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/dports/biology/ncbi-cxx-toolkit/ncbi_cxx--25_2_0/src/algo/cobalt/unit_test/
H A Dclusterer_unit_test.cpp83 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()
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/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/tests/
H A Dtest_flat.py51 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',
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H A Dtest_hdbscan.py359 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)
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/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/
H A Dprediction.py370 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,
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H A Dflat.py112 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
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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 DMultiKMeansPlusPlusClusterer.java37 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()
/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/test/recognition/
H A Dtest_recognition_cg.cpp124 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()
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/dports/biology/ncbi-cxx-toolkit/ncbi_cxx--25_2_0/src/algo/cobalt/demo/
H A Dclusterer_app.cpp194 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()
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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 DFuzzyKMeansClustererTest.java89 … 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()
H A DDBSCANClustererTest.java152 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()
/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/outlier/clustering/
H A DKMeansOutlierDetection.java83 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()
H A DSilhouetteOutlierDetection.java89 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()
/dports/science/py-scikit-learn/scikit-learn-1.0.2/examples/cluster/
H A Dplot_inductive_clustering.py54 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)
H A Dplot_kmeans_silhouette_analysis.py72 clusterer = KMeans(n_clusters=n_clusters, random_state=10) variable
73 cluster_labels = clusterer.fit_predict(X)
133 centers = clusterer.cluster_centers_
/dports/textproc/py-nltk/nltk-3.4.1/nltk/cluster/
H A Dkmeans.py206 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))
H A Dem.py194 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)
H A Dgaac.py154 clusterer = GAAClusterer(4)
155 clusters = clusterer.cluster(vectors, True)
157 print('Clusterer:', clusterer)
163 clusterer.dendrogram().show()
168 print(clusterer.classify(vector))
/dports/math/mlpack/mlpack-3.4.2/src/mlpack/methods/gmm/
H A Dem_fit.hpp66 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
/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/doc/tutorials/content/sources/correspondence_grouping/
H A Dcorrespondence_grouping.cpp309 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()
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/dports/graphics/pcl-pointclouds/pcl-pcl-1.12.0/doc/tutorials/content/sources/global_hypothesis_verification/
H A Dglobal_hypothesis_verification.cpp347 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()
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/dports/graphics/tesseract/tesseract-5.0.0/src/training/
H A Dmftraining.cpp93 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()
/dports/biology/mummer/mummer-4.0.0beta2-2-g277dac5/src/tigr/
H A Dmgaps_main.cc133 …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()
/dports/math/apache-commons-math/commons-math3-3.6.1-src/src/test/java/org/apache/commons/math3/stat/clustering/
H A DDBSCANClustererTest.java153 …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()
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/
H A D_birch.py695 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_)
/dports/math/py-hdbscan/hdbscan-0.8.27/
H A DREADME.rst80 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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