autoConfusion Confusion_condense (Confusion me, const char32 *search, const char32 *replace,
long maximumNumberOfReplaces, int use_regexp) {
try {
long nmatches, nstringmatches;
if (my rowLabels == 0 || my columnLabels == 0) {
Melder_throw (U"No row or column labels.");
}
autostring32vector rowLabels (strs_replace (my rowLabels, 1, my numberOfRows, search, replace,
maximumNumberOfReplaces, &nmatches, &nstringmatches, use_regexp), 1, my numberOfRows);
autostring32vector columnLabels (strs_replace (my columnLabels, 1, my numberOfColumns, search, replace,
maximumNumberOfReplaces, &nmatches, &nstringmatches, use_regexp), 1, my numberOfColumns);
autoStrings srow = Thing_new (Strings);
srow -> numberOfStrings = my numberOfRows;
srow -> strings = rowLabels.transfer();
autoStrings scol = Thing_new (Strings);
scol -> numberOfStrings = my numberOfColumns;
scol -> strings = columnLabels.transfer();
/* Find dimension of new Confusion */
autoDistributions dcol = Strings_to_Distributions (scol.get());
long nresp = dcol -> numberOfRows;
autoDistributions drow = Strings_to_Distributions (srow.get());
long nstim = drow -> numberOfRows;
autoConfusion thee = Confusion_create (nstim, nresp);
NUMstrings_copyElements (drow -> rowLabels, thy rowLabels, 1, nstim);
NUMstrings_copyElements (dcol -> rowLabels, thy columnLabels, 1, nresp);
autoNUMvector<long> rowIndex (1, my numberOfRows);
create_index (srow -> strings, 1, my numberOfRows, drow -> rowLabels, 1, nstim, rowIndex.peek());
autoNUMvector<long> columnIndex (1, my numberOfColumns);
create_index (scol -> strings, 1, my numberOfColumns, dcol -> rowLabels, 1, nresp, columnIndex.peek());
for (long i = 1; i <= my numberOfRows; i++) {
for (long j = 1; j <= my numberOfColumns; j++) {
thy data [rowIndex [i]][columnIndex[j]] += my data[i][j];
}
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": not condensed.");
}
}
Confusion ClassificationTable_to_Confusion (ClassificationTable me, int onlyClassLabels) {
try {
autoStrings responses = TableOfReal_extractColumnLabelsAsStrings (me);
autoStrings s2 = TableOfReal_extractRowLabelsAsStrings (me);
autoDistributions d2 = Strings_to_Distributions (s2.peek());
autoStrings stimuli = TableOfReal_extractRowLabelsAsStrings (d2.peek());
autoConfusion thee = Confusion_createFromStringses ((onlyClassLabels ? responses.peek() : stimuli.peek()), responses.peek());
Confusion_and_ClassificationTable_increase (thee.peek(), me);
return thee.transfer();
} catch (MelderError) {
Melder_throw (me, U": confusions cannot be calculated.");
}
}
