Changeset 14783
- Timestamp:
- 03/25/17 12:22:43 (7 years ago)
- Location:
- branches/TSNE/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE
- Files:
-
- 1 added
- 1 deleted
- 3 edited
Legend:
- Unmodified
- Added
- Removed
-
branches/TSNE/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/SPtree.cs
r14742 r14783 61 61 62 62 namespace HeuristicLab.Algorithms.DataAnalysis { 63 /// <summary> 64 /// Space partitioning tree 65 /// </summary> 63 66 [StorableClass] 64 67 public class SPTree : DeepCloneable, ISPTree { -
branches/TSNE/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/TSNE.cs
r14782 r14783 175 175 public TSNEAnalysis() { 176 176 Problem = new RegressionProblem(); 177 Parameters.Add(new ValueParameter<IDistance<RealVector>>(DistanceParameterName, "The distance function used to differentiate similar from non-similar points", new Euclid ianDistance()));177 Parameters.Add(new ValueParameter<IDistance<RealVector>>(DistanceParameterName, "The distance function used to differentiate similar from non-similar points", new EuclideanDistance())); 178 178 Parameters.Add(new FixedValueParameter<DoubleValue>(PerplexityParameterName, "Perplexity-Parameter of TSNE. Comparable to k in a k-nearest neighbour algorithm. Recommended Value is Floor(number of points /3) or lower", new DoubleValue(25))); 179 179 Parameters.Add(new OptionalValueParameter<DoubleValue>(ThetaParameterName, "Value describing how much appoximated gradients my differ from exact gradients. Set to 0 for exact calculation and in [0,1] otherwise \n CAUTION: exact calculation of forces requires building a non-sparse N*N matrix where N is the number of data points\n This may exceed memory limitations", new DoubleValue(0.1))); -
branches/TSNE/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/VPTree.cs
r14767 r14783 63 63 64 64 namespace HeuristicLab.Algorithms.DataAnalysis { 65 /// <summary> 66 /// Vantage point tree (or VP tree) is a metric tree that segregates data in a metric space by choosing 67 /// a position in the space (the "vantage point") and partitioning the data points into two parts: 68 /// those points that are nearer to the vantage point than a threshold, and those points that are not. 69 /// By recursively applying this procedure to partition the data into smaller and smaller sets, a tree 70 /// data structure is created where neighbors in the tree are likely to be neighbors in the space. 71 /// </summary> 72 /// <typeparam name="T"></typeparam> 65 73 [StorableClass] 66 74 public class VPTree<T> : DeepCloneable, IVPTree<T> where T : class, IDeepCloneable {
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