Index: trunk/sources/HeuristicLab.Problems.QuadraticAssignment.Views/3.3/MultidimensionalScaling.cs
===================================================================
--- trunk/sources/HeuristicLab.Problems.QuadraticAssignment.Views/3.3/MultidimensionalScaling.cs	(revision 12753)
+++ 	(revision )
@@ -1,130 +1,0 @@
-﻿#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 <http://www.gnu.org/licenses/>.
- */
-#endregion
-
-using System;
-using HeuristicLab.Data;
-
-namespace HeuristicLab.Problems.QuadraticAssignment.Views {
-  public static class MultidimensionalScaling {
-
-    /// <summary>
-    /// Performs the Kruskal-Shepard algorithm and applies a gradient descent method
-    /// to fit the coordinates such that the difference between the fit distances
-    /// and the actual distances is minimal.
-    /// </summary>
-    /// <param name="distances">A symmetric NxN matrix that specifies the distances between each element i and j. Diagonal elements are ignored.</param>
-    /// <param name="stress">Returns the stress between the fit distances and the actual distances.</param>
-    /// <returns>A Nx2 matrix where the first column represents the x- and the second column the y coordinates.</returns>
-    public static DoubleMatrix MetricByDistance(DoubleMatrix distances, out double stress) {
-      if (distances == null) throw new ArgumentNullException("distances");
-      if (distances.Rows != distances.Columns) throw new ArgumentException("Distance matrix must be a square matrix.", "distances");
-      stress = 0.0;
-
-      int dimension = distances.Rows;
-      if (dimension == 1) return new DoubleMatrix(new double[,] { { 0, 0 } });
-      else if (dimension == 2) return new DoubleMatrix(new double[,] { { 0, distances[0, 1] } });
-
-      DoubleMatrix coordinates = new DoubleMatrix(dimension, 2);
-      double rad = (2 * Math.PI) / coordinates.Rows;
-      for (int i = 0; i < dimension; i++) {
-        coordinates[i, 0] = 10 * Math.Cos(rad * i);
-        coordinates[i, 1] = 10 * Math.Sin(rad * i);
-      }
-
-      double epsg = 1e-7;
-      double epsf = 0;
-      double epsx = 0;
-      int maxits = 0;
-      alglib.mincgstate state = null;
-      alglib.mincgreport rep;
-
-      for (int iterations = 0; iterations < 10; iterations++) {
-        for (int i = 0; i < dimension; i++) {
-          double[] c = new double[] { coordinates[i, 0], coordinates[i, 1] };
-
-          if (iterations == 0 && i == 0) {
-            alglib.mincgcreate(c, out state);
-            alglib.mincgsetcond(state, epsg, epsf, epsx, maxits);
-          } else {
-            alglib.mincgrestartfrom(state, c);
-          }
-          alglib.mincgoptimize(state, StressGradient, null, new Info(coordinates, distances, i));
-          alglib.mincgresults(state, out c, out rep);
-
-          coordinates[i, 0] = c[0];
-          coordinates[i, 1] = c[1];
-        }
-      }
-      stress = CalculateNormalizedStress(dimension, distances, coordinates);
-      return coordinates;
-    }
-
-    private static void StressGradient(double[] x, ref double func, double[] grad, object obj) {
-      func = 0; grad[0] = 0; grad[1] = 0;
-      Info info = (obj as Info);
-      for (int i = 0; i < info.Coordinates.Rows; i++) {
-        double c = info.Distances[info.Row, i];
-        if (i != info.Row) {
-          double a = info.Coordinates[i, 0];
-          double b = info.Coordinates[i, 1];
-          func += Stress(x, c, a, b);
-          grad[0] += ((2 * x[0] - 2 * a) * Math.Sqrt(x[1] * x[1] - 2 * b * x[1] + x[0] * x[0] - 2 * a * x[0] + b * b + a * a) - 2 * c * x[0] + 2 * a * c) / Math.Sqrt(x[1] * x[1] - 2 * b * x[1] + x[0] * x[0] - 2 * a * x[0] + b * b + a * a);
-          grad[1] += ((2 * x[1] - 2 * b) * Math.Sqrt(x[1] * x[1] - 2 * b * x[1] + x[0] * x[0] - 2 * a * x[0] + b * b + a * a) - 2 * c * x[1] + 2 * b * c) / Math.Sqrt(x[1] * x[1] - 2 * b * x[1] + x[0] * x[0] - 2 * a * x[0] + b * b + a * a);
-        }
-      }
-    }
-
-    private static double Stress(double[] x, double distance, double xCoord, double yCoord) {
-      return Stress(x[0], x[1], distance, xCoord, yCoord);
-    }
-
-    private static double Stress(double x, double y, double distance, double otherX, double otherY) {
-      double d = Math.Sqrt((x - otherX) * (x - otherX)
-                         + (y - otherY) * (y - otherY));
-      return (d - distance) * (d - distance);
-    }
-
-    public static double CalculateNormalizedStress(int dimension, DoubleMatrix distances, DoubleMatrix coordinates) {
-      double stress = 0;
-      for (int i = 0; i < dimension - 1; i++) {
-        for (int j = i + 1; j < dimension; j++) {
-          if (distances[i, j] != 0) {
-            stress += Stress(coordinates[i, 0], coordinates[i, 1], distances[i, j], coordinates[j, 0], coordinates[j, 1])
-              / (distances[i, j] * distances[i, j]);
-          }
-        }
-      }
-      return stress;
-    }
-
-    private class Info {
-      public DoubleMatrix Coordinates { get; set; }
-      public DoubleMatrix Distances { get; set; }
-      public int Row { get; set; }
-
-      public Info(DoubleMatrix c, DoubleMatrix d, int r) {
-        Coordinates = c;
-        Distances = d;
-        Row = r;
-      }
-    }
-  }
-}
