clusterer.cluster_hierarchy_
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