/// <summary>
/// Initializes a new instance of the <see cref="RelevanceOfStroke"/> class.
/// </summary>
/// <param name="transitions">The transitions</param>
/// <param name="iterations">The iterations</param>
public RelevanceOfStroke(Transitions transitions, int iterations = 50)
: base(transitions)
{
this.Iterations = iterations;
this.CountedWinningProbabilities = DenseVector.Create(
2,
i =>
{
var w = i == 0 ? MatchPlayer.First : MatchPlayer.Second;
return(this.Match.Rallies.Count(r => r.Winner == w) /
(double)this.Match.Rallies.Count);
});
var probabilities = transitions.TransitionProbabilities;
var start = MakeStartVector(probabilities.ColumnCount);
this.WinningProbabilities = Markov.Simulate(probabilities, start, iterations)
.SubVector(probabilities.ColumnCount - 2, 2);
this.ByScorer = this.ComputeRelevanceOfStroke();
// Transform relevance of stroke to support plotting
this.ByStriker = new DenseMatrix(
this.ByScorer.RowCount,
this.ByScorer.ColumnCount);
foreach (var pair in this.ByScorer.EnumerateRowsIndexed())
{
var v = pair.Item2;
var sourceRow = pair.Item1;
var targetColumn = sourceRow % 2;
var targetRow = sourceRow - targetColumn;
this.ByStriker.SetColumn(
targetColumn,
targetRow,
v.Count,
targetColumn == 0 ? v : DenseVector.OfEnumerable(v.Reverse()));
}
}
/// <summary>
/// Computes the relevance of stroke.
/// </summary>
/// <returns>The relevance of strokes</returns>
private Matrix <double> ComputeRelevanceOfStroke()
{
var probabilities = this.Transitions.TransitionProbabilities;
var relevance = new DenseMatrix(probabilities.RowCount - 2, 2);
var start = MakeStartVector(probabilities.ColumnCount);
for (int j = 2; j < probabilities.RowCount - 2; ++j)
{
// Compute the relevance for each row in the transition matrix.
// We can skip the zero rows, because there are no transitions
// to zero strokes anyway
for (int k = 0; k < 2; ++k)
{
// and for each win/loose combination. k==0 means the first
// player won, k==1 means the second player won.
var m = probabilities.Clone();
var p = m[j, m.ColumnCount - 2 + k];
var d = Delta(p);
for (int i = 0; i < m.ColumnCount; ++i)
{
if (m[j, i] != 0)
{
m[j, i] = m[j, i] - ((d * m[j, i]) / (1 - p));
}
}
m[j, m.ColumnCount - 2 + k] = p + d;
// For even rows, we take the winning probability of the first player,
// for odd rows, the probability of the second player.
var result = Markov.Simulate(m, start, this.Iterations)[m.ColumnCount - 2 + (j % 2)];
var pWin = this.WinningProbabilities[j % 2];
relevance[j, k] = result - pWin;
}
}
return(relevance);
}
