#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 . * * Author: Sabine Winkler */ #endregion using System.Collections.Generic; using System.Linq; using HEAL.Attic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Problems.DataAnalysis.Symbolic; using HeuristicLab.Random; namespace HeuristicLab.Problems.GrammaticalEvolution { [StorableType("73D43A23-02FF-4BD8-9834-55D8A90E0FCE")] [Item("GESymbolicExpressionGrammar", "Represents a grammar for functional expressions for grammatical evolution.")] public class GESymbolicExpressionGrammar : DataAnalysisGrammar, ISymbolicDataAnalysisGrammar { [StorableConstructor] protected GESymbolicExpressionGrammar(StorableConstructorFlag _) : base(_) { } protected GESymbolicExpressionGrammar(GESymbolicExpressionGrammar original, Cloner cloner) : base(original, cloner) { } public GESymbolicExpressionGrammar() : base(ItemAttribute.GetName(typeof(GESymbolicExpressionGrammar)), ItemAttribute.GetDescription(typeof(GESymbolicExpressionGrammar))) { // empty ctor is necessary to allow creation of new GEGrammars from the GUI. // the problem creates a new correctly configured grammar when the grammar is set } internal GESymbolicExpressionGrammar(IEnumerable variableNames, int nConstants) : base(ItemAttribute.GetName(typeof(GESymbolicExpressionGrammar)), ItemAttribute.GetDescription(typeof(GESymbolicExpressionGrammar))) { // this ctor is called by the problem as only the problem knows the allowed input variables Initialize(variableNames, nConstants); } public override IDeepCloneable Clone(Cloner cloner) { return new GESymbolicExpressionGrammar(this, cloner); } private void Initialize(IEnumerable variableNames, int nConstants) { #region symbol declaration var add = new Addition(); var sub = new Subtraction(); var mul = new Multiplication(); var div = new Division(); var mean = new Average(); var log = new Logarithm(); var pow = new Power(); var square = new Square(); var root = new Root(); var sqrt = new SquareRoot(); var exp = new Exponential(); // we use our own random number generator here because we assume // that grammars are only initialized once when setting the grammar in the problem. // This means everytime the grammar parameter in the problem is changed // we initialize the constants to new values var rand = new MersenneTwister(); // warm up for (int i = 0; i < 1000; i++) rand.NextDouble(); var constants = new List(nConstants); for (int i = 0; i < nConstants; i++) { var constant = new Constant(); do { var constVal = rand.NextDouble() * 20.0 - 10.0; constant.Name = string.Format("{0:0.000}", constVal); constant.MinValue = constVal; constant.MaxValue = constVal; constant.ManipulatorSigma = 0.0; constant.ManipulatorMu = 0.0; constant.MultiplicativeManipulatorSigma = 0.0; } while (constants.Any(c => c.Name == constant.Name)); // unlikely, but it could happen that the same constant value is sampled twice. so we resample if necessary. constants.Add(constant); } var variables = new List(); foreach (var variableName in variableNames) { var variableSymbol = new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable(); variableSymbol.Name = variableName; variableSymbol.WeightManipulatorMu = 0.0; variableSymbol.WeightManipulatorSigma = 0.0; variableSymbol.WeightMu = 1.0; variableSymbol.WeightSigma = 0.0; variableSymbol.MultiplicativeWeightManipulatorSigma = 0.0; variableSymbol.AllVariableNames = new[] { variableName }; variableSymbol.VariableNames = new[] { variableName }; variables.Add(variableSymbol); } #endregion AddSymbol(add); AddSymbol(sub); AddSymbol(mul); AddSymbol(div); AddSymbol(mean); AddSymbol(log); AddSymbol(pow); AddSymbol(square); AddSymbol(root); AddSymbol(sqrt); AddSymbol(exp); constants.ForEach(AddSymbol); variables.ForEach(AddSymbol); #region subtree count configuration SetSubtreeCount(add, 2, 2); SetSubtreeCount(sub, 2, 2); SetSubtreeCount(mul, 2, 2); SetSubtreeCount(div, 2, 2); SetSubtreeCount(mean, 2, 2); SetSubtreeCount(log, 1, 1); SetSubtreeCount(pow, 2, 2); SetSubtreeCount(square, 1, 1); SetSubtreeCount(root, 2, 2); SetSubtreeCount(sqrt, 1, 1); SetSubtreeCount(exp, 1, 1); constants.ForEach((c) => SetSubtreeCount(c, 0, 0)); variables.ForEach((v) => SetSubtreeCount(v, 0, 0)); #endregion var functions = new ISymbol[] { add, sub, mul, div, mean, log, pow, root, square, sqrt }; var terminalSymbols = variables.Concat(constants); var allSymbols = functions.Concat(terminalSymbols); #region allowed child symbols configuration foreach (var s in allSymbols) { AddAllowedChildSymbol(StartSymbol, s); } foreach (var parentSymb in functions) foreach (var childSymb in allSymbols) { AddAllowedChildSymbol(parentSymb, childSymb); } #endregion } } }