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source: branches/DataAnalysis.PopulationDiversityAnalysis/HeuristicLab.Problems.DataAnalysis/3.3/Tests/Util.cs @ 12730

Last change on this file since 12730 was 4951, checked in by swinkler, 14 years ago

Branched DataAnalysis plugin into DataAnalysis.PopulationDiversity branch. (#1278)

File size: 4.5 KB
Line 
1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2010 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.Encodings.SymbolicExpressionTreeEncoding.Creators;
27using HeuristicLab.Problems.DataAnalysis.Symbolic;
28using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols;
29using HeuristicLab.Random;
30using Microsoft.VisualStudio.TestTools.UnitTesting;
31namespace HeuristicLab.Problems.DataAnalysis.Tests {
32  internal class Util {
33
34    public static void InitTree(SymbolicExpressionTree tree, MersenneTwister twister, List<string> varNames) {
35      foreach (var node in tree.IterateNodesPostfix()) {
36        if (node is VariableTreeNode) {
37          var varNode = node as VariableTreeNode;
38          varNode.Weight = twister.NextDouble() * 20.0 - 10.0;
39          varNode.VariableName = varNames[twister.Next(varNames.Count)];
40        } else if (node is ConstantTreeNode) {
41          var constantNode = node as ConstantTreeNode;
42          constantNode.Value = twister.NextDouble() * 20.0 - 10.0;
43        }
44      }
45    }
46
47
48    public static SymbolicExpressionTree[] CreateRandomTrees(MersenneTwister twister, Dataset dataset, ISymbolicExpressionGrammar grammar, int popSize) {
49      return CreateRandomTrees(twister, dataset, grammar, popSize, 1, 200, 3, 3);
50    }
51
52    public static SymbolicExpressionTree[] CreateRandomTrees(MersenneTwister twister, Dataset dataset, ISymbolicExpressionGrammar grammar,
53      int popSize, int minSize, int maxSize,
54      int maxFunctionDefinitions, int maxFunctionArguments) {
55      foreach (Variable variableSymbol in grammar.Symbols.OfType<Variable>()) {
56        variableSymbol.VariableNames = dataset.VariableNames.Skip(1);
57      }
58      SymbolicExpressionTree[] randomTrees = new SymbolicExpressionTree[popSize];
59      for (int i = 0; i < randomTrees.Length; i++) {
60        randomTrees[i] = ProbabilisticTreeCreator.Create(twister, grammar, maxSize, 10, maxFunctionDefinitions, maxFunctionArguments);
61      }
62      return randomTrees;
63    }
64
65
66    public static Dataset CreateRandomDataset(MersenneTwister twister, int rows, int columns) {
67      double[,] data = new double[rows, columns];
68      for (int i = 0; i < rows; i++) {
69        for (int j = 0; j < columns; j++) {
70          data[i, j] = twister.NextDouble() * 2.0 - 1.0;
71        }
72      }
73      IEnumerable<string> variableNames = new string[] { "y" }.Concat(Enumerable.Range(0, columns - 1).Select(x => "x" + x.ToString()));
74      Dataset ds = new Dataset(variableNames, data);
75      return ds;
76    }
77
78    public static double NodesPerSecond(long nNodes, Stopwatch watch) {
79      return nNodes / (watch.ElapsedMilliseconds / 1000.0);
80    }
81
82    public static void EvaluateTrees(SymbolicExpressionTree[] trees, ISymbolicExpressionTreeInterpreter interpreter, Dataset dataset, int repetitions) {
83      double[] estimation = new double[dataset.Rows];
84      // warm up
85      for (int i = 0; i < trees.Length; i++) {
86        estimation = interpreter.GetSymbolicExpressionTreeValues(trees[i], dataset, Enumerable.Range(0, dataset.Rows)).ToArray();
87      }
88
89      Stopwatch watch = new Stopwatch();
90      long nNodes = 0;
91      for (int rep = 0; rep < repetitions; rep++) {
92        watch.Start();
93        for (int i = 0; i < trees.Length; i++) {
94          nNodes += trees[i].Size * (dataset.Rows - 1);
95          estimation = interpreter.GetSymbolicExpressionTreeValues(trees[i], dataset, Enumerable.Range(0, dataset.Rows)).ToArray();
96        }
97        watch.Stop();
98      }
99      Assert.Inconclusive("Random tree evaluation performance of " + interpreter.GetType() + ":" +
100        watch.ElapsedMilliseconds + "ms " +
101        Util.NodesPerSecond(nNodes, watch) + " nodes/sec");
102    }
103  }
104}
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