/// <summary>
/// Calculates the quality of the move <paramref name="move"/> by evaluating the changes.
/// The runtime complexity of this method is O(N) with N being the size of the permutation.
/// </summary>
/// <param name="assignment">The current permutation.</param>
/// <param name="move">The move that is to be evaluated if it was applied to the current permutation.</param>
/// <param name="weights">The weights matrix.</param>
/// <param name="distances">The distances matrix.</param>
/// <returns>The relative change in quality if <paramref name="move"/> was applied to <paramref name="assignment"/>.</returns>
public static double Apply(Permutation assignment, ScrambleMove move, DoubleMatrix weights, DoubleMatrix distances) {
double moveQuality = 0;
int min = move.StartIndex;
int max = min + move.ScrambledIndices.Length - 1;
for (int i = min; i <= max; i++) {
int locI = assignment[i];
int newlocI = assignment[min + move.ScrambledIndices[i - min]];
if (locI == newlocI) continue;
for (int j = 0; j < assignment.Length; j++) {
int locJ = assignment[j];
if (j >= min && j <= max) {
int newlocJ = assignment[min + move.ScrambledIndices[j - min]];
moveQuality += weights[i, j] * (distances[newlocI, newlocJ] - distances[locI, locJ]);
if (locJ == newlocJ)
moveQuality += weights[j, i] * (distances[newlocJ, newlocI] - distances[locJ, locI]);
} else {
moveQuality += weights[i, j] * (distances[newlocI, locJ] - distances[locI, locJ]);
moveQuality += weights[j, i] * (distances[locJ, newlocI] - distances[locJ, locI]);
}
}
}
return moveQuality;
}
private ScrambleMove(ScrambleMove original, Cloner cloner)
: base(original, cloner)
{
this.StartIndex = original.StartIndex;
if (original.ScrambledIndices != null)
{
this.ScrambledIndices = (int[])original.ScrambledIndices.Clone();
}
}
public static ScrambleMove[] Apply(Permutation permutation, IRandom random, int sampleSize)
{
int length = permutation.Length;
ScrambleMove[] moves = new ScrambleMove[sampleSize];
for (int i = 0; i < sampleSize; i++)
{
moves[i] = GenerateRandomMove(permutation, random);
}
return(moves);
}
public override IOperation Apply()
{
ScrambleMove move = ScrambleMoveParameter.ActualValue;
Permutation permutation = PermutationParameter.ActualValue;
DoubleValue moveQuality = MoveQualityParameter.ActualValue;
DoubleValue quality = QualityParameter.ActualValue;
ScrambleManipulator.Apply(permutation, move.StartIndex, move.ScrambledIndices);
quality.Value = moveQuality.Value;
return(base.Apply());
}
private ScrambleMove(ScrambleMove original, Cloner cloner)
: base(original, cloner) {
this.StartIndex = original.StartIndex;
if (original.ScrambledIndices != null)
this.ScrambledIndices = (int[])original.ScrambledIndices.Clone();
}
public static ScrambleMove[] Apply(Permutation permutation, IRandom random, int sampleSize) {
int length = permutation.Length;
ScrambleMove[] moves = new ScrambleMove[sampleSize];
for (int i = 0; i < sampleSize; i++) {
moves[i] = GenerateRandomMove(permutation, random);
}
return moves;
}
