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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SymbolicTimeSeriesPrognosisExpressionTreeInterpreter.cs @ 8851

Last change on this file since 8851 was 8798, checked in by mkommend, 11 years ago

#1081: Reintegrated time series modeling branch into trunk.

File size: 6.9 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2011 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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;
84      EvaluatedSolutions.Value++; // increment the evaluated solutions counter
85      var state = PrepareInterpreterState(tree, dataset, targetVariableCache);
86      var rowsEnumerator = rows.GetEnumerator();
87      var horizonsEnumerator = horizons.GetEnumerator();
88
89      // produce a n-step forecast for all rows
90      while (rowsEnumerator.MoveNext() & horizonsEnumerator.MoveNext()) {
91        int row = rowsEnumerator.Current;
92        int horizon = horizonsEnumerator.Current;
93        double[] vProgs = new double[horizon];
94
95        for (int i = 0; i < horizon; i++) {
96          int localRow = i + row; // create a local variable for the ref parameter
97          vProgs[i] = Evaluate(dataset, ref localRow, state);
98          targetVariableCache[localRow] = vProgs[i];
99          invalidateCacheIndexes.Add(localRow);
100          state.Reset();
101        }
102        yield return vProgs;
103
104        int j = 0;
105        foreach (var targetValue in dataset.GetDoubleValues(targetVariable, invalidateCacheIndexes)) {
106          targetVariableCache[invalidateCacheIndexes[j]] = targetValue;
107          j++;
108        }
109        invalidateCacheIndexes.Clear();
110      }
111
112      if (rowsEnumerator.MoveNext() || horizonsEnumerator.MoveNext())
113        throw new ArgumentException("Number of elements in rows and horizon enumerations doesn't match.");
114    }
115
116    private InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, Dataset dataset, double[] targetVariableCache) {
117      Instruction[] code = SymbolicExpressionTreeCompiler.Compile(tree, OpCodes.MapSymbolToOpCode);
118      int necessaryArgStackSize = 0;
119      foreach (Instruction instr in code) {
120        if (instr.opCode == OpCodes.Variable) {
121          var variableTreeNode = (VariableTreeNode)instr.dynamicNode;
122          if (variableTreeNode.VariableName == TargetVariable)
123            instr.iArg0 = targetVariableCache;
124          else
125            instr.iArg0 = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
126        } else if (instr.opCode == OpCodes.LagVariable) {
127          var variableTreeNode = (LaggedVariableTreeNode)instr.dynamicNode;
128          if (variableTreeNode.VariableName == TargetVariable)
129            instr.iArg0 = targetVariableCache;
130          else
131            instr.iArg0 = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
132        } else if (instr.opCode == OpCodes.VariableCondition) {
133          var variableTreeNode = (VariableConditionTreeNode)instr.dynamicNode;
134          if (variableTreeNode.VariableName == TargetVariable)
135            instr.iArg0 = targetVariableCache;
136          else
137            instr.iArg0 = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
138        } else if (instr.opCode == OpCodes.Call) {
139          necessaryArgStackSize += instr.nArguments + 1;
140        }
141      }
142
143      return new InterpreterState(code, necessaryArgStackSize);
144    }
145  }
146}
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