public static WaveletCollection FromWaveletData(IEnumerable <double> waveletData)
{
int index = 0;
double smoothingCoefficient = waveletData.ElementAt(index++);
List <List <double> > decomposition = new List <List <double> >();
int levelCount = (int)Scalars.Logarithm(2, waveletData.Count());
for (int levelIndex = 0; levelIndex < levelCount; levelIndex++)
{
List <double> level = new List <double>();
int itemCount = (int)Scalars.Exponentiate(2, levelIndex);
for (int itemIndex = 0; itemIndex < itemCount; itemIndex++)
{
level.Add(waveletData.ElementAt(index++));
}
decomposition.Add(level);
}
return(new WaveletCollection(smoothingCoefficient, decomposition));
}
// Intended for synthesis
// Input \ Output | Real | Imaginary
// ---------------|------|----------
// Real | + | -
// Imaginary | + | +
static MatrixComplex GetReverseTransformation(int size)
{
MatrixComplex transformation = new MatrixComplex(size, size);
Complex factor = (2 * Math.PI / size) * Complex.ImaginaryOne;
for (int row = 0; row < transformation.RowCount; row++)
{
for (int column = 0; column < transformation.ColumnCount; column++)
{
transformation[row, column] = Scalars.Exponentiate(-factor * row * column);
}
}
return((1 / Scalars.SquareRoot(size)) * transformation);
}
public WaveletCollection(double smoothingCoefficient, IEnumerable <IEnumerable <double> > decomposition)
{
if (decomposition == null)
{
throw new ArgumentNullException("levels");
}
for (int levelIndex = 0; levelIndex < decomposition.Count(); levelIndex++)
{
if (decomposition.ElementAt(levelIndex).Count() != (int)Scalars.Exponentiate(2, levelIndex))
{
throw new ArgumentException("parameter 'decomposition' doesn't have the correct format");
}
}
this.smoothingCoefficient = smoothingCoefficient;
this.decomposition = decomposition;
}
public double NextExponential(double minimum, double maximum)
{
return(Scalars.Logarithm(NextDouble(Scalars.Exponentiate(minimum), Scalars.Exponentiate(maximum))));
}
