#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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System.Collections.Generic;
using System.Linq;
using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using DoubleVector = MathNet.Numerics.LinearAlgebra.Vector<double>;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
  [StorableType("C913F5C6-AF16-4F2C-BA93-2D5AE1F8E68A")]
  [Item("FullFunctionalVectorExpressionGrammar", "Represents a grammar for functional expressions using all available functions.")]
  public class FullFunctionalVectorExpressionGrammar : DataAnalysisGrammar, ISymbolicDataAnalysisGrammar {
    [StorableConstructor]
    protected FullFunctionalVectorExpressionGrammar(StorableConstructorFlag _) : base(_) { }
    protected FullFunctionalVectorExpressionGrammar(FullFunctionalVectorExpressionGrammar original, Cloner cloner) : base(original, cloner) { }
    public FullFunctionalVectorExpressionGrammar()
      : base(ItemAttribute.GetName(typeof(FullFunctionalVectorExpressionGrammar)), ItemAttribute.GetDescription(typeof(FullFunctionalVectorExpressionGrammar))) {
      Initialize();
    }

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

    private void Initialize() {
      var add = new Addition();
      var sub = new Subtraction();
      var mul = new Multiplication();
      var div = new Division();

      var sin = new Sine();
      var cos = new Cosine();
      var tan = new Tangent();

      var log = new Logarithm();
      var exp = new Exponential();


      var pow = new Power { InitialFrequency = 0.0 };
      var root = new Root { InitialFrequency = 0.0 };
      var square = new Square { InitialFrequency = 0.0 };
      var sqrt = new SquareRoot { InitialFrequency = 0.0 };
      var cube = new Cube { InitialFrequency = 0.0 };
      var cubeRoot = new CubeRoot { InitialFrequency = 0.0 };

      var constant = new Constant { MinValue = -20, MaxValue = 20 };
      var variable = new Variable();
      var binFactorVariable = new BinaryFactorVariable();
      var factorVariable = new FactorVariable();
      var vectorVariable = new Variable() { Name = "Vector Variable" };

      var sum = new Sum();
      var mean = new Average() { Name = "Mean" };


      var allSymbols = new List<Symbol>() {
        add, sub, mul, div,
        sin, cos, tan,
        log, exp,
        square, sqrt, cube, cubeRoot, pow, root,
        constant, variable, binFactorVariable, factorVariable, vectorVariable,
        sum, mean
      };
      var unaryFunctionSymbols = new List<Symbol>() {
        sin, cos, tan,
        log, exp
      };

      var binaryFunctionSymbols = new List<Symbol>() { pow, root };
      var ternarySymbols = new List<Symbol>() { add, sub, mul, div };
      var terminalSymbols = new List<Symbol>() { variable, binFactorVariable, factorVariable, vectorVariable, constant };

      var aggregationSymbols = new List<Symbol>() { sum, mean };

      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);
      }
      foreach (var aggrSymb in aggregationSymbols) {
        SetSubtreeCount(aggrSymb, 1, 1);
      }


      // 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);
      }
    }

    public override void ConfigureVariableSymbols(IDataAnalysisProblemData problemData) {
      base.ConfigureVariableSymbols(problemData);

      var dataset = problemData.Dataset;
      foreach (var varSymbol in Symbols.OfType<VariableBase>().Where(sym => sym.Name == "Variable")) {
        if (!varSymbol.Fixed) {
          varSymbol.AllVariableNames = problemData.InputVariables.Select(x => x.Value).Where(x => dataset.VariableHasType<double>(x));
          varSymbol.VariableNames = problemData.AllowedInputVariables.Where(x => dataset.VariableHasType<double>(x));
        }
      }
      foreach (var varSymbol in Symbols.OfType<VariableBase>().Where(sym => sym.Name == "Vector Variable")) {
        if (!varSymbol.Fixed) {
          varSymbol.AllVariableNames = problemData.InputVariables.Select(x => x.Value).Where(x => dataset.VariableHasType<DoubleVector>(x));
          varSymbol.VariableNames = problemData.AllowedInputVariables.Where(x => dataset.VariableHasType<DoubleVector>(x));
        }
      }
    }
  }
}