| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/ |
| test_cluster.py | 42 assert_equal(list(nx.clustering(G,weight='weight').values()),[])
43 assert_equal(nx.clustering(G),{})
47 assert_equal(list(nx.clustering(G,weight='weight').values()),
49 assert_equal(nx.clustering(G,weight='weight'),
55 assert_equal(list(nx.clustering(G,weight='weight').values()),
57 assert_equal(nx.clustering(G,1),0)
58 assert_equal(list(nx.clustering(G,[1,2],weight='weight').values()),[0, 0])
59 assert_equal(nx.clustering(G,1,weight='weight'),0)
60 assert_equal(nx.clustering(G,[1,2],weight='weight'),{1: 0, 2: 0})
64 assert_equal(list(nx.clustering(G,weight='weight').values()),[1, 1, 1, 1, 1]
[all...] |
| /packages/apps/Gallery2/src/com/android/gallery3d/data/ |
| ClusterAlbumSet.java | 81 Clustering clustering; local
85 clustering = new TimeClustering(context);
88 clustering = new LocationClustering(context);
91 clustering = new TagClustering(context);
94 clustering = new FaceClustering(context);
97 clustering = new SizeClustering(context);
101 clustering.run(mBaseSet);
102 int n = clustering.getNumberOfClusters();
106 String childName = clustering.getClusterName(i)
[all...] |
| Clustering.java | 21 public abstract class Clustering {
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| TagClustering.java | 27 public class TagClustering extends Clustering {
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| FaceClustering.java | 28 public class FaceClustering extends Clustering {
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| SizeClustering.java | 26 public class SizeClustering extends Clustering {
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/ |
| cluster.py | 11 __all__ = [ 'clustering',
16 # functions for computing clustering of pairs
31 r"""Compute a bipartite clustering coefficient for nodes.
33 The bipartie clustering coefficient is a measure of local density
41 and `c_{uv}` is the pairwise clustering coefficient between nodes
71 Compute bipartite clustering for these nodes. The default
75 The pariwise bipartite clustering method to be used in the computation.
80 clustering : dictionary
81 A dictionary keyed by node with the clustering coefficient value.
88 >>> c = bipartite.clustering(G)
129 clustering = latapy_clustering variable
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/ |
| test_cluster.py | 7 # Test functions for different kinds of bipartite clustering coefficients
26 assert_equal(bipartite.clustering(G,mode='dot'),answer)
27 assert_equal(bipartite.clustering(G,mode='min'),answer)
28 assert_equal(bipartite.clustering(G,mode='max'),answer)
32 bipartite.clustering(nx.complete_graph(4))
36 bipartite.clustering(nx.path_graph(4),mode='foo')
41 assert_equal(bipartite.clustering(G,mode='dot'),answer)
42 assert_equal(bipartite.clustering(G,mode='max'),answer)
44 assert_equal(bipartite.clustering(G,mode='min'),answer)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/ |
| cluster.py | 16 __all__= ['triangles', 'average_clustering', 'clustering', 'transitivity',
86 Used for weighted clustering.
118 r"""Compute the average clustering coefficient for the graph G.
120 The clustering coefficient for the graph is the average,
133 Compute average clustering for nodes in this container.
140 If False include only the nodes with nonzero clustering in the average.
145 Average clustering
156 to use the clustering function to get a list and then take the average.
162 .. [1] Generalizations of the clustering coefficient to weighted
166 .. [2] Marcus Kaiser, Mean clustering coefficients: the role of isolated
175 def clustering(G, nodes=None, weight=None): function
[all...] |
| /packages/apps/Gallery2/src/com/android/gallery3d/app/ |
| FilterUtils.java | 23 // This class handles filtering and clustering.
26 // doesn't make sense to use more than one). Also each clustering operation
36 // 1. We can not change this set to use another clustering condition (like
39 // The reason is in both cases the 7th set may not exist in the new clustering.
192 // Add a specified clustering to the path.
218 // Change the topmost clustering to the specified type.
223 // Remove the topmost clustering (if any) from the path.
|
| /external/ceres-solver/internal/ceres/ |
| single_linkage_clustering.h | 51 // during the clustering process.
56 // single linkage clustering algorithm. Edges with weight less than
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| canonical_views_clustering.h | 31 // An implementation of the Canonical Views clustering algorithm from
38 // Ceres uses this algorithm to perform view clustering for
60 // canonical views clustering algorithm.
99 // It is possible depending on the configuration of the clustering
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| canonical_views_clustering.cc | 55 // Compute the canonical views clustering of the vertices of the
60 // configuration of the clustering algorithm that some of the
94 VLOG(2) << "Canonical views clustering time (secs): "
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| visibility_based_preconditioner.h | 82 // CLUSTER_JACOBI identifies these camera pairs by clustering cameras,
84 // clustering in the current implementation is done using the
86 // canonical_views_clustering.h). For the purposes of clustering, the
|
| visibility_based_preconditioner_test.cc | 252 // // Override the clustering to be a single clustering containing all
299 // // Override the clustering to be equal number of cameras.
326 // // Override the clustering to be 3 clusters.
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/clustering/ |
| package.html | 19 <body>Clustering algorithms</body>
|
| Clusterable.java | 18 package org.apache.commons.math.stat.clustering;
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| Cluster.java | 18 package org.apache.commons.math.stat.clustering;
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| KMeansPlusPlusClusterer.java | 18 package org.apache.commons.math.stat.clustering;
30 * Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
84 * Runs the K-means++ clustering algorithm.
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| EuclideanIntegerPoint.java | 18 package org.apache.commons.math.stat.clustering;
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| /frameworks/ml/bordeaux/service/src/android/bordeaux/services/ |
| MotionStatsAggregator.java | 34 m.put(CURRENT_MOTION,"Running"); //TODO maybe use clustering for user motion
|
| /external/skia/site/dev/tools/ |
| skiaperf.md | 13 clustering](https://perf.skia.org/clusters/).
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| /external/llvm/test/CodeGen/R600/ |
| schedule-global-loads.ll | 6 ; FIXME: This currently doesn't do a great job of clustering the
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| /external/ceres-solver/include/ceres/ |
| types.h | 113 // Visibility clustering based preconditioners.
117 // preconditioner. This is done using a clustering algorithm. The
118 // available visibility clustering algorithms are described below.
131 // This clustering algorithm can be quite slow, but gives high
132 // quality clusters. The original visibility based clustering paper
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| /external/llvm/include/llvm/CodeGen/ |
| SchedulerRegistry.h | 96 /// DFA driven list scheduler with clustering heuristic to control
|
