/// <summary>
/// Switch between different metrics.
/// </summary>
/// <param name="PR"></param>
/// <param name="similarityMetric"></param>
/// <returns></returns>
private static void ComputeSimilarities(PrefRelations PR,
Metric.SimilarityMetric similarityMetric, int maxCountOfNeighbors,
double minSimilarityThreshold, out SimilarityData neighborsByObject,
out HashSet <Tuple <int, int> > strongSimilarityIndicators)
{
int dimension = PR.UserCount;
HashSet <Tuple <int, int> > strongSimilarityIndicators_out = new HashSet <Tuple <int, int> >();
SimilarityData neighborsByObject_out = new SimilarityData(maxCountOfNeighbors);
// Compute similarity for the lower triangular
Object lockMe = new Object();
Parallel.For(0, dimension, i =>
{
Utils.PrintEpoch("Progress current/total", i, dimension);
for (int j = 0; j < dimension; j++)
{
if (i == j)
{
continue;
} // Skip self similarity
else if (i > j)
{
switch (similarityMetric)
{
case SimilarityMetric.CosinePrefRelations:
double cosinePR = Metric.cosinePR(PR, i, j);
lock (lockMe)
{
if (cosinePR > minSimilarityThreshold)
{
strongSimilarityIndicators_out.Add(new Tuple <int, int>(i, j));
}
neighborsByObject_out.AddSimilarityData(i, j, cosinePR);
neighborsByObject_out.AddSimilarityData(j, i, cosinePR);
}
break;
// More metrics to be added here.
}
}
}
});
neighborsByObject = neighborsByObject_out;
strongSimilarityIndicators = strongSimilarityIndicators_out;
}
private static void ComputeSimilarities(Matrix <double> R,
Metric.SimilarityMetric similarityMetric, int maxCountOfNeighbors,
double minSimilarityThreshold, out SimilarityData neighborsByObject,
out HashSet <Tuple <int, int> > strongSimilarityIndicators)
{
int dimension = R.RowCount;
List <Vector <double> > rows = new List <Vector <double> >(R.EnumerateRows());
// I assume that the rows are enumerated from first to last
Debug.Assert(rows[0].Sum() == R.Row(0).Sum());
Debug.Assert(rows[rows.Count - 1].Sum() == R.Row(rows.Count - 1).Sum());
List <Tuple <int, int> > strongSimilarityIndicators_out = new List <Tuple <int, int> >();
SimilarityData neighborsByObject_out = new SimilarityData(maxCountOfNeighbors);
Object lockMe = new Object();
Parallel.For(0, dimension, indexOfRow =>
{
Utils.PrintEpoch("Progress current/total", indexOfRow, dimension);
Dictionary <Tuple <int, int>, double> similarityCache = new Dictionary <Tuple <int, int>, double>();
List <Tuple <int, int> > strongSimilarityIndocatorCache = new List <Tuple <int, int> >();
for (int indexOfNeighbor = 0; indexOfNeighbor < dimension; indexOfNeighbor++)
{
if (indexOfRow == indexOfNeighbor)
{
continue;
} // Skip self similarity
else if (indexOfRow > indexOfNeighbor)
{
switch (similarityMetric)
{
case Metric.SimilarityMetric.CosineRating:
// TODO: make a note that it really matters to make it sparse, it computes differently!
double cosine = Metric.CosineR((SparseVector)rows[indexOfRow], (SparseVector)rows[indexOfNeighbor]);
if (cosine > minSimilarityThreshold)
{
strongSimilarityIndocatorCache.Add(new Tuple <int, int>(indexOfRow, indexOfNeighbor));
strongSimilarityIndocatorCache.Add(new Tuple <int, int>(indexOfNeighbor, indexOfRow));
}
similarityCache[new Tuple <int, int>(indexOfRow, indexOfNeighbor)] = cosine;
similarityCache[new Tuple <int, int>(indexOfNeighbor, indexOfRow)] = cosine;
break;
case Metric.SimilarityMetric.PearsonRating:
double pearson = Metric.PearsonR((SparseVector)rows[indexOfRow], (SparseVector)rows[indexOfNeighbor]);
if (pearson > minSimilarityThreshold)
{
strongSimilarityIndocatorCache.Add(new Tuple <int, int>(indexOfRow, indexOfNeighbor));
strongSimilarityIndocatorCache.Add(new Tuple <int, int>(indexOfNeighbor, indexOfRow));
}
similarityCache[new Tuple <int, int>(indexOfRow, indexOfNeighbor)] = pearson;
similarityCache[new Tuple <int, int>(indexOfNeighbor, indexOfRow)] = pearson;
break;
}
}
}
lock (lockMe)
{
foreach (var entry in similarityCache)
{
neighborsByObject_out.AddSimilarityData(entry.Key.Item1, entry.Key.Item2, entry.Value);
}
strongSimilarityIndicators_out.AddRange(strongSimilarityIndocatorCache);
}
});
neighborsByObject = neighborsByObject_out;
neighborsByObject.SortAndRemoveNeighbors();
strongSimilarityIndicators = new HashSet <Tuple <int, int> >(strongSimilarityIndicators_out);
}
