#region License Information
/* HeuristicLab
* Copyright (C) 2002-2014 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);
}
}
}
}