/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki/src/main/java/de/lmu/ifi/dbs/elki/evaluation/ |
H A D | AutomaticEvaluation.java | 79 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()
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/dports/biology/mmseqs2/MMseqs2-13-45111/src/util/ |
H A D | mergeclusters.cpp | 17 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()
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/evaluation/clustering/pairsegments/ |
H A D | ClusterPairSegmentAnalysis.java | 70 List<Clustering<?>> clusterings = Clustering.getClusteringResults(result); in processNewResult() local 72 if(clusterings.size() < 2) { in processNewResult() 77 Segments segments = new Segments(clusterings); in processNewResult()
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H A D | Segments.java | 84 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()
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H A D | Segment.java | 59 public Segment(int clusterings) { in Segment() argument 60 clusterIds = new int[clusterings]; in Segment()
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/unsupervised/tests/ |
H A D | test_owkmeans.py | 210 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 …]
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/unsupervised/ |
H A D | owkmeans.py | 167 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)
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/visualizers/pairsegments/ |
H A D | CircleSegmentsVisualizer.java | 374 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()
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/addons/batikvis/src/main/java/de/lmu/ifi/dbs/elki/visualization/ |
H A D | VisualizerContext.java | 174 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()
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/addons/uncertain/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/uncertain/ |
H A D | RepresentativeUncertainClustering.java | 188 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()
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/dports/math/igraph/igraph-0.9.5/tests/unit/ |
H A D | igraph_split_join_distance.out | 12 Differently sized clusterings
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/docs/cross-validation/metrics/ |
H A D | rand-index.md | 4 …les that are assigned in the same or different clusters in the predicted and empirical clusterings.
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/dports/misc/orange3/orange3-3.29.1/Orange/widgets/visualize/tests/ |
H A D | test_owheatmap.py | 107 clusterings = (Clustering.None_, Clustering.Clustering, 111 for col_clust in clusterings: 117 for row_clust in clusterings:
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/dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/addons/3dpc/src/main/java/de/lmu/ifi/dbs/elki/visualization/parallel3d/ |
H A D | OpenGL3DParallelCoordinates.java | 157 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()
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/dports/mail/nextcloud-mail/mail/vendor/rubix/ml/docs/clusterers/ |
H A D | gaussian-mixture.md | 4 …to be learned as well. For this reason, GMMs are especially useful for clusterings that are of dif…
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/dports/math/R/R-4.1.2/src/library/stats/man/ |
H A D | cophenetic.Rd | 37 represent hierarchical clusterings (total indexed hierarchies) can be
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/dports/math/libRmath/R-4.1.1/src/library/stats/man/ |
H A D | cophenetic.Rd | 37 represent hierarchical clusterings (total indexed hierarchies) can be
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/dports/math/R-cran-igraph/igraph/man/ |
H A D | compare.Rd | 49 Meila M: Comparing clusterings by the variation of information.
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/dports/science/py-libpysal/libpysal-4.5.1/libpysal/cg/ |
H A D | rtree.py | 937 clusterings = [ 940 score, bestcluster = max([(silhouette_coeff(c), c) for c in clusterings])
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/doc/modules/ |
H A D | clustering.rst | 1134 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
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H A D | mixture.rst | 298 *clusterings with an infinite, unbounded, number of partitions*.
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/dports/science/py-scipy/scipy-1.7.1/doc/release/ |
H A D | 0.7.0-notes.rst | 246 class represents a hierarchical clusterings as a field-navigable tree 250 function plots hierarchical clusterings as a dendrogram, using
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/dports/math/py-hdbscan/hdbscan-0.8.27/ |
H A D | README.rst | 142 or export the hierarchy, and to extract flat clusterings at a given
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H A D | PKG-INFO | 150 or export the hierarchy, and to extract flat clusterings at a given
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/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan.egg-info/ |
H A D | PKG-INFO | 150 or export the hierarchy, and to extract flat clusterings at a given
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