Changeset 15532


Ignore:
Timestamp:
12/18/17 14:12:18 (12 months ago)
Author:
bwerth
Message:

#2850 merged Weighted TSNE to trunk

Location:
trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4
Files:
13 edited
1 copied

Legend:

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  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4

    • Property svn:mergeinfo set to (toggle deleted branches)
      /branches/Weighted TSNE/3.4mergedeligible
      /stable/HeuristicLab.Algorithms.DataAnalysis/3.4mergedeligible
      /branches/1721-RandomForestPersistence/HeuristicLab.Algorithms.DataAnalysis/3.410321-10322
      /branches/Async/HeuristicLab.Algorithms.DataAnalysis/3.413329-15286
      /branches/Benchmarking/sources/HeuristicLab.Algorithms.DataAnalysis/3.46917-7005
      /branches/ClassificationModelComparison/HeuristicLab.Algorithms.DataAnalysis/3.49070-13099
      /branches/CloningRefactoring/HeuristicLab.Algorithms.DataAnalysis/3.44656-4721
      /branches/DataAnalysis Refactoring/HeuristicLab.Algorithms.DataAnalysis/3.45471-5808
      /branches/DataAnalysis SolutionEnsembles/HeuristicLab.Algorithms.DataAnalysis/3.45815-6180
      /branches/DataAnalysis/HeuristicLab.Algorithms.DataAnalysis/3.44458-4459,​4462,​4464
      /branches/DataPreprocessing/HeuristicLab.Algorithms.DataAnalysis/3.410085-11101
      /branches/GP.Grammar.Editor/HeuristicLab.Algorithms.DataAnalysis/3.46284-6795
      /branches/GP.Symbols (TimeLag, Diff, Integral)/HeuristicLab.Algorithms.DataAnalysis/3.45060
      /branches/HeuristicLab.DatasetRefactor/sources/HeuristicLab.Algorithms.DataAnalysis/3.411570-12508
      /branches/HeuristicLab.Problems.Orienteering/HeuristicLab.Algorithms.DataAnalysis/3.411130-12721
      /branches/HeuristicLab.RegressionSolutionGradientView/HeuristicLab.Algorithms.DataAnalysis/3.413819-14091
      /branches/HeuristicLab.TimeSeries/HeuristicLab.Algorithms.DataAnalysis/3.48116-8789
      /branches/LogResidualEvaluator/HeuristicLab.Algorithms.DataAnalysis/3.410202-10483
      /branches/NET40/sources/HeuristicLab.Algorithms.DataAnalysis/3.45138-5162
      /branches/ParallelEngine/HeuristicLab.Algorithms.DataAnalysis/3.45175-5192
      /branches/ProblemInstancesRegressionAndClassification/HeuristicLab.Algorithms.DataAnalysis/3.47773-7810
      /branches/QAPAlgorithms/HeuristicLab.Algorithms.DataAnalysis/3.46350-6627
      /branches/Restructure trunk solution/HeuristicLab.Algorithms.DataAnalysis/3.46828
      /branches/SpectralKernelForGaussianProcesses/HeuristicLab.Algorithms.DataAnalysis/3.410204-10479
      /branches/SuccessProgressAnalysis/HeuristicLab.Algorithms.DataAnalysis/3.45370-5682
      /branches/Trunk/HeuristicLab.Algorithms.DataAnalysis/3.46829-6865
      /branches/VNS/HeuristicLab.Algorithms.DataAnalysis/3.45594-5752
      /branches/histogram/HeuristicLab.Algorithms.DataAnalysis/3.45959-6341
      /branches/symbreg-factors-2650/HeuristicLab.Algorithms.DataAnalysis/3.414232-14825
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/FixedDataAnalysisAlgorithm.cs

    r15287 r15532  
    3030namespace HeuristicLab.Algorithms.DataAnalysis {
    3131  [StorableClass]
    32   public abstract class FixedDataAnalysisAlgorithm<T> : BasicAlgorithm where T : class, IDataAnalysisProblem {
     32  public abstract class FixedDataAnalysisAlgorithm<T> : BasicAlgorithm, IDataAnalysisAlgorithm<T> where T : class, IDataAnalysisProblem {
    3333    #region Properties
    3434    public override Type ProblemType {
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessRegression.cs

    r14185 r15532  
    3939  [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 160)]
    4040  [StorableClass]
    41   public sealed class GaussianProcessRegression : GaussianProcessBase, IStorableContent {
     41  public sealed class GaussianProcessRegression : GaussianProcessBase, IStorableContent, IDataAnalysisAlgorithm<IRegressionProblem> {
    4242    public string Filename { get; set; }
    4343
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj

    r15209 r15532  
    320320    <Compile Include="TSNE\Distances\IndexedItemDistance.cs" />
    321321    <Compile Include="TSNE\Distances\ManhattanDistance.cs" />
     322  <Compile Include="TSNE\Distances\WeightedEuclideanDistance.cs" />
    322323    <Compile Include="TSNE\Distances\IDistance.cs" />
    323324    <Compile Include="TSNE\PriorityQueue.cs" />
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/KernelRidgeRegression/KernelRidgeRegression.cs

    r15248 r15532  
    3636  [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 100)]
    3737  [StorableClass]
    38   public sealed class KernelRidgeRegression : BasicAlgorithm {
     38  public sealed class KernelRidgeRegression : BasicAlgorithm, IDataAnalysisAlgorithm<IRegressionProblem> {
    3939    private const string SolutionResultName = "Kernel ridge regression solution";
    4040
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/Distances/CosineDistance.cs

    r15234 r15532  
    2222using System;
    2323using System.Collections.Generic;
    24 using System.Linq;
    2524using HeuristicLab.Common;
    2625using HeuristicLab.Core;
     
    2827
    2928namespace HeuristicLab.Algorithms.DataAnalysis {
    30 
    3129  /// <summary>
    3230  /// The angular distance as defined as a normalized distance measure dependent on the angle between two vectors.
     
    3533  [Item("CosineDistance", "The angular distance as defined as a normalized distance measure dependent on the angle between two vectors.")]
    3634  public class CosineDistance : DistanceBase<IEnumerable<double>> {
    37 
    3835    #region HLConstructors & Cloning
    3936    [StorableConstructor]
     
    4845
    4946    #region statics
    50     public static double GetDistance(IReadOnlyList<double> point1, IReadOnlyList<double> point2) {
    51       if (point1.Count != point2.Count) throw new ArgumentException("Cosine distance not defined on vectors of different length");
    52       var innerprod = 0.0;
    53       var length1 = 0.0;
    54       var length2 = 0.0;
    55 
    56       for (var i = 0; i < point1.Count; i++) {
    57         double d1 = point1[i], d2 = point2[i];
    58         innerprod += d1 * d2;
    59         length1 += d1 * d1;
    60         length2 += d2 * d2;
     47    public static double GetDistance(IEnumerable<double> point1, IEnumerable<double> point2) {
     48      using (IEnumerator<double> p1Enum = point1.GetEnumerator(), p2Enum = point2.GetEnumerator()) {
     49        var innerprod = 0.0;
     50        var length1 = 0.0;
     51        var length2 = 0.0;
     52        while (p1Enum.MoveNext() & p2Enum.MoveNext()) {
     53          double d1 = p1Enum.Current, d2 = p2Enum.Current;
     54          innerprod += d1 * d2;
     55          length1 += d1 * d1;
     56          length2 += d2 * d2;
     57        }
     58        var divisor = Math.Sqrt(length1 * length2);
     59        if (divisor.IsAlmost(0)) throw new ArgumentException("Cosine distance is not defined on vectors of length 0");
     60        if (p1Enum.MoveNext() || p2Enum.MoveNext()) throw new ArgumentException("Cosine distance not defined on vectors of different length");
     61        return 1 - innerprod / divisor;
    6162      }
    62       var l = Math.Sqrt(length1 * length2);
    63       if (l.IsAlmost(0)) throw new ArgumentException("Cosine distance is not defined on vectors of length 0");
    64       return 1 - innerprod / l;
    6563    }
    6664    #endregion
    6765    public override double Get(IEnumerable<double> a, IEnumerable<double> b) {
    68       return GetDistance(a.ToArray(), b.ToArray());
     66      return GetDistance(a, b);
    6967    }
    7068  }
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/Distances/DistanceBase.cs

    r15207 r15532  
    2929  [StorableClass]
    3030  public abstract class DistanceBase<T> : Item, IDistance<T> {
    31 
    3231    #region HLConstructors & Cloning
    3332    [StorableConstructor]
     
    4443
    4544    public double Get(object x, object y) {
    46       return Get((T)x, (T)y);
     45      return Get((T) x, (T) y);
    4746    }
    4847
    4948    public IComparer GetDistanceComparer(object item) {
    50       return new DistanceComparer((T)item, this);
     49      return new DistanceComparer((T) item, this);
    5150    }
    5251
    53     private class DistanceComparer : IComparer<T>, IComparer {
     52    internal class DistanceComparer : IComparer<T>, IComparer {
    5453      private readonly T item;
    5554      private readonly IDistance<T> dist;
     
    6564
    6665      public int Compare(object x, object y) {
    67         return Compare((T)x, (T)y);
     66        return Compare((T) x, (T) y);
    6867      }
    6968    }
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/Distances/EuclideanDistance.cs

    r15207 r15532  
    3131  [Item("EuclideanDistance", "A norm function that uses Euclidean distance")]
    3232  public class EuclideanDistance : DistanceBase<IEnumerable<double>> {
    33 
    3433    #region HLConstructors & Cloning
    3534    [StorableConstructor]
    3635    protected EuclideanDistance(bool deserializing) : base(deserializing) { }
    3736    protected EuclideanDistance(EuclideanDistance original, Cloner cloner) : base(original, cloner) { }
    38     public override IDeepCloneable Clone(Cloner cloner) { return new EuclideanDistance(this, cloner); }
     37    public override IDeepCloneable Clone(Cloner cloner) {
     38      return new EuclideanDistance(this, cloner);
     39    }
    3940    public EuclideanDistance() { }
    4041    #endregion
    4142
    42     public static double GetDistance(IReadOnlyList<double> point1, IReadOnlyList<double> point2) {
    43       if (point1.Count != point2.Count) throw new ArgumentException("Euclidean distance not defined on vectors of different length");
    44       var sum = 0.0;
    45       for (var i = 0; i < point1.Count; i++) {
    46         var d = point1[i] - point2[i];
    47         sum += d * d;
     43    public static double GetDistance(IEnumerable<double> point1, IEnumerable<double> point2) {
     44      using (IEnumerator<double> p1Enum = point1.GetEnumerator(), p2Enum = point2.GetEnumerator()) {
     45        var sum = 0.0;
     46        while (p1Enum.MoveNext() & p2Enum.MoveNext()) {
     47          var d = p1Enum.Current - p2Enum.Current;
     48          sum += d * d;
     49        }
     50        if (p1Enum.MoveNext() || p2Enum.MoveNext()) throw new ArgumentException("Euclidean distance not defined on vectors of different length");
     51        return Math.Sqrt(sum);
    4852      }
    49 
    50       return Math.Sqrt(sum);
    5153    }
    5254
    5355    public override double Get(IEnumerable<double> a, IEnumerable<double> b) {
    54       return GetDistance(a.ToArray(), b.ToArray());
     56      return GetDistance(a, b);
    5557    }
    5658  }
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/Distances/ManhattanDistance.cs

    r15207 r15532  
    3131  [Item("ManhattanDistance", "A distance function that uses block distance")]
    3232  public class ManhattanDistance : DistanceBase<IEnumerable<double>> {
    33 
    3433    #region HLConstructors & Cloning
    3534    [StorableConstructor]
     
    4544    #endregion
    4645
    47     public static double GetDistance(double[] point1, double[] point2) {
    48       if (point1.Length != point2.Length) throw new ArgumentException("Manhattan distance not defined on vectors of different length");
    49       var sum = 0.0;
    50       for (var i = 0; i < point1.Length; i++)
    51         sum += Math.Abs(point1[i] + point2[i]);
    52       return sum;
     46    public static double GetDistance(IEnumerable<double> point1, IEnumerable<double> point2) {
     47      using (IEnumerator<double> p1Enum = point1.GetEnumerator(), p2Enum = point2.GetEnumerator()) {
     48        var sum = 0.0;
     49        while (p1Enum.MoveNext() & p2Enum.MoveNext())
     50          sum += Math.Abs(p1Enum.Current - p2Enum.Current);
     51        if (p1Enum.MoveNext() || p2Enum.MoveNext()) throw new ArgumentException("Manhattan distance not defined on vectors of different length");
     52        return sum;
     53      }
    5354    }
    5455
    5556    public override double Get(IEnumerable<double> a, IEnumerable<double> b) {
    56       return GetDistance(a.ToArray(), b.ToArray());
     57      return GetDistance(a, b);
    5758    }
    5859  }
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/TSNEAlgorithm.cs

    r15234 r15532  
    5353    }
    5454    public new IDataAnalysisProblem Problem {
    55       get { return (IDataAnalysisProblem)base.Problem; }
     55      get { return (IDataAnalysisProblem) base.Problem; }
    5656      set { base.Problem = value; }
    5757    }
    5858
    59     #region parameter names
     59    #region Parameter names
    6060    private const string DistanceFunctionParameterName = "DistanceFunction";
    6161    private const string PerplexityParameterName = "Perplexity";
     
    7272    private const string ClassesNameParameterName = "ClassesName";
    7373    private const string NormalizationParameterName = "Normalization";
     74    private const string RandomInitializationParameterName = "RandomInitialization";
    7475    private const string UpdateIntervalParameterName = "UpdateInterval";
    7576    #endregion
    7677
    77     #region result names
     78    #region Result names
    7879    private const string IterationResultName = "Iteration";
    7980    private const string ErrorResultName = "Error";
     
    8384    #endregion
    8485
    85     #region parameter properties
     86    #region Parameter properties
    8687    public IFixedValueParameter<DoubleValue> PerplexityParameter {
    87       get { return Parameters[PerplexityParameterName] as IFixedValueParameter<DoubleValue>; }
     88      get { return (IFixedValueParameter<DoubleValue>) Parameters[PerplexityParameterName]; }
    8889    }
    8990    public IFixedValueParameter<PercentValue> ThetaParameter {
    90       get { return Parameters[ThetaParameterName] as IFixedValueParameter<PercentValue>; }
     91      get { return (IFixedValueParameter<PercentValue>) Parameters[ThetaParameterName]; }
    9192    }
    9293    public IFixedValueParameter<IntValue> NewDimensionsParameter {
    93       get { return Parameters[NewDimensionsParameterName] as IFixedValueParameter<IntValue>; }
     94      get { return (IFixedValueParameter<IntValue>) Parameters[NewDimensionsParameterName]; }
    9495    }
    9596    public IConstrainedValueParameter<IDistance<double[]>> DistanceFunctionParameter {
    96       get { return Parameters[DistanceFunctionParameterName] as IConstrainedValueParameter<IDistance<double[]>>; }
     97      get { return (IConstrainedValueParameter<IDistance<double[]>>) Parameters[DistanceFunctionParameterName]; }
    9798    }
    9899    public IFixedValueParameter<IntValue> MaxIterationsParameter {
    99       get { return Parameters[MaxIterationsParameterName] as IFixedValueParameter<IntValue>; }
     100      get { return (IFixedValueParameter<IntValue>) Parameters[MaxIterationsParameterName]; }
    100101    }
    101102    public IFixedValueParameter<IntValue> StopLyingIterationParameter {
    102       get { return Parameters[StopLyingIterationParameterName] as IFixedValueParameter<IntValue>; }
     103      get { return (IFixedValueParameter<IntValue>) Parameters[StopLyingIterationParameterName]; }
    103104    }
    104105    public IFixedValueParameter<IntValue> MomentumSwitchIterationParameter {
    105       get { return Parameters[MomentumSwitchIterationParameterName] as IFixedValueParameter<IntValue>; }
     106      get { return (IFixedValueParameter<IntValue>) Parameters[MomentumSwitchIterationParameterName]; }
    106107    }
    107108    public IFixedValueParameter<DoubleValue> InitialMomentumParameter {
    108       get { return Parameters[InitialMomentumParameterName] as IFixedValueParameter<DoubleValue>; }
     109      get { return (IFixedValueParameter<DoubleValue>) Parameters[InitialMomentumParameterName]; }
    109110    }
    110111    public IFixedValueParameter<DoubleValue> FinalMomentumParameter {
    111       get { return Parameters[FinalMomentumParameterName] as IFixedValueParameter<DoubleValue>; }
     112      get { return (IFixedValueParameter<DoubleValue>) Parameters[FinalMomentumParameterName]; }
    112113    }
    113114    public IFixedValueParameter<DoubleValue> EtaParameter {
    114       get { return Parameters[EtaParameterName] as IFixedValueParameter<DoubleValue>; }
     115      get { return (IFixedValueParameter<DoubleValue>) Parameters[EtaParameterName]; }
    115116    }
    116117    public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
    117       get { return Parameters[SetSeedRandomlyParameterName] as IFixedValueParameter<BoolValue>; }
     118      get { return (IFixedValueParameter<BoolValue>) Parameters[SetSeedRandomlyParameterName]; }
    118119    }
    119120    public IFixedValueParameter<IntValue> SeedParameter {
    120       get { return Parameters[SeedParameterName] as IFixedValueParameter<IntValue>; }
     121      get { return (IFixedValueParameter<IntValue>) Parameters[SeedParameterName]; }
    121122    }
    122123    public IConstrainedValueParameter<StringValue> ClassesNameParameter {
    123       get { return Parameters[ClassesNameParameterName] as IConstrainedValueParameter<StringValue>; }
     124      get { return (IConstrainedValueParameter<StringValue>) Parameters[ClassesNameParameterName]; }
    124125    }
    125126    public IFixedValueParameter<BoolValue> NormalizationParameter {
    126       get { return Parameters[NormalizationParameterName] as IFixedValueParameter<BoolValue>; }
     127      get { return (IFixedValueParameter<BoolValue>) Parameters[NormalizationParameterName]; }
     128    }
     129    public IFixedValueParameter<BoolValue> RandomInitializationParameter {
     130      get { return (IFixedValueParameter<BoolValue>) Parameters[RandomInitializationParameterName]; }
    127131    }
    128132    public IFixedValueParameter<IntValue> UpdateIntervalParameter {
    129       get { return Parameters[UpdateIntervalParameterName] as IFixedValueParameter<IntValue>; }
     133      get { return (IFixedValueParameter<IntValue>) Parameters[UpdateIntervalParameterName]; }
    130134    }
    131135    #endregion
     
    187191      set { NormalizationParameter.Value.Value = value; }
    188192    }
    189 
     193    public bool RandomInitialization {
     194      get { return RandomInitializationParameter.Value.Value; }
     195      set { RandomInitializationParameter.Value.Value = value; }
     196    }
    190197    public int UpdateInterval {
    191198      get { return UpdateIntervalParameter.Value.Value; }
     
    194201    #endregion
    195202
     203    #region Storable poperties
     204    [Storable]
     205    private Dictionary<string, List<int>> dataRowNames;
     206    [Storable]
     207    private Dictionary<string, ScatterPlotDataRow> dataRows;
     208    [Storable]
     209    private TSNEStatic<double[]>.TSNEState state;
     210    [Storable]
     211    private int iter;
     212    #endregion
     213
    196214    #region Constructors & Cloning
    197215    [StorableConstructor]
    198216    private TSNEAlgorithm(bool deserializing) : base(deserializing) { }
    199217
     218    [StorableHook(HookType.AfterDeserialization)]
     219    private void AfterDeserialization() {
     220      if (Parameters.ContainsKey(RandomInitializationParameterName))
     221        Parameters.Add(new FixedValueParameter<BoolValue>(RandomInitializationParameterName, "Wether data points should be randomly initialized or according to the first 2 dimensions", new BoolValue(true)));
     222      RegisterParameterEvents();
     223    }
    200224    private TSNEAlgorithm(TSNEAlgorithm original, Cloner cloner) : base(original, cloner) {
    201225      if (original.dataRowNames != null)
    202         this.dataRowNames = new Dictionary<string, List<int>>(original.dataRowNames);
     226        dataRowNames = new Dictionary<string, List<int>>(original.dataRowNames);
    203227      if (original.dataRows != null)
    204         this.dataRows = original.dataRows.ToDictionary(kvp => kvp.Key, kvp => cloner.Clone(kvp.Value));
     228        dataRows = original.dataRows.ToDictionary(kvp => kvp.Key, kvp => cloner.Clone(kvp.Value));
    205229      if (original.state != null)
    206         this.state = cloner.Clone(original.state);
    207       this.iter = original.iter;
    208     }
    209     public override IDeepCloneable Clone(Cloner cloner) { return new TSNEAlgorithm(this, cloner); }
     230        state = cloner.Clone(original.state);
     231      iter = original.iter;
     232    }
     233    public override IDeepCloneable Clone(Cloner cloner) {
     234      return new TSNEAlgorithm(this, cloner);
     235    }
    210236    public TSNEAlgorithm() {
    211237      var distances = new ItemSet<IDistance<double[]>>(ApplicationManager.Manager.GetInstances<IDistance<double[]>>());
     
    213239      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)));
    214240      Parameters.Add(new FixedValueParameter<PercentValue>(ThetaParameterName, "Value describing how much appoximated " +
    215                                                                               "gradients my differ from exact gradients. Set to 0 for exact calculation and in [0,1] otherwise. " +
    216                                                                               "Appropriate values for theta are between 0.1 and 0.7 (default = 0.5). CAUTION: exact calculation of " +
    217                                                                               "forces requires building a non-sparse N*N matrix where N is the number of data points. This may " +
    218                                                                               "exceed memory limitations. The function is designed to run on large (N > 5000) data sets. It may give" +
    219                                                                               " poor performance on very small data sets(it is better to use a standard t - SNE implementation on such data).", new PercentValue(0)));
     241                                                                               "gradients my differ from exact gradients. Set to 0 for exact calculation and in [0,1] otherwise. " +
     242                                                                               "Appropriate values for theta are between 0.1 and 0.7 (default = 0.5). CAUTION: exact calculation of " +
     243                                                                               "forces requires building a non-sparse N*N matrix where N is the number of data points. This may " +
     244                                                                               "exceed memory limitations. The function is designed to run on large (N > 5000) data sets. It may give" +
     245                                                                               " poor performance on very small data sets(it is better to use a standard t - SNE implementation on such data).", new PercentValue(0)));
    220246      Parameters.Add(new FixedValueParameter<IntValue>(NewDimensionsParameterName, "Dimensionality of projected space (usually 2 for easy visual analysis)", new IntValue(2)));
    221247      Parameters.Add(new FixedValueParameter<IntValue>(MaxIterationsParameterName, "Maximum number of iterations for gradient descent.", new IntValue(1000)));
     
    230256      Parameters.Add(new FixedValueParameter<BoolValue>(NormalizationParameterName, "Whether the data should be zero centered and have variance of 1 for each variable, so different scalings are ignored.", new BoolValue(true)));
    231257      Parameters.Add(new FixedValueParameter<IntValue>(UpdateIntervalParameterName, "The interval after which the results will be updated.", new IntValue(50)));
     258      Parameters.Add(new FixedValueParameter<BoolValue>(RandomInitializationParameterName, "Wether data points should be randomly initialized or according to the first 2 dimensions", new BoolValue(true)));
     259
    232260      Parameters[UpdateIntervalParameterName].Hidden = true;
    233261
     
    238266      EtaParameter.Hidden = false;
    239267      Problem = new RegressionProblem();
    240     }
    241     #endregion
    242 
    243     [Storable]
    244     private Dictionary<string, List<int>> dataRowNames;
    245     [Storable]
    246     private Dictionary<string, ScatterPlotDataRow> dataRows;
    247     [Storable]
    248     private TSNEStatic<double[]>.TSNEState state;
    249     [Storable]
    250     private int iter;
     268      RegisterParameterEvents();
     269    }
     270    #endregion
    251271
    252272    public override void Prepare() {
     
    259279    protected override void Run(CancellationToken cancellationToken) {
    260280      var problemData = Problem.ProblemData;
    261       // set up and initialized everything if necessary
     281      // set up and initialize everything if necessary
     282      var wdist = DistanceFunction as WeightedEuclideanDistance;
     283      if (wdist != null) wdist.Initialize(problemData);
    262284      if (state == null) {
    263285        if (SetSeedRandomly) Seed = new System.Random().Next();
    264         var random = new MersenneTwister((uint)Seed);
     286        var random = new MersenneTwister((uint) Seed);
    265287        var dataset = problemData.Dataset;
    266288        var allowedInputVariables = problemData.AllowedInputVariables.ToArray();
    267         var data = new double[dataset.Rows][];
    268         for (var row = 0; row < dataset.Rows; row++)
    269           data[row] = allowedInputVariables.Select(col => dataset.GetDoubleValue(col, row)).ToArray();
    270 
    271         if (Normalization) data = NormalizeData(data);
    272 
    273         state = TSNEStatic<double[]>.CreateState(data, DistanceFunction, random, NewDimensions, Perplexity, Theta,
    274           StopLyingIteration, MomentumSwitchIteration, InitialMomentum, FinalMomentum, Eta);
    275 
    276         SetUpResults(data);
     289        var allindices = Problem.ProblemData.AllIndices.ToArray();
     290
     291        // jagged array is required to meet the static method declarations of TSNEStatic<T>
     292        var data = Enumerable.Range(0, dataset.Rows).Select(x => new double[allowedInputVariables.Length]).ToArray();
     293        var col = 0;
     294        foreach (var s in allowedInputVariables) {
     295          var row = 0;
     296          foreach (var d in dataset.GetDoubleValues(s)) {
     297            data[row][col] = d;
     298            row++;
     299          }
     300          col++;
     301        }
     302
     303        if (Normalization) data = NormalizeInputData(data);
     304        state = TSNEStatic<double[]>.CreateState(data, DistanceFunction, random, NewDimensions, Perplexity, Theta, StopLyingIteration, MomentumSwitchIteration, InitialMomentum, FinalMomentum, Eta, RandomInitialization);
     305        SetUpResults(allindices);
    277306        iter = 0;
    278307      }
    279308      for (; iter < MaxIterations && !cancellationToken.IsCancellationRequested; iter++) {
    280         if (iter % UpdateInterval == 0)
    281           Analyze(state);
     309        if (iter % UpdateInterval == 0) Analyze(state);
    282310        TSNEStatic<double[]>.Iterate(state);
    283311      }
     
    296324      Problem.ProblemDataChanged += OnProblemDataChanged;
    297325    }
     326
    298327    protected override void DeregisterProblemEvents() {
    299328      base.DeregisterProblemEvents();
     
    301330    }
    302331
     332    protected override void OnStopped() {
     333      base.OnStopped();
     334      state = null;
     335      dataRowNames = null;
     336      dataRows = null;
     337    }
     338
    303339    private void OnProblemDataChanged(object sender, EventArgs args) {
    304340      if (Problem == null || Problem.ProblemData == null) return;
     341      OnPerplexityChanged(this, null);
     342      OnColumnsChanged(this, null);
     343      Problem.ProblemData.Changed += OnPerplexityChanged;
     344      Problem.ProblemData.Changed += OnColumnsChanged;
     345      Problem.ProblemData.Dataset.RowsChanged += OnPerplexityChanged;
     346      Problem.ProblemData.Dataset.ColumnsChanged += OnColumnsChanged;
    305347      if (!Parameters.ContainsKey(ClassesNameParameterName)) return;
    306348      ClassesNameParameter.ValidValues.Clear();
     
    308350    }
    309351
     352    private void OnColumnsChanged(object sender, EventArgs e) {
     353      if (Problem == null || Problem.ProblemData == null || Problem.ProblemData.Dataset == null || !Parameters.ContainsKey(DistanceFunctionParameterName)) return;
     354      DistanceFunctionParameter.ValidValues.OfType<WeightedEuclideanDistance>().Single().AdaptToProblemData(Problem.ProblemData);
     355    }
     356
     357    private void RegisterParameterEvents() {
     358      PerplexityParameter.Value.ValueChanged -= OnPerplexityChanged;
     359      PerplexityParameter.Value.ValueChanged += OnPerplexityChanged;
     360    }
     361
     362    private void OnPerplexityChanged(object sender, EventArgs e) {
     363      if (Problem == null || Problem.ProblemData == null || Problem.ProblemData.Dataset == null || !Parameters.ContainsKey(PerplexityParameterName)) return;
     364      PerplexityParameter.Value.ValueChanged -= OnPerplexityChanged;
     365      PerplexityParameter.Value.Value = Math.Max(1, Math.Min((Problem.ProblemData.Dataset.Rows - 1) / 3.0, Perplexity));
     366      PerplexityParameter.Value.ValueChanged += OnPerplexityChanged;
     367    }
    310368    #endregion
    311369
    312370    #region Helpers
    313     private void SetUpResults(IReadOnlyCollection<double[]> data) {
     371    private void SetUpResults(IReadOnlyList<int> allIndices) {
    314372      if (Results == null) return;
    315373      var results = Results;
     
    318376      var problemData = Problem.ProblemData;
    319377
     378      if (!results.ContainsKey(IterationResultName)) results.Add(new Result(IterationResultName, new IntValue(0)));
     379      if (!results.ContainsKey(ErrorResultName)) results.Add(new Result(ErrorResultName, new DoubleValue(0)));
     380      if (!results.ContainsKey(ScatterPlotResultName)) results.Add(new Result(ScatterPlotResultName, "Plot of the projected data", new ScatterPlot(DataResultName, "")));
     381      if (!results.ContainsKey(DataResultName)) results.Add(new Result(DataResultName, "Projected Data", new DoubleMatrix()));
     382      if (!results.ContainsKey(ErrorPlotResultName)) {
     383        var errortable = new DataTable(ErrorPlotResultName, "Development of errors during gradient descent") {
     384          VisualProperties = {
     385            XAxisTitle = "UpdateIntervall",
     386            YAxisTitle = "Error",
     387            YAxisLogScale = true
     388          }
     389        };
     390        errortable.Rows.Add(new DataRow("Errors"));
     391        errortable.Rows["Errors"].VisualProperties.StartIndexZero = true;
     392        results.Add(new Result(ErrorPlotResultName, errortable));
     393      }
     394
    320395      //color datapoints acording to classes variable (be it double or string)
    321       if (problemData.Dataset.VariableNames.Contains(ClassesName)) {
    322         if ((problemData.Dataset as Dataset).VariableHasType<string>(ClassesName)) {
    323           var classes = problemData.Dataset.GetStringValues(ClassesName).ToArray();
    324           for (var i = 0; i < classes.Length; i++) {
    325             if (!dataRowNames.ContainsKey(classes[i])) dataRowNames.Add(classes[i], new List<int>());
    326             dataRowNames[classes[i]].Add(i);
    327           }
    328         } else if ((problemData.Dataset as Dataset).VariableHasType<double>(ClassesName)) {
    329           var classValues = problemData.Dataset.GetDoubleValues(ClassesName).ToArray();
    330           var max = classValues.Max() + 0.1;
    331           var min = classValues.Min() - 0.1;
    332           const int contours = 8;
    333           for (var i = 0; i < contours; i++) {
    334             var contourname = GetContourName(i, min, max, contours);
    335             dataRowNames.Add(contourname, new List<int>());
    336             dataRows.Add(contourname, new ScatterPlotDataRow(contourname, "", new List<Point2D<double>>()));
    337             dataRows[contourname].VisualProperties.Color = GetHeatMapColor(i, contours);
    338             dataRows[contourname].VisualProperties.PointSize = i + 3;
    339           }
    340           for (var i = 0; i < classValues.Length; i++) {
    341             dataRowNames[GetContourName(classValues[i], min, max, contours)].Add(i);
    342           }
    343         }
    344       } else {
     396      if (!problemData.Dataset.VariableNames.Contains(ClassesName)) {
    345397        dataRowNames.Add("Training", problemData.TrainingIndices.ToList());
    346398        dataRowNames.Add("Test", problemData.TestIndices.ToList());
    347       }
    348 
    349       if (!results.ContainsKey(IterationResultName)) results.Add(new Result(IterationResultName, new IntValue(0)));
    350       else ((IntValue)results[IterationResultName].Value).Value = 0;
    351 
    352       if (!results.ContainsKey(ErrorResultName)) results.Add(new Result(ErrorResultName, new DoubleValue(0)));
    353       else ((DoubleValue)results[ErrorResultName].Value).Value = 0;
    354 
    355       if (!results.ContainsKey(ErrorPlotResultName)) results.Add(new Result(ErrorPlotResultName, new DataTable(ErrorPlotResultName, "Development of errors during gradient descent")));
    356       else results[ErrorPlotResultName].Value = new DataTable(ErrorPlotResultName, "Development of errors during gradient descent");
    357 
    358       var plot = results[ErrorPlotResultName].Value as DataTable;
    359       if (plot == null) throw new ArgumentException("could not create/access error data table in results collection");
    360 
    361       if (!plot.Rows.ContainsKey("errors")) plot.Rows.Add(new DataRow("errors"));
    362       plot.Rows["errors"].Values.Clear();
    363       plot.Rows["errors"].VisualProperties.StartIndexZero = true;
    364 
    365       results.Add(new Result(ScatterPlotResultName, "Plot of the projected data", new ScatterPlot(DataResultName, "")));
    366       results.Add(new Result(DataResultName, "Projected Data", new DoubleMatrix()));
     399        return;
     400      }
     401      var classificationData = problemData as ClassificationProblemData;
     402      if (classificationData != null && classificationData.TargetVariable.Equals(ClassesName)) {
     403        var classNames = classificationData.ClassValues.Zip(classificationData.ClassNames, (v, n) => new {v, n}).ToDictionary(x => x.v, x => x.n);
     404        var classes = classificationData.Dataset.GetDoubleValues(classificationData.TargetVariable, allIndices).Select(v => classNames[v]).ToArray();
     405        for (var i = 0; i < classes.Length; i++) {
     406          if (!dataRowNames.ContainsKey(classes[i])) dataRowNames.Add(classes[i], new List<int>());
     407          dataRowNames[classes[i]].Add(i);
     408        }
     409      }
     410      else if (((Dataset) problemData.Dataset).VariableHasType<string>(ClassesName)) {
     411        var classes = problemData.Dataset.GetStringValues(ClassesName, allIndices).ToArray();
     412        for (var i = 0; i < classes.Length; i++) {
     413          if (!dataRowNames.ContainsKey(classes[i])) dataRowNames.Add(classes[i], new List<int>());
     414          dataRowNames[classes[i]].Add(i);
     415        }
     416      }
     417      else if (((Dataset) problemData.Dataset).VariableHasType<double>(ClassesName)) {
     418        var clusterdata = new Dataset(problemData.Dataset.DoubleVariables, problemData.Dataset.DoubleVariables.Select(v => problemData.Dataset.GetDoubleValues(v, allIndices).ToList()));
     419        const int contours = 8;
     420        Dictionary<int, string> contourMap;
     421        IClusteringModel clusterModel;
     422        double[][] borders;
     423        CreateClusters(clusterdata, ClassesName, contours, out clusterModel, out contourMap, out borders);
     424        var contourorder = borders.Select((x, i) => new {x, i}).OrderBy(x => x.x[0]).Select(x => x.i).ToArray();
     425        for (var i = 0; i < contours; i++) {
     426          var c = contourorder[i];
     427          var contourname = contourMap[c];
     428          dataRowNames.Add(contourname, new List<int>());
     429          dataRows.Add(contourname, new ScatterPlotDataRow(contourname, "", new List<Point2D<double>>()));
     430          dataRows[contourname].VisualProperties.Color = GetHeatMapColor(i, contours);
     431        }
     432        var allClusters = clusterModel.GetClusterValues(clusterdata, Enumerable.Range(0, clusterdata.Rows)).ToArray();
     433        for (var i = 0; i < clusterdata.Rows; i++) dataRowNames[contourMap[allClusters[i] - 1]].Add(i);
     434      }
     435      else if (((Dataset) problemData.Dataset).VariableHasType<DateTime>(ClassesName)) {
     436        var clusterdata = new Dataset(problemData.Dataset.DateTimeVariables, problemData.Dataset.DateTimeVariables.Select(v => problemData.Dataset.GetDoubleValues(v, allIndices).ToList()));
     437        const int contours = 8;
     438        Dictionary<int, string> contourMap;
     439        IClusteringModel clusterModel;
     440        double[][] borders;
     441        CreateClusters(clusterdata, ClassesName, contours, out clusterModel, out contourMap, out borders);
     442        var contourorder = borders.Select((x, i) => new {x, i}).OrderBy(x => x.x[0]).Select(x => x.i).ToArray();
     443        for (var i = 0; i < contours; i++) {
     444          var c = contourorder[i];
     445          var contourname = contourMap[c];
     446          dataRowNames.Add(contourname, new List<int>());
     447          dataRows.Add(contourname, new ScatterPlotDataRow(contourname, "", new List<Point2D<double>>()));
     448          dataRows[contourname].VisualProperties.Color = GetHeatMapColor(i, contours);
     449        }
     450        var allClusters = clusterModel.GetClusterValues(clusterdata, Enumerable.Range(0, clusterdata.Rows)).ToArray();
     451        for (var i = 0; i < clusterdata.Rows; i++) dataRowNames[contourMap[allClusters[i] - 1]].Add(i);
     452      }
     453      else {
     454        dataRowNames.Add("Training", problemData.TrainingIndices.ToList());
     455        dataRowNames.Add("Test", problemData.TestIndices.ToList());
     456      }
    367457    }
    368458
     
    372462      var plot = results[ErrorPlotResultName].Value as DataTable;
    373463      if (plot == null) throw new ArgumentException("Could not create/access error data table in results collection.");
    374       var errors = plot.Rows["errors"].Values;
     464      var errors = plot.Rows["Errors"].Values;
    375465      var c = tsneState.EvaluateError();
    376466      errors.Add(c);
    377       ((IntValue)results[IterationResultName].Value).Value = tsneState.iter;
    378       ((DoubleValue)results[ErrorResultName].Value).Value = errors.Last();
    379 
    380       var ndata = Normalize(tsneState.newData);
     467      ((IntValue) results[IterationResultName].Value).Value = tsneState.iter;
     468      ((DoubleValue) results[ErrorResultName].Value).Value = errors.Last();
     469
     470      var ndata = NormalizeProjectedData(tsneState.newData);
    381471      results[DataResultName].Value = new DoubleMatrix(ndata);
    382472      var splot = results[ScatterPlotResultName].Value as ScatterPlot;
     
    386476    private void FillScatterPlot(double[,] lowDimData, ScatterPlot plot) {
    387477      foreach (var rowName in dataRowNames.Keys) {
    388         if (!plot.Rows.ContainsKey(rowName))
     478        if (!plot.Rows.ContainsKey(rowName)) {
    389479          plot.Rows.Add(dataRows.ContainsKey(rowName) ? dataRows[rowName] : new ScatterPlotDataRow(rowName, "", new List<Point2D<double>>()));
     480          plot.Rows[rowName].VisualProperties.PointSize = 8;
     481        }
    390482        plot.Rows[rowName].Points.Replace(dataRowNames[rowName].Select(i => new Point2D<double>(lowDimData[i, 0], lowDimData[i, 1])));
    391483      }
    392484    }
    393485
    394     private static double[,] Normalize(double[,] data) {
     486    private static double[,] NormalizeProjectedData(double[,] data) {
    395487      var max = new double[data.GetLength(1)];
    396488      var min = new double[data.GetLength(1)];
     
    398490      for (var i = 0; i < max.Length; i++) max[i] = min[i] = data[0, i];
    399491      for (var i = 0; i < data.GetLength(0); i++)
    400         for (var j = 0; j < data.GetLength(1); j++) {
    401           var v = data[i, j];
    402           max[j] = Math.Max(max[j], v);
    403           min[j] = Math.Min(min[j], v);
    404         }
     492      for (var j = 0; j < data.GetLength(1); j++) {
     493        var v = data[i, j];
     494        max[j] = Math.Max(max[j], v);
     495        min[j] = Math.Min(min[j], v);
     496      }
    405497      for (var i = 0; i < data.GetLength(0); i++) {
    406498        for (var j = 0; j < data.GetLength(1); j++) {
    407499          var d = max[j] - min[j];
    408           var s = data[i, j] - (max[j] + min[j]) / 2;  //shift data
    409           if (d.IsAlmost(0)) res[i, j] = data[i, j];   //no scaling possible
    410           else res[i, j] = s / d;  //scale data
     500          var s = data[i, j] - (max[j] + min[j]) / 2; //shift data
     501          if (d.IsAlmost(0)) res[i, j] = data[i, j]; //no scaling possible
     502          else res[i, j] = s / d; //scale data
    411503        }
    412504      }
     
    414506    }
    415507
    416     private static double[][] NormalizeData(IReadOnlyList<double[]> data) {
     508    private static double[][] NormalizeInputData(IReadOnlyList<IReadOnlyList<double>> data) {
    417509      // as in tSNE implementation by van der Maaten
    418       var n = data[0].Length;
     510      var n = data[0].Count;
    419511      var mean = new double[n];
    420512      var max = new double[n];
     
    432524
    433525    private static Color GetHeatMapColor(int contourNr, int noContours) {
    434       var q = (double)contourNr / noContours;  // q in [0,1]
    435       var c = q < 0.5 ? Color.FromArgb((int)(q * 2 * 255), 255, 0) : Color.FromArgb(255, (int)((1 - q) * 2 * 255), 0);
    436       return c;
    437     }
    438 
    439     private static string GetContourName(double value, double min, double max, int noContours) {
    440       var size = (max - min) / noContours;
    441       var contourNr = (int)((value - min) / size);
    442       return GetContourName(contourNr, min, max, noContours);
    443     }
    444 
    445     private static string GetContourName(int i, double min, double max, int noContours) {
    446       var size = (max - min) / noContours;
    447       return "[" + (min + i * size) + ";" + (min + (i + 1) * size) + ")";
     526      return ConvertTotalToRgb(0, noContours, contourNr);
     527    }
     528
     529    private static void CreateClusters(IDataset data, string target, int contours, out IClusteringModel contourCluster, out Dictionary<int, string> contourNames, out double[][] borders) {
     530      var cpd = new ClusteringProblemData((Dataset) data, new[] {target});
     531      contourCluster = KMeansClustering.CreateKMeansSolution(cpd, contours, 3).Model;
     532
     533      borders = Enumerable.Range(0, contours).Select(x => new[] {double.MaxValue, double.MinValue}).ToArray();
     534      var clusters = contourCluster.GetClusterValues(cpd.Dataset, cpd.AllIndices).ToArray();
     535      var targetvalues = cpd.Dataset.GetDoubleValues(target).ToArray();
     536      foreach (var i in cpd.AllIndices) {
     537        var cl = clusters[i] - 1;
     538        var clv = targetvalues[i];
     539        if (borders[cl][0] > clv) borders[cl][0] = clv;
     540        if (borders[cl][1] < clv) borders[cl][1] = clv;
     541      }
     542
     543      contourNames = new Dictionary<int, string>();
     544      for (var i = 0; i < contours; i++)
     545        contourNames.Add(i, "[" + borders[i][0] + ";" + borders[i][1] + "]");
     546    }
     547
     548    private static Color ConvertTotalToRgb(double low, double high, double cell) {
     549      var colorGradient = ColorGradient.Colors;
     550      var range = high - low;
     551      var h = Math.Min(cell / range * colorGradient.Count, colorGradient.Count - 1);
     552      return colorGradient[(int) h];
    448553    }
    449554    #endregion
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/TSNEStatic.cs

    r15207 r15532  
    6565  [StorableClass]
    6666  public class TSNEStatic<T> {
    67 
    6867    [StorableClass]
    6968    public sealed class TSNEState : DeepCloneable {
     
    170169      [StorableConstructor]
    171170      public TSNEState(bool deserializing) { }
    172       public TSNEState(T[] data, IDistance<T> distance, IRandom random, int newDimensions, double perplexity, double theta, int stopLyingIter, int momSwitchIter, double momentum, double finalMomentum, double eta) {
     171
     172      public TSNEState(IReadOnlyList<T> data, IDistance<T> distance, IRandom random, int newDimensions, double perplexity,
     173        double theta, int stopLyingIter, int momSwitchIter, double momentum, double finalMomentum, double eta, bool randomInit) {
    173174        this.distance = distance;
    174175        this.random = random;
     
    183184
    184185        // initialize
    185         noDatapoints = data.Length;
     186        noDatapoints = data.Count;
    186187        if (noDatapoints - 1 < 3 * perplexity)
    187188          throw new ArgumentException("Perplexity too large for the number of data points!");
     
    193194        gains = new double[noDatapoints, newDimensions];
    194195        for (var i = 0; i < noDatapoints; i++)
    195           for (var j = 0; j < newDimensions; j++)
    196             gains[i, j] = 1.0;
     196        for (var j = 0; j < newDimensions; j++)
     197          gains[i, j] = 1.0;
    197198
    198199        p = null;
     
    212213        var rand = new NormalDistributedRandom(random, 0, 1);
    213214        for (var i = 0; i < noDatapoints; i++)
    214           for (var j = 0; j < newDimensions; j++)
    215             newData[i, j] = rand.NextDouble() * .0001;
     215        for (var j = 0; j < newDimensions; j++)
     216          newData[i, j] = rand.NextDouble() * .0001;
     217
     218        if (!(data[0] is IReadOnlyList<double>) || randomInit) return;
     219        for (var i = 0; i < noDatapoints; i++)
     220        for (var j = 0; j < newDimensions; j++) {
     221          var row = (IReadOnlyList<double>) data[i];
     222          newData[i, j] = row[j % row.Count];
     223        }
    216224      }
    217225      #endregion
    218226
    219227      public double EvaluateError() {
    220         return exact ?
    221           EvaluateErrorExact(p, newData, noDatapoints, newDimensions) :
    222           EvaluateErrorApproximate(rowP, colP, valP, newData, theta);
     228        return exact ? EvaluateErrorExact(p, newData, noDatapoints, newDimensions) : EvaluateErrorApproximate(rowP, colP, valP, newData, theta);
    223229      }
    224230
    225231      #region Helpers
    226       private static void CalculateApproximateSimilarities(T[] data, IDistance<T> distance, double perplexity, out int[] rowP, out int[] colP, out double[] valP) {
     232      private static void CalculateApproximateSimilarities(IReadOnlyList<T> data, IDistance<T> distance, double perplexity, out int[] rowP, out int[] colP, out double[] valP) {
    227233        // Compute asymmetric pairwise input similarities
    228         ComputeGaussianPerplexity(data, distance, out rowP, out colP, out valP, perplexity, (int)(3 * perplexity));
     234        ComputeGaussianPerplexity(data, distance, out rowP, out colP, out valP, perplexity, (int) (3 * perplexity));
    229235        // Symmetrize input similarities
    230236        int[] sRowP, symColP;
     
    235241        valP = sValP;
    236242        var sumP = .0;
    237         for (var i = 0; i < rowP[data.Length]; i++) sumP += valP[i];
    238         for (var i = 0; i < rowP[data.Length]; i++) valP[i] /= sumP;
    239       }
    240 
    241       private static double[,] CalculateExactSimilarites(T[] data, IDistance<T> distance, double perplexity) {
     243        for (var i = 0; i < rowP[data.Count]; i++) sumP += valP[i];
     244        for (var i = 0; i < rowP[data.Count]; i++) valP[i] /= sumP;
     245      }
     246      private static double[,] CalculateExactSimilarites(IReadOnlyList<T> data, IDistance<T> distance, double perplexity) {
    242247        // Compute similarities
    243         var p = new double[data.Length, data.Length];
     248        var p = new double[data.Count, data.Count];
    244249        ComputeGaussianPerplexity(data, distance, p, perplexity);
    245250        // Symmetrize input similarities
    246         for (var n = 0; n < data.Length; n++) {
    247           for (var m = n + 1; m < data.Length; m++) {
     251        for (var n = 0; n < data.Count; n++) {
     252          for (var m = n + 1; m < data.Count; m++) {
    248253            p[n, m] += p[m, n];
    249254            p[m, n] = p[n, m];
     
    251256        }
    252257        var sumP = .0;
    253         for (var i = 0; i < data.Length; i++) for (var j = 0; j < data.Length; j++) sumP += p[i, j];
    254         for (var i = 0; i < data.Length; i++) for (var j = 0; j < data.Length; j++) p[i, j] /= sumP;
     258        for (var i = 0; i < data.Count; i++) {
     259          for (var j = 0; j < data.Count; j++) {
     260            sumP += p[i, j];
     261          }
     262        }
     263        for (var i = 0; i < data.Count; i++) {
     264          for (var j = 0; j < data.Count; j++) {
     265            p[i, j] /= sumP;
     266          }
     267        }
    255268        return p;
    256269      }
    257 
    258270      private static void ComputeGaussianPerplexity(IReadOnlyList<T> x, IDistance<T> distance, out int[] rowP, out int[] colP, out double[] valP, double perplexity, int k) {
    259271        if (perplexity > k) throw new ArgumentException("Perplexity should be lower than k!");
     
    290302
    291303          // Iterate until we found a good perplexity
    292           var iter = 0; double sumP = 0;
     304          var iter = 0;
     305          double sumP = 0;
    293306          while (!found && iter < 200) {
    294 
    295307            // Compute Gaussian kernel row
    296308            for (var m = 0; m < k; m++) curP[m] = Math.Exp(-beta * distances[m + 1]);
     
    307319            if (hdiff < tol && -hdiff < tol) {
    308320              found = true;
    309             } else {
     321            }
     322            else {
    310323              if (hdiff > 0) {
    311324                minBeta = beta;
     
    314327                else
    315328                  beta = (beta + maxBeta) / 2.0;
    316               } else {
     329              }
     330              else {
    317331                maxBeta = beta;
    318332                if (minBeta.IsAlmost(double.MinValue) || minBeta.IsAlmost(double.MaxValue))
     
    335349        }
    336350      }
    337       private static void ComputeGaussianPerplexity(T[] x, IDistance<T> distance, double[,] p, double perplexity) {
     351      private static void ComputeGaussianPerplexity(IReadOnlyList<T> x, IDistance<T> distance, double[,] p, double perplexity) {
    338352        // Compute the distance matrix
    339353        var dd = ComputeDistances(x, distance);
    340354
    341         var n = x.Length;
     355        var n = x.Count;
    342356        // Compute the Gaussian kernel row by row
    343357        for (var i = 0; i < n; i++) {
     
    352366          // Iterate until we found a good perplexity
    353367          var iter = 0;
    354           while (!found && iter < 200) {      // 200 iterations as in tSNE implementation by van der Maarten
     368          while (!found && iter < 200) { // 200 iterations as in tSNE implementation by van der Maarten
    355369
    356370            // Compute Gaussian kernel row
     
    369383            if (hdiff < tol && -hdiff < tol) {
    370384              found = true;
    371             } else {
     385            }
     386            else {
    372387              if (hdiff > 0) {
    373388                minBeta = beta;
     
    376391                else
    377392                  beta = (beta + maxBeta) / 2.0;
    378               } else {
     393              }
     394              else {
    379395                maxBeta = beta;
    380396                if (minBeta.IsAlmost(double.MinValue) || minBeta.IsAlmost(double.MaxValue))
     
    393409        }
    394410      }
    395 
    396       private static double[][] ComputeDistances(T[] x, IDistance<T> distance) {
    397         var res = new double[x.Length][];
    398         for (var r = 0; r < x.Length; r++) {
    399           var rowV = new double[x.Length];
     411      private static double[][] ComputeDistances(IReadOnlyList<T> x, IDistance<T> distance) {
     412        var res = new double[x.Count][];
     413        for (var r = 0; r < x.Count; r++) {
     414          var rowV = new double[x.Count];
    400415          // all distances must be symmetric
    401416          for (var c = 0; c < r; c++) {
     
    403418          }
    404419          rowV[r] = 0.0; // distance to self is zero for all distances
    405           for (var c = r + 1; c < x.Length; c++) {
     420          for (var c = r + 1; c < x.Count; c++) {
    406421            rowV[c] = distance.Get(x[r], x[c]);
    407422          }
     
    411426        // return x.Select(m => x.Select(n => distance.Get(m, n)).ToArray()).ToArray();
    412427      }
    413 
    414428      private static double EvaluateErrorExact(double[,] p, double[,] y, int n, int d) {
    415429        // Compute the squared Euclidean distance matrix
     
    425439              q[n1, m] = 1 / (1 + dd[n1, m]);
    426440              sumQ += q[n1, m];
    427             } else q[n1, m] = double.Epsilon;
     441            }
     442            else q[n1, m] = double.Epsilon;
    428443          }
    429444        }
     
    433448        var c = .0;
    434449        for (var i = 0; i < n; i++)
    435           for (var j = 0; j < n; j++) {
    436             c += p[i, j] * Math.Log((p[i, j] + float.Epsilon) / (q[i, j] + float.Epsilon));
    437           }
     450        for (var j = 0; j < n; j++) {
     451          c += p[i, j] * Math.Log((p[i, j] + float.Epsilon) / (q[i, j] + float.Epsilon));
     452        }
    438453        return c;
    439454      }
    440 
    441455      private static double EvaluateErrorApproximate(IReadOnlyList<int> rowP, IReadOnlyList<int> colP, IReadOnlyList<double> valP, double[,] y, double theta) {
    442456        // Get estimate of normalization term
     
    463477      }
    464478      private static void SymmetrizeMatrix(IReadOnlyList<int> rowP, IReadOnlyList<int> colP, IReadOnlyList<double> valP, out int[] symRowP, out int[] symColP, out double[] symValP) {
    465 
    466479        // Count number of elements and row counts of symmetric matrix
    467480        var n = rowP.Count - 1;
     
    469482        for (var j = 0; j < n; j++) {
    470483          for (var i = rowP[j]; i < rowP[j + 1]; i++) {
    471 
    472484            // Check whether element (col_P[i], n) is present
    473485            var present = false;
     
    497509        var offset = new int[n];
    498510        for (var j = 0; j < n; j++) {
    499           for (var i = rowP[j]; i < rowP[j + 1]; i++) {                                  // considering element(n, colP[i])
     511          for (var i = rowP[j]; i < rowP[j + 1]; i++) { // considering element(n, colP[i])
    500512
    501513            // Check whether element (col_P[i], n) is present
     
    549561    public static double[,] Run(T[] data, IDistance<T> distance, IRandom random,
    550562      int newDimensions = 2, double perplexity = 25, int iterations = 1000,
    551       double theta = 0,
    552       int stopLyingIter = 0, int momSwitchIter = 0, double momentum = .5,
     563      double theta = 0, int stopLyingIter = 0, int momSwitchIter = 0, double momentum = .5,
    553564      double finalMomentum = .8, double eta = 10.0
    554       ) {
     565    ) {
    555566      var state = CreateState(data, distance, random, newDimensions, perplexity,
    556567        theta, stopLyingIter, momSwitchIter, momentum, finalMomentum, eta);
     
    565576      int newDimensions = 2, double perplexity = 25, double theta = 0,
    566577      int stopLyingIter = 0, int momSwitchIter = 0, double momentum = .5,
    567       double finalMomentum = .8, double eta = 10.0
    568       ) {
    569       return new TSNEState(data, distance, random, newDimensions, perplexity, theta, stopLyingIter, momSwitchIter, momentum, finalMomentum, eta);
     578      double finalMomentum = .8, double eta = 10.0, bool randomInit = true
     579    ) {
     580      return new TSNEState(data, distance, random, newDimensions, perplexity, theta, stopLyingIter, momSwitchIter, momentum, finalMomentum, eta, randomInit);
    570581    }
    571582
     
    580591        for (var j = 0; j < state.newDimensions; j++) {
    581592          state.gains[i, j] = Math.Sign(state.dY[i, j]) != Math.Sign(state.uY[i, j])
    582             ? state.gains[i, j] + .2  // +0.2 nd *0.8 are used in two separate implementations of tSNE -> seems to be correct
     593            ? state.gains[i, j] + .2 // +0.2 nd *0.8 are used in two separate implementations of tSNE -> seems to be correct
    583594            : state.gains[i, j] * .8;
    584 
    585595          if (state.gains[i, j] < .01) state.gains[i, j] = .01;
    586596        }
    587597      }
    588 
    589598
    590599      // Perform gradient update (with momentum and gains)
    591600      for (var i = 0; i < state.noDatapoints; i++)
    592         for (var j = 0; j < state.newDimensions; j++)
    593           state.uY[i, j] = state.currentMomentum * state.uY[i, j] - state.eta * state.gains[i, j] * state.dY[i, j];
     601      for (var j = 0; j < state.newDimensions; j++)
     602        state.uY[i, j] = state.currentMomentum * state.uY[i, j] - state.eta * state.gains[i, j] * state.dY[i, j];
    594603
    595604      for (var i = 0; i < state.noDatapoints; i++)
    596         for (var j = 0; j < state.newDimensions; j++)
    597           state.newData[i, j] = state.newData[i, j] + state.uY[i, j];
     605      for (var j = 0; j < state.newDimensions; j++)
     606        state.newData[i, j] = state.newData[i, j] + state.uY[i, j];
    598607
    599608      // Make solution zero-mean
     
    604613        if (state.exact)
    605614          for (var i = 0; i < state.noDatapoints; i++)
    606             for (var j = 0; j < state.noDatapoints; j++)
    607               state.p[i, j] /= 12.0;
     615          for (var j = 0; j < state.noDatapoints; j++)
     616            state.p[i, j] /= 12.0;
    608617        else
    609618          for (var i = 0; i < state.rowP[state.noDatapoints]; i++)
     
    634643      // Compute final t-SNE gradient
    635644      for (var i = 0; i < n; i++)
    636         for (var j = 0; j < d; j++) {
    637           dC[i, j] = posF[i, j] - negF[i, j] / sumQ;
    638         }
     645      for (var j = 0; j < d; j++) {
     646        dC[i, j] = posF[i, j] - negF[i, j] / sumQ;
     647      }
    639648    }
    640649
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/TSNEUtils.cs

    r14414 r15532  
    3535    }
    3636
    37     internal static IList<T> Swap<T>(this IList<T> list, int indexA, int indexB) {
     37    internal static void Swap<T>(this IList<T> list, int indexA, int indexB) {
    3838      var tmp = list[indexA];
    3939      list[indexA] = list[indexB];
    4040      list[indexB] = tmp;
    41       return list;
    4241    }
    4342
    44     internal static int Partition<T>(this IList<T> list, int left, int right, int pivotindex, IComparer<T> comparer) {
     43    private static int Partition<T>(this IList<T> list, int left, int right, int pivotindex, IComparer<T> comparer) {
    4544      var pivotValue = list[pivotindex];
    4645      list.Swap(pivotindex, right);
     
    6766    /// <param name="comparer">comparer for list elemnts </param>
    6867    /// <returns></returns>
    69     internal static T NthElement<T>(this IList<T> list, int left, int right, int n, IComparer<T> comparer) {
     68    internal static void PartialSort<T>(this IList<T> list, int left, int right, int n, IComparer<T> comparer) {
    7069      while (true) {
    71         if (left == right) return list[left];
    72         var pivotindex = left + (int)Math.Floor(new System.Random().Next() % (right - (double)left + 1));
     70        if (left == right) return;
     71        var pivotindex = left + (int) Math.Floor(new System.Random().Next() % (right - (double) left + 1));
    7372        pivotindex = list.Partition(left, right, pivotindex, comparer);
    74         if (n == pivotindex) return list[n];
     73        if (n == pivotindex) return;
    7574        if (n < pivotindex) right = pivotindex - 1;
    7675        else left = pivotindex + 1;
  • trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/VantagePointTree.cs

    r15207 r15532  
    139139      // Partition around the median distance
    140140      var median = (upper + lower) / 2;
    141       items.NthElement(lower + 1, upper - 1, median, distance.GetDistanceComparer(items[lower]));
     141      items.PartialSort(lower + 1, upper - 1, median, distance.GetDistanceComparer(items[lower]));
    142142
    143143      // Threshold of the new node will be the distance to the median
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