- Timestamp:
- 02/01/21 18:33:07 (4 years ago)
- Location:
- branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4
- Files:
-
- 1 added
- 3 edited
- 1 copied
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branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Grammars/TypeCoherentVectorTimeSeriesExpressionGrammar.cs
r17824 r17830 29 29 30 30 namespace HeuristicLab.Problems.DataAnalysis.Symbolic { 31 [StorableType(" 7EC7B4A7-0E27-4011-B983-B0E15A6944EC")]32 [Item("TypeCoherentVector ExpressionGrammar", "Represents a grammar for functional expressions in which special syntactic constraints are enforced so that vector and scalar expressions are not mixed.")]33 public class TypeCoherentVector ExpressionGrammar : DataAnalysisGrammar, ISymbolicDataAnalysisGrammar {31 [StorableType("88895D71-3D3F-44A7-A531-D5D59963AABE")] 32 [Item("TypeCoherentVectorTimeSeriesExpressionGrammar", "Represents a grammar for functional expressions in which special syntactic constraints are enforced so that vector and scalar expressions are not mixed.")] 33 public class TypeCoherentVectorTimeSeriesExpressionGrammar : DataAnalysisGrammar, ISymbolicDataAnalysisGrammar { 34 34 private const string ArithmeticFunctionsName = "Arithmetic Functions"; 35 35 private const string TrigonometricFunctionsName = "Trigonometric Functions"; … … 40 40 private const string VectorStatisticsName = "Vector Statistics"; 41 41 private const string VectorDistancesName = "Vector Distances"; 42 private const string VectorDistributionCharacteristicsName = "Distribution Characteristics"; 43 private const string VectorTimeSeriesDynamicsName = "Time Series Dynamics"; 42 44 private const string ScalarSymbolsName = "Scalar Symbols"; 43 45 … … 54 56 55 57 [StorableConstructor] 56 protected TypeCoherentVector ExpressionGrammar(StorableConstructorFlag _) : base(_) { }57 protected TypeCoherentVector ExpressionGrammar(TypeCoherentVectorExpressionGrammar original, Cloner cloner) : base(original, cloner) { }58 public TypeCoherentVector ExpressionGrammar()59 : base(ItemAttribute.GetName(typeof(TypeCoherentVector ExpressionGrammar)), ItemAttribute.GetDescription(typeof(TypeCoherentVectorExpressionGrammar))) {58 protected TypeCoherentVectorTimeSeriesExpressionGrammar(StorableConstructorFlag _) : base(_) { } 59 protected TypeCoherentVectorTimeSeriesExpressionGrammar(TypeCoherentVectorTimeSeriesExpressionGrammar original, Cloner cloner) : base(original, cloner) { } 60 public TypeCoherentVectorTimeSeriesExpressionGrammar() 61 : base(ItemAttribute.GetName(typeof(TypeCoherentVectorTimeSeriesExpressionGrammar)), ItemAttribute.GetDescription(typeof(TypeCoherentVectorTimeSeriesExpressionGrammar))) { 60 62 Initialize(); 61 63 } 62 64 public override IDeepCloneable Clone(Cloner cloner) { 63 return new TypeCoherentVector ExpressionGrammar(this, cloner);65 return new TypeCoherentVectorTimeSeriesExpressionGrammar(this, cloner); 64 66 } 65 67 … … 86 88 87 89 var constant = new Constant { MinValue = -20, MaxValue = 20 }; 90 var constantZeroToOne = new Constant { Name = "Constant [0-1]", MinValue = 0, MaxValue = 1 }; 88 91 var variable = new Variable(); 89 92 var binFactorVariable = new BinaryFactorVariable(); … … 91 94 92 95 var mean = new Mean(); 96 var median = new Median() { Enabled = false }; 93 97 var sd = new StandardDeviation(); 94 98 var sum = new Sum(); … … 96 100 var min = new Min() { Enabled = false }; 97 101 var max = new Max() { Enabled = false }; 102 var quantile = new Quantile() { Enabled = false }; 98 103 var variance = new Variance() { Enabled = false }; 99 104 var skewness = new Skewness() { Enabled = false }; … … 124 129 125 130 var vectorvariable = new Variable() { Name = "Vector Variable" }; 131 132 #region TimeSeries Symbols 133 var absoluteEnergy = new AbsoluteEnergy() { Enabled = false }; 134 var binnedEntropy = new BinnedEntropy() { Enabled = false }; 135 var hasLargeStandardDeviation = new HasLargeStandardDeviation() { Enabled = false }; 136 var hasVarianceLargerThanStd = new HasVarianceLargerThanStd() { Enabled = false }; 137 var isSymmetricLooking = new IsSymmetricLooking() { Enabled = false }; 138 var numberDataPointsAboveMean = new NumberDataPointsAboveMean() { Enabled = false }; 139 var numberDataPointsAboveMedian = new NumberDataPointsAboveMedian() { Enabled = false }; 140 var numberDataPointsBelowMean = new NumberDataPointsBelowMean() { Enabled = false }; 141 var numberDataPointsBelowMedian = new NumberDataPointsBelowMedian() { Enabled = false }; 142 143 var arimaModelCoefficients = new ArimaModelCoefficients() { Enabled = false }; 144 var continuousWaveletTransformationCoefficients = new ContinuousWaveletTransformationCoefficients() { Enabled = false }; 145 var fastFourierTransformationCoefficient = new FastFourierTransformationCoefficient() { Enabled = false }; 146 var firstIndexMax = new FirstIndexMax() { Enabled = false }; 147 var firstIndexMin = new FirstIndexMin() { Enabled = false }; 148 var lastIndexMax = new LastIndexMax() { Enabled = false }; 149 var lastIndexMin = new LastIndexMin() { Enabled = false }; 150 var longestStrikeAboveMean = new LongestStrikeAboveMean() { Enabled = false }; 151 var longestStrikeAboveMedian = new LongestStrikeAboveMedian() { Enabled = false }; 152 var longestStrikeBelowMean = new LongestStrikeBelowMean() { Enabled = false }; 153 var longestStrikeBelowMedian = new LongestStrikeBelowMedian() { Enabled = false }; 154 var longestStrikePositive = new LongestStrikePositive() { Enabled = false }; 155 var longestStrikeNegative = new LongestStrikeNegative() { Enabled = false }; 156 var longestStrikeZero = new LongestStrikeZero() { Enabled = false }; 157 var meanAbsoluteChange = new MeanAbsoluteChange() { Enabled = false }; 158 var meanAbsoluteChangeQuantiles = new MeanAbsoluteChangeQuantiles() { Enabled = false }; 159 var meanAutocorrelation = new MeanAutocorrelation() { Enabled = false }; 160 var laggedAutocorrelation = new LaggedAutocorrelation() { Enabled = false }; 161 var meanSecondDerivateCentral = new MeanSecondDerivateCentral() { Enabled = false }; 162 var numberPeaksOfSize = new NumberPeaksOfSize() { Enabled = false }; 163 var largeNumberOfPeaks = new LargeNumberOfPeaks() { Enabled = false }; 164 var timeReversalAsymmetryStatistic = new TimeReversalAsymmetryStatistic() { Enabled = false }; 165 #endregion 126 166 #endregion 127 167 … … 135 175 var exponentialAndLogarithmicSymbols = new GroupSymbol(ExponentialFunctionsName, new List<ISymbol> { exp, log }); 136 176 var powerSymbols = new GroupSymbol(PowerFunctionsName, new List<ISymbol> { square, sqrt, cube, cubeRoot, power, root }); 137 var terminalSymbols = new GroupSymbol(TerminalsName, new List<ISymbol> { constant, variable, binFactorVariable, factorVariable });138 var statisticsSymbols = new GroupSymbol(VectorStatisticsName, new List<ISymbol> { mean, sd, sum, length, min, max, variance, skewness, kurtosis });177 var terminalSymbols = new GroupSymbol(TerminalsName, new List<ISymbol> { constant, constantZeroToOne, variable, binFactorVariable, factorVariable }); 178 var statisticsSymbols = new GroupSymbol(VectorStatisticsName, new List<ISymbol> { mean, median, sd, sum, length, min, max, quantile, variance, skewness, kurtosis }); 139 179 var distancesSymbols = new GroupSymbol(VectorDistancesName, new List<ISymbol> { euclideanDistance, covariance }); 140 var aggregationSymbols = new GroupSymbol(VectorAggregationName, new List<ISymbol> { statisticsSymbols, distancesSymbols }); 180 var distributionCharacteristicsSymbols = new GroupSymbol(VectorDistributionCharacteristicsName, new List<ISymbol> { 181 absoluteEnergy, binnedEntropy, hasLargeStandardDeviation, hasVarianceLargerThanStd, isSymmetricLooking, 182 numberDataPointsAboveMean, numberDataPointsAboveMedian, numberDataPointsBelowMean, numberDataPointsBelowMedian 183 }); 184 var timeSeriesDynamicsSymbols = new GroupSymbol(VectorTimeSeriesDynamicsName, new List<ISymbol> { 185 arimaModelCoefficients, continuousWaveletTransformationCoefficients, fastFourierTransformationCoefficient, 186 firstIndexMax, firstIndexMin, lastIndexMax, lastIndexMin, 187 longestStrikeAboveMean, longestStrikeAboveMedian, longestStrikeBelowMean, longestStrikeBelowMedian, longestStrikePositive, longestStrikePositive, longestStrikeNegative, longestStrikeZero, 188 meanAbsoluteChange, meanAbsoluteChangeQuantiles, meanAutocorrelation, laggedAutocorrelation, meanSecondDerivateCentral, meanSecondDerivateCentral, 189 numberPeaksOfSize, largeNumberOfPeaks, timeReversalAsymmetryStatistic 190 }); 191 var aggregationSymbols = new GroupSymbol(VectorAggregationName, new List<ISymbol> { statisticsSymbols, distancesSymbols, distributionCharacteristicsSymbols, timeSeriesDynamicsSymbols }); 141 192 var scalarSymbols = new GroupSymbol(ScalarSymbolsName, new List<ISymbol>() { arithmeticSymbols, trigonometricSymbols, exponentialAndLogarithmicSymbols, powerSymbols, terminalSymbols, aggregationSymbols }); 142 193 … … 170 221 SetSubtreeCount(exponentialAndLogarithmicSymbols, 1, 1); 171 222 SetSubtreeCount(terminalSymbols, 0, 0); 172 SetSubtreeCount(statisticsSymbols, 1, 1); 223 foreach (var sy in new Symbol[] { mean, median, sd, sum, length, min, max, variance, skewness, kurtosis }) 224 SetSubtreeCount(sy, 1, 1); 225 SetSubtreeCount(quantile, 2, 2); 173 226 SetSubtreeCount(distancesSymbols, 2, 2); 227 #region TimeSeries symbols 228 foreach (var sy in new Symbol[] { 229 absoluteEnergy, hasLargeStandardDeviation, hasVarianceLargerThanStd, isSymmetricLooking, 230 numberDataPointsAboveMean, numberDataPointsAboveMedian, numberDataPointsBelowMean, numberDataPointsBelowMedian 231 }) SetSubtreeCount(sy, 1, 1); 232 foreach (var sy in new Symbol[] { binnedEntropy }) 233 SetSubtreeCount(sy, 2, 2); 234 235 foreach (var sy in new Symbol[] { 236 firstIndexMax, firstIndexMin, lastIndexMax, lastIndexMin, 237 longestStrikeAboveMean, longestStrikeAboveMedian, longestStrikeBelowMean, longestStrikeBelowMedian, 238 longestStrikePositive, longestStrikeNegative, longestStrikeZero, 239 meanAbsoluteChange, meanAutocorrelation, meanSecondDerivateCentral 240 }) SetSubtreeCount(sy, 1, 1); 241 foreach (var sy in new Symbol[] { 242 fastFourierTransformationCoefficient, laggedAutocorrelation, numberPeaksOfSize, timeReversalAsymmetryStatistic 243 }) SetSubtreeCount(sy, 2, 2); 244 foreach (var sy in new Symbol[] { 245 arimaModelCoefficients, continuousWaveletTransformationCoefficients, 246 meanAbsoluteChangeQuantiles, largeNumberOfPeaks 247 }) SetSubtreeCount(sy, 3, 3); 248 #endregion 174 249 175 250 SetSubtreeCount(vectorarithmeticSymbols, 2, 2); … … 197 272 AddAllowedChildSymbol(power, constant, 1); 198 273 AddAllowedChildSymbol(root, constant, 1); 199 AddAllowedChildSymbol(aggregationSymbols, vectorSymbols); 200 AddAllowedChildSymbol(statisticsSymbols, subvector); 274 AddAllowedChildSymbol(aggregationSymbols, vectorSymbols, 0); 275 AddAllowedChildSymbol(statisticsSymbols, subvector, 0); 276 AddAllowedChildSymbol(quantile, constantZeroToOne, 1); 277 AddAllowedChildSymbol(distancesSymbols, vectorSymbols, 1); 278 AddAllowedChildSymbol(distributionCharacteristicsSymbols, vectorSymbols, 0); 279 AddAllowedChildSymbol(distributionCharacteristicsSymbols, subvector, 0); 280 AddAllowedChildSymbol(distributionCharacteristicsSymbols, constantZeroToOne, 1); 281 AddAllowedChildSymbol(timeSeriesDynamicsSymbols, vectorSymbols, 0); 282 AddAllowedChildSymbol(timeSeriesDynamicsSymbols, subvector, 0); 283 AddAllowedChildSymbol(timeSeriesDynamicsSymbols, constantZeroToOne, 1); 284 AddAllowedChildSymbol(timeSeriesDynamicsSymbols, constantZeroToOne, 2); 201 285 202 286 AddAllowedChildSymbol(vectorarithmeticSymbols, vectorSymbols); … … 205 289 AddAllowedChildSymbol(vectorexponentialAndLogarithmicSymbols, vectorSymbols); 206 290 AddAllowedChildSymbol(vectorpowerSymbols, vectorSymbols, 0); 207 AddAllowedChildSymbol(vectorpower, constant , 1);208 AddAllowedChildSymbol(vectorroot, constant , 1);291 AddAllowedChildSymbol(vectorpower, constantZeroToOne, 1); 292 AddAllowedChildSymbol(vectorroot, constantZeroToOne, 1); 209 293 210 294 AddAllowedChildSymbol(subvector, vectorSymbols); -
branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj
r17786 r17830 181 181 <Compile Include="Grammars\FullFunctionalVectorExpressionGrammar.cs" /> 182 182 <Compile Include="Grammars\TypeCoherentVectorExpressionGrammar.cs" /> 183 <Compile Include="Grammars\TypeCoherentVectorTimeSeriesExpressionGrammar.cs" /> 183 184 <Compile Include="Hashing\HashExtensions.cs" /> 184 185 <Compile Include="Hashing\HashUtil.cs" /> … … 260 261 <Compile Include="Symbols\Skewness.cs" /> 261 262 <Compile Include="Symbols\SubVector.cs" /> 263 <Compile Include="Symbols\TimeSeriesSymbols.cs" /> 262 264 <Compile Include="Symbols\WindowedSymbolTreeNode.cs" /> 263 265 <Compile Include="Symbols\WindowedSymbol.cs" /> -
branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/OpCodes.cs
r17726 r17830 91 91 SubVector = 64, 92 92 93 #region Time Series Symbols 94 Median = 100, 95 Quantile = 101, 96 97 AbsoluteEnergy = 102, 98 BinnedEntropy = 103, 99 HasLargeStandardDeviation = 104, 100 HasVarianceLargerThanStd = 105, 101 IsSymmetricLooking = 106, 102 NumberDataPointsAboveMean = 107, 103 NumberDataPointsAboveMedian = 108, 104 NumberDataPointsBelowMean = 109, 105 NumberDataPointsBelowMedian = 110, 106 107 ArimaModelCoefficients = 111, 108 ContinuousWaveletTransformationCoefficients = 112, 109 FastFourierTransformationCoefficient = 113, 110 FirstIndexMax = 124, 111 FirstIndexMin = 125, 112 LastIndexMax = 126, 113 LastIndexMin = 127, 114 LongestStrikeAboveMean = 128, 115 LongestStrikeAboveMedian = 129, 116 LongestStrikeBelowMean = 130, 117 LongestStrikeBelowMedian = 131, 118 LongestStrikePositive = 132, 119 LongestStrikeNegative = 133, 120 LongestStrikeZero = 134, 121 MeanAbsoluteChange = 135, 122 MeanAbsoluteChangeQuantiles = 136, 123 MeanAutocorrelation = 137, 124 LaggedAutocorrelation = 138, 125 MeanSecondDerivateCentral = 139, 126 NumberPeaksOfSize = 140, 127 LargeNumberOfPeaks = 141, 128 TimeReversalAsymmetryStatistic = 142 129 #endregion 93 130 } 94 131 public static class OpCodes { … … 158 195 public const byte Covariance = (byte)OpCode.Covariance; 159 196 public const byte SubVector = (byte)OpCode.SubVector; 197 #region Time Series Symbols 198 public const byte Median = (byte)OpCode.Median; 199 public const byte Quantile = (byte)OpCode.Quantile; 200 201 public const byte AbsoluteEnergy = (byte)OpCode.AbsoluteEnergy; 202 public const byte BinnedEntropy = (byte)OpCode.BinnedEntropy; 203 public const byte HasLargeStandardDeviation = (byte)OpCode.HasLargeStandardDeviation; 204 public const byte HasVarianceLargerThanStd = (byte)OpCode.HasVarianceLargerThanStd; 205 public const byte IsSymmetricLooking = (byte)OpCode.IsSymmetricLooking; 206 public const byte NumberDataPointsAboveMean = (byte)OpCode.NumberDataPointsAboveMean; 207 public const byte NumberDataPointsAboveMedian = (byte)OpCode.NumberDataPointsAboveMedian; 208 public const byte NumberDataPointsBelowMean = (byte)OpCode.NumberDataPointsBelowMean; 209 public const byte NumberDataPointsBelowMedian = (byte)OpCode.NumberDataPointsBelowMedian; 210 211 public const byte ArimaModelCoefficients = (byte)OpCode.ArimaModelCoefficients; 212 public const byte ContinuousWaveletTransformationCoefficients = (byte)OpCode.ContinuousWaveletTransformationCoefficients; 213 public const byte FastFourierTransformationCoefficient = (byte)OpCode.FastFourierTransformationCoefficient; 214 public const byte FirstIndexMax = (byte)OpCode.FirstIndexMax; 215 public const byte FirstIndexMin = (byte)OpCode.FirstIndexMin; 216 public const byte LastIndexMax = (byte)OpCode.LastIndexMax; 217 public const byte LastIndexMin = (byte)OpCode.LastIndexMin; 218 public const byte LongestStrikeAboveMean = (byte)OpCode.LongestStrikeAboveMean; 219 public const byte LongestStrikeAboveMedian = (byte)OpCode.LongestStrikeAboveMedian; 220 public const byte LongestStrikeBelowMean = (byte)OpCode.LongestStrikeBelowMean; 221 public const byte LongestStrikeBelowMedian = (byte)OpCode.LongestStrikeBelowMedian; 222 public const byte LongestStrikePositive = (byte)OpCode.LongestStrikePositive; 223 public const byte LongestStrikeNegative = (byte)OpCode.LongestStrikeNegative; 224 public const byte LongestStrikeZero = (byte)OpCode.LongestStrikeZero; 225 public const byte MeanAbsoluteChange = (byte)OpCode.MeanAbsoluteChange; 226 public const byte MeanAbsoluteChangeQuantiles = (byte)OpCode.MeanAbsoluteChangeQuantiles; 227 public const byte MeanAutocorrelation = (byte)OpCode.MeanAutocorrelation; 228 public const byte LaggedAutocorrelation = (byte)OpCode.LaggedAutocorrelation; 229 public const byte MeanSecondDerivateCentral = (byte)OpCode.MeanSecondDerivateCentral; 230 public const byte NumberPeaksOfSize = (byte)OpCode.NumberPeaksOfSize; 231 public const byte LargeNumberOfPeaks = (byte)OpCode.LargeNumberOfPeaks; 232 public const byte TimeReversalAsymmetryStatistic = (byte)OpCode.TimeReversalAsymmetryStatistic; 233 #endregion 160 234 161 235 … … 226 300 { typeof(Covariance), OpCodes.Covariance }, 227 301 { typeof(SubVector), OpCodes.SubVector }, 302 303 #region Time Series Symbols 304 { typeof(Median), OpCodes.Median }, 305 { typeof(Quantile), OpCodes.Quantile }, 306 307 { typeof(AbsoluteEnergy), OpCodes.AbsoluteEnergy }, 308 { typeof(BinnedEntropy), OpCodes.BinnedEntropy }, 309 { typeof(HasLargeStandardDeviation), OpCodes.HasLargeStandardDeviation }, 310 { typeof(HasVarianceLargerThanStd), OpCodes.HasVarianceLargerThanStd }, 311 { typeof(IsSymmetricLooking), OpCodes.IsSymmetricLooking }, 312 { typeof(NumberDataPointsAboveMean), OpCodes.NumberDataPointsAboveMean }, 313 { typeof(NumberDataPointsAboveMedian), OpCodes.NumberDataPointsAboveMedian }, 314 { typeof(NumberDataPointsBelowMean), OpCodes.NumberDataPointsBelowMean }, 315 { typeof(NumberDataPointsBelowMedian), OpCodes.NumberDataPointsBelowMedian }, 316 317 { typeof(ArimaModelCoefficients), OpCodes.ArimaModelCoefficients }, 318 { typeof(ContinuousWaveletTransformationCoefficients), OpCodes.ContinuousWaveletTransformationCoefficients }, 319 { typeof(FastFourierTransformationCoefficient), OpCodes.FastFourierTransformationCoefficient }, 320 { typeof(FirstIndexMax), OpCodes.FirstIndexMax }, 321 { typeof(FirstIndexMin), OpCodes.FirstIndexMin }, 322 { typeof(LastIndexMax), OpCodes.LastIndexMax }, 323 { typeof(LastIndexMin), OpCodes.LastIndexMin }, 324 { typeof(LongestStrikeAboveMean), OpCodes.LongestStrikeAboveMean }, 325 { typeof(LongestStrikeAboveMedian), OpCodes.LongestStrikeAboveMedian }, 326 { typeof(LongestStrikeBelowMean), OpCodes.LongestStrikeBelowMean }, 327 { typeof(LongestStrikeBelowMedian), OpCodes.LongestStrikeBelowMedian }, 328 { typeof(LongestStrikePositive), OpCodes.LongestStrikePositive }, 329 { typeof(LongestStrikeNegative), OpCodes.LongestStrikeNegative }, 330 { typeof(LongestStrikeZero), OpCodes.LongestStrikeZero }, 331 { typeof(MeanAbsoluteChange), OpCodes.MeanAbsoluteChange }, 332 { typeof(MeanAbsoluteChangeQuantiles), OpCodes.MeanAbsoluteChangeQuantiles }, 333 { typeof(MeanAutocorrelation), OpCodes.MeanAutocorrelation }, 334 { typeof(LaggedAutocorrelation), OpCodes.LaggedAutocorrelation }, 335 { typeof(MeanSecondDerivateCentral), OpCodes.MeanSecondDerivateCentral }, 336 { typeof(NumberPeaksOfSize), OpCodes.NumberPeaksOfSize }, 337 { typeof(LargeNumberOfPeaks), OpCodes.LargeNumberOfPeaks }, 338 { typeof(TimeReversalAsymmetryStatistic), OpCodes.TimeReversalAsymmetryStatistic }, 339 #endregion 228 340 }; 229 341 -
branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeVectorInterpreter.cs
r17786 r17830 209 209 210 210 public SymbolicDataAnalysisExpressionTreeVectorInterpreter() 211 : this("SymbolicDataAnalysisExpressionTreeVectorInterpreter", "Interpreter for symbolic expression trees including vector arithmetic.") { 212 } 211 : this("SymbolicDataAnalysisExpressionTreeVectorInterpreter", "Interpreter for symbolic expression trees including vector arithmetic.") { } 213 212 214 213 protected SymbolicDataAnalysisExpressionTreeVectorInterpreter(string name, string description) … … 752 751 } 753 752 753 #region Time Series Symbols 754 case OpCodes.Median: { 755 var cur = Evaluate(dataset, ref row, state, traceDict); 756 cur = AggregateApply(cur, 757 s => s, 758 v => Statistics.Median(v)); 759 TraceEvaluation(currentInstr, cur); 760 return cur; 761 } 762 case OpCodes.Quantile: { 763 var cur = Evaluate(dataset, ref row, state, traceDict); 764 var q = Evaluate(dataset, ref row, state, traceDict); 765 cur = AggregateApply(cur, 766 s => s, 767 v => Statistics.Quantile(v, q.Scalar)); 768 TraceEvaluation(currentInstr, cur); 769 return cur; 770 } 771 772 case OpCodes.AbsoluteEnergy: { 773 var cur = Evaluate(dataset, ref row, state, traceDict); 774 cur = AggregateApply(cur, 775 s => s * s, 776 v => v.PointwisePower(2.0).Sum()); 777 TraceEvaluation(currentInstr, cur); 778 return cur; 779 } 780 781 case OpCodes.BinnedEntropy: { 782 var cur = Evaluate(dataset, ref row, state, traceDict); 783 var m = Evaluate(dataset, ref row, state, traceDict); 784 cur = AggregateApply(cur, 785 s => 0, 786 v => { 787 int bins = (int)Math.Round(m.Scalar); 788 double minValue = v.Minimum(); 789 double maxValue = v.Maximum(); 790 double intervalWidth = (maxValue - minValue) / bins; 791 int totalValues = v.Count; 792 double sum = 0; 793 for (int i = 0; i < Math.Max(bins, v.Count); i++) { 794 double binMin = minValue * i; 795 double binMax = binMin + intervalWidth; 796 double countBin = v.Map(e => (e > binMin && e < binMax) ? 1 : 0).Sum(); 797 double percBin = countBin / totalValues; 798 sum += percBin * Math.Log(percBin); 799 } 800 801 return sum; 802 }); 803 TraceEvaluation(currentInstr, cur); 804 return cur; 805 } 806 case OpCodes.HasLargeStandardDeviation: { 807 var cur = Evaluate(dataset, ref row, state, traceDict); 808 cur = AggregateApply(cur, 809 s => 0, 810 v => Statistics.PopulationStandardDeviation(v) > (Statistics.Maximum(v) - Statistics.Minimum(v)) / 2 ? 1 : 0); 811 TraceEvaluation(currentInstr, cur); 812 return cur; 813 } 814 case OpCodes.HasVarianceLargerThanStd: { 815 var cur = Evaluate(dataset, ref row, state, traceDict); 816 cur = AggregateApply(cur, 817 s => 0, 818 v => Statistics.PopulationVariance(v) > Statistics.StandardDeviation(v) ? 1 : 0); 819 TraceEvaluation(currentInstr, cur); 820 return cur; 821 } 822 case OpCodes.IsSymmetricLooking: { 823 var cur = Evaluate(dataset, ref row, state, traceDict); 824 cur = AggregateApply(cur, 825 s => 0, 826 v => Math.Abs(Statistics.Mean(v) - Statistics.Median(v)) < (Statistics.Maximum(v) - Statistics.Minimum(v)) / 2 ? 1 : 0); 827 TraceEvaluation(currentInstr, cur); 828 return cur; 829 } 830 case OpCodes.NumberDataPointsAboveMean: { 831 var cur = Evaluate(dataset, ref row, state, traceDict); 832 cur = AggregateApply(cur, 833 s => 0, 834 v => { 835 double mean = Statistics.Mean(v); 836 return v.Map(e => e > mean ? 1 : 0).Sum(); 837 }); 838 TraceEvaluation(currentInstr, cur); 839 return cur; 840 } 841 case OpCodes.NumberDataPointsAboveMedian: { 842 var cur = Evaluate(dataset, ref row, state, traceDict); 843 cur = AggregateApply(cur, 844 s => 0, 845 v => { 846 double median = Statistics.Median(v); 847 return v.Map(e => e > median ? 1 : 0).Sum(); 848 }); 849 TraceEvaluation(currentInstr, cur); 850 return cur; 851 } 852 case OpCodes.NumberDataPointsBelowMean: { 853 var cur = Evaluate(dataset, ref row, state, traceDict); 854 cur = AggregateApply(cur, 855 s => 0, 856 v => { 857 double mean = Statistics.Mean(v); 858 return v.Map(e => e < mean ? 1 : 0).Sum(); 859 }); 860 TraceEvaluation(currentInstr, cur); 861 return cur; 862 } 863 case OpCodes.NumberDataPointsBelowMedian: { 864 var cur = Evaluate(dataset, ref row, state, traceDict); 865 cur = AggregateApply(cur, 866 s => 0, 867 v => { 868 double median = Statistics.Median(v); 869 return v.Map(e => e < median ? 1 : 0).Sum(); 870 }); 871 TraceEvaluation(currentInstr, cur); 872 return cur; 873 } 874 875 case OpCodes.ArimaModelCoefficients: { 876 var cur = Evaluate(dataset, ref row, state, traceDict); 877 var i = Evaluate(dataset, ref row, state, traceDict); 878 var k = Evaluate(dataset, ref row, state, traceDict); 879 cur = AggregateApply(cur, 880 s => 0, 881 v => throw new NotImplementedException("")); 882 TraceEvaluation(currentInstr, cur); 883 return cur; 884 } 885 case OpCodes.ContinuousWaveletTransformationCoefficients: { 886 var cur = Evaluate(dataset, ref row, state, traceDict); 887 var a = Evaluate(dataset, ref row, state, traceDict); 888 var b = Evaluate(dataset, ref row, state, traceDict); 889 cur = AggregateApply(cur, 890 s => 0, 891 v => throw new NotImplementedException("")); 892 TraceEvaluation(currentInstr, cur); 893 return cur; 894 } 895 case OpCodes.FastFourierTransformationCoefficient: { 896 var cur = Evaluate(dataset, ref row, state, traceDict); 897 var k = Evaluate(dataset, ref row, state, traceDict); 898 cur = AggregateApply(cur, 899 s => 0, 900 v => throw new NotImplementedException("")); 901 TraceEvaluation(currentInstr, cur); 902 return cur; 903 } 904 case OpCodes.FirstIndexMax: { 905 var cur = Evaluate(dataset, ref row, state, traceDict); 906 cur = AggregateApply(cur, 907 s => 0, 908 v => (double)v.MaximumIndex() / v.Count); 909 TraceEvaluation(currentInstr, cur); 910 return cur; 911 } 912 case OpCodes.FirstIndexMin: { 913 var cur = Evaluate(dataset, ref row, state, traceDict); 914 cur = AggregateApply(cur, 915 s => 0, 916 v => (double)v.MinimumIndex() / v.Count); 917 TraceEvaluation(currentInstr, cur); 918 return cur; 919 } 920 case OpCodes.LastIndexMax: { 921 var cur = Evaluate(dataset, ref row, state, traceDict); 922 cur = AggregateApply(cur, 923 s => 0, 924 v => (double)(v.Count - DoubleVector.Build.DenseOfEnumerable(v.Reverse()).MaximumIndex()) / v.Count); 925 926 TraceEvaluation(currentInstr, cur); 927 return cur; 928 } 929 case OpCodes.LastIndexMin: { 930 var cur = Evaluate(dataset, ref row, state, traceDict); 931 cur = AggregateApply(cur, 932 s => 0, 933 v => (double)(v.Count - DoubleVector.Build.DenseOfEnumerable(v.Reverse()).MinimumIndex()) / v.Count); 934 TraceEvaluation(currentInstr, cur); 935 return cur; 936 } 937 case OpCodes.LongestStrikeAboveMean: { 938 var cur = Evaluate(dataset, ref row, state, traceDict); 939 cur = AggregateApply(cur, 940 s => 0, 941 v => LongestStrikeAbove(v, Statistics.Mean(v))); 942 TraceEvaluation(currentInstr, cur); 943 return cur; 944 } 945 case OpCodes.LongestStrikeAboveMedian: { 946 var cur = Evaluate(dataset, ref row, state, traceDict); 947 cur = AggregateApply(cur, 948 s => 0, 949 v => LongestStrikeAbove(v, Statistics.Median(v))); 950 TraceEvaluation(currentInstr, cur); 951 return cur; 952 } 953 case OpCodes.LongestStrikeBelowMean: { 954 var cur = Evaluate(dataset, ref row, state, traceDict); 955 cur = AggregateApply(cur, 956 s => 0, 957 v => LongestStrikeBelow(v, Statistics.Mean(v))); 958 TraceEvaluation(currentInstr, cur); 959 return cur; 960 } 961 case OpCodes.LongestStrikeBelowMedian: { 962 var cur = Evaluate(dataset, ref row, state, traceDict); 963 cur = AggregateApply(cur, 964 s => 0, 965 v => LongestStrikeBelow(v, Statistics.Median(v))); 966 TraceEvaluation(currentInstr, cur); 967 return cur; 968 } 969 case OpCodes.LongestStrikePositive: { 970 var cur = Evaluate(dataset, ref row, state, traceDict); 971 cur = AggregateApply(cur, 972 s => 0, 973 v => LongestStrikeAbove(v, 0)); 974 TraceEvaluation(currentInstr, cur); 975 return cur; 976 } 977 case OpCodes.LongestStrikeNegative: { 978 var cur = Evaluate(dataset, ref row, state, traceDict); 979 cur = AggregateApply(cur, 980 s => 0, 981 v => LongestStrikeAbove(v, 0)); 982 TraceEvaluation(currentInstr, cur); 983 return cur; 984 } 985 case OpCodes.LongestStrikeZero: { 986 var cur = Evaluate(dataset, ref row, state, traceDict); 987 cur = AggregateApply(cur, 988 s => 0, 989 v => LongestStrikeEqual(v, 0)); 990 TraceEvaluation(currentInstr, cur); 991 return cur; 992 } 993 case OpCodes.MeanAbsoluteChange: { 994 var cur = Evaluate(dataset, ref row, state, traceDict); 995 cur = AggregateApply(cur, 996 s => 0, 997 v => { 998 double sum = 0.0; 999 for (int i = 0; i < v.Count - 1; i++) { 1000 sum += Math.Abs(v[i + 1] - v[i]); 1001 } 1002 1003 return sum / v.Count; 1004 }); 1005 TraceEvaluation(currentInstr, cur); 1006 return cur; 1007 } 1008 case OpCodes.MeanAbsoluteChangeQuantiles: { 1009 var cur = Evaluate(dataset, ref row, state, traceDict); 1010 var ql = Evaluate(dataset, ref row, state, traceDict); 1011 var qu = Evaluate(dataset, ref row, state, traceDict); 1012 cur = AggregateApply(cur, 1013 s => 0, 1014 v => { 1015 var lowerBound = Statistics.Quantile(v, ql.Scalar); 1016 var upperBound = Statistics.Quantile(v, qu.Scalar); 1017 var inBounds = v.Select(e => e > lowerBound && e < upperBound).ToList(); 1018 double sum = 0.0; 1019 int count = 0; 1020 for (int i = 0; i < v.Count - 1; i++) { 1021 if (inBounds[i] && inBounds[i + 1]) { 1022 sum += Math.Abs(v[i + 1] - v[i]); 1023 count++; 1024 } 1025 } 1026 1027 return sum / count; 1028 }); 1029 TraceEvaluation(currentInstr, cur); 1030 return cur; 1031 } 1032 case OpCodes.MeanAutocorrelation: { 1033 var cur = Evaluate(dataset, ref row, state, traceDict); 1034 cur = AggregateApply(cur, 1035 s => 0, 1036 v => { 1037 double sum = 0.0; 1038 double mean = Statistics.Mean(v); 1039 for (int l = 0; l < v.Count; l++) { 1040 for (int i = 0; i < v.Count - l; i++) { 1041 sum += (v[i] - mean) * (v[i + l] - mean); 1042 } 1043 } 1044 1045 return sum / (v.Count - 1) / Statistics.PopulationVariance(v); 1046 }); 1047 TraceEvaluation(currentInstr, cur); 1048 return cur; 1049 } 1050 case OpCodes.LaggedAutocorrelation: { 1051 var cur = Evaluate(dataset, ref row, state, traceDict); 1052 var lVal = Evaluate(dataset, ref row, state, traceDict); 1053 cur = AggregateApply(cur, 1054 s => 0, 1055 v => { 1056 double sum = 0.0; 1057 int l = (int)Math.Round(lVal.Scalar); 1058 double mean = Statistics.Mean(v); 1059 for (int i = 0; i < v.Count - l; i++) { 1060 sum += (v[i] - mean) * (v[i + l] - mean); 1061 } 1062 1063 return sum / Statistics.PopulationVariance(v); 1064 }); 1065 TraceEvaluation(currentInstr, cur); 1066 return cur; 1067 } 1068 case OpCodes.MeanSecondDerivateCentral: { 1069 var cur = Evaluate(dataset, ref row, state, traceDict); 1070 cur = AggregateApply(cur, 1071 s => 0, 1072 v => { 1073 double sum = 0.0; 1074 for (int i = 1; i < v.Count - 1; i++) { 1075 sum += (v[i - 1] - 2 * v[i] + v[i + 1]) / 2; 1076 } 1077 1078 return sum / (v.Count - 2); 1079 }); 1080 TraceEvaluation(currentInstr, cur); 1081 return cur; 1082 } 1083 case OpCodes.NumberPeaksOfSize: { 1084 var cur = Evaluate(dataset, ref row, state, traceDict); 1085 var l = Evaluate(dataset, ref row, state, traceDict); 1086 cur = AggregateApply(cur, 1087 s => 0, 1088 v => CountNumberOfPeaks(v, l.Scalar)); 1089 TraceEvaluation(currentInstr, cur); 1090 return cur; 1091 } 1092 case OpCodes.LargeNumberOfPeaks: { 1093 var cur = Evaluate(dataset, ref row, state, traceDict); 1094 var l = Evaluate(dataset, ref row, state, traceDict); 1095 var m = Evaluate(dataset, ref row, state, traceDict); 1096 cur = AggregateApply(cur, 1097 s => 0, 1098 v => CountNumberOfPeaks(v, l.Scalar) > m.Scalar ? 1.0 : 0.0); 1099 TraceEvaluation(currentInstr, cur); 1100 return cur; 1101 } 1102 case OpCodes.TimeReversalAsymmetryStatistic: { 1103 var cur = Evaluate(dataset, ref row, state, traceDict); 1104 var l = Evaluate(dataset, ref row, state, traceDict); 1105 cur = AggregateApply(cur, 1106 s => 0, 1107 v => { 1108 int lag = (int)Math.Round(l.Scalar); 1109 double sum = 0.0; 1110 for (int i = 0; i < v.Count - 2 * lag; i++) { 1111 sum += Math.Pow(v[i + 2 * lag], 2) * v[i + lag] - v[i + lag] * Math.Pow(v[i], 2); 1112 } 1113 1114 return sum / (v.Count - 2 * lag); 1115 }); 1116 TraceEvaluation(currentInstr, cur); 1117 return cur; 1118 } 1119 #endregion 1120 754 1121 default: 755 1122 throw new NotSupportedException($"Unsupported OpCode: {currentInstr.opCode}"); 756 1123 } 757 1124 } 1125 1126 private static int LongestStrikeAbove(DoubleVector v, double threshold) { 1127 int longestStrike = 0, currentStrike = 0; 1128 for (int i = 0; i < v.Count; i++) { 1129 if (v[i] > threshold) { 1130 currentStrike++; 1131 longestStrike = Math.Max(longestStrike, currentStrike); 1132 } else 1133 currentStrike = 0; 1134 } 1135 return longestStrike; 1136 } 1137 private static int LongestStrikeBelow(DoubleVector v, double threshold) { 1138 int longestStrike = 0, currentStrike = 0; 1139 for (int i = 0; i < v.Count; i++) { 1140 if (v[i] < threshold) { 1141 currentStrike++; 1142 longestStrike = Math.Max(longestStrike, currentStrike); 1143 } else 1144 currentStrike = 0; 1145 } 1146 return longestStrike; 1147 } 1148 1149 private static int LongestStrikeEqual(DoubleVector v, double value, double epsilon = double.Epsilon) { 1150 int longestStrike = 0, currentStrike = 0; 1151 for (int i = 0; i < v.Count; i++) { 1152 if (v[i].IsAlmost(epsilon)) { 1153 currentStrike++; 1154 longestStrike = Math.Max(longestStrike, currentStrike); 1155 } else 1156 currentStrike = 0; 1157 } 1158 return longestStrike; 1159 } 1160 private static int CountNumberOfPeaks(DoubleVector v, double heightDifference) { 1161 int count = 0; 1162 for (int i = 0; i < v.Count; i++) { 1163 bool largerThanPrev = i == 0 || v[i] > v[i - 1] + heightDifference; 1164 bool largerThanNext = i == v.Count - 1 || v[i] > v[i + 1] + heightDifference; 1165 if (largerThanPrev && largerThanNext) 1166 count++; 1167 } 1168 return count; 1169 } 758 1170 } 759 1171 }
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