source: branches/2988_ModelsOfModels2/HeuristicLab.Algorithms.EMM/EMMGrammar.cs @ 16724

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2019 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HEAL.Attic;
using HeuristicLab.Problems.DataAnalysis.Symbolic;

namespace HeuristicLab.Algorithms.EvolvmentModelsOfModels {
  [StorableType("44B0829C-1CB5-4BE9-9514-BBA54FAB2912")]
  [Item("EMMGrammar", "Represents a grammar for functional expressions using all available functions.")]
  public class EMMGrammar : SymbolicExpressionGrammar, ISymbolicDataAnalysisGrammar {
    [StorableConstructor]
    protected EMMGrammar(StorableConstructorFlag _) : base(_) { }
    protected EMMGrammar(EMMGrammar original, Cloner cloner) : base(original, cloner) { }
    public EMMGrammar()
      : base(ItemAttribute.GetName(typeof(EMMGrammar)), ItemAttribute.GetDescription(typeof(EMMGrammar))) {
      Initialize();
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new EMMGrammar(this, cloner);
    }

    private void Initialize() {
      var add = new Addition();
      var sub = new Subtraction();
      var mul = new Multiplication();
      var div = new Division();
      var aq = new AnalyticQuotient();
      var mean = new Average();
      var sin = new Sine();
      var cos = new Cosine();
      var tan = new Tangent();
      var log = new Logarithm();
      var abs = new Absolute();
      var pow = new Power();
      pow.InitialFrequency = 0.0;
      var square = new Square();
      square.InitialFrequency = 0.0;
      var cube = new Cube();
      cube.InitialFrequency = 0.0;
      var root = new Root();
      root.InitialFrequency = 0.0;
      var sqrt = new SquareRoot();
      sqrt.InitialFrequency = 0.0;
      var cubeRoot = new CubeRoot();
      cubeRoot.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();
      var treeModel = new TreeModel();
      constant.MinValue = -20;
      constant.MaxValue = 20;
      var variableSymbol = new HeuristicLab.Problems.DataAnalysis.Symbolic.Variable();
      var binFactorVariable = new BinaryFactorVariable();
      var factorVariable = new FactorVariable();
      var laggedVariable = new LaggedVariable();
      laggedVariable.InitialFrequency = 0.0;
      var autoregressiveVariable = new AutoregressiveTargetVariable();
      autoregressiveVariable.InitialFrequency = 0.0;
      autoregressiveVariable.Enabled = false;

      var allSymbols = new List<Symbol>() { add, sub, mul, div, aq, mean, abs, sin, cos, tan, log, square, cube, pow, sqrt, cubeRoot, 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, binFactorVariable, factorVariable, laggedVariable,autoregressiveVariable, variableCondition,
        treeModel};
      var unaryFunctionSymbols = new List<Symbol>() { abs, square, sqrt, cube, cubeRoot, 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<Symbol>() { pow, root, gt, lt, aq, variableCondition };
      var ternarySymbols = new List<Symbol>() { add, sub, mul, div, mean, and, or, xor };
      var terminalSymbols = new List<Symbol>() { variableSymbol, binFactorVariable, factorVariable, constant, laggedVariable, autoregressiveVariable, treeModel };

      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);
      }
    }
  }
}