#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 . */ #endregion using System; using System.Collections.Generic; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.Data; using HeuristicLab.Encodings.ConditionActionEncoding; using HeuristicLab.Encodings.VariableVector; using HeuristicLab.Parameters; using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; using HeuristicLab.Problems.ConditionActionClassification; using HeuristicLab.Problems.DataAnalysis; namespace HeuristicLab.Problems.VariableVectorClassification { [StorableClass] [Item("CombinedIntegerVectorClassificationProblemData", "A problem data for LCS.")] public class VariableVectorClassificationProblemData : ConditionActionClassificationProblemData, IVariableVectorClassificationProblemData { #region parameter properites public IValueParameter SampleVariableVectorParameter { get { return (IValueParameter)Parameters["SampleVariableVector"]; } } public IFixedValueParameter SpreadPercentageParameter { get { return (IFixedValueParameter)Parameters["SpreadPercentage"]; } } #endregion #region properties public VariableVector SampleVariableVector { get { return SampleVariableVectorParameter.Value; } } public PercentValue SpreadPercentage { get { return SpreadPercentageParameter.Value; } } #endregion [StorableConstructor] protected VariableVectorClassificationProblemData(bool deserializing) : base(deserializing) { } protected VariableVectorClassificationProblemData(VariableVectorClassificationProblemData original, Cloner cloner) : base(original, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new VariableVectorClassificationProblemData(this, cloner); } public VariableVectorClassificationProblemData(Dataset dataset, IEnumerable allowedConditionVariables, IEnumerable allowedActionVariables) : base(dataset, allowedConditionVariables, allowedActionVariables) { Parameters.Add(new ValueParameter("SampleVariableVector", "", GenerateSampleVariableVector(dataset, allowedConditionVariables, allowedActionVariables))); Parameters.Add(new FixedValueParameter("SpreadPercentage", "", new PercentValue(0.5))); } private VariableVector GenerateSampleVariableVector(Dataset dataset, IEnumerable allowedConditionVariables, IEnumerable allowedActionVariables) { var conditionVariables = GetVariablesOfDataSet(dataset, allowedConditionVariables); var actionVariables = GetVariablesOfDataSet(dataset, allowedActionVariables); if (actionVariables.Count() == 0 || !actionVariables.All(x => x is IActionVariable)) { throw new ArgumentException("Action variable can not be empty and all action variables have to be of type int or string."); } return new VariableVector(conditionVariables, actionVariables); } private IEnumerable GetVariablesOfDataSet(DataAnalysis.Dataset dataset, IEnumerable allowedVariables) { var variables = new List(); foreach (var variableName in allowedVariables) { var variableValues = dataset.GetValues(variableName); HeuristicLab.Encodings.VariableVector.IVariable variable; if (variableValues is List) { variable = new StringVariable(variableName, (variableValues as List).Distinct()); } else if (variableValues is List) { var doubleValues = (variableValues as List).Distinct(); if (doubleValues.All(x => x % 1 == 0)) { // ToList call is necessary, because otherwise it wouldn't be possible to serialize it variable = new IntVariable(variableName, doubleValues.Select(x => Convert.ToInt32(x)).ToList()); } else { variable = new DoubleVariable(variableName, doubleValues); } } else { throw new ArgumentException("There is no matching variable type for the values in the dataset"); } variables.Add(variable); } return variables; } public override IInput FetchInput(int rowNumber) { if (!fetchInputCache.ContainsKey(rowNumber)) { VariableVectorInput input = new VariableVectorInput(); IEnumerable variableNames = SampleVariableVector.Condition.VariableDictionary.Keys.Union(SampleVariableVector.Action.VariableDictionary.Keys); foreach (var variableName in variableNames) { input.InputDictionary.Add(variableName, Dataset.GetValue(rowNumber, variableName)); } fetchInputCache.Add(rowNumber, input); } return fetchInputCache[rowNumber]; } protected IDictionary fetchActionCache = new Dictionary(); public override IAction FetchAction(int rowNumber) { if (!fetchActionCache.ContainsKey(rowNumber)) { var action = SampleVariableVector.Action.GetEmptyCopy(); foreach (var variableName in action.VariableDictionary.Keys) { action.VariableDictionary[variableName].SetTo(Dataset.GetValue(rowNumber, variableName)); } fetchActionCache.Add(rowNumber, action); } return fetchActionCache[rowNumber]; } protected override void ActionConditionVariablesChanged() { SampleVariableVectorParameter.Value = GenerateSampleVariableVector(Dataset, AllowedConditionVariables, AllowedActionVariables); } } }