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Changeset 14783


Ignore:
Timestamp:
03/25/17 12:22:43 (7 years ago)
Author:
gkronber
Message:

#2700 renamed EuclideanDistance and added some comments

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  
    6161
    6262namespace HeuristicLab.Algorithms.DataAnalysis {
     63  /// <summary>
     64  /// Space partitioning tree
     65  /// </summary>
    6366  [StorableClass]
    6467  public class SPTree : DeepCloneable, ISPTree {
  • branches/TSNE/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/TSNE.cs

    r14782 r14783  
    175175    public TSNEAnalysis() {
    176176      Problem = new RegressionProblem();
    177       Parameters.Add(new ValueParameter<IDistance<RealVector>>(DistanceParameterName, "The distance function used to differentiate similar from non-similar points", new EuclidianDistance()));
     177      Parameters.Add(new ValueParameter<IDistance<RealVector>>(DistanceParameterName, "The distance function used to differentiate similar from non-similar points", new EuclideanDistance()));
    178178      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)));
    179179      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  
    6363
    6464namespace 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>
    6573  [StorableClass]
    6674  public class VPTree<T> : DeepCloneable, IVPTree<T> where T : class, IDeepCloneable {
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