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source: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SymbolicTimeSeriesPrognosisExpressionTreeInterpreter.cs @ 10104

Last change on this file since 10104 was 9976, checked in by gkronber, 11 years ago

#2021: merged linear interpreter for symbolic data analysis solutions to stable (r9828,r9830,r9837,r9840,r9871,r9944)

File size: 7.0 KB
RevLine 
[8798]1#region License Information
2/* HeuristicLab
[9462]3 * Copyright (C) 2002-2013 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
[8798]4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
33  [StorableClass]
34  [Item("SymbolicTimeSeriesPrognosisInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.")]
35  public sealed class SymbolicTimeSeriesPrognosisExpressionTreeInterpreter : SymbolicDataAnalysisExpressionTreeInterpreter, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter {
36    private const string TargetVariableParameterName = "TargetVariable";
37
38    public IFixedValueParameter<StringValue> TargetVariableParameter {
39      get { return (IFixedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
40    }
41
42    public string TargetVariable {
43      get { return TargetVariableParameter.Value.Value; }
44      set { TargetVariableParameter.Value.Value = value; }
45    }
46
47    [ThreadStatic]
48    private static double[] targetVariableCache;
49    [ThreadStatic]
50    private static List<int> invalidateCacheIndexes;
51
52    [StorableConstructor]
53    private SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(bool deserializing) : base(deserializing) { }
54    private SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(SymbolicTimeSeriesPrognosisExpressionTreeInterpreter original, Cloner cloner) : base(original, cloner) { }
55    public override IDeepCloneable Clone(Cloner cloner) {
56      return new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(this, cloner);
57    }
58
59    internal SymbolicTimeSeriesPrognosisExpressionTreeInterpreter()
60      : base("SymbolicTimeSeriesPrognosisInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.") {
61      Parameters.Add(new FixedValueParameter<StringValue>(TargetVariableParameterName));
62      TargetVariableParameter.Hidden = true;
63    }
64
65    public SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(string targetVariable)
66      : this() {
67      TargetVariable = targetVariable;
68    }
69
70    // for each row several (=#horizon) future predictions
71    public IEnumerable<IEnumerable<double>> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, IEnumerable<int> rows, int horizon) {
72      return GetSymbolicExpressionTreeValues(tree, dataset, rows, rows.Select(row => horizon));
73    }
74
75    public IEnumerable<IEnumerable<double>> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, Dataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
76      if (CheckExpressionsWithIntervalArithmetic.Value)
77        throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
78      if (targetVariableCache == null || targetVariableCache.GetLength(0) < dataset.Rows)
79        targetVariableCache = dataset.GetDoubleValues(TargetVariable).ToArray();
80      if (invalidateCacheIndexes == null)
81        invalidateCacheIndexes = new List<int>(10);
82
83      string targetVariable = TargetVariable;
[9004]84      lock (EvaluatedSolutions) {
85        EvaluatedSolutions.Value++; // increment the evaluated solutions counter
86      }
87      var state = PrepareInterpreterState(tree, dataset, targetVariableCache, TargetVariable);
[8798]88      var rowsEnumerator = rows.GetEnumerator();
89      var horizonsEnumerator = horizons.GetEnumerator();
90
91      // produce a n-step forecast for all rows
92      while (rowsEnumerator.MoveNext() & horizonsEnumerator.MoveNext()) {
93        int row = rowsEnumerator.Current;
94        int horizon = horizonsEnumerator.Current;
95        double[] vProgs = new double[horizon];
96
97        for (int i = 0; i < horizon; i++) {
98          int localRow = i + row; // create a local variable for the ref parameter
99          vProgs[i] = Evaluate(dataset, ref localRow, state);
100          targetVariableCache[localRow] = vProgs[i];
101          invalidateCacheIndexes.Add(localRow);
102          state.Reset();
103        }
104        yield return vProgs;
105
106        int j = 0;
107        foreach (var targetValue in dataset.GetDoubleValues(targetVariable, invalidateCacheIndexes)) {
108          targetVariableCache[invalidateCacheIndexes[j]] = targetValue;
109          j++;
110        }
111        invalidateCacheIndexes.Clear();
112      }
113
114      if (rowsEnumerator.MoveNext() || horizonsEnumerator.MoveNext())
115        throw new ArgumentException("Number of elements in rows and horizon enumerations doesn't match.");
116    }
117
[9004]118    private static InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, Dataset dataset, double[] targetVariableCache, string targetVariable) {
[8798]119      Instruction[] code = SymbolicExpressionTreeCompiler.Compile(tree, OpCodes.MapSymbolToOpCode);
120      int necessaryArgStackSize = 0;
121      foreach (Instruction instr in code) {
122        if (instr.opCode == OpCodes.Variable) {
123          var variableTreeNode = (VariableTreeNode)instr.dynamicNode;
[9004]124          if (variableTreeNode.VariableName == targetVariable)
[9976]125            instr.data = targetVariableCache;
[8798]126          else
[9976]127            instr.data = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
[8798]128        } else if (instr.opCode == OpCodes.LagVariable) {
129          var variableTreeNode = (LaggedVariableTreeNode)instr.dynamicNode;
[9004]130          if (variableTreeNode.VariableName == targetVariable)
[9976]131            instr.data = targetVariableCache;
[8798]132          else
[9976]133            instr.data = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
[8798]134        } else if (instr.opCode == OpCodes.VariableCondition) {
135          var variableTreeNode = (VariableConditionTreeNode)instr.dynamicNode;
[9004]136          if (variableTreeNode.VariableName == targetVariable)
[9976]137            instr.data = targetVariableCache;
[8798]138          else
[9976]139            instr.data = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
[8798]140        } else if (instr.opCode == OpCodes.Call) {
141          necessaryArgStackSize += instr.nArguments + 1;
142        }
143      }
144
145      return new InterpreterState(code, necessaryArgStackSize);
146    }
147  }
148}
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