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/* ClassificationTable.cpp
*
* Copyright (C) 1993-2021 David Weenink
*
* This code is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or (at
* your option) any later version.
*
* This code is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this work. If not, see <http://www.gnu.org/licenses/>.
*/
/*
djmw 1998
djmw 20020315 GPL header
djmw 20040422 Added ClassificationTable_to_Categories_maximumProbability
djmw 20040623 Added ClassificationTable_to_Strings_maximumProbability
djmw 20040824 Added Strings_extensions.h header
djmw 20101122 ClassificationTable_to_Correlation_columns
djmw 20110304 Thing_new
*/
#include "ClassificationTable.h"
#include "Distributions_and_Strings.h"
#include "Strings_extensions.h"
#include "NUM2.h"
Thing_implement (ClassificationTable, TableOfReal, 0);
autoClassificationTable ClassificationTable_create (integer numberOfRows, integer numberOfClasses) {
try {
autoClassificationTable me = Thing_new (ClassificationTable);
TableOfReal_init (me.get(), numberOfRows, numberOfClasses);
return me;
} catch (MelderError) {
Melder_throw (U"ClassificationTable not created.");
}
}
autoConfusion ClassificationTable_to_Confusion (ClassificationTable me, bool onlyClassLabels) {
try {
autoStrings responses = TableOfReal_extractColumnLabelsAsStrings (me);
autoStrings s2 = TableOfReal_extractRowLabelsAsStrings (me);
autoDistributions d2 = Strings_to_Distributions (s2.get());
autoStrings stimuli = TableOfReal_extractRowLabelsAsStrings (d2.get());
autoConfusion thee = Confusion_createFromStringses (( onlyClassLabels ? responses.get() : stimuli.get() ), responses.get());
Confusion_ClassificationTable_increase (thee.get(), me);
return thee;
} catch (MelderError) {
Melder_throw (me, U": confusions cannot be calculated.");
}
}
void Confusion_ClassificationTable_increase (Confusion me, ClassificationTable thee) {
Melder_require (my numberOfColumns == thy numberOfColumns,
U"The number of columns should be equal.");
for (integer irow = 1; irow <= thy numberOfRows; irow ++) {
const integer index = TableOfReal_getColumnIndexAtMaximumInRow (thee, irow);
Confusion_increase (me, thy rowLabels [irow].get(), my columnLabels [index].get());
}
}
autoStrings ClassificationTable_to_Strings_maximumProbability (ClassificationTable me) {
try {
autoStrings thee = Strings_createFixedLength (my numberOfRows);
Melder_assert (my numberOfColumns > 0);
for (integer irow = 1; irow <= my numberOfRows; irow ++) {
const integer col = NUMmaxPos (my data.row (irow));
if (my columnLabels [col])
Strings_replace (thee.get(), irow, my columnLabels [col].get());
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": strings cannot be created.");
}
}
autoCategories ClassificationTable_to_Categories_maximumProbability (ClassificationTable me) {
try {
autoCategories thee = Categories_create ();
Melder_assert (my numberOfColumns > 0);
for (integer irow = 1; irow <= my numberOfRows; irow ++) {
const integer col = NUMmaxPos (my data.row (irow));
OrderedOfString_append (thee.get(), my columnLabels [col].get());
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": no Categories created.");
}
}
autoCorrelation ClassificationTable_to_Correlation_columns (ClassificationTable me) {
try {
autoCorrelation thee = Correlation_create (my numberOfColumns);
for (integer icol = 1; icol <= thy numberOfColumns; icol ++) {
const conststring32 label = my columnLabels [icol].get();
TableOfReal_setRowLabel (thee.get(), icol, label);
TableOfReal_setColumnLabel (thee.get(), icol, label);
}
for (integer irow = 1; irow <= thy numberOfColumns; irow ++) {
thy data [irow] [irow] = 1.0;
for (integer icol = irow + 1; icol <= thy numberOfColumns; icol ++) {
const double n12 = NUMinner (my data.column (irow), my data.column (icol));
const double n11 = NUMinner (my data.column (irow), my data.column (irow));
const double n22 = NUMinner (my data.column (icol), my data.column (icol));
// probabilities might be very low!
if (n12 > 0.0 && n22 > 0.0) {
thy data [irow] [icol] = thy data [icol] [irow] = n12 / sqrt (n11 * n22);
}
}
}
thy numberOfObservations = my numberOfRows;
return thee;
} catch (MelderError) {
Melder_throw (me, U": no correlation created.");
}
}
/* End of file ClassificationTable.cpp */
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