#region License Information /* HeuristicLab * Copyright (C) 2002-2016 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 System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; namespace HeuristicLab.Problems.DataAnalysis.Symbolic { [StorableClass] [Item("FullFunctionalExpressionGrammar", "Represents a grammar for functional expressions using all available functions.")] public class FullFunctionalExpressionGrammar : SymbolicExpressionGrammar, ISymbolicDataAnalysisGrammar { [StorableConstructor] protected FullFunctionalExpressionGrammar(bool deserializing) : base(deserializing) { } protected FullFunctionalExpressionGrammar(FullFunctionalExpressionGrammar original, Cloner cloner) : base(original, cloner) { } public FullFunctionalExpressionGrammar() : base(ItemAttribute.GetName(typeof(FullFunctionalExpressionGrammar)), ItemAttribute.GetDescription(typeof(FullFunctionalExpressionGrammar))) { Initialize(); } public override IDeepCloneable Clone(Cloner cloner) { return new FullFunctionalExpressionGrammar(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 pow = new Power(); pow.InitialFrequency = 0.0; var square = new Square(); square.InitialFrequency = 0.0; var root = new Root(); root.InitialFrequency = 0.0; var sqrt = new SquareRoot(); sqrt.InitialFrequency = 0.0; var airyA = new AiryA(); airyA.InitialFrequency = 0.0; var airyB = new AiryB(); airyB.InitialFrequency = 0.0; var bessel = new Bessel(); bessel.InitialFrequency = 0.0; var cosineIntegral = new CosineIntegral(); cosineIntegral.InitialFrequency = 0.0; var dawson = new Dawson(); dawson.InitialFrequency = 0.0; var erf = new Erf(); erf.InitialFrequency = 0.0; var expIntegralEi = new ExponentialIntegralEi(); expIntegralEi.InitialFrequency = 0.0; var fresnelCosineIntegral = new FresnelCosineIntegral(); fresnelCosineIntegral.InitialFrequency = 0.0; var fresnelSineIntegral = new FresnelSineIntegral(); fresnelSineIntegral.InitialFrequency = 0.0; var gamma = new Gamma(); gamma.InitialFrequency = 0.0; var hypCosineIntegral = new HyperbolicCosineIntegral(); hypCosineIntegral.InitialFrequency = 0.0; var hypSineIntegral = new HyperbolicSineIntegral(); hypSineIntegral.InitialFrequency = 0.0; var norm = new Norm(); norm.InitialFrequency = 0.0; var psi = new Psi(); psi.InitialFrequency = 0.0; var sineIntegral = new SineIntegral(); sineIntegral.InitialFrequency = 0.0; 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 xor = new Xor(); var timeLag = new TimeLag(); timeLag.InitialFrequency = 0.0; var integral = new Integral(); integral.InitialFrequency = 0.0; var derivative = new Derivative(); derivative.InitialFrequency = 0.0; var variableCondition = new VariableCondition(); variableCondition.InitialFrequency = 0.0; var constant = new Constant(); constant.MinValue = -20; constant.MaxValue = 20; var variableSymbol = new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable(); var laggedVariable = new LaggedVariable(); laggedVariable.InitialFrequency = 0.0; var autoregressiveVariable = new AutoregressiveTargetVariable(); autoregressiveVariable.InitialFrequency = 0.0; autoregressiveVariable.Enabled = false; var allSymbols = new List() { add, sub, mul, div, mean, sin, cos, tan, log, square, pow, sqrt, root, exp, airyA, airyB, bessel, cosineIntegral, dawson, erf, expIntegralEi, fresnelCosineIntegral, fresnelSineIntegral, gamma, hypCosineIntegral, hypSineIntegral, norm, psi, sineIntegral, @if, gt, lt, and, or, not,xor, timeLag, integral, derivative, constant, variableSymbol, laggedVariable,autoregressiveVariable, variableCondition }; var unaryFunctionSymbols = new List() { square, sqrt, sin, cos, tan, log, exp, not, timeLag, integral, derivative, airyA, airyB, bessel, cosineIntegral, dawson, erf, expIntegralEi, fresnelCosineIntegral, fresnelSineIntegral, gamma, hypCosineIntegral, hypSineIntegral, norm, psi, sineIntegral }; var binaryFunctionSymbols = new List() { pow, root, gt, lt, variableCondition }; var ternarySymbols = new List() { add, sub, mul, div, mean, and, or, xor }; var terminalSymbols = new List() { variableSymbol, constant, laggedVariable, autoregressiveVariable }; foreach (var symb in allSymbols) AddSymbol(symb); foreach (var funSymb in ternarySymbols) { SetSubtreeCount(funSymb, 1, 3); } foreach (var funSymb in unaryFunctionSymbols) { SetSubtreeCount(funSymb, 1, 1); } foreach (var funSymb in binaryFunctionSymbols) { SetSubtreeCount(funSymb, 2, 2); } foreach (var terminalSymbol in terminalSymbols) { SetSubtreeCount(terminalSymbol, 0, 0); } SetSubtreeCount(@if, 3, 3); // allow each symbol as child of the start symbol foreach (var symb in allSymbols) { AddAllowedChildSymbol(StartSymbol, symb); AddAllowedChildSymbol(DefunSymbol, symb); } // allow each symbol as child of every other symbol (except for terminals that have maxSubtreeCount == 0) foreach (var parent in allSymbols.Except(terminalSymbols)) { foreach (var child in allSymbols) AddAllowedChildSymbol(parent, child); } } } }