source: branches/3040_VectorBasedGP/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeVectorInterpreter.cs @ 18058

#region License Information
/* HeuristicLab
 * Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;
using HEAL.Attic;
using MathNet.Numerics;
using MathNet.Numerics.Statistics;
using DoubleVector = MathNet.Numerics.LinearAlgebra.Vector<double>;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
  [StorableType("DE68A1D9-5AFC-4DDD-AB62-29F3B8FC28E0")]
  [Item("SymbolicDataAnalysisExpressionTreeVectorInterpreter", "Interpreter for symbolic expression trees including vector arithmetic.")]
  public class SymbolicDataAnalysisExpressionTreeVectorInterpreter : ParameterizedNamedItem, ISymbolicDataAnalysisExpressionTreeInterpreter {
    [StorableType("2612504E-AD5F-4AE2-B60E-98A5AB59E164")]
    public enum Aggregation {
      Mean,
      Median,
      Sum,
      First,
      L1Norm,
      L2Norm,
      NaN,
      Exception
    }
    public static double Aggregate(Aggregation aggregation, DoubleVector vector) {
      switch (aggregation) {
        case Aggregation.Mean: return Statistics.Mean(vector);
        case Aggregation.Median: return Statistics.Median(vector);
        case Aggregation.Sum: return vector.Sum();
        case Aggregation.First: return vector.First();
        case Aggregation.L1Norm: return vector.L1Norm();
        case Aggregation.L2Norm: return vector.L2Norm();
        case Aggregation.NaN: return double.NaN;
        case Aggregation.Exception: throw new InvalidOperationException("Result of the tree is not a scalar.");
        default: throw new ArgumentOutOfRangeException(nameof(aggregation), aggregation, null);
      }
    }

    [StorableType("73DCBB45-916F-4139-8ADC-57BA610A1B66")]
    public enum VectorLengthStrategy {
      ExceptionIfDifferent,
      FillShorterWithNaN,
      FillShorterWithNeutralElement,
      CutLonger,
      ResampleToLonger,
      ResampleToShorter,
      CycleShorter
    }

    #region Implementation VectorLengthStrategy
    public static (DoubleVector, DoubleVector) ExceptionIfDifferent(DoubleVector lhs, DoubleVector rhs) {
      if (lhs.Count != rhs.Count)
        throw new InvalidOperationException($"Vector Lengths incompatible ({lhs.Count} vs. {rhs.Count}");
      return (lhs, rhs);
    }

    public static (DoubleVector, DoubleVector) FillShorter(DoubleVector lhs, DoubleVector rhs, double fillElement) {
      var targetLength = Math.Max(lhs.Count, rhs.Count);

      DoubleVector PadVector(DoubleVector v) {
        if (v.Count == targetLength) return v;
        var p = DoubleVector.Build.Dense(targetLength, fillElement);
        v.CopySubVectorTo(p, 0, 0, v.Count);
        return p;
      }

      return (PadVector(lhs), PadVector(rhs));
    }

    public static (DoubleVector, DoubleVector) CutLonger(DoubleVector lhs, DoubleVector rhs) {
      var targetLength = Math.Min(lhs.Count, rhs.Count);

      DoubleVector CutVector(DoubleVector v) {
        if (v.Count == targetLength) return v;
        return v.SubVector(0, targetLength);
      }

      return (CutVector(lhs), CutVector(rhs));
    }

    private static DoubleVector ResampleToLength(DoubleVector v, int targetLength) {
      if (v.Count == targetLength) return v;

      var indices = Enumerable.Range(0, v.Count).Select(x => (double)x);
      var interpolation = Interpolate.Linear(indices, v);

      var resampledIndices = Enumerable.Range(0, targetLength).Select(i => (double)i / targetLength * v.Count);
      var interpolatedValues = resampledIndices.Select(interpolation.Interpolate);

      return DoubleVector.Build.DenseOfEnumerable(interpolatedValues);
    }
    public static (DoubleVector, DoubleVector) ResampleToLonger(DoubleVector lhs, DoubleVector rhs) {
      var maxLength = Math.Max(lhs.Count, rhs.Count);
      return (ResampleToLength(lhs, maxLength), ResampleToLength(rhs, maxLength));
    }
    public static (DoubleVector, DoubleVector) ResampleToShorter(DoubleVector lhs, DoubleVector rhs) {
      var minLength = Math.Min(lhs.Count, rhs.Count);
      return (ResampleToLength(lhs, minLength), ResampleToLength(rhs, minLength));
    }

    public static (DoubleVector, DoubleVector) CycleShorter(DoubleVector lhs, DoubleVector rhs) {
      var targetLength = Math.Max(lhs.Count, rhs.Count);

      DoubleVector CycleVector(DoubleVector v) {
        if (v.Count == targetLength) return v;
        var cycledValues = Enumerable.Range(0, targetLength).Select(i => v[i % v.Count]);
        return DoubleVector.Build.DenseOfEnumerable(cycledValues);
      }

      return (CycleVector(lhs), CycleVector(rhs));
    }
    #endregion

    public static (DoubleVector lhs, DoubleVector rhs) ApplyVectorLengthStrategy(VectorLengthStrategy strategy, DoubleVector lhs, DoubleVector rhs,
      double neutralElement = double.NaN) {

      switch (strategy) {
        case VectorLengthStrategy.ExceptionIfDifferent: return ExceptionIfDifferent(lhs, rhs);
        case VectorLengthStrategy.FillShorterWithNaN: return FillShorter(lhs, rhs, double.NaN);
        case VectorLengthStrategy.FillShorterWithNeutralElement: return FillShorter(lhs, rhs, neutralElement);
        case VectorLengthStrategy.CutLonger: return CutLonger(lhs, rhs);
        case VectorLengthStrategy.ResampleToLonger: return ResampleToLonger(lhs, rhs);
        case VectorLengthStrategy.ResampleToShorter: return ResampleToShorter(lhs, rhs);
        case VectorLengthStrategy.CycleShorter: return CycleShorter(lhs, rhs);
        default: throw new ArgumentOutOfRangeException(nameof(strategy), strategy, null);
      }
    }

    #region Aggregation Symbols
    private static Type[] AggregationSymbols = new[] {
      typeof(Sum), typeof(Mean), typeof(Length), typeof(StandardDeviation), typeof(Variance),
      typeof(EuclideanDistance), typeof(Covariance)
    };
    #endregion

    private const string EvaluatedSolutionsParameterName = "EvaluatedSolutions";
    private const string FinalAggregationParameterName = "FinalAggregation";
    private const string DifferentVectorLengthStrategyParameterName = "DifferentVectorLengthStrategy";

    public override bool CanChangeName {
      get { return false; }
    }

    public override bool CanChangeDescription {
      get { return false; }
    }

    #region parameter properties
    public IFixedValueParameter<IntValue> EvaluatedSolutionsParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters[EvaluatedSolutionsParameterName]; }
    }
    public IFixedValueParameter<EnumValue<Aggregation>> FinalAggregationParameter {
      get { return (IFixedValueParameter<EnumValue<Aggregation>>)Parameters[FinalAggregationParameterName]; }
    }
    public IFixedValueParameter<EnumValue<VectorLengthStrategy>> DifferentVectorLengthStrategyParameter {
      get { return (IFixedValueParameter<EnumValue<VectorLengthStrategy>>)Parameters[DifferentVectorLengthStrategyParameterName]; }
    }
    #endregion

    #region properties
    public int EvaluatedSolutions {
      get { return EvaluatedSolutionsParameter.Value.Value; }
      set { EvaluatedSolutionsParameter.Value.Value = value; }
    }
    public Aggregation FinalAggregation {
      get { return FinalAggregationParameter.Value.Value; }
      set { FinalAggregationParameter.Value.Value = value; }
    }
    public VectorLengthStrategy DifferentVectorLengthStrategy {
      get { return DifferentVectorLengthStrategyParameter.Value.Value; }
      set { DifferentVectorLengthStrategyParameter.Value.Value = value; }
    }
    #endregion

    [StorableConstructor]
    protected SymbolicDataAnalysisExpressionTreeVectorInterpreter(StorableConstructorFlag _) : base(_) { }

    protected SymbolicDataAnalysisExpressionTreeVectorInterpreter(SymbolicDataAnalysisExpressionTreeVectorInterpreter original, Cloner cloner)
      : base(original, cloner) { }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new SymbolicDataAnalysisExpressionTreeVectorInterpreter(this, cloner);
    }

    public SymbolicDataAnalysisExpressionTreeVectorInterpreter()
      : this("SymbolicDataAnalysisExpressionTreeVectorInterpreter", "Interpreter for symbolic expression trees including vector arithmetic.") { }

    protected SymbolicDataAnalysisExpressionTreeVectorInterpreter(string name, string description)
      : base(name, description) {
      Parameters.Add(new FixedValueParameter<IntValue>(EvaluatedSolutionsParameterName, "A counter for the total number of solutions the interpreter has evaluated", new IntValue(0)));
      Parameters.Add(new FixedValueParameter<EnumValue<Aggregation>>(FinalAggregationParameterName, "If root node of the expression tree results in a Vector it is aggregated according to this parameter", new EnumValue<Aggregation>(Aggregation.Mean)));
      Parameters.Add(new FixedValueParameter<EnumValue<VectorLengthStrategy>>(DifferentVectorLengthStrategyParameterName, "", new EnumValue<VectorLengthStrategy>(VectorLengthStrategy.ExceptionIfDifferent)));
    }

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() {
      if (!Parameters.ContainsKey(FinalAggregationParameterName)) {
        Parameters.Add(new FixedValueParameter<EnumValue<Aggregation>>(FinalAggregationParameterName, "If root node of the expression tree results in a Vector it is aggregated according to this parameter", new EnumValue<Aggregation>(Aggregation.Mean)));
      }
      if (!Parameters.ContainsKey(DifferentVectorLengthStrategyParameterName)) {
        Parameters.Add(new FixedValueParameter<EnumValue<VectorLengthStrategy>>(DifferentVectorLengthStrategyParameterName, "", new EnumValue<VectorLengthStrategy>(VectorLengthStrategy.ExceptionIfDifferent)));
      }
    }

    #region IStatefulItem
    public void InitializeState() {
      EvaluatedSolutions = 0;
    }

    public void ClearState() { }
    #endregion

    private readonly object syncRoot = new object();
    public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows) {
      lock (syncRoot) {
        EvaluatedSolutions++; // increment the evaluated solutions counter
      }
      var state = PrepareInterpreterState(tree, dataset);

      foreach (var rowEnum in rows) {
        int row = rowEnum;
        var result = Evaluate(dataset, ref row, state);
        if (result.IsScalar)
          yield return result.Scalar;
        else if (result.IsVector) {
          yield return Aggregate(FinalAggregation, result.Vector);
        } else
          yield return double.NaN;
        state.Reset();
      }
    }

    public IEnumerable<Dictionary<ISymbolicExpressionTreeNode, EvaluationResult>> GetIntermediateNodeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows) {
      var state = PrepareInterpreterState(tree, dataset);

      foreach (var rowEnum in rows) {
        int row = rowEnum;
        var traceDict = new Dictionary<ISymbolicExpressionTreeNode, EvaluationResult>();
        var result = Evaluate(dataset, ref row, state, traceDict);
        traceDict.Add(tree.Root.GetSubtree(0), result); // Add StartSymbol
        yield return traceDict;
        state.Reset();
      }
    }

    private static InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, IDataset dataset) {
      Instruction[] code = SymbolicExpressionTreeCompiler.Compile(tree, OpCodes.MapSymbolToOpCode);
      int necessaryArgStackSize = 0;
      foreach (Instruction instr in code) {
        if (instr.opCode == OpCodes.Variable) {
          var variableTreeNode = (VariableTreeNode)instr.dynamicNode;
          if (dataset.VariableHasType<double>(variableTreeNode.VariableName))
            instr.data = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
          else if (dataset.VariableHasType<DoubleVector>(variableTreeNode.VariableName))
            instr.data = dataset.GetReadOnlyDoubleVectorValues(variableTreeNode.VariableName);
          else throw new NotSupportedException($"Type of variable {variableTreeNode.VariableName} is not supported.");
        } else if (instr.opCode == OpCodes.FactorVariable) {
          var factorTreeNode = instr.dynamicNode as FactorVariableTreeNode;
          instr.data = dataset.GetReadOnlyStringValues(factorTreeNode.VariableName);
        } else if (instr.opCode == OpCodes.BinaryFactorVariable) {
          var factorTreeNode = instr.dynamicNode as BinaryFactorVariableTreeNode;
          instr.data = dataset.GetReadOnlyStringValues(factorTreeNode.VariableName);
        } else if (instr.opCode == OpCodes.LagVariable) {
          var laggedVariableTreeNode = (LaggedVariableTreeNode)instr.dynamicNode;
          instr.data = dataset.GetReadOnlyDoubleValues(laggedVariableTreeNode.VariableName);
        } else if (instr.opCode == OpCodes.VariableCondition) {
          var variableConditionTreeNode = (VariableConditionTreeNode)instr.dynamicNode;
          instr.data = dataset.GetReadOnlyDoubleValues(variableConditionTreeNode.VariableName);
        } else if (instr.opCode == OpCodes.Call) {
          necessaryArgStackSize += instr.nArguments + 1;
        }
      }
      return new InterpreterState(code, necessaryArgStackSize);
    }


    public struct EvaluationResult {
      public double Scalar { get; }
      public bool IsScalar => !double.IsNaN(Scalar);

      public DoubleVector Vector { get; }
      public bool IsVector => !(Vector.Count == 1 && double.IsNaN(Vector[0]));

      public bool IsNaN => !IsScalar && !IsVector;

      public EvaluationResult(double scalar) {
        Scalar = scalar;
        Vector = NaNVector;
      }
      public EvaluationResult(DoubleVector vector) {
        if (vector == null) throw new ArgumentNullException(nameof(vector));
        Vector = vector;
        Scalar = double.NaN;
      }

      public override string ToString() {
        if (IsScalar) return Scalar.ToString();
        if (IsVector) return Vector.ToVectorString();
        return "NaN";
      }

      private static readonly DoubleVector NaNVector = DoubleVector.Build.Dense(1, double.NaN);
      public static readonly EvaluationResult NaN = new EvaluationResult(double.NaN);
    }

    private static EvaluationResult ArithmeticApply(EvaluationResult lhs, EvaluationResult rhs,
      Func<DoubleVector, DoubleVector, (DoubleVector, DoubleVector)> lengthStrategy,
      Func<double, double, double> ssFunc = null,
      Func<double, DoubleVector, DoubleVector> svFunc = null,
      Func<DoubleVector, double, DoubleVector> vsFunc = null,
      Func<DoubleVector, DoubleVector, DoubleVector> vvFunc = null) {

      if (lhs.IsScalar && rhs.IsScalar && ssFunc != null) return new EvaluationResult(ssFunc(lhs.Scalar, rhs.Scalar));
      if (lhs.IsScalar && rhs.IsVector && svFunc != null) return new EvaluationResult(svFunc(lhs.Scalar, rhs.Vector));
      if (lhs.IsVector && rhs.IsScalar && vsFunc != null) return new EvaluationResult(vsFunc(lhs.Vector, rhs.Scalar));
      if (lhs.IsVector && rhs.IsVector && vvFunc != null) {
        if (lhs.Vector.Count == rhs.Vector.Count) {
          return new EvaluationResult(vvFunc(lhs.Vector, rhs.Vector));
        } else {
          var (lhsVector, rhsVector) = lengthStrategy(lhs.Vector, rhs.Vector);
          return new EvaluationResult(vvFunc(lhsVector, rhsVector));
        }
      }
      return EvaluationResult.NaN;
    }

    private static EvaluationResult FunctionApply(EvaluationResult val,
      Func<double, double> sFunc = null,
      Func<DoubleVector, DoubleVector> vFunc = null) {
      if (val.IsScalar && sFunc != null) return new EvaluationResult(sFunc(val.Scalar));
      if (val.IsVector && vFunc != null) return new EvaluationResult(vFunc(val.Vector));
      return EvaluationResult.NaN;
    }
    private static EvaluationResult AggregateApply(EvaluationResult val,
      Func<double, double> sFunc = null,
      Func<DoubleVector, double> vFunc = null) {
      if (val.IsScalar && sFunc != null) return new EvaluationResult(sFunc(val.Scalar));
      if (val.IsVector && vFunc != null) return new EvaluationResult(vFunc(val.Vector));
      return EvaluationResult.NaN;
    }

    private static EvaluationResult WindowedAggregateApply(EvaluationResult val, WindowedSymbolTreeNode node,
      Func<double, double> sFunc = null,
      Func<DoubleVector, double> vFunc = null) {

      // Parameters are interpreted as start and end with wrapping
      var start = node.Offset;
      var end = node.Length;

      DoubleVector SubVector(DoubleVector v) {
        int startIdx = (int)Math.Round(start * v.Count);
        int endIdx = (int)Math.Round(end * v.Count);
        int size = v.Count;
        if (startIdx < endIdx) {
          return v.SubVector(startIdx, count: endIdx - startIdx);
        } else { // wrap around
          var resultVector = DoubleVector.Build.Dense(size: size - (startIdx - endIdx));
          v.CopySubVectorTo(resultVector, startIdx, 0, size - startIdx); // copy [startIdx:size] to [0:size-startIdx]
          v.CopySubVectorTo(resultVector, 0, size - startIdx, endIdx);   // copy [0:endIdx]      to [size-startIdx:size]
          return resultVector;
        }
      }

      if (val.IsScalar && sFunc != null) return new EvaluationResult(sFunc(val.Scalar));
      if (val.IsVector && vFunc != null) return new EvaluationResult(vFunc(SubVector(val.Vector)));
      return EvaluationResult.NaN;
    }
    private static EvaluationResult WindowedFunctionApply(EvaluationResult val, IWindowedSymbolTreeNode node,
      Func<double, double> sFunc = null,
      Func<DoubleVector, DoubleVector> vFunc = null) {
      // Parameters are interpreted as start and end with wrapping
      var start = node.Offset;
      var end = node.Length;

      DoubleVector SubVector(DoubleVector v) {
        int startIdx = (int)Math.Round(start * v.Count);
        int endIdx = (int)Math.Round(end * v.Count);
        int size = v.Count;
        if (startIdx < endIdx) {
          return v.SubVector(startIdx, count: endIdx - startIdx);
        } else { // wrap around
          var resultVector = DoubleVector.Build.Dense(size: size - (startIdx - endIdx));
          v.CopySubVectorTo(resultVector, startIdx, 0, size - startIdx); // copy [startIdx:size] to [0:size-startIdx]
          v.CopySubVectorTo(resultVector, 0, size - startIdx, endIdx);   // copy [0:endIdx]      to [size-startIdx:size]
          return resultVector;
        }
      }

      if (val.IsScalar && sFunc != null) return new EvaluationResult(sFunc(val.Scalar));
      if (val.IsVector && vFunc != null) return new EvaluationResult(vFunc(SubVector(val.Vector)));
      return EvaluationResult.NaN;
    }

    private static EvaluationResult AggregateMultipleApply(EvaluationResult lhs, EvaluationResult rhs,
      Func<DoubleVector, DoubleVector, (DoubleVector, DoubleVector)> lengthStrategy,
      Func<double, double, double> ssFunc = null,
      Func<double, DoubleVector, double> svFunc = null,
      Func<DoubleVector, double, double> vsFunc = null,
      Func<DoubleVector, DoubleVector, double> vvFunc = null) {
      if (lhs.IsScalar && rhs.IsScalar && ssFunc != null) return new EvaluationResult(ssFunc(lhs.Scalar, rhs.Scalar));
      if (lhs.IsScalar && rhs.IsVector && svFunc != null) return new EvaluationResult(svFunc(lhs.Scalar, rhs.Vector));
      if (lhs.IsVector && rhs.IsScalar && vsFunc != null) return new EvaluationResult(vsFunc(lhs.Vector, rhs.Scalar));
      if (lhs.IsVector && rhs.IsVector && vvFunc != null) {
        if (lhs.Vector.Count == rhs.Vector.Count) {
          return new EvaluationResult(vvFunc(lhs.Vector, rhs.Vector));
        } else {
          var (lhsVector, rhsVector) = lengthStrategy(lhs.Vector, rhs.Vector);
          return new EvaluationResult(vvFunc(lhsVector, rhsVector));
        }
      }
      return EvaluationResult.NaN;
    }

    public virtual Type GetNodeType(ISymbolicExpressionTreeNode node) {
      if (node.DataType != null)
        return node.DataType;

      if (AggregationSymbols.Contains(node.Symbol.GetType()))
        return typeof(double);

      var argumentTypes = node.Subtrees.Select(GetNodeType);
      if (argumentTypes.Any(t => t == typeof(DoubleVector)))
        return typeof(DoubleVector);

      return typeof(double);
    }


    public virtual EvaluationResult Evaluate(IDataset dataset, ref int row, InterpreterState state,
      IDictionary<ISymbolicExpressionTreeNode, EvaluationResult> traceDict = null) {

      void TraceEvaluation(Instruction instr, EvaluationResult result) {
        traceDict?.Add(instr.dynamicNode, result);
      }

      Instruction currentInstr = state.NextInstruction();
      switch (currentInstr.opCode) {
        case OpCodes.Add: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            for (int i = 1; i < currentInstr.nArguments; i++) {
              var op = Evaluate(dataset, ref row, state, traceDict);
              cur = ArithmeticApply(cur, op,
                (lhs, rhs) => ApplyVectorLengthStrategy(DifferentVectorLengthStrategy, lhs, rhs, 0.0),
                (s1, s2) => s1 + s2,
                (s1, v2) => s1 + v2,
                (v1, s2) => v1 + s2,
                (v1, v2) => v1 + v2);
            }
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Sub: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            for (int i = 1; i < currentInstr.nArguments; i++) {
              var op = Evaluate(dataset, ref row, state, traceDict);
              cur = ArithmeticApply(cur, op,
                (lhs, rhs) => ApplyVectorLengthStrategy(DifferentVectorLengthStrategy, lhs, rhs, 0.0),
                (s1, s2) => s1 - s2,
                (s1, v2) => s1 - v2,
                (v1, s2) => v1 - s2,
                (v1, v2) => v1 - v2);
            }
            if (currentInstr.nArguments == 1)
              cur = FunctionApply(cur,
                s => -s,
                v => -v);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Mul: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            for (int i = 1; i < currentInstr.nArguments; i++) {
              var op = Evaluate(dataset, ref row, state, traceDict);
              cur = ArithmeticApply(cur, op,
                (lhs, rhs) => ApplyVectorLengthStrategy(DifferentVectorLengthStrategy, lhs, rhs, 1.0),
                (s1, s2) => s1 * s2,
                (s1, v2) => s1 * v2,
                (v1, s2) => v1 * s2,
                (v1, v2) => v1.PointwiseMultiply(v2));
            }
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Div: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            for (int i = 1; i < currentInstr.nArguments; i++) {
              var op = Evaluate(dataset, ref row, state, traceDict);
              cur = ArithmeticApply(cur, op,
                (lhs, rhs) => ApplyVectorLengthStrategy(DifferentVectorLengthStrategy, lhs, rhs, 1.0),
                (s1, s2) => s1 / s2,
                (s1, v2) => s1 / v2,
                (v1, s2) => v1 / s2,
                (v1, v2) => v1 / v2);
            }
            if (currentInstr.nArguments == 1)
              cur = FunctionApply(cur,
                s => 1 / s,
                v => 1 / v);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Absolute: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur, Math.Abs, DoubleVector.Abs);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Tanh: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur, Math.Tanh, DoubleVector.Tanh);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Cos: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur, Math.Cos, DoubleVector.Cos);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Sin: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur, Math.Sin, DoubleVector.Sin);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Tan: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur, Math.Tan, DoubleVector.Tan);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Square: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur,
              s => Math.Pow(s, 2),
              v => v.PointwisePower(2));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Cube: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur,
              s => Math.Pow(s, 3),
              v => v.PointwisePower(3));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Power: {
            var x = Evaluate(dataset, ref row, state, traceDict);
            var y = Evaluate(dataset, ref row, state, traceDict);
            var cur = ArithmeticApply(x, y,
              (lhs, rhs) => lhs.Count < rhs.Count
                ? CutLonger(lhs, rhs)
                : ApplyVectorLengthStrategy(DifferentVectorLengthStrategy, lhs, rhs, 1.0),
              (s1, s2) => Math.Pow(s1, Math.Round(s2)),
              (s1, v2) => DoubleVector.Build.Dense(v2.Count, s1).PointwisePower(DoubleVector.Round(v2)),
              (v1, s2) => v1.PointwisePower(Math.Round(s2)),
              (v1, v2) => v1.PointwisePower(DoubleVector.Round(v2)));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.SquareRoot: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur,
              s => Math.Sqrt(s),
              v => DoubleVector.Sqrt(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.CubeRoot: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur,
              s => s < 0 ? -Math.Pow(-s, 1.0 / 3.0) : Math.Pow(s, 1.0 / 3.0),
              v => v.Map(s => s < 0 ? -Math.Pow(-s, 1.0 / 3.0) : Math.Pow(s, 1.0 / 3.0)));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Root: {
            var x = Evaluate(dataset, ref row, state, traceDict);
            var y = Evaluate(dataset, ref row, state, traceDict);
            var cur = ArithmeticApply(x, y,
              (lhs, rhs) => lhs.Count < rhs.Count
                ? CutLonger(lhs, rhs)
                : ApplyVectorLengthStrategy(DifferentVectorLengthStrategy, lhs, rhs, 1.0),
              (s1, s2) => Math.Pow(s1, 1.0 / Math.Round(s2)),
              (s1, v2) => DoubleVector.Build.Dense(v2.Count, s1).PointwisePower(1.0 / DoubleVector.Round(v2)),
              (v1, s2) => v1.PointwisePower(1.0 / Math.Round(s2)),
              (v1, v2) => v1.PointwisePower(1.0 / DoubleVector.Round(v2)));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Exp: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur,
              s => Math.Exp(s),
              v => DoubleVector.Exp(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Log: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = FunctionApply(cur,
              s => Math.Log(s),
              v => DoubleVector.Log(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Sum: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => s,
              v => v.Sum());
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Mean: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => s,
              v => Statistics.Mean(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.StandardDeviation: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => Statistics.PopulationStandardDeviation(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Length: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 1,
              v => v.Count);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Min: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => s,
              v => Statistics.Minimum(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Max: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => s,
              v => Statistics.Maximum(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Variance: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => Statistics.PopulationVariance(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Skewness: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => double.NaN,
              v => Statistics.PopulationSkewness(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Kurtosis: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => double.NaN,
              v => Statistics.PopulationKurtosis(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.EuclideanDistance: {
            var x1 = Evaluate(dataset, ref row, state, traceDict);
            var x2 = Evaluate(dataset, ref row, state, traceDict);
            var cur = AggregateMultipleApply(x1, x2,
              (lhs, rhs) => ApplyVectorLengthStrategy(DifferentVectorLengthStrategy, lhs, rhs, 0.0),
              (s1, s2) => s1 - s2,
              (s1, v2) => Math.Sqrt((s1 - v2).PointwisePower(2).Sum()),
              (v1, s2) => Math.Sqrt((v1 - s2).PointwisePower(2).Sum()),
              (v1, v2) => Math.Sqrt((v1 - v2).PointwisePower(2).Sum()));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Covariance: {
            var x1 = Evaluate(dataset, ref row, state, traceDict);
            var x2 = Evaluate(dataset, ref row, state, traceDict);
            var cur = AggregateMultipleApply(x1, x2,
              (lhs, rhs) => ApplyVectorLengthStrategy(DifferentVectorLengthStrategy, lhs, rhs, 0.0),
              (s1, s2) => 0,
              (s1, v2) => 0,
              (v1, s2) => 0,
              (v1, v2) => Statistics.PopulationCovariance(v1, v2));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.SubVector: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            return WindowedFunctionApply(cur, (WindowedSymbolTreeNode)currentInstr.dynamicNode,
              s => s,
              v => v);
          }
        case OpCodes.Variable: {
            if (row < 0 || row >= dataset.Rows) return EvaluationResult.NaN;
            var variableTreeNode = (VariableTreeNode)currentInstr.dynamicNode;
            if (currentInstr.data is IList<double> doubleList) {
              var cur = new EvaluationResult(doubleList[row] * variableTreeNode.Weight);
              TraceEvaluation(currentInstr, cur);
              return cur;
            }
            if (currentInstr.data is IList<DoubleVector> doubleVectorList) {
              var cur = new EvaluationResult(doubleVectorList[row] * variableTreeNode.Weight);
              TraceEvaluation(currentInstr, cur);
              return cur;
            }
            throw new NotSupportedException($"Unsupported type of variable: {currentInstr.data.GetType().GetPrettyName()}");
          }
        case OpCodes.BinaryFactorVariable: {
            if (row < 0 || row >= dataset.Rows) return EvaluationResult.NaN;
            var factorVarTreeNode = currentInstr.dynamicNode as BinaryFactorVariableTreeNode;
            var cur = new EvaluationResult(((IList<string>)currentInstr.data)[row] == factorVarTreeNode.VariableValue ? factorVarTreeNode.Weight : 0);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.FactorVariable: {
            if (row < 0 || row >= dataset.Rows) return EvaluationResult.NaN;
            var factorVarTreeNode = currentInstr.dynamicNode as FactorVariableTreeNode;
            var cur = new EvaluationResult(factorVarTreeNode.GetValue(((IList<string>)currentInstr.data)[row]));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Constant: {
            var constTreeNode = (ConstantTreeNode)currentInstr.dynamicNode;
            var cur = new EvaluationResult(constTreeNode.Value);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }

        #region Time Series Symbols
        case OpCodes.Median: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => s,
              v => Statistics.Median(v));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.Quantile: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var q = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => s,
              v => Statistics.Quantile(v, q.Scalar));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }

        case OpCodes.AbsoluteEnergy: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => s * s,
              v => v.PointwisePower(2.0).Sum());
            TraceEvaluation(currentInstr, cur);
            return cur;
          }

        case OpCodes.BinnedEntropy: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var m = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                int bins = Math.Max((int)Math.Round(m.Scalar), 1);
                double minValue = v.Minimum();
                double maxValue = v.Maximum();
                double intervalWidth = (maxValue - minValue) / bins;
                int totalValues = v.Count;
                double sum = 0;
                for (int i = 0; i < Math.Max(bins, v.Count); i++) {
                  double binMin = minValue * i;
                  double binMax = binMin + intervalWidth;
                  double countBin = v.Map(e => (e > binMin && e < binMax) ? 1.0 : 0.0).Sum();
                  double percBin = countBin / totalValues;
                  sum += percBin * Math.Log(percBin);
                }

                return sum;
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.HasLargeStandardDeviation: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => Statistics.PopulationStandardDeviation(v) > (Statistics.Maximum(v) - Statistics.Minimum(v)) / 2 ? 1.0 : 0.0);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.HasVarianceLargerThanStd: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => Statistics.PopulationVariance(v) > Statistics.StandardDeviation(v) ? 1.0 : 0.0);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.IsSymmetricLooking: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => Math.Abs(Statistics.Mean(v) - Statistics.Median(v)) < (Statistics.Maximum(v) - Statistics.Minimum(v)) / 2 ? 1.0 : 0.0);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.NumberDataPointsAboveMean: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                double mean = Statistics.Mean(v);
                return v.Map(e => e > mean ? 1.0 : 0.0).Sum();
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.NumberDataPointsAboveMedian: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                double median = Statistics.Median(v);
                return v.Map(e => e > median ? 1.0 : 0.0).Sum();
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.NumberDataPointsBelowMean: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                double mean = Statistics.Mean(v);
                return v.Map(e => e < mean ? 1.0 : 0.0).Sum();
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.NumberDataPointsBelowMedian: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                double median = Statistics.Median(v);
                return v.Map(e => e < median ? 1.0 : 0.0).Sum();
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }

        case OpCodes.ArimaModelCoefficients: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var i = Evaluate(dataset, ref row, state, traceDict);
            var k = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => throw new NotImplementedException(""));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.ContinuousWaveletTransformationCoefficients: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var a = Evaluate(dataset, ref row, state, traceDict);
            var b = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => throw new NotImplementedException(""));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.FastFourierTransformationCoefficient: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var k = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => throw new NotImplementedException(""));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.FirstIndexMax: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => (double)v.MaximumIndex() / v.Count);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.FirstIndexMin: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => (double)v.MinimumIndex() / v.Count);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LastIndexMax: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => (double)(v.Count - DoubleVector.Build.DenseOfEnumerable(v.Reverse()).MaximumIndex()) / v.Count);

            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LastIndexMin: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => (double)(v.Count - DoubleVector.Build.DenseOfEnumerable(v.Reverse()).MinimumIndex()) / v.Count);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LongestStrikeAboveMean: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => LongestStrikeAbove(v, Statistics.Mean(v)));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LongestStrikeAboveMedian: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => LongestStrikeAbove(v, Statistics.Median(v)));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LongestStrikeBelowMean: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => LongestStrikeBelow(v, Statistics.Mean(v)));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LongestStrikeBelowMedian: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => LongestStrikeBelow(v, Statistics.Median(v)));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LongestStrikePositive: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => LongestStrikeAbove(v, 0));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LongestStrikeNegative: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => LongestStrikeAbove(v, 0));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LongestStrikeZero: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => LongestStrikeEqual(v, 0));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.MeanAbsoluteChange: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                double sum = 0.0;
                for (int i = 0; i < v.Count - 1; i++) {
                  sum += Math.Abs(v[i + 1] - v[i]);
                }

                return sum / v.Count;
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.MeanAbsoluteChangeQuantiles: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var ql = Evaluate(dataset, ref row, state, traceDict);
            var qu = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                var lowerBound = Statistics.Quantile(v, ql.Scalar);
                var upperBound = Statistics.Quantile(v, qu.Scalar);
                var inBounds = v.Select(e => e > lowerBound && e < upperBound).ToList();
                double sum = 0.0;
                int count = 0;
                for (int i = 0; i < v.Count - 1; i++) {
                  if (inBounds[i] && inBounds[i + 1]) {
                    sum += Math.Abs(v[i + 1] - v[i]);
                    count++;
                  }
                }

                return sum / count;
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.MeanAutocorrelation: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                double sum = 0.0;
                double mean = Statistics.Mean(v);
                for (int l = 0; l < v.Count; l++) {
                  for (int i = 0; i < v.Count - l; i++) {
                    sum += (v[i] - mean) * (v[i + l] - mean);
                  }
                }

                return sum / (v.Count - 1) / Statistics.PopulationVariance(v);
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LaggedAutocorrelation: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var lVal = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                double sum = 0.0;
                int l = Math.Max((int)Math.Round(lVal.Scalar), 0);
                double mean = Statistics.Mean(v);
                for (int i = 0; i < v.Count - l; i++) {
                  sum += (v[i] - mean) * (v[i + l] - mean);
                }

                return sum / Statistics.PopulationVariance(v);
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.MeanSecondDerivateCentral: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                double sum = 0.0;
                for (int i = 1; i < v.Count - 1; i++) {
                  sum += (v[i - 1] - 2 * v[i] + v[i + 1]) / 2;
                }

                return sum / (v.Count - 2);
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.NumberPeaksOfSize: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var l = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => CountNumberOfPeaks(v, l.Scalar));
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.LargeNumberOfPeaks: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var l = Evaluate(dataset, ref row, state, traceDict);
            var m = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => CountNumberOfPeaks(v, l.Scalar) > m.Scalar ? 1.0 : 0.0);
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        case OpCodes.TimeReversalAsymmetryStatistic: {
            var cur = Evaluate(dataset, ref row, state, traceDict);
            var l = Evaluate(dataset, ref row, state, traceDict);
            cur = AggregateApply(cur,
              s => 0,
              v => {
                int lag = Math.Max((int)Math.Round(l.Scalar), 0);
                double sum = 0.0;
                for (int i = 0; i < v.Count - 2 * lag; i++) {
                  sum += Math.Pow(v[i + 2 * lag], 2) * v[i + lag] - v[i + lag] * Math.Pow(v[i], 2);
                }

                return sum / (v.Count - 2 * lag);
              });
            TraceEvaluation(currentInstr, cur);
            return cur;
          }
        #endregion

        default:
          throw new NotSupportedException($"Unsupported OpCode: {currentInstr.opCode}");
      }
    }

    private static int LongestStrikeAbove(DoubleVector v, double threshold) {
      int longestStrike = 0, currentStrike = 0;
      for (int i = 0; i < v.Count; i++) {
        if (v[i] > threshold) {
          currentStrike++;
          longestStrike = Math.Max(longestStrike, currentStrike);
        } else
          currentStrike = 0;
      }
      return longestStrike;
    }
    private static int LongestStrikeBelow(DoubleVector v, double threshold) {
      int longestStrike = 0, currentStrike = 0;
      for (int i = 0; i < v.Count; i++) {
        if (v[i] < threshold) {
          currentStrike++;
          longestStrike = Math.Max(longestStrike, currentStrike);
        } else
          currentStrike = 0;
      }
      return longestStrike;
    }

    private static int LongestStrikeEqual(DoubleVector v, double value, double epsilon = double.Epsilon) {
      int longestStrike = 0, currentStrike = 0;
      for (int i = 0; i < v.Count; i++) {
        if (v[i].IsAlmost(epsilon)) {
          currentStrike++;
          longestStrike = Math.Max(longestStrike, currentStrike);
        } else
          currentStrike = 0;
      }
      return longestStrike;
    }
    private static int CountNumberOfPeaks(DoubleVector v, double heightDifference) {
      int count = 0;
      for (int i = 0; i < v.Count; i++) {
        bool largerThanPrev = i == 0 || v[i] > v[i - 1] + heightDifference;
        bool largerThanNext = i == v.Count - 1 || v[i] > v[i + 1] + heightDifference;
        if (largerThanPrev && largerThanNext)
          count++;
      }
      return count;
    }
  }
}