source: branches/2457_ExpertSystem/HeuristicLab.Analysis.FitnessLandscape/3.3/Utilities/DoubleMatrixCharacteristics.cs @ 18183

#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 HeuristicLab.Common;
using HeuristicLab.Data;
using System;
using System.Linq;

namespace HeuristicLab.Analysis.FitnessLandscape {
  public static class DoubleMatrixCharacteristics {
    public static double CoeffVariation(DoubleMatrix m) {
      var avg = m.Average();
      var stdDev = m.StandardDeviation();
      return stdDev / avg;
    }

    public static double Sparsity(DoubleMatrix m) {
      return m.Count(x => x == 0) / (double)(m.Rows * m.Columns);
    }

    public static double Disparity(DoubleMatrix m) {
      if (m.Rows != m.Columns) throw new ArgumentException("not a quadratic matrix", "m");
      var n = m.Rows;
      var avg = m.Sum() / n;
      var disparity = 0.0;
      for (var i = 0; i < n; i++) {
        var tmp = 0.0;
        for (var j = 0; j < n; j++) {
          tmp += m[i, j];
        }
        disparity += (tmp - avg) * (tmp - avg);
      }
      return Math.Sqrt(disparity / n) / avg;
    }

    public static double Skewness(DoubleMatrix m) {
      double mean = 0, variance = 0, skewness = 0, kurtosis = 0;
      alglib.basestat.samplemoments(m.ToArray(), m.Rows * m.Columns, ref mean, ref variance, ref skewness, ref kurtosis);
      return skewness;
    }

    public static double Asymmetry(DoubleMatrix m) {
      if (m.Rows != m.Columns) throw new ArgumentException("not a quadratic matrix", "m");
      var n = m.Rows;
      var count = 0;
      for (var i = 0; i < n; i++) {
        for (var j = 0; j < i; j++) {
          var v = Math.Abs(m[i, j] - m[j, i]);
          if (v > 0) {
            count++;
          }
        }
      }
      return count / (n * (n - 1) / 2.0);
    }
  }
}