#region License Information
/* HeuristicLab
* Copyright (C) Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Random;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis.Tests {
internal class Util {
public static void InitTree(ISymbolicExpressionTree tree, MersenneTwister twister, List varNames) {
foreach (var node in tree.IterateNodesPostfix()) {
if (node is VariableTreeNode) {
var varNode = node as VariableTreeNode;
varNode.Weight = twister.NextDouble() * 20.0 - 10.0;
varNode.VariableName = varNames[twister.Next(varNames.Count)];
} else if (node is NumberTreeNode) {
var numNode = node as NumberTreeNode;
numNode.Value = twister.NextDouble() * 20.0 - 10.0;
}
}
}
public static ISymbolicExpressionTree[] CreateRandomTrees(MersenneTwister twister, Dataset dataset, ISymbolicExpressionGrammar grammar, int popSize) {
return CreateRandomTrees(twister, dataset, grammar, popSize, 200);
}
public static ISymbolicExpressionTree[] CreateRandomTrees(MersenneTwister twister, Dataset dataset, ISymbolicExpressionGrammar grammar,
int popSize, int maxSize) {
foreach (Variable variableSymbol in grammar.Symbols.OfType()) {
variableSymbol.VariableNames = dataset.VariableNames.Skip(1);
}
ISymbolicExpressionTree[] randomTrees = new ISymbolicExpressionTree[popSize];
for (int i = 0; i < randomTrees.Length; i++) {
randomTrees[i] = ProbabilisticTreeCreator.Create(twister, grammar, maxSize, 10);
}
return randomTrees;
}
public static Dataset CreateRandomDataset(MersenneTwister twister, int rows, int columns) {
double[,] data = new double[rows, columns];
for (int i = 0; i < rows; i++) {
for (int j = 0; j < columns; j++) {
data[i, j] = twister.NextDouble() * 2.0 - 1.0;
}
}
IEnumerable variableNames = new string[] { "y" }.Concat(Enumerable.Range(0, columns - 1).Select(x => "x" + x.ToString()));
Dataset ds = new Dataset(variableNames, data);
return ds;
}
public static double NodesPerSecond(long nNodes, Stopwatch watch) {
return nNodes / (watch.ElapsedMilliseconds / 1000.0);
}
private const int horizon = 10;
public static double CalculateEvaluatedNodesPerSec(ISymbolicExpressionTree[] trees, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter interpreter, Dataset dataset, int repetitions) {
interpreter.TargetVariable = dataset.VariableNames.First();
// warm up
IEnumerable rows = Enumerable.Range(0, dataset.Rows - horizon);
long nNodes = 0;
for (int i = 0; i < trees.Length; i++) {
nNodes += trees[i].Length * (dataset.Rows - horizon) * horizon;
interpreter.GetSymbolicExpressionTreeValues(trees[i], dataset, rows, horizon).Count(); // count needs to evaluate all rows
}
Stopwatch watch = new Stopwatch();
for (int rep = 0; rep < repetitions; rep++) {
watch.Start();
for (int i = 0; i < trees.Length; i++) {
interpreter.GetSymbolicExpressionTreeValues(trees[i], dataset, rows, horizon).Count(); // count needs to evaluate all rows
}
watch.Stop();
}
Console.WriteLine("Random tree evaluation performance of " + interpreter.GetType() + ": " +
watch.ElapsedMilliseconds + "ms " +
Util.NodesPerSecond(nNodes * repetitions, watch) + " nodes/sec");
return Util.NodesPerSecond(nNodes * repetitions, watch);
}
}
}