| /dports/math/apache-commons-math/commons-math3-3.6.1-src/src/main/java/org/apache/commons/math3/ml/clustering/ |
| H A D | DBSCANClusterer.java | 64 private final int minPts; field in DBSCANClusterer
83 public DBSCANClusterer(final double eps, final int minPts) in DBSCANClusterer() argument
85 this(eps, minPts, new EuclideanDistance()); in DBSCANClusterer()
96 public DBSCANClusterer(final double eps, final int minPts, final DistanceMeasure measure) in DBSCANClusterer() argument
103 if (minPts < 0) { in DBSCANClusterer()
104 throw new NotPositiveException(minPts); in DBSCANClusterer()
107 this.minPts = minPts; in DBSCANClusterer()
123 return minPts; in getMinPts()
147 if (neighbors.size() >= minPts) { in cluster()
185 if (currentNeighbors.size() >= minPts) { in expandCluster()
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| /dports/math/apache-commons-math/commons-math3-3.6.1-src/src/main/java/org/apache/commons/math3/stat/clustering/ |
| H A D | DBSCANClusterer.java | 69 private final int minPts; field in DBSCANClusterer
86 public DBSCANClusterer(final double eps, final int minPts) in DBSCANClusterer() argument
91 if (minPts < 0) { in DBSCANClusterer()
92 throw new NotPositiveException(minPts); in DBSCANClusterer()
95 this.minPts = minPts; in DBSCANClusterer()
113 return minPts; in getMinPts()
140 if (neighbors.size() >= minPts) { in cluster()
178 if (currentNeighbors.size() >= minPts) { in expandCluster()
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| /dports/graphics/geos/geos-3.9.1/src/precision/ |
| H A D | MinimumClearance.cpp | 62 std::vector<Coordinate> minPts; in compute() member in geos::precision::MinimumClearance::compute::MinClearanceDistance
69 minPts[0] = p; in compute()
70 seg.closestPoint(p, minPts[1]); in compute()
76 minPts(std::vector<Coordinate>(2)) in compute()
82 return &minPts; in compute()
129 minPts[0] = *p1; in compute()
130 minPts[1] = *p2; in compute()
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| /dports/math/jts/jts-jts-1.18.1/modules/core/src/main/java/org/locationtech/jts/precision/ |
| H A D | MinimumClearance.java | 232 private Coordinate[] minPts = new Coordinate[2]; field in MinimumClearance.MinClearanceDistance
236 return minPts; in getCoordinates()
268 minPts[0] = p1; in vertexDistance()
269 minPts[1] = p2; in vertexDistance()
304 minPts[0] = p; in updatePts()
306 minPts[1] = new Coordinate(seg.closestPoint(p)); in updatePts()
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| /dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/hierarchical/ |
| H A D | AbstractHDBSCAN.java | 70 protected final int minPts; field in AbstractHDBSCAN
78 public AbstractHDBSCAN(DistanceFunction<? super O> distanceFunction, int minPts) { in AbstractHDBSCAN() argument
80 this.minPts = minPts; in AbstractHDBSCAN()
91 protected WritableDoubleDataStore computeCoreDists(DBIDs ids, KNNQuery<O> knnQ, int minPts) { in computeCoreDists() argument
96 coredists.put(iter, knnQ.getKNNForDBID(iter, minPts).getKNNDistance()); in computeCoreDists()
300 protected int minPts; field in AbstractHDBSCAN.Parameterizer
309 minPts = minptsP.getValue(); in makeOptions()
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| H A D | HDBSCANLinearMemory.java | 96 public HDBSCANLinearMemory(DistanceFunction<? super O> distanceFunction, int minPts) { in HDBSCANLinearMemory() argument
97 super(distanceFunction, minPts); in HDBSCANLinearMemory()
109 final KNNQuery<O> knnQ = db.getKNNQuery(distQ, minPts); in run()
115 final WritableDoubleDataStore coredists = computeCoreDists(ids, knnQ, minPts); in run()
155 return new HDBSCANLinearMemory<>(distanceFunction, minPts); in makeInstance()
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| H A D | SLINKHDBSCANLinearMemory.java | 83 public SLINKHDBSCANLinearMemory(DistanceFunction<? super O> distanceFunction, int minPts) { in SLINKHDBSCANLinearMemory() argument
84 super(distanceFunction, minPts); in SLINKHDBSCANLinearMemory()
96 final KNNQuery<O> knnQ = db.getKNNQuery(distQ, minPts); in run()
102 final WritableDoubleDataStore coredists = computeCoreDists(ids, knnQ, minPts); in run()
253 return new SLINKHDBSCANLinearMemory<>(distanceFunction, minPts); in makeInstance()
|
| /dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/cash/ |
| H A D | CASHIntervalSplit.java | 66 private int minPts; field in CASHIntervalSplit
79 public CASHIntervalSplit(Relation<ParameterizationFunction> database, int minPts) { in CASHIntervalSplit() argument
83 this.minPts = minPts; in CASHIntervalSplit()
161 if(childIDs.size() < minPts) { in determineIDs()
|
| /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 | FDBSCAN.java | 110 protected int minPts; field in FDBSCAN.Parameterizer
123 minPts = minPtsP.intValue(); in makeOptions()
144 return new FDBSCAN(epsilon, sampleSize, threshold, seed, minPts); in makeInstance()
|
| /dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/correlation/ |
| H A D | CASH.java | 116 protected int minPts; field in CASH
162 public CASH(int minPts, int maxLevel, int minDim, double jitter, boolean adjust) { in CASH() argument
164 this.minPts = minPts; in CASH()
300 if(currentInterval.getIDs().size() >= minPts) { in doRun()
316 else if(noiseIDs.size() >= minPts) { in doRun()
364 CASHIntervalSplit split = new CASHIntervalSplit(relation, minPts); in initHeap()
393 if(intervalIDs != null && intervalIDs.size() >= minPts) { in initHeap()
770 protected int minPts; field in CASH.Parameterizer
798 minPts = minptsP.getValue(); in makeOptions()
823 return new CASH<>(minPts, maxLevel, minDim, jitter, adjust); in makeInstance()
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| /dports/misc/elki/elki-release0.7.1-1166-gfb1fffdf3/elki-clustering/src/main/java/de/lmu/ifi/dbs/elki/algorithm/clustering/optics/ |
| H A D | FastOPTICS.java | 121 int minPts; field in FastOPTICS
136 this.minPts = minpts; in FastOPTICS()
154 index.computeSetsBounds(rel, minPts, ids); // project points in run()
212 return minPts; in getMinPts()
|
| /dports/math/pdal/PDAL-2.3.0/plugins/i3s/lepcc/src/ |
| H A D | Test_C_Api.cpp | 325 double minPts = 1e16, maxPts = 0; in main() local
450 minPts = (std::min)(minPts, (double)nPts); in main()
678 std::cout << "points per tile [min, max] = [ " << minPts << ", " << maxPts << " ]" << endl; in main()
|
| /dports/science/apbs/apbs-pdb2pqr-apbs-1.5-102-g500c1473/apbs/externals/pb_s_am/pbsam/src/ |
| H A D | Solvmat.cpp | 85 int minPts = 200; in calc_n_grid_pts() local
88 if (r < cutoff ) nFinal = minPts; in calc_n_grid_pts()
91 nFinal = int ( minPts + gradient * (r-cutoff) * (r-cutoff) ); in calc_n_grid_pts()
|
| /dports/math/py-hdbscan/hdbscan-0.8.27/notebooks/ |
| H A D | Performance data generation .ipynb | 54 " 'minPts={}'.format(min_points),\n",
|
| /dports/graphics/qgis/qgis-3.22.3/i18n/ |
| H A D | qgis_ja.ts | 29922 <translation>最小クラスタサイズ（minPts）</translation>
29954 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29957 このアルゴリズムでは、最小クラスタサイズ（minPts）とクラスタ化されたポイント間で許容される最大距離（eps）の2つのパラメータが必要です。</translation>
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| H A D | qgis_fi.ts | 29605 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29608 Algoritmi vaatii kaksi parametria, klusterin minimikoon ("minPts") ja klusteroitujen pist…
|
| H A D | qgis_da.ts | 29705 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29708 Algoritmen kræver to parametre, en mindste klyngestørrelse ("minPts") og den maksimale af…
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| H A D | qgis_hu.ts | 29818 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29821 Az algoritmushoz két paraméter szükséges, egy minimális csoport méret (“minPts”), és a maximális me…
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| H A D | qgis_ko.ts | 29855 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29858 이 알고리즘에는 최소 클러스터 크기 ( "minPts")와 클러스터된 지점간 허용되는 최대 거리 ( "eps")의 두 가지 매개 변수가 필요합…
|
| H A D | qgis_ro.ts | 29736 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29739 Algoritmul necesită doi parametri, o dimensiune minimă a grupului (“minPts”) și distanța maximă per…
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| H A D | qgis_gl.ts | 29805 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29808 O algoritmo precisa dous parámetros, un tamaño mínimo de agrupación ("minPts") e unha dis…
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| H A D | qgis_pl.ts | 29750 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29753 Algorytm wymaga dwóch parametrów, minimalnego rozmiaru klastra ("minPts") i maksymalnej o…
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| H A D | qgis_fr.ts | 29850 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29852 …ux paramètres, le nombre minimum de points pour former un cluster ("minPts"), et la dist…
|
| H A D | qgis_ru.ts | 29880 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29883 Алгоритму требуется два параметра: минимальный размера кластера (”minPts“) и максимальное расстояни…
|
| H A D | qgis_it.ts | 29917 The algorithm requires two parameters, a minimum cluster size (“minPts”), and the maximum distance …
29920 L'algoritmo richiede due parametri, una dimensione minima dei cluster ("minPts") e l…
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