1  #region License Information


2  /* HeuristicLab


3  * Copyright (C) 20022019 Heuristic and Evolutionary Algorithms Laboratory (HEAL)


4  *


5  * This file is part of HeuristicLab.


6  *


7  * HeuristicLab is free software: you can redistribute it and/or modify


8  * it under the terms of the GNU General Public License as published by


9  * the Free Software Foundation, either version 3 of the License, or


10  * (at your option) any later version.


11  *


12  * HeuristicLab is distributed in the hope that it will be useful,


13  * but WITHOUT ANY WARRANTY; without even the implied warranty of


14  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the


15  * GNU General Public License for more details.


16  *


17  * You should have received a copy of the GNU General Public License


18  * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.


19  */


20  #endregion


21 


22  using System.Linq;


23  using HeuristicLab.Common;


24  using HeuristicLab.Core;


25  using HeuristicLab.Optimization;


26  using HeuristicLab.Parameters;


27  using HEAL.Attic;


28  using HeuristicLab.Problems.DataAnalysis;


29 


30  namespace HeuristicLab.Algorithms.DataAnalysis {


31  [Item("Random", "Initializes the matrix randomly.")]


32  [StorableType("C799E0AA1DC544BF98F96CFEF5521BBC")]


33  public sealed class RandomInitializer : NcaInitializer, IStochasticOperator {


34  public ILookupParameter<IRandom> RandomParameter {


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


36  }


37 


38  [StorableConstructor]


39  private RandomInitializer(StorableConstructorFlag _) : base(_) { }


40  private RandomInitializer(RandomInitializer original, Cloner cloner) : base(original, cloner) { }


41  public RandomInitializer()


42  : base() {


43  Parameters.Add(new LookupParameter<IRandom>("Random", "The random number generator to use."));


44  }


45 


46  public override IDeepCloneable Clone(Cloner cloner) {


47  return new RandomInitializer(this, cloner);


48  }


49 


50  public override double[,] Initialize(IClassificationProblemData data, int dimensions) {


51  var attributes = data.AllowedInputVariables.Count();


52 


53  var random = RandomParameter.ActualValue;


54  var matrix = new double[attributes, dimensions];


55  for (int i = 0; i < attributes; i++)


56  for (int j = 0; j < dimensions; j++)


57  matrix[i, j] = random.NextDouble();


58 


59  return matrix;


60  }


61  }


62  }

