Changeset 15545

Timestamp:

12/19/17 12:14:52 (7 years ago)

Author:

mkommend

Message:

#2850: Corrected after deserialization hook of tSNE.

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/TSNEAlgorithm.cs (modified) (15 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/TSNE/TSNEAlgorithm.cs

@@ -53 +53 @@
     }
     public new IDataAnalysisProblem Problem {
-      get { return (IDataAnalysisProblem) base.Problem; }
+      get { return (IDataAnalysisProblem)base.Problem; }
       set { base.Problem = value; }
     }
@@ -86 +86 @@
     #region Parameter properties
     public IFixedValueParameter<DoubleValue> PerplexityParameter {
-      get { return (IFixedValueParameter<DoubleValue>) Parameters[PerplexityParameterName]; }
+      get { return (IFixedValueParameter<DoubleValue>)Parameters[PerplexityParameterName]; }
     }
     public IFixedValueParameter<PercentValue> ThetaParameter {
-      get { return (IFixedValueParameter<PercentValue>) Parameters[ThetaParameterName]; }
+      get { return (IFixedValueParameter<PercentValue>)Parameters[ThetaParameterName]; }
     }
     public IFixedValueParameter<IntValue> NewDimensionsParameter {
-      get { return (IFixedValueParameter<IntValue>) Parameters[NewDimensionsParameterName]; }
+      get { return (IFixedValueParameter<IntValue>)Parameters[NewDimensionsParameterName]; }
     }
     public IConstrainedValueParameter<IDistance<double[]>> DistanceFunctionParameter {
-      get { return (IConstrainedValueParameter<IDistance<double[]>>) Parameters[DistanceFunctionParameterName]; }
+      get { return (IConstrainedValueParameter<IDistance<double[]>>)Parameters[DistanceFunctionParameterName]; }
     }
     public IFixedValueParameter<IntValue> MaxIterationsParameter {
-      get { return (IFixedValueParameter<IntValue>) Parameters[MaxIterationsParameterName]; }
+      get { return (IFixedValueParameter<IntValue>)Parameters[MaxIterationsParameterName]; }
     }
     public IFixedValueParameter<IntValue> StopLyingIterationParameter {
-      get { return (IFixedValueParameter<IntValue>) Parameters[StopLyingIterationParameterName]; }
+      get { return (IFixedValueParameter<IntValue>)Parameters[StopLyingIterationParameterName]; }
     }
     public IFixedValueParameter<IntValue> MomentumSwitchIterationParameter {
-      get { return (IFixedValueParameter<IntValue>) Parameters[MomentumSwitchIterationParameterName]; }
+      get { return (IFixedValueParameter<IntValue>)Parameters[MomentumSwitchIterationParameterName]; }
     }
     public IFixedValueParameter<DoubleValue> InitialMomentumParameter {
-      get { return (IFixedValueParameter<DoubleValue>) Parameters[InitialMomentumParameterName]; }
+      get { return (IFixedValueParameter<DoubleValue>)Parameters[InitialMomentumParameterName]; }
     }
     public IFixedValueParameter<DoubleValue> FinalMomentumParameter {
-      get { return (IFixedValueParameter<DoubleValue>) Parameters[FinalMomentumParameterName]; }
+      get { return (IFixedValueParameter<DoubleValue>)Parameters[FinalMomentumParameterName]; }
     }
     public IFixedValueParameter<DoubleValue> EtaParameter {
-      get { return (IFixedValueParameter<DoubleValue>) Parameters[EtaParameterName]; }
+      get { return (IFixedValueParameter<DoubleValue>)Parameters[EtaParameterName]; }
     }
     public IFixedValueParameter<BoolValue> SetSeedRandomlyParameter {
-      get { return (IFixedValueParameter<BoolValue>) Parameters[SetSeedRandomlyParameterName]; }
+      get { return (IFixedValueParameter<BoolValue>)Parameters[SetSeedRandomlyParameterName]; }
     }
     public IFixedValueParameter<IntValue> SeedParameter {
-      get { return (IFixedValueParameter<IntValue>) Parameters[SeedParameterName]; }
+      get { return (IFixedValueParameter<IntValue>)Parameters[SeedParameterName]; }
     }
     public IConstrainedValueParameter<StringValue> ClassesNameParameter {
-      get { return (IConstrainedValueParameter<StringValue>) Parameters[ClassesNameParameterName]; }
+      get { return (IConstrainedValueParameter<StringValue>)Parameters[ClassesNameParameterName]; }
     }
     public IFixedValueParameter<BoolValue> NormalizationParameter {
-      get { return (IFixedValueParameter<BoolValue>) Parameters[NormalizationParameterName]; }
+      get { return (IFixedValueParameter<BoolValue>)Parameters[NormalizationParameterName]; }
     }
     public IFixedValueParameter<BoolValue> RandomInitializationParameter {
-      get { return (IFixedValueParameter<BoolValue>) Parameters[RandomInitializationParameterName]; }
+      get { return (IFixedValueParameter<BoolValue>)Parameters[RandomInitializationParameterName]; }
     }
     public IFixedValueParameter<IntValue> UpdateIntervalParameter {
-      get { return (IFixedValueParameter<IntValue>) Parameters[UpdateIntervalParameterName]; }
+      get { return (IFixedValueParameter<IntValue>)Parameters[UpdateIntervalParameterName]; }
     }
     #endregion
@@ -218 +218 @@
     [StorableHook(HookType.AfterDeserialization)]
     private void AfterDeserialization() {
-      if (Parameters.ContainsKey(RandomInitializationParameterName))
+      if (!Parameters.ContainsKey(RandomInitializationParameterName))
         Parameters.Add(new FixedValueParameter<BoolValue>(RandomInitializationParameterName, "Wether data points should be randomly initialized or according to the first 2 dimensions", new BoolValue(true)));
       RegisterParameterEvents();
@@ -284 +284 @@
       if (state == null) {
         if (SetSeedRandomly) Seed = new System.Random().Next();
-        var random = new MersenneTwister((uint) Seed);
+        var random = new MersenneTwister((uint)Seed);
         var dataset = problemData.Dataset;
         var allowedInputVariables = problemData.AllowedInputVariables.ToArray();
@@ -401 +401 @@
       var classificationData = problemData as ClassificationProblemData;
       if (classificationData != null && classificationData.TargetVariable.Equals(ClassesName)) {
-        var classNames = classificationData.ClassValues.Zip(classificationData.ClassNames, (v, n) => new {v, n}).ToDictionary(x => x.v, x => x.n);
+        var classNames = classificationData.ClassValues.Zip(classificationData.ClassNames, (v, n) => new { v, n }).ToDictionary(x => x.v, x => x.n);
         var classes = classificationData.Dataset.GetDoubleValues(classificationData.TargetVariable, allIndices).Select(v => classNames[v]).ToArray();
         for (var i = 0; i < classes.Length; i++) {
@@ -407 +407 @@
           dataRowNames[classes[i]].Add(i);
         }
-      }
-      else if (((Dataset) problemData.Dataset).VariableHasType<string>(ClassesName)) {
+      } else if (((Dataset)problemData.Dataset).VariableHasType<string>(ClassesName)) {
         var classes = problemData.Dataset.GetStringValues(ClassesName, allIndices).ToArray();
         for (var i = 0; i < classes.Length; i++) {
@@ -414 +413 @@
           dataRowNames[classes[i]].Add(i);
         }
-      }
-      else if (((Dataset) problemData.Dataset).VariableHasType<double>(ClassesName)) {
+      } else if (((Dataset)problemData.Dataset).VariableHasType<double>(ClassesName)) {
         var clusterdata = new Dataset(problemData.Dataset.DoubleVariables, problemData.Dataset.DoubleVariables.Select(v => problemData.Dataset.GetDoubleValues(v, allIndices).ToList()));
         const int contours = 8;
@@ -422 +420 @@
         double[][] borders;
         CreateClusters(clusterdata, ClassesName, contours, out clusterModel, out contourMap, out borders);
-        var contourorder = borders.Select((x, i) => new {x, i}).OrderBy(x => x.x[0]).Select(x => x.i).ToArray();
+        var contourorder = borders.Select((x, i) => new { x, i }).OrderBy(x => x.x[0]).Select(x => x.i).ToArray();
         for (var i = 0; i < contours; i++) {
           var c = contourorder[i];
@@ -432 +430 @@
         var allClusters = clusterModel.GetClusterValues(clusterdata, Enumerable.Range(0, clusterdata.Rows)).ToArray();
         for (var i = 0; i < clusterdata.Rows; i++) dataRowNames[contourMap[allClusters[i] - 1]].Add(i);
-      }
-      else if (((Dataset) problemData.Dataset).VariableHasType<DateTime>(ClassesName)) {
+      } else if (((Dataset)problemData.Dataset).VariableHasType<DateTime>(ClassesName)) {
         var clusterdata = new Dataset(problemData.Dataset.DateTimeVariables, problemData.Dataset.DateTimeVariables.Select(v => problemData.Dataset.GetDoubleValues(v, allIndices).ToList()));
         const int contours = 8;
@@ -440 +437 @@
         double[][] borders;
         CreateClusters(clusterdata, ClassesName, contours, out clusterModel, out contourMap, out borders);
-        var contourorder = borders.Select((x, i) => new {x, i}).OrderBy(x => x.x[0]).Select(x => x.i).ToArray();
+        var contourorder = borders.Select((x, i) => new { x, i }).OrderBy(x => x.x[0]).Select(x => x.i).ToArray();
         for (var i = 0; i < contours; i++) {
           var c = contourorder[i];
@@ -450 +447 @@
         var allClusters = clusterModel.GetClusterValues(clusterdata, Enumerable.Range(0, clusterdata.Rows)).ToArray();
         for (var i = 0; i < clusterdata.Rows; i++) dataRowNames[contourMap[allClusters[i] - 1]].Add(i);
-      }
-      else {
+      } else {
         dataRowNames.Add("Training", problemData.TrainingIndices.ToList());
         dataRowNames.Add("Test", problemData.TestIndices.ToList());
@@ -465 +461 @@
       var c = tsneState.EvaluateError();
       errors.Add(c);
-      ((IntValue) results[IterationResultName].Value).Value = tsneState.iter;
-      ((DoubleValue) results[ErrorResultName].Value).Value = errors.Last();
+      ((IntValue)results[IterationResultName].Value).Value = tsneState.iter;
+      ((DoubleValue)results[ErrorResultName].Value).Value = errors.Last();
 
       var ndata = NormalizeProjectedData(tsneState.newData);
@@ -490 +486 @@
       for (var i = 0; i < max.Length; i++) max[i] = min[i] = data[0, i];
       for (var i = 0; i < data.GetLength(0); i++)
-      for (var j = 0; j < data.GetLength(1); j++) {
-        var v = data[i, j];
-        max[j] = Math.Max(max[j], v);
-        min[j] = Math.Min(min[j], v);
-      }
+        for (var j = 0; j < data.GetLength(1); j++) {
+          var v = data[i, j];
+          max[j] = Math.Max(max[j], v);
+          min[j] = Math.Min(min[j], v);
+        }
       for (var i = 0; i < data.GetLength(0); i++) {
         for (var j = 0; j < data.GetLength(1); j++) {
@@ -528 +524 @@
 
     private static void CreateClusters(IDataset data, string target, int contours, out IClusteringModel contourCluster, out Dictionary<int, string> contourNames, out double[][] borders) {
-      var cpd = new ClusteringProblemData((Dataset) data, new[] {target});
+      var cpd = new ClusteringProblemData((Dataset)data, new[] { target });
       contourCluster = KMeansClustering.CreateKMeansSolution(cpd, contours, 3).Model;
 
-      borders = Enumerable.Range(0, contours).Select(x => new[] {double.MaxValue, double.MinValue}).ToArray();
+      borders = Enumerable.Range(0, contours).Select(x => new[] { double.MaxValue, double.MinValue }).ToArray();
       var clusters = contourCluster.GetClusterValues(cpd.Dataset, cpd.AllIndices).ToArray();
       var targetvalues = cpd.Dataset.GetDoubleValues(target).ToArray();
@@ -550 +546 @@
       var range = high - low;
       var h = Math.Min(cell / range * colorGradient.Count, colorGradient.Count - 1);
-      return colorGradient[(int) h];
+      return colorGradient[(int)h];
     }
     #endregion