source: branches/HivePerformance/sources/HeuristicLab.Problems.TestFunctions/3.3/Evaluators/RandomEvaluator.cs @ 9378

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2012 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.RealVectorEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

namespace HeuristicLab.Problems.TestFunctions {
  /// <summary>
  /// A function that returns a random variable in [0;1) independent of the inputs.
  /// </summary
  [Item("RandomEvaluator", "Returns a random value in [0;1) that is independent of the inputs.")]
  [StorableClass]
  public class RandomEvaluator : SingleObjectiveTestFunctionProblemEvaluator, IStochasticOperator {
    /// <summary>
    /// It does not really matter.
    /// </summary>
    public override bool Maximization {
      get { return false; }
    }
    /// <summary>
    /// The minimum value that can be "found" is 0.
    /// </summary>
    public override double BestKnownQuality {
      get { return 0; }
    }
    /// <summary>
    /// Gets the lower and upper bound of the function.
    /// </summary>
    public override DoubleMatrix Bounds {
      get { return new DoubleMatrix(new double[,] { { -100, 100 } }); }
    }
    /// <summary>
    /// Gets the minimum problem size (1).
    /// </summary>
    public override int MinimumProblemSize {
      get { return 1; }
    }
    /// <summary>
    /// Gets the (theoretical) maximum problem size (2^31 - 1).
    /// </summary>
    public override int MaximumProblemSize {
      get { return int.MaxValue; }
    }

    public ILookupParameter<IRandom> RandomParameter {
      get { return (ILookupParameter<IRandom>)Parameters["Random"]; }
    }

    [StorableConstructor]
    protected RandomEvaluator(bool deserializing) : base(deserializing) { }
    protected RandomEvaluator(RandomEvaluator original, Cloner cloner) : base(original, cloner) { }
    public RandomEvaluator()
      : base() {
      Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator to use."));
    }

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

    public override RealVector GetBestKnownSolution(int dimension) {
      return new RealVector(dimension);
    }

    public override double EvaluateFunction(RealVector point) {
      return ExecutionContext == null ? new System.Random().NextDouble() : RandomParameter.ActualValue.NextDouble();
    }
  }
}