static autoPCA NUMdmatrix_to_PCA (double **m, long numberOfRows, long numberOfColumns, bool byColumns) {
try {
if (! NUMdmatrix_hasFiniteElements(m, 1, numberOfRows, 1, numberOfColumns)) {
Melder_throw (U"At least one of the matrix elements is not finite or undefined.");
}
if (NUMfrobeniusnorm (numberOfRows, numberOfColumns, m) == 0.0) {
Melder_throw (U"All values in your table are zero.");
}
autoNUMmatrix<double> mcopy;
long numberOfRows2, numberOfColumns2;
if (byColumns) {
if (numberOfColumns < numberOfRows) {
Melder_warning (U"The number of columns in your table is less than the number of rows. ");
}
numberOfRows2 = numberOfColumns, numberOfColumns2 = numberOfRows;
mcopy.reset (1, numberOfRows2, 1, numberOfColumns2);
for (long i = 1; i <= numberOfRows2; i ++) { // transpose
for (long j = 1; j <= numberOfColumns2; j++) {
mcopy [i] [j] = m [j] [i];
}
}
} else {
if (numberOfRows < numberOfColumns) {
Melder_warning (U"The number of rows in your table is less than the number of columns. ");
}
numberOfRows2 = numberOfRows, numberOfColumns2 = numberOfColumns;
mcopy.reset (1, numberOfRows2, 1, numberOfColumns2);
NUMmatrix_copyElements<double>(m, mcopy.peek(), 1, numberOfRows2, 1, numberOfColumns2);
}
autoPCA thee = Thing_new (PCA);
thy centroid = NUMvector<double> (1, numberOfColumns2);
NUMcentreColumns (mcopy.peek(), 1, numberOfRows2, 1, numberOfColumns2, thy centroid);
Eigen_initFromSquareRoot (thee.get(), mcopy.peek(), numberOfRows2, numberOfColumns2);
thy labels = NUMvector<char32 *> (1, numberOfColumns2);
PCA_setNumberOfObservations (thee.get(), numberOfRows2);
/*
The covariance matrix C = A'A / (N-1). However, we have calculated
the eigenstructure for A'A. This has no consequences for the
eigenvectors, but the eigenvalues have to be divided by (N-1).
*/
for (long i = 1; i <= thy numberOfEigenvalues; i++) {
thy eigenvalues [i] /= (numberOfRows2 - 1);
}
return thee;
} catch (MelderError) {
Melder_throw (U"No PCA created from ", byColumns ? U"columns." : U"rows.");
}
}
ERPTier ERPTier_extractEventsWhereColumn_string (ERPTier me, Table table,
long columnNumber, int which_Melder_STRING, const wchar_t *criterion)
{
try {
Table_checkSpecifiedColumnNumberWithinRange (table, columnNumber);
if (my events -> size != table -> rows -> size)
Melder_throw (me, " & ", table, ": the number of rows in the table (", table -> rows -> size,
") doesn't match the number of events (", my events -> size, ").");
autoERPTier thee = Thing_new (ERPTier);
Function_init (thee.peek(), my xmin, my xmax);
thy numberOfChannels = my numberOfChannels;
thy channelNames = NUMvector <wchar_t *> (1, thy numberOfChannels);
for (long ichan = 1; ichan <= thy numberOfChannels; ichan ++) {
thy channelNames [ichan] = Melder_wcsdup (my channelNames [ichan]);
}
thy events = SortedSetOfDouble_create ();
for (long ievent = 1; ievent <= my events -> size; ievent ++) {
ERPPoint oldEvent = my event (ievent);
TableRow row = table -> row (ievent);
if (Melder_stringMatchesCriterion (row -> cells [columnNumber]. string, which_Melder_STRING, criterion)) {
autoERPPoint newEvent = Data_copy (oldEvent);
Collection_addItem (thy events, newEvent.transfer());
}
}
if (thy events -> size == 0) {
Melder_warning ("No event matches criterion.");
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": events not extracted.");
}
}
void structGraphicsScreen :: v_getMouseLocation (double *xWC, double *yWC) {
#if cairo
GdkEvent *gevent = gdk_display_get_event (d_display);
if (gevent != NULL) {
if (gevent -> type == GDK_BUTTON_RELEASE) {
theMouseDown = false;
}
gdk_event_free (gevent);
}
gint xDC, yDC;
gdk_window_get_pointer (d_window, & xDC, & yDC, NULL);
Graphics_DCtoWC (this, xDC, yDC, xWC, yWC);
#elif cocoa
#elif win
POINT pos;
if (! GetCursorPos (& pos)) { Melder_warning (L"Cannot get cursor position."); return; }
ScreenToClient (d_winWindow, & pos);
Graphics_DCtoWC (this, pos. x, pos. y, xWC, yWC);
#elif mac
if (HIGetMousePosition != NULL && false) { // AVAILABLE_MAC_OS_X_VERSION_10_5_AND_LATER
//Melder_casual ("HIGetMousePosition exists");
HIPoint mouseLoc;
HIGetMousePosition (kHICoordSpaceWindow, GetWindowFromPort (d_macPort), & mouseLoc);
Graphics_DCtoWC (this, mouseLoc. x, mouseLoc. y, xWC, yWC);
} else {
Point mouseLoc;
GetMouse (& mouseLoc); // AVAILABLE_MAC_OS_X_VERSION_10_0_AND_LATER_BUT_DEPRECATED_IN_MAC_OS_X_VERSION_10_5
Graphics_DCtoWC (this, mouseLoc. h, mouseLoc. v, xWC, yWC);
}
#endif
}
autoERPTier ERPTier_extractEventsWhereColumn_number (ERPTier me, Table table, long columnNumber, int which_Melder_NUMBER, double criterion) {
try {
Table_checkSpecifiedColumnNumberWithinRange (table, columnNumber);
Table_numericize_Assert (table, columnNumber); // extraction should work even if cells are not defined
if (my events -> size != table -> rows -> size)
Melder_throw (me, U" & ", table, U": the number of rows in the table (", table -> rows -> size,
U") doesn't match the number of events (", my events -> size, U").");
autoERPTier thee = Thing_new (ERPTier);
Function_init (thee.peek(), my xmin, my xmax);
thy numberOfChannels = my numberOfChannels;
thy channelNames = NUMvector <char32 *> (1, thy numberOfChannels);
for (long ichan = 1; ichan <= thy numberOfChannels; ichan ++) {
thy channelNames [ichan] = Melder_dup (my channelNames [ichan]);
}
thy events = SortedSetOfDouble_create ();
for (long ievent = 1; ievent <= my events -> size; ievent ++) {
ERPPoint oldEvent = my event (ievent);
TableRow row = table -> row (ievent);
if (Melder_numberMatchesCriterion (row -> cells [columnNumber]. number, which_Melder_NUMBER, criterion)) {
autoERPPoint newEvent = Data_copy (oldEvent);
Collection_addItem_move (thy events.get(), newEvent.move());
}
}
if (thy events -> size == 0) {
Melder_warning (U"No event matches criterion.");
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": events not extracted.");
}
}
autoERPTier ERPTier_extractEventsWhereColumn_string (ERPTier me, Table table,
long columnNumber, int which_Melder_STRING, const char32 *criterion)
{
try {
Table_checkSpecifiedColumnNumberWithinRange (table, columnNumber);
if (my points.size != table -> rows.size)
Melder_throw (me, U" & ", table, U": the number of rows in the table (", table -> rows.size,
U") doesn't match the number of events (", my points.size, U").");
autoERPTier thee = Thing_new (ERPTier);
Function_init (thee.get(), my xmin, my xmax);
thy numberOfChannels = my numberOfChannels;
thy channelNames = NUMvector <char32 *> (1, thy numberOfChannels);
for (long ichan = 1; ichan <= thy numberOfChannels; ichan ++) {
thy channelNames [ichan] = Melder_dup (my channelNames [ichan]);
}
for (long ievent = 1; ievent <= my points.size; ievent ++) {
ERPPoint oldEvent = my points.at [ievent];
TableRow row = table -> rows.at [ievent];
if (Melder_stringMatchesCriterion (row -> cells [columnNumber]. string, which_Melder_STRING, criterion)) {
autoERPPoint newEvent = Data_copy (oldEvent);
thy points. addItem_move (newEvent.move());
}
}
if (thy points.size == 0) {
Melder_warning (U"No event matches criterion.");
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": events not extracted.");
}
}
void NUMlinprog_run (NUMlinprog me) {
try {
glp_smcp parm;
glp_init_smcp (& parm);
parm. msg_lev = GLP_MSG_OFF;
my status = glp_simplex (my linearProgram, & parm);
switch (my status) {
case GLP_EBADB: Melder_throw (U"Unable to start the search, because the initial basis is invalid.");
case GLP_ESING: Melder_throw (U"Unable to start the search, because the basis matrix is singular.");
case GLP_ECOND: Melder_throw (U"Unable to start the search, because the basis matrix is ill-conditioned.");
case GLP_EBOUND: Melder_throw (U"Unable to start the search, because some variables have incorrect bounds.");
case GLP_EFAIL: Melder_throw (U"Search prematurely terminated due to solver failure.");
case GLP_EOBJLL: Melder_throw (U"Search prematurely terminated: lower limit reached.");
case GLP_EOBJUL: Melder_throw (U"Search prematurely terminated: upper limit reached.");
case GLP_EITLIM: Melder_throw (U"Search prematurely terminated: iteration limit exceeded.");
case GLP_ETMLIM: Melder_throw (U"Search prematurely terminated: time limit exceeded.");
case GLP_ENOPFS: Melder_throw (U"The problem has no primal feasible solution.");
case GLP_ENODFS: Melder_throw (U"The problem has no dual feasible solution.");
default: break;
}
my status = glp_get_status (my linearProgram);
switch (my status) {
case GLP_INFEAS: Melder_throw (U"Solution is infeasible.");
case GLP_NOFEAS: Melder_throw (U"Problem has no feasible solution.");
case GLP_UNBND: Melder_throw (U"Problem has unbounded solution.");
case GLP_UNDEF: Melder_throw (U"Solution is undefined.");
default: break;
}
if (my status == GLP_FEAS) {
Melder_warning (U"Linear programming solution is feasible but not optimal.");
}
} catch (MelderError) {
Melder_throw (U"Linear programming: not run.");
}
}
autoMatrix Matrix_solveEquation (Matrix me, double /* tolerance */) {
try {
long nr = my ny, nc = my nx - 1;
if (nc == 0) {
Melder_throw (U"Matrix_solveEquation: there must be at least 2 columns in the matrix.");
}
if (nr < nc) {
Melder_warning (U"Matrix_solveEquation: solution is not unique (fewer equations than unknowns).");
}
autoNUMmatrix<double> u (1, nr, 1, nc);
autoNUMvector<double> b (1, nr);
autoNUMvector<double> x (1, nc);
autoMatrix thee = Matrix_create (0.5, 0.5 + nc, nc, 1, 1, 0.5, 1.5, 1, 1, 1);
for (long i = 1; i <= nr; i++) {
for (long j = 1; j <= nc; j++) {
u[i][j] = my z[i][j];
}
b[i] = my z[i][my nx];
}
NUMsolveEquation (u.peek(), nr, nc, b.peek(), 0, x.peek());
for (long j = 1; j <= nc; j++) {
thy z[1][j] = x[j];
}
return thee;
} catch (MelderError) {
Melder_throw (U"Matrix equation not solved.");
}
}
autoConfusion Confusion_groupStimuli (Confusion me, const char32 *labels, const char32 *newLabel, long newpos) {
try {
long ncondense = Melder_countTokens (labels);
autoNUMvector<long> irow (1, my numberOfRows);
for (long i = 1; i <= my numberOfRows; i++) {
irow[i] = i;
}
for (char32 *token = Melder_firstToken (labels); token != nullptr; token = Melder_nextToken ()) {
for (long i = 1; i <= my numberOfRows; i++) {
if (Melder_equ (token, my rowLabels[i])) {
irow[i] = 0;
break;
}
}
}
long nfound = 0;
for (long i = 1; i <= my numberOfRows; i++) {
if (irow[i] == 0) {
nfound ++;
}
}
if (nfound == 0) {
Melder_throw (U"Invalid stimulus labels.");
}
if (nfound != ncondense) {
Melder_warning (U"One or more of the given stimulus labels are suspect.");
}
long newnstim = my numberOfRows - nfound + 1;
if (newpos < 1) {
newpos = 1;
}
if (newpos > newnstim) {
newpos = newnstim;
}
autoConfusion thee = Confusion_create (newnstim, my numberOfColumns);
NUMstrings_copyElements (my columnLabels, thy columnLabels, 1, my numberOfColumns);
TableOfReal_setRowLabel (thee.get(), newpos, newLabel);
long inewrow = 1;
for (long i = 1; i <= my numberOfRows; i++) {
long rowpos = newpos;
if (irow[i] > 0) {
if (inewrow == newpos) {
inewrow++;
}
rowpos = inewrow;
inewrow++;
TableOfReal_setRowLabel (thee.get(), rowpos, my rowLabels[i]);
}
for (long j = 1; j <= my numberOfColumns; j++) {
thy data[rowpos][j] += my data[i][j];
}
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": stimuli not grouped.");
}
}
void Graphics_setWsViewport (Graphics me,
long x1DC, long x2DC, long y1DC, long y2DC)
{
if (x1DC < my d_x1DCmin || x2DC > my d_x2DCmax || y1DC < my d_y1DCmin || y2DC > my d_y2DCmax) {
Melder_warning (U"Graphics_setWsViewport: coordinates too large:\n",
x1DC, U"..", x2DC, U" x ", y1DC, U"..", y2DC,
U" goes outside ",
my d_x1DCmin, U"..", my d_x2DCmax, U" x ", my d_y1DCmin, U"..", my d_y2DCmax,
U"."
);
x1DC = my d_x1DCmin;
x2DC = my d_x2DCmax;
y1DC = my d_y1DCmin;
y2DC = my d_y2DCmax;
}
my d_x1DC = x1DC;
my d_x2DC = x2DC;
my d_y1DC = y1DC;
my d_y2DC = y2DC;
#if win
if (my screen && my printer) {
GraphicsScreen mescreen = (GraphicsScreen) me;
/*
* Map page coordinates to paper coordinates.
*/
mescreen -> d_x1DC -= GetDeviceCaps (mescreen -> d_gdiGraphicsContext, PHYSICALOFFSETX);
mescreen -> d_x2DC -= GetDeviceCaps (mescreen -> d_gdiGraphicsContext, PHYSICALOFFSETX);
mescreen -> d_y1DC -= GetDeviceCaps (mescreen -> d_gdiGraphicsContext, PHYSICALOFFSETY);
mescreen -> d_y2DC -= GetDeviceCaps (mescreen -> d_gdiGraphicsContext, PHYSICALOFFSETY);
}
#endif
computeTrafo (me);
}
autoConfusion Confusion_groupResponses (Confusion me, const char32 *labels, const char32 *newLabel, long newpos) {
try {
long ncondense = Melder_countTokens (labels);
autoNUMvector<long> icol (1, my numberOfColumns);
for (long i = 1; i <= my numberOfColumns; i++) {
icol[i] = i;
}
for (char32 *token = Melder_firstToken (labels); token != 0; token = Melder_nextToken ()) {
for (long i = 1; i <= my numberOfColumns; i++) {
if (Melder_equ (token, my columnLabels[i])) {
icol[i] = 0;
break;
}
}
}
long nfound = 0;
for (long i = 1; i <= my numberOfColumns; i++) {
if (icol[i] == 0) {
nfound ++;
}
}
if (nfound == 0) {
Melder_throw (U"Invalid response labels.");
}
if (nfound != ncondense) {
Melder_warning (U"One or more of the given response labels are suspect.");
}
long newnresp = my numberOfColumns - nfound + 1;
if (newpos < 1) {
newpos = 1;
}
if (newpos > newnresp) {
newpos = newnresp;
}
autoConfusion thee = Confusion_create (my numberOfRows, newnresp);
NUMstrings_copyElements (my rowLabels, thy rowLabels, 1, my numberOfRows);
TableOfReal_setColumnLabel (thee.get(), newpos, newLabel);
long inewcol = 1;
for (long i = 1; i <= my numberOfColumns; i++) {
long colpos = newpos;
if (icol[i] > 0) {
if (inewcol == newpos) {
inewcol++;
}
colpos = inewcol;
inewcol++;
TableOfReal_setColumnLabel (thee.get(), colpos, my columnLabels[i]);
}
for (long j = 1; j <= my numberOfRows; j++) {
thy data[j][colpos] += my data[j][i];
}
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": responses not grouped.");
}
}
void FormantFilter_drawFilterFunctions (FormantFilter me, Graphics g, double bandwidth,
int toFreqScale, int fromFilter, int toFilter, double zmin, double zmax,
int dbScale, double ymin, double ymax, int garnish) {
if (! checkLimits (me, FilterBank_HERTZ, toFreqScale, & fromFilter, & toFilter,
& zmin, & zmax, dbScale, & ymin, & ymax)) {
return;
}
if (bandwidth <= 0) {
Melder_warning (U"Bandwidth must be greater than zero.");
}
long n = 1000;
autoNUMvector<double>a (1, n);
Graphics_setInner (g);
Graphics_setWindow (g, zmin, zmax, ymin, ymax);
for (long j = fromFilter; j <= toFilter; j++) {
double df = (zmax - zmin) / (n - 1);
double fc = my y1 + (j - 1) * my dy;
long ibegin, iend;
for (long i = 1; i <= n; i++) {
double f = zmin + (i - 1) * df;
double z = scaleFrequency (f, toFreqScale, FilterBank_HERTZ);
if (z == NUMundefined) {
a[i] = NUMundefined;
} else {
a[i] = NUMformantfilter_amplitude (fc, bandwidth, z);
if (dbScale) {
a[i] = to_dB (a[i], 10, ymin);
}
}
}
setDrawingLimits (a.peek(), n, ymin, ymax, &ibegin, &iend);
if (ibegin <= iend) {
double fmin = zmin + (ibegin - 1) * df;
double fmax = zmax - (n - iend) * df;
Graphics_function (g, a.peek(), ibegin, iend, fmin, fmax);
}
}
Graphics_unsetInner (g);
if (garnish) {
double distance = dbScale ? 10 : 1;
char32 const *ytext = dbScale ? U"Amplitude (dB)" : U"Amplitude";
Graphics_drawInnerBox (g);
Graphics_marksBottom (g, 2, 1, 1, 0);
Graphics_marksLeftEvery (g, 1, distance, 1, 1, 0);
Graphics_textLeft (g, 1, ytext);
Graphics_textBottom (g, 1, GetFreqScaleText (toFreqScale));
}
}
void FilterBank_drawFrequencyScales (I, Graphics g, int horizontalScale, double xmin,
double xmax, int verticalScale, double ymin, double ymax, int garnish) {
iam (FilterBank);
int myFreqScale = FilterBank_getFrequencyScale (me);
if (xmin < 0 || xmax < 0 || ymin < 0 || ymax < 0) {
Melder_warning (U"Frequencies must be >= 0.");
return;
}
if (xmin >= xmax) {
double xmint = my ymin;
double xmaxt = my ymax;
if (ymin < ymax) {
xmint = scaleFrequency (ymin, verticalScale, myFreqScale);
xmaxt = scaleFrequency (ymax, verticalScale, myFreqScale);
}
xmin = scaleFrequency (xmint, myFreqScale, horizontalScale);
xmax = scaleFrequency (xmaxt, myFreqScale, horizontalScale);
}
if (ymin >= ymax) {
ymin = scaleFrequency (xmin, horizontalScale, verticalScale);
ymax = scaleFrequency (xmax, horizontalScale, verticalScale);
}
long n = 2000;
autoNUMvector<double> a (1, n);
Graphics_setInner (g);
Graphics_setWindow (g, xmin, xmax, ymin, ymax);
double df = (xmax - xmin) / (n - 1);
for (long i = 1; i <= n; i++) {
double f = xmin + (i - 1) * df;
a[i] = scaleFrequency (f, horizontalScale, verticalScale);
}
long ibegin, iend;
setDrawingLimits (a.peek(), n, ymin, ymax, & ibegin, & iend);
if (ibegin <= iend) {
double fmin = xmin + (ibegin - 1) * df;
double fmax = xmax - (n - iend) * df;
Graphics_function (g, a.peek(), ibegin, iend, fmin, fmax);
}
Graphics_unsetInner (g);
if (garnish) {
Graphics_drawInnerBox (g);
Graphics_marksLeft (g, 2, 1, 1, 0);
Graphics_textLeft (g, 1, GetFreqScaleText (verticalScale));
Graphics_marksBottom (g, 2, 1, 1, 0);
Graphics_textBottom (g, 1, GetFreqScaleText (horizontalScale));
}
}
static int checkLimits (Matrix me, int fromFreqScale, int toFreqScale, int *fromFilter,
int *toFilter, double *zmin, double *zmax, int dbScale,
double *ymin, double *ymax) {
if (*fromFilter == 0) {
*fromFilter = 1;
}
if (*toFilter == 0) {
*toFilter = my ny;
}
if (*toFilter < *fromFilter) {
*fromFilter = 1;
*toFilter = my ny;
}
if (*fromFilter < 1) {
*fromFilter = 1;
}
if (*toFilter > my ny) {
*toFilter = my ny;
}
if (*fromFilter > *toFilter) {
Melder_warning (U"Filter numbers must be in range [1, ", my ny, U"]");
return 0;
}
if (*zmin < 0 || *zmax < 0) {
Melder_warning (U"Frequencies must be positive.");
return 0;
}
if (*zmax <= *zmin) {
*zmin = scaleFrequency (my ymin, fromFreqScale, toFreqScale);
*zmax = scaleFrequency (my ymax, fromFreqScale, toFreqScale);
}
if (*ymax <= *ymin) {
*ymax = 1; *ymin = 0;
if (dbScale) {
*ymax = 0; *ymin = -60;
}
}
return 1;
}
autoPCA TableOfReal_to_PCA (I) {
iam (TableOfReal);
try {
long m = my numberOfRows, n = my numberOfColumns;
if (! TableOfReal_areAllCellsDefined (me, 0, 0, 0, 0)) {
Melder_throw (U"Undefined cells.");
}
if (m < 2) {
Melder_throw (U"There is not enough data to perform a PCA.\nYour table has less than 2 rows.");
}
if (m < n) {
Melder_warning (U"The number of rows in your table is less than the \nnumber of columns. ");
}
if (NUMfrobeniusnorm (m, n, my data) == 0) {
Melder_throw (U"All values in your table are zero.");
}
autoPCA thee = Thing_new (PCA);
autoNUMmatrix<double> a (NUMmatrix_copy (my data, 1, m, 1, n), 1, 1);
thy centroid = NUMvector<double> (1, n);
for (long j = 1; j <= n; j++) {
double colmean = a[1][j];
for (long i = 2; i <= m; i++) {
colmean += a[i][j];
}
colmean /= m;
for (long i = 1; i <= m; i++) {
a[i][j] -= colmean;
}
thy centroid[j] = colmean;
}
Eigen_initFromSquareRoot (thee.peek(), a.peek(), m, n);
thy labels = NUMvector<char32 *> (1, n);
NUMstrings_copyElements (my columnLabels, thy labels, 1, n);
PCA_setNumberOfObservations (thee.peek(), m);
/*
The covariance matrix C = A'A / (N-1). However, we have calculated
the eigenstructure for A'A. This has no consequences for the
eigenvectors, but the eigenvalues have to be divided by (N-1).
*/
for (long i = 1; i <= thy numberOfEigenvalues; i++) {
thy eigenvalues[i] /= (m - 1);
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": PCA not created.");
}
}
static void cb_optionChanged (GuiObject w, XtPointer void_me, XtPointer call) {
iam (GuiOptionMenu);
(void) call;
for (int i = 1; i <= my d_options -> size; i ++) {
GuiMenuItem item = static_cast <GuiMenuItem> (my d_options -> item [i]);
if (item -> d_widget == w) {
XtVaSetValues (my d_xmCascadeButton, XmNlabelString, Melder_peekWcsToUtf8 (item -> d_widget -> name), NULL);
XmToggleButtonSetState (item -> d_widget, TRUE, FALSE);
if (Melder_debug == 11) {
Melder_warning (i, " \"", item -> d_widget -> name, "\"");
}
} else {
XmToggleButtonSetState (item -> d_widget, FALSE, FALSE);
}
}
}
static void LongSound_init (LongSound me, MelderFile file) {
MelderFile_copy (file, & my file);
MelderFile_open (file); // BUG: should be auto, but that requires an implemented .transfer()
my f = file -> filePointer;
my audioFileType = MelderFile_checkSoundFile (file, & my numberOfChannels, & my encoding, & my sampleRate, & my startOfData, & my nx);
if (my audioFileType == 0)
Melder_throw (U"File not recognized (LongSound only supports AIFF, AIFC, WAV, NeXT/Sun, NIST and FLAC).");
if (my encoding == Melder_SHORTEN || my encoding == Melder_POLYPHONE)
Melder_throw (U"LongSound does not support sound files compressed with \"shorten\".");
if (my nx < 1)
Melder_throw (U"Audio file contains 0 samples.");
my xmin = 0.0;
my dx = 1 / my sampleRate;
my xmax = my nx * my dx;
my x1 = 0.5 * my dx;
my numberOfBytesPerSamplePoint = Melder_bytesPerSamplePoint (my encoding);
my bufferLength = prefs_bufferLength;
for (;;) {
my nmax = my bufferLength * my numberOfChannels * my sampleRate * (1 + 3 * MARGIN);
try {
my buffer = NUMvector <int16> (0, my nmax * my numberOfChannels);
break;
} catch (MelderError) {
my bufferLength *= 0.5; // try 30, 15, or 7.5 seconds
if (my bufferLength < 5.0) // too short to be good
throw;
Melder_clearError (); // delete out-of-memory message
}
}
my imin = 1;
my imax = 0;
my flacDecoder = nullptr;
if (my audioFileType == Melder_FLAC) {
my flacDecoder = FLAC__stream_decoder_new ();
FLAC__stream_decoder_init_FILE (my flacDecoder, my f, _LongSound_FLAC_write, nullptr, _LongSound_FLAC_error, me);
}
my mp3f = nullptr;
if (my audioFileType == Melder_MP3) {
my mp3f = mp3f_new ();
mp3f_set_file (my mp3f, my f);
mp3f_set_callback (my mp3f, _LongSound_MP3_convert, me);
if (! mp3f_analyze (my mp3f))
Melder_throw (U"Unable to analyze MP3 file.");
Melder_warning (U"Time measurements in MP3 files can be off by several tens of milliseconds. "
U"Please convert to WAV file if you need time precision or annotation.");
}
}
Formant LPC_to_Formant (LPC me, double margin) {
try {
double samplingFrequency = 1.0 / my samplingPeriod;
long nmax = my maxnCoefficients, err = 0;
long interval = nmax > 20 ? 1 : 10;
if (nmax > 99) {
Melder_throw ("We cannot find the roots of a polynomial of order > 99.");
}
if (margin >= samplingFrequency / 4) {
Melder_throw ("Margin must be smaller than ", samplingFrequency / 4, ".");
}
autoFormant thee = Formant_create (my xmin, my xmax, my nx, my dx, my x1, (nmax + 1) / 2);
autoMelderProgress progress (L"LPC to Formant");
for (long i = 1; i <= my nx; i++) {
Formant_Frame formant = & thy d_frames[i];
LPC_Frame lpc = & my d_frames[i];
// Initialisation of Formant_Frame is taken care of in Roots_into_Formant_Frame!
try {
LPC_Frame_into_Formant_Frame (lpc, formant, my samplingPeriod, margin);
} catch (MelderError) {
Melder_clearError();
err++;
}
if ( (interval == 1 || (i % interval) == 1)) {
Melder_progress ( (double) i / my nx, L"LPC to Formant: frame ", Melder_integer (i),
L" out of ", Melder_integer (my nx), L".");
}
}
Formant_sort (thee.peek());
if (err > 0) {
Melder_warning (Melder_integer (err), L" formant frames out of ", Melder_integer (my nx), L" suspect.");
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, ": no Formant created.");
}
}
autoSoundList TextGrid_Sound_extractNonemptyIntervals (TextGrid me, Sound sound, long tierNumber, bool preserveTimes) {
try {
IntervalTier tier = TextGrid_checkSpecifiedTierIsIntervalTier (me, tierNumber);
autoSoundList list = SoundList_create ();
for (long iseg = 1; iseg <= tier -> intervals.size; iseg ++) {
TextInterval segment = tier -> intervals.at [iseg];
if (segment -> text && segment -> text [0] != U'\0') {
autoSound interval = Sound_extractPart (sound, segment -> xmin, segment -> xmax, kSound_windowShape_RECTANGULAR, 1.0, preserveTimes);
Thing_setName (interval.get(), segment -> text ? segment -> text : U"untitled");
list -> addItem_move (interval.move());
}
}
if (list->size == 0) Melder_warning (U"No non-empty intervals were found.");
return list;
} catch (MelderError) {
Melder_throw (me, U" & ", sound, U": non-empty intervals not extracted.");
}
}
Collection TextGrid_Sound_extractNonemptyIntervals (TextGrid me, Sound sound, long tierNumber, int preserveTimes) {
try {
IntervalTier tier = TextGrid_checkSpecifiedTierIsIntervalTier (me, tierNumber);
autoCollection collection = Collection_create (NULL, tier -> intervals -> size);
for (long iseg = 1; iseg <= tier -> intervals -> size; iseg ++) {
TextInterval segment = (TextInterval) tier -> intervals -> item [iseg];
if (segment -> text != NULL && segment -> text [0] != '\0') {
autoSound interval = Sound_extractPart (sound, segment -> xmin, segment -> xmax, kSound_windowShape_RECTANGULAR, 1.0, preserveTimes);
Thing_setName (interval.peek(), segment -> text ? segment -> text : L"untitled");
Collection_addItem (collection.peek(), interval.transfer());
}
}
if (collection -> size == 0) Melder_warning (L"No non-empty intervals were found.");
return collection.transfer();
} catch (MelderError) {
Melder_throw (me, " & ", sound, ": non-empty intervals not extracted.");
}
}
void Discriminant_drawConcentrationEllipses (Discriminant me, Graphics g, double scale, bool confidence, char32 *label,
int discriminantDirections, long d1, long d2, double xmin, double xmax, double ymin, double ymax, int fontSize, int garnish)
{
long numberOfFunctions = Discriminant_getNumberOfFunctions (me);
if (! discriminantDirections) {
SSCPList_drawConcentrationEllipses (my groups.get(), g, scale, confidence, label, d1, d2, xmin, xmax, ymin, ymax, fontSize, garnish);
return;
}
if (numberOfFunctions <= 1) {
Melder_warning (U"Discriminant_drawConcentrationEllipses: Nothing drawn "
U"because there is only one dimension in the discriminant space.");
return;
}
// Project SSCPs on eigenvectors.
if (d1 == 0 && d2 == 0) {
d1 = 1;
d2 = MIN (numberOfFunctions, d1 + 1);
} else if (d1 < 0 || d2 > numberOfFunctions) {
return;
}
double *v1 = my eigen -> eigenvectors [d1];
double *v2 = my eigen -> eigenvectors [d2];
autoSSCPList thee = SSCPList_toTwoDimensions (my groups.get(), v1, v2);
SSCPList_drawConcentrationEllipses (thee.get(), g, scale, confidence, label, 1, 2, xmin, xmax, ymin, ymax, fontSize, 0);
if (garnish) {
char32 llabel[40];
Graphics_drawInnerBox (g);
Graphics_marksLeft (g, 2, true, true, false);
Melder_sprint (llabel,40, U"function ", d2);
Graphics_textLeft (g, true, llabel);
Graphics_marksBottom (g, 2, true, true, false);
Melder_sprint (llabel,40, U"function ", d1);
Graphics_textBottom (g, true, llabel);
}
}
void Matrix_scale (Matrix me, int choice) {
double min, max, extremum;
long nZero = 0;
if (choice == 2) { /* by row */
for (long i = 1; i <= my ny; i++) {
Matrix_getWindowExtrema (me, 1, my nx, i, i, &min, &max);
extremum = fabs (max) > fabs (min) ? fabs (max) : fabs (min);
if (extremum == 0.0) {
nZero++;
} else for (long j = 1; j <= my nx; j++) {
my z[i][j] /= extremum;
}
}
} else if (choice == 3) { /* by col */
for (long j = 1; j <= my nx; j++) {
Matrix_getWindowExtrema (me, j, j, 1, my ny, &min, &max);
extremum = fabs (max) > fabs (min) ? fabs (max) : fabs (min);
if (extremum == 0.0) {
nZero++;
} else for (long i = 1; i <= my ny; i++) {
my z[i][j] /= extremum;
}
}
} else if (choice == 1) { /* overall */
Matrix_getWindowExtrema (me, 1, my nx, 1, my ny, &min, &max);
extremum = fabs (max) > fabs (min) ? fabs (max) : fabs (min);
if (extremum == 0.0) {
nZero++;
} else {
for (long i = 1; i <= my ny; i++) {
for (long j = 1; j <= my nx; j++) {
my z[i][j] /= extremum;
}
}
}
} else {
Melder_flushError (U"Matrix_scale: choice must be > 0 && <= 3.");
return;
}
if (nZero) {
Melder_warning (U"Matrix_scale: extremum == 0, (part of) matrix unscaled.");
}
}
// xmin, xmax in hz versus bark/mel or lin
void BandFilterSpectrogram_drawFrequencyScale (BandFilterSpectrogram me, Graphics g, double xmin, double xmax, double ymin, double ymax, int garnish) {
if (xmin < 0 || xmax < 0 || ymin < 0 || ymax < 0) {
Melder_warning (U"Frequencies must be >= 0.");
return;
}
// scale is in hertz
if (xmin >= xmax) { // autoscaling
xmin = 0;
xmax = my v_frequencyToHertz (my ymax);
}
if (ymin >= ymax) { // autoscaling
ymin = my ymin;
ymax = my ymax;
}
long n = 2000;
Graphics_setInner (g);
Graphics_setWindow (g, xmin, xmax, ymin, ymax);
double dx = (xmax - xmin) / (n - 1);
double x1 = xmin, y1 = my v_hertzToFrequency (x1);
for (long i = 2; i <= n; i++) {
double x2 = x1 + dx, y2 = my v_hertzToFrequency (x2);
if (NUMdefined (y1) && NUMdefined (y2)) {
double xo1, yo1, xo2, yo2;
if (NUMclipLineWithinRectangle (x1, y1, x2, y2, xmin, ymin, xmax, ymax, &xo1, &yo1, &xo2, &yo2)) {
Graphics_line (g, xo1, yo1, xo2, yo2);
}
}
x1 = x2; y1 = y2;
}
Graphics_unsetInner (g);
if (garnish) {
Graphics_drawInnerBox (g);
Graphics_marksLeft (g, 2, 1, 1, 0);
Graphics_textLeft (g, 1, Melder_cat (U"Frequency (", my v_getFrequencyUnit (), U")"));
Graphics_marksBottom (g, 2, 1, 1, 0);
Graphics_textBottom (g, 1, U"Frequency (Hz)");
}
}
autoResultsMFC ExperimentMFC_extractResults (ExperimentMFC me) {
try {
if (my trial == 0 || my trial <= my numberOfTrials)
Melder_warning (U"The experiment was not finished. Only the first ", my trial - 1 + my pausing, U" responses are valid.");
autoResultsMFC thee = ResultsMFC_create (my numberOfTrials);
for (long trial = 1; trial <= my numberOfTrials; trial ++) {
char32 *pipe = my stimulus [my stimuli [trial]]. visibleText ?
str32chr (my stimulus [my stimuli [trial]]. visibleText, U'|') : nullptr;
thy result [trial]. stimulus = Melder_dup (Melder_cat (my stimulus [my stimuli [trial]]. name, pipe));
//if (my responses [trial] < 1) Melder_throw (U"No response for trial ", trial, U".")
thy result [trial]. response = Melder_dup (my responses [trial] ? my response [my responses [trial]]. name : U"");
thy result [trial]. goodness = my goodnesses [trial];
thy result [trial]. reactionTime = my reactionTimes [trial];
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": results not extracted.");
}
}
autoSoundList TextGrid_Sound_extractIntervalsWhere (TextGrid me, Sound sound, long tierNumber,
int comparison_Melder_STRING, const char32 *text, bool preserveTimes)
{
try {
IntervalTier tier = TextGrid_checkSpecifiedTierIsIntervalTier (me, tierNumber);
autoSoundList list = SoundList_create ();
long count = 0;
for (long iseg = 1; iseg <= tier -> intervals.size; iseg ++) {
TextInterval segment = tier -> intervals.at [iseg];
if (Melder_stringMatchesCriterion (segment -> text, comparison_Melder_STRING, text)) {
autoSound interval = Sound_extractPart (sound, segment -> xmin, segment -> xmax, kSound_windowShape_RECTANGULAR, 1.0, preserveTimes);
Thing_setName (interval.get(), Melder_cat (sound -> name ? sound -> name : U"", U"_", text, U"_", ++ count));
list -> addItem_move (interval.move());
}
}
if (list->size == 0)
Melder_warning (U"No label that ", kMelder_string_getText (comparison_Melder_STRING), U" the text \"", text, U"\" was found.");
return list;
} catch (MelderError) {
Melder_throw (me, U" & ", sound, U": intervals not extracted.");
}
}
LinearRegression Table_to_LinearRegression (Table me) {
try {
long numberOfIndependentVariables = my numberOfColumns - 1, numberOfParameters = my numberOfColumns;
long numberOfCells = my rows -> size, icell, ivar;
if (numberOfParameters < 1) /* Includes intercept. */
Melder_throw (U"Not enough columns (has to be more than 1).");
if (numberOfCells < numberOfParameters) {
Melder_warning (U"Solution is not unique (more parameters than cases).");
}
autoNUMmatrix <double> u (1, numberOfCells, 1, numberOfParameters);
autoNUMvector <double> b (1, numberOfCells);
autoNUMvector <double> x (1, numberOfParameters);
autoLinearRegression thee = LinearRegression_create ();
for (ivar = 1; ivar <= numberOfIndependentVariables; ivar ++) {
double minimum = Table_getMinimum (me, ivar);
double maximum = Table_getMaximum (me, ivar);
Regression_addParameter (thee.peek(), my columnHeaders [ivar]. label, minimum, maximum, 0.0);
}
for (icell = 1; icell <= numberOfCells; icell ++) {
for (ivar = 1; ivar < numberOfParameters; ivar ++) {
u [icell] [ivar] = Table_getNumericValue_Assert (me, icell, ivar);
}
u [icell] [numberOfParameters] = 1.0; /* For the intercept. */
b [icell] = Table_getNumericValue_Assert (me, icell, my numberOfColumns); // the dependent variable
}
NUMsolveEquation (u.peek(), numberOfCells, numberOfParameters, b.peek(), NUMeps * numberOfCells, x.peek());
thy intercept = x [numberOfParameters];
for (ivar = 1; ivar <= numberOfIndependentVariables; ivar ++) {
RegressionParameter parm = static_cast<RegressionParameter> (thy parameters -> item [ivar]);
parm -> value = x [ivar];
}
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, U": linear regression not performed.");
}
}
Polygon Polygon_createSimple (wchar_t *xystring) {
try {
long numberOfPoints;
autoNUMvector<double> xys (NUMstring_to_numbers (xystring, &numberOfPoints), 1);
if (numberOfPoints < 6) {
Melder_throw ("There must be at least 3 points (= x,y pairs) in the Polygon");
}
if (numberOfPoints % 2 != 0) {
Melder_throw ("One value is missing.");
}
numberOfPoints /= 2; // x,y pairs
autoPolygon me = Polygon_create (numberOfPoints);
for (long i = 1; i <= numberOfPoints; i++) {
my x[i] = xys[2 * i - 1];
my y[i] = xys[2 * i];
if (i > 1 && my x[i] == my x[i - 1] && my y[i] == my y[i - 1]) {
Melder_warning ("Two successives vertices are equal.");
}
}
return me.transfer();
} catch (MelderError) {
Melder_throw ("Polygon not created.");
}
}
Collection TextGrid_Sound_extractIntervalsWhere (TextGrid me, Sound sound, long tierNumber,
int comparison_Melder_STRING, const wchar_t *text, int preserveTimes)
{
try {
IntervalTier tier = TextGrid_checkSpecifiedTierIsIntervalTier (me, tierNumber);
autoCollection collection = Collection_create (NULL, tier -> intervals -> size);
long count = 0;
for (long iseg = 1; iseg <= tier -> intervals -> size; iseg ++) {
TextInterval segment = (TextInterval) tier -> intervals -> item [iseg];
if (Melder_stringMatchesCriterion (segment -> text, comparison_Melder_STRING, text)) {
autoSound interval = Sound_extractPart (sound, segment -> xmin, segment -> xmax, kSound_windowShape_RECTANGULAR, 1.0, preserveTimes);
wchar_t name [1000];
swprintf (name, 1000, L"%ls_%ls_%ld", sound -> name ? sound -> name : L"", text, ++ count);
Thing_setName (interval.peek(), name);
Collection_addItem (collection.peek(), interval.transfer());
}
}
if (collection -> size == 0)
Melder_warning ("No label that ", kMelder_string_getText (comparison_Melder_STRING), " the text \"", text, "\" was found.");
return collection.transfer();
} catch (MelderError) {
Melder_throw (me, " & ", sound, ": intervals not extracted.");
}
}
static LogisticRegression _Table_to_LogisticRegression (Table me, long *factors, long numberOfFactors, long dependent1, long dependent2) {
long numberOfParameters = numberOfFactors + 1;
long numberOfCells = my rows -> size, numberOfY0 = 0, numberOfY1 = 0, numberOfData = 0;
double logLikelihood = 1e300, previousLogLikelihood = 2e300;
if (numberOfParameters < 1) // includes intercept
Melder_throw ("Not enough columns (has to be more than 1).");
/*
* Divide up the contents of the table into a number of independent variables (x) and two dependent variables (y0 and y1).
*/
autoNUMmatrix <double> x (1, numberOfCells, 0, numberOfFactors); // column 0 is the intercept
autoNUMvector <double> y0 (1, numberOfCells);
autoNUMvector <double> y1 (1, numberOfCells);
autoNUMvector <double> meanX (1, numberOfFactors);
autoNUMvector <double> stdevX (1, numberOfFactors);
autoNUMmatrix <double> smallMatrix (0, numberOfFactors, 0, numberOfParameters);
autoLogisticRegression thee = LogisticRegression_create (my columnHeaders [dependent1]. label, my columnHeaders [dependent2]. label);
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
double minimum = Table_getMinimum (me, factors [ivar]);
double maximum = Table_getMaximum (me, factors [ivar]);
Regression_addParameter (thee.peek(), my columnHeaders [factors [ivar]]. label, minimum, maximum, 0.0);
}
for (long icell = 1; icell <= numberOfCells; icell ++) {
y0 [icell] = Table_getNumericValue_Assert (me, icell, dependent1);
y1 [icell] = Table_getNumericValue_Assert (me, icell, dependent2);
numberOfY0 += y0 [icell];
numberOfY1 += y1 [icell];
numberOfData += y0 [icell] + y1 [icell];
x [icell] [0] = 1.0; /* Intercept. */
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
x [icell] [ivar] = Table_getNumericValue_Assert (me, icell, factors [ivar]);
meanX [ivar] += x [icell] [ivar] * (y0 [icell] + y1 [icell]);
}
}
if (numberOfY0 == 0 && numberOfY1 == 0)
Melder_throw ("No data in either class. Cannot determine result.");
if (numberOfY0 == 0)
Melder_throw ("No data in class ", my columnHeaders [dependent1]. label, ". Cannot determine result.");
if (numberOfY1 == 0)
Melder_throw ("No data in class ", my columnHeaders [dependent2]. label, ". Cannot determine result.");
/*
* Normalize the data.
*/
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
meanX [ivar] /= numberOfData;
for (long icell = 1; icell <= numberOfCells; icell ++) {
x [icell] [ivar] -= meanX [ivar];
}
}
for (long icell = 1; icell <= numberOfCells; icell ++) {
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
stdevX [ivar] += x [icell] [ivar] * x [icell] [ivar] * (y0 [icell] + y1 [icell]);
}
}
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
stdevX [ivar] = sqrt (stdevX [ivar] / numberOfData);
for (long icell = 1; icell <= numberOfCells; icell ++) {
x [icell] [ivar] /= stdevX [ivar];
}
}
/*
* Initial state of iteration: the null model.
*/
thy intercept = log ((double) numberOfY1 / (double) numberOfY0); // initial state of intercept: best guess for average log odds
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
RegressionParameter parm = static_cast<RegressionParameter> (thy parameters -> item [ivar]);
parm -> value = 0.0; // initial state of dependence: none
}
long iteration = 1;
for (; iteration <= 100; iteration ++) {
previousLogLikelihood = logLikelihood;
for (long ivar = 0; ivar <= numberOfFactors; ivar ++) {
for (long jvar = ivar; jvar <= numberOfParameters; jvar ++) {
smallMatrix [ivar] [jvar] = 0.0;
}
}
/*
* Compute the current log likelihood.
*/
logLikelihood = 0.0;
for (long icell = 1; icell <= numberOfCells; icell ++) {
double fittedLogit = thy intercept, fittedP, fittedQ, fittedLogP, fittedLogQ, fittedPQ, fittedVariance;
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
RegressionParameter parm = static_cast<RegressionParameter> (thy parameters -> item [ivar]);
fittedLogit += parm -> value * x [icell] [ivar];
}
/*
* Basically we have fittedP = 1.0 / (1.0 + exp (- fittedLogit)),
* but that works neither for fittedP values near 0 nor for values near 1.
*/
if (fittedLogit > 15.0) {
/*
* For large fittedLogit, fittedLogP = ln (1/(1+exp(-fittedLogit))) = -ln (1+exp(-fittedLogit)) =~ - exp(-fittedLogit)
*/
fittedLogP = - exp (- fittedLogit);
fittedLogQ = - fittedLogit;
fittedPQ = exp (- fittedLogit);
fittedP = exp (fittedLogP);
fittedQ = 1.0 - fittedP;
} else if (fittedLogit < -15.0) {
fittedLogP = fittedLogit;
fittedLogQ = - exp (fittedLogit);
fittedPQ = exp (fittedLogit);
fittedP = exp (fittedLogP);
fittedQ = 1 - fittedP;
} else {
fittedP = 1.0 / (1.0 + exp (- fittedLogit));
fittedLogP = log (fittedP);
fittedQ = 1.0 - fittedP;
fittedLogQ = log (fittedQ);
fittedPQ = fittedP * fittedQ;
}
logLikelihood += -2 * (y1 [icell] * fittedLogP + y0 [icell] * fittedLogQ);
/*
* Matrix shifting stuff.
* Suppose a + b Sk + c Tk = ln (pk / qk),
* where {a, b, c} are the coefficients to be optimized,
* Sk and Tk are properties of stimulus k,
* and pk and qk are the fitted probabilities for y1 and y0, respectively, given stimulus k.
* Then ln pk = - ln (1 + qk / pk) = - ln (1 + exp (- (a + b Sk + c Tk)))
* d ln pk / da = 1 / (1 + exp (a + b Sk + c Tk)) = qk
* d ln pk / db = qk Sk
* d ln pk / dc = qk Tk
* d ln qk / da = - pk
* Now LL = Sum(k) (y1k ln pk + y0k ln qk)
* so that dLL/da = Sum(k) (y1k d ln pk / da + y0k ln qk / da) = Sum(k) (y1k qk - y0k pk)
*/
fittedVariance = fittedPQ * (y0 [icell] + y1 [icell]);
for (long ivar = 0; ivar <= numberOfFactors; ivar ++) {
/*
* The last column gets the gradient of LL: dLL/da, dLL/db, dLL/dc.
*/
smallMatrix [ivar] [numberOfParameters] += x [icell] [ivar] * (y1 [icell] * fittedQ - y0 [icell] * fittedP);
for (long jvar = ivar; jvar <= numberOfFactors; jvar ++) {
smallMatrix [ivar] [jvar] += x [icell] [ivar] * x [icell] [jvar] * fittedVariance;
}
}
}
if (fabs (logLikelihood - previousLogLikelihood) < 1e-11) {
break;
}
/*
* Make matrix symmetric.
*/
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
for (long jvar = 0; jvar < ivar; jvar ++) {
smallMatrix [ivar] [jvar] = smallMatrix [jvar] [ivar];
}
}
/*
* Invert matrix in the simplest way, and shift and wipe the last column with it.
*/
for (long ivar = 0; ivar <= numberOfFactors; ivar ++) {
double pivot = smallMatrix [ivar] [ivar]; /* Save diagonal. */
smallMatrix [ivar] [ivar] = 1.0;
for (long jvar = 0; jvar <= numberOfParameters; jvar ++) {
smallMatrix [ivar] [jvar] /= pivot;
}
for (long jvar = 0; jvar <= numberOfFactors; jvar ++) {
if (jvar != ivar) {
double temp = smallMatrix [jvar] [ivar];
smallMatrix [jvar] [ivar] = 0.0;
for (long kvar = 0; kvar <= numberOfParameters; kvar ++) {
smallMatrix [jvar] [kvar] -= temp * smallMatrix [ivar] [kvar];
}
}
}
}
/*
* Update the parameters from the last column of smallMatrix.
*/
thy intercept += smallMatrix [0] [numberOfParameters];
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
RegressionParameter parm = static_cast<RegressionParameter> (thy parameters -> item [ivar]);
parm -> value += smallMatrix [ivar] [numberOfParameters];
}
}
if (iteration > 100) {
Melder_warning (L"Logistic regression has not converged in 100 iterations. The results are unreliable.");
}
for (long ivar = 1; ivar <= numberOfFactors; ivar ++) {
RegressionParameter parm = static_cast<RegressionParameter> (thy parameters -> item [ivar]);
parm -> value /= stdevX [ivar];
thy intercept -= parm -> value * meanX [ivar];
}
return thee.transfer();
}
BarkFilter Sound_to_BarkFilter (Sound me, double analysisWidth, double dt,
double f1_bark, double fmax_bark, double df_bark) {
try {
double t1, nyquist = 0.5 / my dx, samplingFrequency = 2 * nyquist;
double windowDuration = 2 * analysisWidth; /* gaussian window */
double zmax = NUMhertzToBark2 (nyquist);
double fmin_bark = 0;
long nt, frameErrorCount = 0;
// Check defaults.
if (f1_bark <= 0) {
f1_bark = 1;
}
if (fmax_bark <= 0) {
fmax_bark = zmax;
}
if (df_bark <= 0) {
df_bark = 1;
}
fmax_bark = MIN (fmax_bark, zmax);
long nf = floor ( (fmax_bark - f1_bark) / df_bark + 0.5);
if (nf <= 0) {
Melder_throw ("The combination of filter parameters is not valid.");
}
Sampled_shortTermAnalysis (me, windowDuration, dt, & nt, & t1);
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoBarkFilter thee = BarkFilter_create (my xmin, my xmax, nt, dt, t1,
fmin_bark, fmax_bark, nf, df_bark, f1_bark);
autoMelderProgress progess (L"BarkFilter analysis");
for (long i = 1; i <= nt; i++) {
double t = Sampled_indexToX (thee.peek(), i);
Sound_into_Sound (me, sframe.peek(), t - windowDuration / 2);
Sounds_multiply (sframe.peek(), window.peek());
if (! Sound_into_BarkFilter_frame (sframe.peek(), thee.peek(), i)) {
frameErrorCount++;
}
if ( (i % 10) == 1) {
Melder_progress ( (double) i / nt, L"BarkFilter analysis: frame ",
Melder_integer (i), L" from ", Melder_integer (nt), L"."); therror
}
}
if (frameErrorCount > 0) {
Melder_warning (L"Analysis results of ", Melder_integer (frameErrorCount), L" frame(s) out of ",
Melder_integer (nt), L" will be suspect.");
}
double ref = FilterBank_DBREF * gaussian_window_squared_correction (window -> nx);
NUMdmatrix_to_dBs (thy z, 1, thy ny, 1, thy nx, ref, FilterBank_DBFAC, FilterBank_DBFLOOR);
return thee.transfer();
} catch (MelderError) {
FormantFilter Sound_and_Pitch_to_FormantFilter (Sound me, Pitch thee, double analysisWidth, double dt,
double f1_hz, double fmax_hz, double df_hz, double relative_bw) {
try {
double t1, windowDuration = 2 * analysisWidth; /* gaussian window */
double nyquist = 0.5 / my dx, samplingFrequency = 2 * nyquist, fmin_hz = 0;
long nt, f0_undefined = 0;
if (my xmin > thy xmin || my xmax > thy xmax) Melder_throw
("The domain of the Sound is not included in the domain of the Pitch.");
double f0_median = Pitch_getQuantile (thee, thy xmin, thy xmax, 0.5, kPitch_unit_HERTZ);
if (f0_median == NUMundefined || f0_median == 0) {
f0_median = 100;
Melder_warning (L"Pitch values undefined. Bandwith fixed to 100 Hz. ");
}
if (f1_hz <= 0) {
f1_hz = 100;
}
if (fmax_hz <= 0) {
fmax_hz = nyquist;
}
if (df_hz <= 0) {
df_hz = f0_median / 2;
}
if (relative_bw <= 0) {
relative_bw = 1.1;
}
fmax_hz = MIN (fmax_hz, nyquist);
long nf = floor ( (fmax_hz - f1_hz) / df_hz + 0.5);
Sampled_shortTermAnalysis (me, windowDuration, dt, &nt, &t1);
autoFormantFilter him = FormantFilter_create (my xmin, my xmax, nt, dt, t1,
fmin_hz, fmax_hz, nf, df_hz, f1_hz);
// Temporary objects
autoSound sframe = Sound_createSimple (1, windowDuration, samplingFrequency);
autoSound window = Sound_createGaussian (windowDuration, samplingFrequency);
autoMelderProgress progress (L"Sound & Pitch: To FormantFilter");
for (long i = 1; i <= nt; i++) {
double t = Sampled_indexToX (him.peek(), i);
double b, f0 = Pitch_getValueAtTime (thee, t, kPitch_unit_HERTZ, 0);
if (f0 == NUMundefined || f0 == 0) {
f0_undefined++; f0 = f0_median;
}
b = relative_bw * f0;
Sound_into_Sound (me, sframe.peek(), t - windowDuration / 2);
Sounds_multiply (sframe.peek(), window.peek());
Sound_into_FormantFilter_frame (sframe.peek(), him.peek(), i, b);
if ( (i % 10) == 1) {
Melder_progress ( (double) i / nt, L"Frame ", Melder_integer (i), L" out of ",
Melder_integer (nt), L".");
}
}
double ref = FilterBank_DBREF * gaussian_window_squared_correction (window -> nx);
NUMdmatrix_to_dBs (his z, 1, his ny, 1, his nx, ref, FilterBank_DBFAC, FilterBank_DBFLOOR);
return him.transfer();
} catch (MelderError) {
Melder_throw ("FormantFilter not created from Pitch & FormantFilter.");
}
}
