#region License Information /* HeuristicLab * Copyright (C) 2002-2010 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.Collections.Generic; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding.Symbols; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.DataAnalysis.MultiVariate.Symbolic; using HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols; namespace HeuristicLab.Problems.DataAnalysis.MultiVariate.Regression.Symbolic { [StorableClass] [Item("SymbolicVectorRegressionGrammar", "Represents a grammar for symbolic vector regression using all available functions.")] public class SymbolicVectorRegressionGrammar : MultiVariateExpressionGrammar { public SymbolicVectorRegressionGrammar() : this(1) { } [StorableConstructor] protected SymbolicVectorRegressionGrammar(bool deserializing) : base(deserializing) { } protected SymbolicVectorRegressionGrammar(SymbolicVectorRegressionGrammar original, Cloner cloner) : base(original, cloner) { } public SymbolicVectorRegressionGrammar(int dimension) : base(dimension) { Initialize(); } public override IDeepCloneable Clone(Cloner cloner) { return new SymbolicVectorRegressionGrammar(this, cloner); } private void Initialize() { var add = new Addition(); var sub = new Subtraction(); var mul = new Multiplication(); var div = new Division(); var mean = new Average(); var sin = new Sine(); var cos = new Cosine(); var tan = new Tangent(); var log = new Logarithm(); var exp = new Exponential(); var @if = new IfThenElse(); var gt = new GreaterThan(); var lt = new LessThan(); var and = new And(); var or = new Or(); var not = new Not(); var constant = new Constant(); constant.MinValue = -20; constant.MaxValue = 20; var variableSymbol = new HeuristicLab.Problems.DataAnalysis.Symbolic.Symbols.Variable(); var allSymbols = new List() { add, sub, mul, div, mean, sin, cos, tan, log, exp, @if, gt, lt, and, or, not, constant, variableSymbol }; var unaryFunctionSymbols = new List() { sin, cos, tan, log, exp, not }; var binaryFunctionSymbols = new List() { gt, lt }; var functionSymbols = new List() { add, sub, mul, div, mean, and, or }; foreach (var symb in allSymbols) AddSymbol(symb); foreach (var funSymb in functionSymbols) { SetMinSubtreeCount(funSymb, 1); SetMaxSubtreeCount(funSymb, 3); } foreach (var funSymb in unaryFunctionSymbols) { SetMinSubtreeCount(funSymb, 1); SetMaxSubtreeCount(funSymb, 1); } foreach (var funSymb in binaryFunctionSymbols) { SetMinSubtreeCount(funSymb, 2); SetMaxSubtreeCount(funSymb, 2); } SetMinSubtreeCount(@if, 3); SetMaxSubtreeCount(@if, 3); SetMinSubtreeCount(constant, 0); SetMaxSubtreeCount(constant, 0); SetMinSubtreeCount(variableSymbol, 0); SetMaxSubtreeCount(variableSymbol, 0); SetMinSubtreeCount(StartSymbol, Dimension); SetMaxSubtreeCount(StartSymbol, Dimension); SetMinSubtreeCount(constant, 0); SetMaxSubtreeCount(constant, 0); SetMinSubtreeCount(variableSymbol, 0); SetMaxSubtreeCount(variableSymbol, 0); // allow all symbols as children of the start-symbol foreach (Symbol symb in allSymbols) { for (int i = 0; i < GetMaxSubtreeCount(StartSymbol); i++) SetAllowedChild(StartSymbol, symb, i); } // allow all symbols as children of all symbols foreach (var parent in allSymbols) { for (int i = 0; i < GetMaxSubtreeCount(parent); i++) foreach (var child in allSymbols) { SetAllowedChild(parent, child, i); } } } } }