public static int CastStructToInterfaceAndWriteInBoxed(Coord2 arg)
{
INormalize tmp = arg;
var y = (int)tmp.Norm();
return(arg.X + y); // arg.X should not change
}
public (double Probability, double Normalization, VectorData Vector) GetVector(TextVectorCell[] cells)
{
if (cells is null)
{
throw new ArgumentNullException(nameof(cells));
}
log.LogDebug("GetVector");
var vectorCells = new List <VectorCell>();
var vector = new double[featureTable.Count];
for (var i = 0; i < featureTable.Count; i++)
{
vector[i] = 0;
}
var unknownIndexes = vector.Length;
foreach (TextVectorCell textCell in cells)
{
VectorCell cell = GetCell(textCell);
if (cell != null)
{
vector[cell.Index] = cell.X;
vectorCells.Add(cell);
}
else
{
// if inverted exist in database, it is very likely that normal version has opposite meaning
cell = GetCell(new TextVectorCell(textCell.Name.GetOpposite(), Math.Abs(textCell.Value)));
if (cell != null)
{
var theata = textCell.Name.IsInverted() ? cell.Theta / 2 : cell.Theta / 4;
cell = new VectorCell(unknownIndexes, textCell, -theata);
vectorCells.Add(cell);
unknownIndexes++;
}
}
}
INormalize normalized = vector.Normalize(NormalizationType.L2);
vector = normalized.GetNormalized.ToArray();
var probability = Classifier.Probability(vector);
// do not normalize data - SVM operates with normalized already. Second time normalization is not required.
return(probability, normalized.Coeficient, new VectorData(vectorCells.ToArray(), unknownIndexes, Classifier.Model.Threshold, NormalizationType.None));
}
public static double CastStructToInterface(Coord arg)
{
INormalize tmp = arg;
return(tmp.Norm());
}
