public WaveletD4Transform(int dataDim)
{
dim = HaarTransform.RoundDimension(dataDim);
InitializeConstants();
D4 = WaveTransforms.App.ScriptApp.New.NumberTable(dim, dim);
// Algorithm adapated from: http://www.bearcave.com/misl/misl_tech/wavelets/daubechies/daub.java
for (int row = 0; row < dim; row++)
{
double[] v = (D4.Matrix as double[][])[row];
Array.Clear(v, 0, v.Length);
v[row] = 1.0;
for (int n = v.Length; n >= 4; n >>= 1)
{
TransformD4(v, n);
}
}
int interval = dim / 16;
for (int k = 0; k < dim; k++)
{
D4.RowSpecList[k].Type = D4.ColumnSpecList[k].Group = (short)(k / interval);
}
List <int> rows = new List <int>();
for (int row = 0; row < dataDim; row++)
{
rows.Add(row);
}
D4 = D4.SelectRows(rows);
}
public WalshTransform(int dataDim)
{
dim = HaarTransform.RoundDimension(dataDim);
walsh = WaveTransforms.App.ScriptApp.New.NumberTable(dim, dim);
//
// The Walsh matrix differs from Hadamard in ordering of the base vectors:
// the former uses the sequency order (bit-reversal or Gray code permutation)
// while the latter uses nature order resulted
// from recursive calculation.
//
// The following code is adapted from http://www.musicdsp.org/showone.php?id=18.
int log2 = Log2(dim);
for (int row = 0; row < dim; row++)
{
double[] v = (walsh.Matrix as double[][])[row];
v[row] = 1.0;
for (int i = 0; i < log2; i++)
{
int i2 = (1 << i);
for (int j = 0; j < (1 << log2); j += 2 * i2)
{
for (int k = 0; k < i2; k++)
{
int jk = j + k;
double a = v[jk] + v[jk + i2];
v[jk + i2] = v[jk] - v[jk + i2];
v[jk] = a;
}
}
}
}
//
// perform the bit-reserve and gray code re-ordering.
//
int[] order = new int[dim];
int[] grayCode = GrayOrder(log2);
for (int i = 0; i < dim; i++)
{
order[i] = ReverseOrder(log2, grayCode[i]);
}
// Permutate the rows.
double[][] newM = new double[dim][];
double[][] M = walsh.Matrix as double[][];
for (int row = 0; row < dim; row++)
{
newM[row] = M[order[row]];
}
for (int row = 0; row < dim; row++)
{
M[row] = newM[row];
}
int interval = dim / 16;
for (int k = 0; k < dim; k++)
{
walsh.RowSpecList[k].Type = walsh.ColumnSpecList[k].Group = (short)(k / interval);
}
// Remove the redudant rows.
List <int> rows = new List <int>();
for (int row = 0; row < dataDim; row++)
{
rows.Add(row);
}
walsh = walsh.SelectRows(rows);
// Normalize the matrix.
double normFactor = Math.Pow(2, -log2 / 2.0);
for (int row = 0; row < walsh.Rows; row++)
{
for (int col = 0; col < walsh.Columns; col++)
{
walsh.Matrix[row][col] *= normFactor;
}
}
}
