#region License Information /* HeuristicLab * Copyright (C) 2002-2015 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.Collections.Generic; using System.Drawing; using System.Linq; using HeuristicLab.Common; using HeuristicLab.Core; using HeuristicLab.DataPreprocessing.Interfaces; namespace HeuristicLab.DataPreprocessing { [Item("Histogram", "Represents the histogram grid.")] public class HistogramContent : PreprocessingChartContent { public static new Image StaticItemImage { get { return HeuristicLab.Common.Resources.VSImageLibrary.Statistics; } } private const int MAX_DISTINCT_VALUES_FOR_CLASSIFCATION = 20; private int classifierVariableIndex = 0; public int ClassifierVariableIndex { get { return this.classifierVariableIndex; } set { this.classifierVariableIndex = value; } } public bool IsDetailedChartViewEnabled { get; set; } public HistogramContent(IFilteredPreprocessingData preprocessingData) : base(preprocessingData) { AllInOneMode = false; } public HistogramContent(HistogramContent content, Cloner cloner) : base(content, cloner) { } public override IDeepCloneable Clone(Cloner cloner) { return new HistogramContent(this, cloner); } public IEnumerable GetVariableNamesForHistogramClassification() { List doubleVariableNames = new List(); //only return variable names from type double for (int i = 0; i < PreprocessingData.Columns; ++i) { if (PreprocessingData.VariableHasType(i)) { double distinctValueCount = PreprocessingData.GetValues(i).GroupBy(x => x).Count(); bool distinctValuesOk = distinctValueCount <= MAX_DISTINCT_VALUES_FOR_CLASSIFCATION; if (distinctValuesOk) doubleVariableNames.Add(PreprocessingData.GetVariableName(i)); } } return doubleVariableNames; } } }