/*
This is for multi-channel "sounds" like EEG signals.
The cross-correlation between channel i and channel j is defined as
sum(k=1..nsamples, (z[i][k] - mean[i])(z[j][k + tau] - mean[j]))*samplingTime
*/
autoCrossCorrelationTable Sound_to_CrossCorrelationTable (Sound me, double startTime, double endTime, double lagStep) {
try {
if (endTime <= startTime) {
startTime = my xmin;
endTime = my xmax;
}
long lag = (long) floor (lagStep / my dx); // ppgb: voor al dit soort dingen geldt: waarom afronden naar beneden?
long i1 = Sampled_xToNearestIndex (me, startTime);
if (i1 < 1) {
i1 = 1;
}
long i2 = Sampled_xToNearestIndex (me, endTime);
if (i2 > my nx) {
i2 = my nx;
}
i2 -= lag;
long nsamples = i2 - i1 + 1;
if (nsamples <= my ny) {
Melder_throw (U"Not enough samples, choose a longer interval.");
}
autoCrossCorrelationTable thee = CrossCorrelationTable_create (my ny);
NUMcrossCorrelate_rows (my z, my ny, i1, i2, lag, thy data, thy centroid, my dx);
thy numberOfObservations = nsamples;
return thee;
} catch (MelderError) {
Melder_throw (me, U": CrossCorrelationTable not created.");
}
}
/*
This is for multi-channel "sounds" like EEG signals.
The cross-correlation between channel i and channel j is defined as
sum(k=1..nsamples, (z[i][k] - mean[i])(z[j][k + tau] - mean[j]))*samplingTime
*/
CrossCorrelationTable Sound_to_CrossCorrelationTable (Sound me, double startTime, double endTime, double lagTime) {
try {
if (endTime <= startTime) {
startTime = my xmin;
endTime = my xmax;
}
long lag = lagTime / my dx;
long i1 = Sampled_xToNearestIndex (me, startTime);
if (i1 < 1) {
i1 = 1;
}
long i2 = Sampled_xToNearestIndex (me, endTime);
if (i2 > my nx) {
i2 = my nx;
}
i2 -= lag;
long nsamples = i2 - i1 + 1;
if (nsamples <= my ny) {
Melder_throw ("Not enough samples, choose a longer interval.");
}
autoCrossCorrelationTable thee = CrossCorrelationTable_create (my ny);
NUMcrossCorrelate_rows (my z, my ny, i1, i2, lag, thy data, thy centroid, my dx);
thy numberOfObservations = nsamples;
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": CrossCorrelationTable not created.");
}
}
autoCrossCorrelationTable CrossCorrelationTable_and_Diagonalizer_diagonalize (CrossCorrelationTable me, Diagonalizer thee) {
try {
if (my numberOfRows != thy numberOfRows) {
Melder_throw (U"The CrossCorrelationTable and the Diagonalizer matrix dimensions must be equal.");
}
autoCrossCorrelationTable him = CrossCorrelationTable_create (my numberOfColumns);
NUMdmatrices_multiply_VCVp (his data, thy data, my numberOfColumns, my numberOfColumns, my data, 1);
return him;
} catch (MelderError) {
Melder_throw (U"CrossCorrelationTable not diagonalized.");
}
}
/*
* Generate n different cct's that have a common diagonalizer.
*/
autoCrossCorrelationTables CrossCorrelationTables_createTestSet (long dimension, long n, int firstPositiveDefinite, double sigma) {
try {
// Start with a square matrix with random gaussian elements and make its singular value decomposition UDV'
// The V matrix will be the common diagonalizer matrix that we use.
autoNUMmatrix<double> d (1, dimension, 1, dimension);
for (long i = 1; i <= dimension; i++) { // Generate the rotation matrix
for (long j = 1; j <= dimension; j++) {
d[i][j] = NUMrandomGauss (0, 1);
}
}
autoNUMmatrix<double> v (1, dimension, 1, dimension);
autoSVD svd = SVD_create_d (d.peek(), dimension, dimension);
autoCrossCorrelationTables me = CrossCorrelationTables_create ();
for (long i = 1; i <= dimension; i++) {
for (long j = 1; j <= dimension; j++) {
d[i][j] = 0;
}
}
// Start with a diagonal matrix D and calculate V'DV
for (long k = 1; k <= n; k++) {
autoCrossCorrelationTable ct = CrossCorrelationTable_create (dimension);
double low = k == 1 && firstPositiveDefinite ? 0.1 : -1;
for (long i = 1; i <= dimension; i++) {
d[i][i] = NUMrandomUniform (low, 1);
}
for (long i = 1; i <= dimension; i++) {
for (long j = 1; j <= dimension; j++) {
v[i][j] = NUMrandomGauss (svd -> v[i][j], sigma);
}
}
// we need V'DV, however our V has eigenvectors row-wise -> VDV'
NUMdmatrices_multiply_VCVp (ct -> data, v.peek(), dimension, dimension, d.peek(), 1);
Collection_addItem_move (me.peek(), ct.move());
}
return me;
} catch (MelderError) {
Melder_throw (U"CrossCorrelationTables test set not created.");
}
}
/* Calculate the CrossCorrelationTable between the channels of two multichannel sounds irrespective of the domains.
* Both sounds are treated as if their domain runs from 0 to duration.
* Outside the chosen interval the sounds are assumed to be zero
*/
autoCrossCorrelationTable Sounds_to_CrossCorrelationTable_combined (Sound me, Sound thee, double relativeStartTime, double relativeEndTime, double lagStep) {
try {
if (my dx != thy dx) {
Melder_throw (U"Sampling frequencies must be equal.");
}
if (relativeEndTime <= relativeStartTime) {
relativeStartTime = my xmin;
relativeEndTime = my xmax;
}
long ndelta = (long) floor (lagStep / my dx), nchannels = my ny + thy ny;
long i1 = Sampled_xToNearestIndex (me, relativeStartTime);
if (i1 < 1) {
i1 = 1;
}
long i2 = Sampled_xToNearestIndex (me, relativeEndTime);
if (i2 > my nx) {
i2 = my nx;
}
i2 -= ndelta;
long nsamples = i2 - i1 + 1;
if (nsamples <= nchannels) {
Melder_throw (U"Not enough samples");
}
autoCrossCorrelationTable him = CrossCorrelationTable_create (nchannels);
autoNUMvector<double *> data (1, nchannels);
for (long i = 1; i <= my ny; i++) {
data[i] = my z[i];
}
for (long i = 1; i <= thy ny; i++) {
data[i + my ny] = thy z[i];
}
NUMcrossCorrelate_rows (data.peek(), nchannels, i1, i2, ndelta, his data, his centroid, my dx);
his numberOfObservations = nsamples;
return him;
} catch (MelderError) {
Melder_throw (me, U": CrossCorrelationTable not created.");
}
}
autoCrossCorrelationTable CrossCorrelationTable_createSimple (char32 *covars, char32 *centroid, long numberOfSamples) {
try {
long dimension = Melder_countTokens (centroid);
long ncovars = Melder_countTokens (covars);
long ncovars_wanted = dimension * (dimension + 1) / 2;
if (ncovars != ncovars_wanted) Melder_throw (U"The number of matrix elements and the number of "
U"centroid elements are not in concordance. There should be \"d(d+1)/2\" matrix values and \"d\" centroid values.");
autoCrossCorrelationTable me = CrossCorrelationTable_create (dimension);
// Construct the full matrix from the upper-diagonal elements
long inum = 1, irow = 1;
for (char32 *token = Melder_firstToken (covars); token != nullptr && inum <= ncovars_wanted; token = Melder_nextToken (), inum++) {
double number;
long nmissing = (irow - 1) * irow / 2;
long inumc = inum + nmissing;
irow = (inumc - 1) / dimension + 1;
long icol = ( (inumc - 1) % dimension) + 1;
Interpreter_numericExpression (nullptr, token, &number);
my data[irow][icol] = my data[icol][irow] = number;
if (icol == dimension) {
irow++;
}
}
inum = 1;
for (char32 *token = Melder_firstToken (centroid); token != nullptr && inum <= dimension; token = Melder_nextToken (), inum++) {
double number;
Interpreter_numericExpression (nullptr, token, &number);
my centroid[inum] = number;
}
my numberOfObservations = numberOfSamples;
return me;
} catch (MelderError) {
Melder_throw (U"CrossCorrelationTable not created.");
}
}
