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source: trunk/sources/HeuristicLab.Tests/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis-3.4/Util.cs @ 9785

Last change on this file since 9785 was 9785, checked in by abeham, 9 years ago

#2088: Added test attributes for Problems.DataAnalysis

File size: 4.6 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2013 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.Diagnostics;
25using System.Linq;
26using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
27using HeuristicLab.Random;
28
29namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Tests {
30  internal class Util {
31    public static void InitTree(ISymbolicExpressionTree tree, MersenneTwister twister, List<string> varNames) {
32      foreach (var node in tree.IterateNodesPostfix()) {
33        if (node is VariableTreeNode) {
34          var varNode = node as VariableTreeNode;
35          varNode.Weight = twister.NextDouble() * 20.0 - 10.0;
36          varNode.VariableName = varNames[twister.Next(varNames.Count)];
37        } else if (node is ConstantTreeNode) {
38          var constantNode = node as ConstantTreeNode;
39          constantNode.Value = twister.NextDouble() * 20.0 - 10.0;
40        }
41      }
42    }
43
44
45    public static ISymbolicExpressionTree[] CreateRandomTrees(MersenneTwister twister, Dataset dataset, ISymbolicExpressionGrammar grammar, int popSize) {
46      return CreateRandomTrees(twister, dataset, grammar, popSize, 1, 200, 3, 3);
47    }
48
49    public static ISymbolicExpressionTree[] CreateRandomTrees(MersenneTwister twister, Dataset dataset, ISymbolicExpressionGrammar grammar,
50      int popSize, int minSize, int maxSize,
51      int maxFunctionDefinitions, int maxFunctionArguments) {
52      foreach (Variable variableSymbol in grammar.Symbols.OfType<Variable>()) {
53        variableSymbol.VariableNames = dataset.VariableNames.Skip(1);
54      }
55      ISymbolicExpressionTree[] randomTrees = new ISymbolicExpressionTree[popSize];
56      for (int i = 0; i < randomTrees.Length; i++) {
57        randomTrees[i] = ProbabilisticTreeCreator.Create(twister, grammar, maxSize, 10);
58      }
59      return randomTrees;
60    }
61
62    public static Dataset CreateRandomDataset(MersenneTwister twister, int rows, int columns) {
63      double[,] data = new double[rows, columns];
64      for (int i = 0; i < rows; i++) {
65        for (int j = 0; j < columns; j++) {
66          data[i, j] = twister.NextDouble() * 2.0 - 1.0;
67        }
68      }
69      IEnumerable<string> variableNames = new string[] { "y" }.Concat(Enumerable.Range(0, columns - 1).Select(x => "x" + x.ToString()));
70      Dataset ds = new Dataset(variableNames, data);
71      return ds;
72    }
73
74    public static double NodesPerSecond(long nNodes, Stopwatch watch) {
75      return nNodes / (watch.ElapsedMilliseconds / 1000.0);
76    }
77
78    private const int horizon = 10;
79    public static double CalculateEvaluatedNodesPerSec(ISymbolicExpressionTree[] trees, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, Dataset dataset, int repetitions) {
80      interpreter.TargetVariable = dataset.VariableNames.First();
81      // warm up
82      IEnumerable<int> rows = Enumerable.Range(0, dataset.Rows - horizon);
83      long nNodes = 0;
84      for (int i = 0; i < trees.Length; i++) {
85        nNodes += trees[i].Length * (dataset.Rows - horizon) * horizon;
86        interpreter.GetSymbolicExpressionTreeValues(trees[i], dataset, rows, horizon).Count(); // count needs to evaluate all rows
87      }
88
89      Stopwatch watch = new Stopwatch();
90      for (int rep = 0; rep < repetitions; rep++) {
91        watch.Start();
92        for (int i = 0; i < trees.Length; i++) {
93          interpreter.GetSymbolicExpressionTreeValues(trees[i], dataset, rows, horizon).Count(); // count needs to evaluate all rows
94        }
95        watch.Stop();
96      }
97      Console.WriteLine("Random tree evaluation performance of " + interpreter.GetType() + ": " +
98        watch.ElapsedMilliseconds + "ms " +
99        Util.NodesPerSecond(nNodes * repetitions, watch) + " nodes/sec");
100      return Util.NodesPerSecond(nNodes * repetitions, watch);
101    }
102  }
103}
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