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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki/src/main/java/de/lmu/ifi/dbs/elki/evaluation/
H A DAutomaticEvaluation.java79 Collection<Clustering<?>> clusterings = ResultUtil.filterResults(hier, db, Clustering.class); in autoEvaluateOutliers() local
80 if(clusterings.isEmpty()) { in autoEvaluateOutliers()
84 Clustering<?> basec = clusterings.iterator().next(); in autoEvaluateOutliers()
125 …Collection<Clustering<?>> clusterings = ResultUtil.filterResults(hier, newResult, Clustering.class… in autoEvaluateClusterings() local
127 LOG.warning("Number of new clustering results: " + clusterings.size()); in autoEvaluateClusterings()
129 for(Iterator<Clustering<?>> c = clusterings.iterator(); c.hasNext();) { in autoEvaluateClusterings()
144 if(!clusterings.isEmpty()) { in autoEvaluateClusterings()
161 …Collection<Clustering<?>> clusterings = ResultUtil.filterResults(db.getHierarchy(), result, Cluste… in ensureClusteringResult() local
162 if(clusterings.isEmpty()) { in ensureClusteringResult()
/dports/biology/mmseqs2/MMseqs2-13-45111/src/util/
H A Dmergeclusters.cpp17 std::list<std::string> clusterings; in mergeclusters() local
19 clusterings.push_back(par.filenames[i]); in mergeclusters()
31 std::string firstClu = clusterings.front(); in mergeclusters()
33 clusterings.pop_front(); in mergeclusters()
68 while (!clusterings.empty()) { in mergeclusters()
71 std::string cluStep = clusterings.front(); in mergeclusters()
73 clusterings.pop_front(); in mergeclusters()
/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/evaluation/clustering/pairsegments/
H A DClusterPairSegmentAnalysis.java70 List<Clustering<?>> clusterings = Clustering.getClusteringResults(result); in processNewResult() local
72 if(clusterings.size() < 2) { in processNewResult()
77 Segments segments = new Segments(clusterings); in processNewResult()
H A DSegments.java84 private List<Clustering<?>> clusterings; field in Segments
121 public Segments(List<Clustering<?>> clusterings) { in Segments() argument
123 this.clusterings = clusterings; in Segments()
124 this.clusteringsCount = clusterings.size(); in Segments()
132 for(Clustering<?> clr : clusterings) { in Segments()
262 return clusterings.get(clusteringID).getLongName(); in getClusteringDescription()
H A DSegment.java59 public Segment(int clusterings) { in Segment() argument
60 clusterIds = new int[clusterings]; in Segment()
/dports/misc/orange3/orange3-3.29.1/Orange/widgets/unsupervised/tests/
H A Dtest_owkmeans.py210 widget.clusterings[widget.k].silhouette_samples = np.arange(303) / 303
271 for km in (widget.clusterings[k] for k in range(3, 9))]
275 self.assertIsInstance(widget.clusterings[3], str)
276 self.assertIsInstance(widget.clusterings[5], str)
277 self.assertIsInstance(widget.clusterings[7], str)
278 self.assertNotIsInstance(widget.clusterings[4], str)
279 self.assertNotIsInstance(widget.clusterings[6], str)
280 self.assertNotIsInstance(widget.clusterings[8], str)
334 widget.clusterings = {k: "error" for k in range(2, 7)}
472 self.assertEqual(widget.clusterings, {})
[all …]
/dports/misc/orange3/orange3-3.29.1/Orange/widgets/unsupervised/
H A Dowkmeans.py167 self.clusterings = {}
326 self.clusterings[self.k_from + idx] = str(ex)
334 self.clusterings[result.k] = result
349 if self.optimize_k and all(isinstance(self.clusterings[i], str)
352 self.Error.failed(self.clusterings[self.k_to])
402 if k not in self.clusterings]
413 if self.k in self.clusterings:
460 self.clusterings = {}
470 (self.clusterings[k] for k in range(self.k_from, self.k_to + 1))]
514 km = self.clusterings.get(k)
/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/pairsegments/
H A DCircleSegmentsVisualizer.java374 final int clusterings = segments.getClusterings(); in drawSegments() local
392 … double radius_delta = (RADIUS_OUTER - RADIUS_INNER - clusterings * RADIUS_DISTANCE) / clusterings; in drawSegments()
410 ArrayList<Element> elems = new ArrayList<>(clusterings); in drawSegments()
414 for(int i = 0; i < clusterings; i++) { in drawSegments()
425 refClustering = Math.min(refClustering + 1, clusterings - 1); in drawSegments()
461 double currentRadius = clusterings * (radius_delta + RADIUS_DISTANCE) + RADIUS_INNER; in drawSegments()
/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/
H A DVisualizerContext.java174 List<Clustering<? extends Model>> clusterings = Clustering.getClusteringResults(db); in makeStyleResult() local
175 if(!clusterings.isEmpty()) { in makeStyleResult()
176 stylepolicy = new ClusterStylingPolicy(clusterings.get(0), stylelib); in makeStyleResult()
/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/
H A DRepresentativeUncertainClustering.java188 ArrayList<Clustering<?>> clusterings = new ArrayList<>(); in run() local
202 clusterings.add(runClusteringAlgorithm(hierarchy, samples, ids, store, dim, "Sample " + i)); in run()
208 DBIDRange rids = DBIDFactory.FACTORY.generateStaticDBIDRange(clusterings.size()); in run()
211 Iterator<Clustering<?>> it2 = clusterings.iterator(); in run()
216 assert (rids.size() == clusterings.size()); in run()
/dports/math/igraph/igraph-0.9.5/tests/unit/
H A Digraph_split_join_distance.out12 Differently sized clusterings
/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/docs/cross-validation/metrics/
H A Drand-index.md4 …les that are assigned in the same or different clusters in the predicted and empirical clusterings.
/dports/misc/orange3/orange3-3.29.1/Orange/widgets/visualize/tests/
H A Dtest_owheatmap.py107 clusterings = (Clustering.None_, Clustering.Clustering,
111 for col_clust in clusterings:
117 for row_clust in clusterings:
/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/addons/3dpc/src/main/java/de/lmu/ifi/dbs/elki/visualization/parallel3d/
H A DOpenGL3DParallelCoordinates.java157 List<Clustering<? extends Model>> clusterings = Clustering.getClusteringResults(db); in getStylePolicy() local
158 if(clusterings.isEmpty()) { in getStylePolicy()
161 return new ClusterStylingPolicy(clusterings.get(0), stylelib); in getStylePolicy()
/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/docs/clusterers/
H A Dgaussian-mixture.md4 …to be learned as well. For this reason, GMMs are especially useful for clusterings that are of dif…
/dports/math/R/R-4.1.2/src/library/stats/man/
H A Dcophenetic.Rd37 represent hierarchical clusterings (total indexed hierarchies) can be
/dports/math/libRmath/R-4.1.1/src/library/stats/man/
H A Dcophenetic.Rd37 represent hierarchical clusterings (total indexed hierarchies) can be
/dports/math/R-cran-igraph/igraph/man/
H A Dcompare.Rd49 Meila M: Comparing clusterings by the variation of information.
/dports/science/py-libpysal/libpysal-4.5.1/libpysal/cg/
H A Drtree.py937 clusterings = [
940 score, bestcluster = max([(silhouette_coeff(c), c) for c in clusterings])
/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/modules/
H A Dclustering.rst1134 similar clusterings have a high (adjusted or unadjusted) Rand index,
1159 clusterings themselves differ significantly. This can be understood
1161 labeling resulting from the clusterings: In practice there often is
1389 for clusterings comparison". Proceedings of the 26th Annual International
1656 hierarchical clusterings". Journal of the American Statistical Association.
1933 the two clusterings.
1965 between two clusterings computed by considering all pairs of samples and
1967 under the true and predicted clusterings.
1971 :math:`C_{00}` : number of pairs with both clusterings having the samples
1982 :math:`C_{11}` : number of pairs with both clusterings having the samples
H A Dmixture.rst298 *clusterings with an infinite, unbounded, number of partitions*.
/dports/science/py-scipy/scipy-1.7.1/doc/release/
H A D0.7.0-notes.rst246 class represents a hierarchical clusterings as a field-navigable tree
250 function plots hierarchical clusterings as a dendrogram, using
/dports/math/py-hdbscan/hdbscan-0.8.27/
H A DREADME.rst142 or export the hierarchy, and to extract flat clusterings at a given
H A DPKG-INFO150 or export the hierarchy, and to extract flat clusterings at a given
/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan.egg-info/
H A DPKG-INFO150 or export the hierarchy, and to extract flat clusterings at a given

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