source: branches/EfficientGlobalOptimization/HeuristicLab.Algorithms.EGO/DiscreteEGO/DiscreteSampleCollector.cs @ 16101

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 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.Data;
using HeuristicLab.Encodings.IntegerVectorEncoding;
using HeuristicLab.Operators;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
using HeuristicLab.Problems.DataAnalysis;

namespace HeuristicLab.Algorithms.EGO {
  [Item("DiscreteSampleCollector", "Collects IntegerVectors into a modifiablbe dataset")]
  [StorableClass]
  public class DiscreteSampleCollector : InstrumentedOperator {
    public override bool CanChangeName => true;

    public ILookupParameter<IntegerVector> IntegerVectorParameter => (ILookupParameter<IntegerVector>)Parameters["IntegerVector"];
    public ILookupParameter<DoubleValue> QualityParameter => (ILookupParameter<DoubleValue>)Parameters["Quality"];
    public ILookupParameter<ModifiableDataset> DatasetParameter => (ILookupParameter<ModifiableDataset>)Parameters["Dataset"];

    [StorableConstructor]
    protected DiscreteSampleCollector(bool deserializing) : base(deserializing) { }
    protected DiscreteSampleCollector(DiscreteSampleCollector original, Cloner cloner) : base(original, cloner) { }
    public DiscreteSampleCollector() {
      Parameters.Add(new LookupParameter<IntegerVector>("IntegerVector", "The vector which should be collected."));
      Parameters.Add(new LookupParameter<DoubleValue>("Quality", "The quality associated which this vector"));
      Parameters.Add(new LookupParameter<ModifiableDataset>("Dataset", "The Dataset in wich new samples are stored."));
    }

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

    public sealed override IOperation InstrumentedApply() {
      var vector = IntegerVectorParameter.ActualValue;
      var quality = QualityParameter.ActualValue.Value;
      var data = DatasetParameter.ActualValue;

      if (data.Columns != vector.Length + 1) {
        if (data.Columns != 0 || data.Rows != 0) throw new OperatorExecutionException(this, "dataset columns do not match samplesize+1");
        var variableNames = vector.Select((x, i) => string.Format("input" + i)).Concat("output".ToEnumerable());
        var variableValues = vector.Select(x => (double)x).Concat(quality.ToEnumerable()).Select(x => new List<double> { x });
        data = DatasetParameter.ActualValue = new ModifiableDataset(variableNames, variableValues);
      } else AddRow(data, vector, quality);

      return base.InstrumentedApply();
    }


    private static void AddRow(ModifiableDataset data, IntegerVector vector, double quality) {
      var row = new object[vector.Length + 1];
      for (var i = 0; i < vector.Length; i++)
        row[i] = (double)vector[i];
      row[vector.Length] = quality;
      data.AddRow(row);
    }



  }
}