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source: trunk/sources/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Tests/Util.cs @ 6488

Last change on this file since 6488 was 5809, checked in by mkommend, 14 years ago

#1418: Reintegrated branch into trunk.

File size: 4.5 KB
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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.Collections.Generic;
23using System.Diagnostics;
24using System.Linq;
25using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
26using HeuristicLab.Problems.DataAnalysis.Symbolic;
27using HeuristicLab.Random;
28using Microsoft.VisualStudio.TestTools.UnitTesting;
29using System;
30namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Tests {
31  internal class Util {
32
33    public static void InitTree(ISymbolicExpressionTree tree, MersenneTwister twister, List<string> varNames) {
34      foreach (var node in tree.IterateNodesPostfix()) {
35        if (node is VariableTreeNode) {
36          var varNode = node as VariableTreeNode;
37          varNode.Weight = twister.NextDouble() * 20.0 - 10.0;
38          varNode.VariableName = varNames[twister.Next(varNames.Count)];
39        } else if (node is ConstantTreeNode) {
40          var constantNode = node as ConstantTreeNode;
41          constantNode.Value = twister.NextDouble() * 20.0 - 10.0;
42        }
43      }
44    }
45
46
47    public static ISymbolicExpressionTree[] CreateRandomTrees(MersenneTwister twister, Dataset dataset, ISymbolicExpressionGrammar grammar, int popSize) {
48      return CreateRandomTrees(twister, dataset, grammar, popSize, 1, 200, 3, 3);
49    }
50
51    public static ISymbolicExpressionTree[] CreateRandomTrees(MersenneTwister twister, Dataset dataset, ISymbolicExpressionGrammar grammar,
52      int popSize, int minSize, int maxSize,
53      int maxFunctionDefinitions, int maxFunctionArguments) {
54      foreach (Variable variableSymbol in grammar.Symbols.OfType<Variable>()) {
55        variableSymbol.VariableNames = dataset.VariableNames.Skip(1);
56      }
57      ISymbolicExpressionTree[] randomTrees = new ISymbolicExpressionTree[popSize];
58      for (int i = 0; i < randomTrees.Length; i++) {
59        randomTrees[i] = ProbabilisticTreeCreator.Create(twister, grammar, maxSize, 10);
60      }
61      return randomTrees;
62    }
63
64
65    public static Dataset CreateRandomDataset(MersenneTwister twister, int rows, int columns) {
66      double[,] data = new double[rows, columns];
67      for (int i = 0; i < rows; i++) {
68        for (int j = 0; j < columns; j++) {
69          data[i, j] = twister.NextDouble() * 2.0 - 1.0;
70        }
71      }
72      IEnumerable<string> variableNames = new string[] { "y" }.Concat(Enumerable.Range(0, columns - 1).Select(x => "x" + x.ToString()));
73      Dataset ds = new Dataset(variableNames, data);
74      return ds;
75    }
76
77    public static double NodesPerSecond(long nNodes, Stopwatch watch) {
78      return nNodes / (watch.ElapsedMilliseconds / 1000.0);
79    }
80
81    public static double CalculateEvaluatedNodesPerSec(ISymbolicExpressionTree[] trees, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, Dataset dataset, int repetitions) {
82      // warm up
83      IEnumerable<int> rows = Enumerable.Range(0, dataset.Rows);
84      long nNodes = 0;
85      double c = 0;
86      for (int i = 0; i < trees.Length; i++) {
87        nNodes += trees[i].Length * (dataset.Rows - 1);
88        c = interpreter.GetSymbolicExpressionTreeValues(trees[i], dataset, rows).Count(); // count needs to evaluate all rows
89      }
90
91      Stopwatch watch = new Stopwatch();
92      for (int rep = 0; rep < repetitions; rep++) {
93        watch.Start();
94        c = 0;
95        for (int i = 0; i < trees.Length; i++) {
96          interpreter.GetSymbolicExpressionTreeValues(trees[i], dataset, rows).Count(); // count needs to evaluate all rows
97        }
98        watch.Stop();
99      }
100      Console.WriteLine("Random tree evaluation performance of " + interpreter.GetType() + ": " +
101        watch.ElapsedMilliseconds + "ms " +
102        Util.NodesPerSecond(nNodes * repetitions, watch) + " nodes/sec");
103      return Util.NodesPerSecond(nNodes * repetitions, watch);
104    }
105  }
106}
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