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Changeset 11202 for branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs

Timestamp:

07/18/14 12:01:13 (10 years ago)

Author:

ascheibe

Message:

merged trunk into hive statistics branch

Location:

branches/HiveStatistics/sources

Files:

: 3 edited

. (modified) (2 props)
HeuristicLab.Problems.DataAnalysis (modified) (1 prop)
HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs (modified) (3 diffs)

Legend:

: Unmodified
: Added
: Removed

branches/HiveStatistics/sources

Property svn:ignore

-                      old
+                      new
 FxCopResults.txt
 Google.ProtocolBuffers-0.9.1.dll
+Google.ProtocolBuffers-2.4.1.473.dll
 HeuristicLab 3.3.5.1.ReSharper.user
 HeuristicLab 3.3.6.0.ReSharper.user
 HeuristicLab.4.5.resharper.user
 HeuristicLab.ExtLibs.6.0.ReSharper.user
+HeuristicLab.Scripting.Development
 HeuristicLab.resharper.user
 ProtoGen.exe
 …
 _ReSharper.HeuristicLab
 _ReSharper.HeuristicLab 3.3
+_ReSharper.HeuristicLab 3.3 Tests
 _ReSharper.HeuristicLab.ExtLibs
 bin
 protoc.exe
-_ReSharper.HeuristicLab 3.3 Tests
-Google.ProtocolBuffers-2.4.1.473.dll

Property svn:mergeinfo changed

/branches/1721-RandomForestPersistence (added)	merged: 10321-10322
/branches/DataPreprocessing (added)	merged: 10085,10095,10134,10143-10148,10157-10169,10179-10189,10191-10194,10216-10221,10234-10260,10303-10304,10307-10308,10310-10317,10319-10320,10341-10345,10367,10369-10372,10374,10377,10380-10383,10532-10539,10542,10544,10546-10552,10554-10555,10557-10559,10571-10574,10580-10583,10585-10590,10609-10638,10657-10673,10676,10689-10719,10733-10742,10762-10766,10768-10773,10775-10786,10802-10821,10841-10849,10851-10854,10863,10866-10883,10898-10902,10904-10905,10908-10909,10911,10913-10916,10918,10921-10922,10925,10930-10935,10938-10940,10942-10952,10962,10964-10965,10967,10969-10973,10976-10982,10987,10990-10995,10997-11004,11009,11011,11037-11039,11042-11045,11064,11068,11070,11098
/branches/HLScript (added)	merged: 10331-10332,10358
/branches/HeuristicLab.Problems.DataAnalysis.Trading (added)	merged: 6123-6128,6131,6136,6138,6861,6937,9176,9213,9743-9745,9796-9799
/branches/LogResidualEvaluator (added)	merged: 10202-10203,10305,10482-10483
/branches/SpectralKernelForGaussianProcesses (added)	merged: 10204-10205,10473,10479
/stable (added)	merged: 10032-10033,11170,11173,11175
/trunk/sources	merged: 9445,9447-9459,9462-9466,9471-9472,9474,9476-9479,9493,9496,9498,9504-9506,9515-9517,9520-9521,9523,9530-9531,9534-9537,9540,9542-9544,9552-9555,9568-9569,9587,9590-9592,9600,9607-9608,9610-9611,9613,9618,9623-9624,9626,9647-9649,9652,9657-9660,9664-9666,9675,9692,9695,9699,9702,9710,9714,9718-9720,9740-9741,9751,9755,9762,9764-9775,9777-9778,9782-9786,9792,9794,9801-9807,9809-9812,9816-9817,9819-9822,9824-9825,9828,9830,9833,9837-9841,9845,9848-9851,9855,9859-9860,9863,9865-9868,9871,9893-9897,9900-9902,9905-9907,9910,9915-9916,9919-9921,9928,9930,9934-9935,9938-9941,9944,9946,9949,9955-9956,9958-9959,9964-9965,9973-9975,9978,9980-9982,9988-9989,9991-9992,9994-9995,9997,10000-10004,10009-10010,10015,10037,10130,10149-10150,10154,10170-10171,10173-10176,10195-10196,10212-10213,10231,10261,10273,10291-10292,10295,10298,10323-10324,10346,10348,10355,10359-10360,10362-10363,10365-10366,10368,10375,10378,10391,10401,10406-10407,10414,10417-10418,10428,10432-10433,10435,10440,10448,10453,10460,10465-10466,10469-10470,10472,10474-10477,10480,10484,10486,10488-10500,10503-10504,10506,10510-10512,10520-10523,10526,10531,10540-10541,10543,10545,10561-10566,10575-10578,10596-10597,10599,10601-10607,10639,10642-10643,10646,10651-10653,10727,10731,10745,10747,10761,10767,10774,10787-10791,10793-10794,10796,10799-10800,10826,10857-10862,10865,10885,10889,10894-10896,10924,10926-10927,10937,10941,10953,10955-10956,10958-10961,10963,10975,10983-10986,10988-10989,10996,11007-11008,11012-11014,11019,11024-11027,11031,11034-11035,11048,11050-11052,11056-11058,11060,11065-11067,11071,11074-11082,11086-11088,11091-11093,11095-11097,11099,11102,11108,11111-11117,11123-11128,11131,11135,11153,11156,11161,11163,11171-11172,11174,11176

branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis

Property svn:mergeinfo changed

/branches/DataPreprocessing/HeuristicLab.Problems.DataAnalysis (added)	merged: 10134,10145-10148,10160,10164,10538,10689-10690,10694-10695,10708,10770,10772,10814,10845,10854,10869,10880,10922,10925,11002,11009,11011,11038,11064,11068,11070
/branches/LogResidualEvaluator/HeuristicLab.Problems.DataAnalysis (added)	merged: 10483
/branches/SpectralKernelForGaussianProcesses/HeuristicLab.Problems.DataAnalysis (added)	merged: 10479
/stable/HeuristicLab.Problems.DataAnalysis (added)	merged: 10032,11170,11173
/trunk/sources/HeuristicLab.Problems.DataAnalysis (added)	merged: 9449,9456,9462,9498,9552,9762,9973,10037,10406,10480,10484,10540,11031,11114,11156,11171,11174

branches/HiveStatistics/sources/HeuristicLab.Problems.DataAnalysis/3.4/Implementation/TimeSeriesPrognosis/TimeSeriesPrognosisSolutionBase.cs

-                      r8764
+                      r11202
 #region License Information
 /* HeuristicLab
  * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
  * This file is part of HeuristicLab.
 …
     protected void CalculateTimeSeriesResults() {
       OnlineCalculatorError errorState;
       double trainingMean = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average();
+      double trainingMean = ProblemData.TrainingIndices.Any() ? ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).Average() : double.NaN;
       var meanModel = new ConstantTimeSeriesPrognosisModel(trainingMean);
 …
       #region Calculate training quality measures
+      IEnumerable<double> trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
+      IEnumerable<double> trainingEstimatedValues = EstimatedTrainingValues.ToList();
+      IEnumerable<double> trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
+      IEnumerable<double> trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
+      TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
+      TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
+      TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
+      TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
+      TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
+      TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity;
+      TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
+      TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity;
+      if (ProblemData.TrainingIndices.Any()) {
+        IEnumerable<double> trainingTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TrainingIndices).ToList();
+        IEnumerable<double> trainingEstimatedValues = EstimatedTrainingValues.ToList();
+        IEnumerable<double> trainingMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
+        IEnumerable<double> trainingAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TrainingIndices).ToList();
+        TrainingDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
+        TrainingDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingDirectionalSymmetry : 0.0;
+        TrainingWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingEstimatedValues, out errorState);
+        TrainingWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TrainingWeightedDirectionalSymmetry : 0.0;
+        TrainingTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingAR1ModelPredictions, trainingEstimatedValues, out errorState);
+        TrainingTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticAR1 : double.PositiveInfinity;
+        TrainingTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(trainingTargetValues.First(), trainingTargetValues, trainingMeanModelPredictions, trainingEstimatedValues, out errorState);
+        TrainingTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TrainingTheilsUStatisticMean : double.PositiveInfinity;
+      }
       #endregion
       #region Calculate test quality measures
+      IEnumerable<double> testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList();
+      IEnumerable<double> testEstimatedValues = EstimatedTestValues.ToList();
+      IEnumerable<double> testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
+      IEnumerable<double> testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
+      TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
+      TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
+      TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
+      TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
+      TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
+      TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity;
+      TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
+      TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity;
+      if (ProblemData.TestIndices.Any()) {
+        IEnumerable<double> testTargetValues = ProblemData.Dataset.GetDoubleValues(ProblemData.TargetVariable, ProblemData.TestIndices).ToList();
+        IEnumerable<double> testEstimatedValues = EstimatedTestValues.ToList();
+        IEnumerable<double> testMeanModelPredictions = meanModel.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
+        IEnumerable<double> testAR1ModelPredictions = AR1model.GetEstimatedValues(ProblemData.Dataset, ProblemData.TestIndices).ToList();
+        TestDirectionalSymmetry = OnlineDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
+        TestDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestDirectionalSymmetry : 0.0;
+        TestWeightedDirectionalSymmetry = OnlineWeightedDirectionalSymmetryCalculator.Calculate(testTargetValues.First(), testTargetValues, testEstimatedValues, out errorState);
+        TestWeightedDirectionalSymmetry = errorState == OnlineCalculatorError.None ? TestWeightedDirectionalSymmetry : 0.0;
+        TestTheilsUStatisticAR1 = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testAR1ModelPredictions, testEstimatedValues, out errorState);
+        TestTheilsUStatisticAR1 = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticAR1 : double.PositiveInfinity;
+        TestTheilsUStatisticMean = OnlineTheilsUStatisticCalculator.Calculate(testTargetValues.First(), testTargetValues, testMeanModelPredictions, testEstimatedValues, out errorState);
+        TestTheilsUStatisticMean = errorState == OnlineCalculatorError.None ? TestTheilsUStatisticMean : double.PositiveInfinity;
+      }
       #endregion
+    }

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