TableOfReal CCA_and_TableOfReal_scores (CCA me, TableOfReal thee, long numberOfFactors) {
try {
long n = thy numberOfRows;
long nx = my x -> dimension, ny = my y -> dimension;
if (ny + nx != thy numberOfColumns) {
Melder_throw (U"The number of columns in the table (", thy numberOfColumns,
U") does not agree with the dimensions of the CCA object (ny + nx = ", ny, U" + ", nx, U").");
}
if (numberOfFactors == 0) {
numberOfFactors = my numberOfCoefficients;
}
if (numberOfFactors < 1 || numberOfFactors > my numberOfCoefficients) {
Melder_throw (U"The number of factors must be in interval [1, ", my numberOfCoefficients, U"].");
}
autoTableOfReal him = TableOfReal_create (n, 2 * numberOfFactors);
TableOfReal phim = him.peek();
NUMstrings_copyElements (thy rowLabels, his rowLabels, 1, thy numberOfRows);
Eigen_and_TableOfReal_project_into (my y, thee, 1, ny, &phim, 1, numberOfFactors);
Eigen_and_TableOfReal_project_into (my x, thee, ny + 1, thy numberOfColumns, &phim, numberOfFactors + 1, his numberOfColumns);
TableOfReal_setSequentialColumnLabels (him.peek(), 1, numberOfFactors, U"y_", 1, 1);
TableOfReal_setSequentialColumnLabels (him.peek(), numberOfFactors + 1, his numberOfColumns, U"x_", 1, 1);
return him.transfer();
} catch (MelderError) {
Melder_throw (me, U": no TableOfReal with scores created.");
}
}
TableOfReal CCA_and_TableOfReal_scores (CCA me, TableOfReal thee, long numberOfFactors)
{
TableOfReal him = NULL;
long n = thy numberOfRows;
long nx = my x -> dimension, ny = my y -> dimension;
if (ny + nx != thy numberOfColumns) return Melder_errorp7 (L"The number "
"of columns in the table (", Melder_integer (thy numberOfColumns), L") does not agree with "
"the dimensions of the CCA object (ny + nx = ", Melder_integer (ny), L" + ", Melder_integer (nx), L").");
if (numberOfFactors == 0) numberOfFactors = my numberOfCoefficients;
if (numberOfFactors < 1 || numberOfFactors > my numberOfCoefficients)
return Melder_errorp3 (L"The number of factors must be in interval "
"[1, ", Melder_integer (my numberOfCoefficients), L"].");
him = TableOfReal_create (n, 2 * numberOfFactors);
if (him == NULL) return NULL;
if (! NUMstrings_copyElements (thy rowLabels, his rowLabels, 1, thy numberOfRows) ||
! Eigen_and_TableOfReal_project_into (my y, thee, 1, ny, &him, 1, numberOfFactors) ||
! Eigen_and_TableOfReal_project_into (my x, thee, ny + 1,
thy numberOfColumns, &him, numberOfFactors + 1, his numberOfColumns) ||
! TableOfReal_setSequentialColumnLabels (him, 1, numberOfFactors, L"y_", 1, 1) ||
! TableOfReal_setSequentialColumnLabels (him, numberOfFactors + 1,
his numberOfColumns, L"x_", 1, 1)) forget (him);
return him;
}
class_methods_end
int Proximity_init (I, long numberOfPoints)
{
iam (Proximity);
return TableOfReal_init (me, numberOfPoints, numberOfPoints) &&
TableOfReal_setSequentialRowLabels (me, 0, 0, NULL, 1, 1) &&
TableOfReal_setSequentialColumnLabels (me, 0, 0, NULL, 1, 1);
}
autoConfiguration Discriminant_and_TableOfReal_to_Configuration (Discriminant me, TableOfReal thee, long numberOfDimensions) {
try {
if (numberOfDimensions == 0) {
numberOfDimensions = Discriminant_getNumberOfFunctions (me);
}
autoConfiguration him = Configuration_create (thy numberOfRows, numberOfDimensions);
Eigen_and_TableOfReal_into_TableOfReal_projectRows (my eigen.get(), thee, 1, him.get(), 1, numberOfDimensions);
TableOfReal_copyLabels (thee, him.get(), 1, 0);
TableOfReal_setSequentialColumnLabels (him.get(), 0, 0, U"Eigenvector ", 1, 1);
return him;
} catch (MelderError) {
Melder_throw (U"Configuration not created.");
}
}
autoConfiguration PCA_and_TableOfReal_to_Configuration (PCA me, TableOfReal thee, long numberOfDimensionsToKeep) {
try {
if (numberOfDimensionsToKeep == 0 || numberOfDimensionsToKeep > my numberOfEigenvalues) {
numberOfDimensionsToKeep = my numberOfEigenvalues;
}
autoConfiguration him = Configuration_create (thy numberOfRows, numberOfDimensionsToKeep);
Eigen_and_TableOfReal_into_TableOfReal_projectRows (me, thee, 1, him.get(), 1, numberOfDimensionsToKeep);
NUMstrings_copyElements (thy rowLabels, his rowLabels, 1, thy numberOfRows);
TableOfReal_setSequentialColumnLabels (him.get(), 0, 0, U"pc", 1, 1);
return him;
} catch (MelderError) {
Melder_throw (U"Configuration not created from PCA & TableOfReal.");
}
}
autoConfiguration Configuration_create (long numberOfPoints, long numberOfDimensions) {
try {
autoConfiguration me = Thing_new (Configuration);
TableOfReal_init (me.peek(), numberOfPoints, numberOfDimensions);
my w = NUMvector<double> (1, numberOfDimensions);
TableOfReal_setSequentialRowLabels (me.peek(), 0, 0, nullptr, 1, 1);
TableOfReal_setSequentialColumnLabels (me.peek(), 0, 0, U"dimension ", 1, 1);
my metric = 2;
Configuration_setDefaultWeights (me.peek());
Configuration_randomize (me.peek());
return me;
} catch (MelderError) {
Melder_throw (U"Configuration not created.");
}
}
autoTableOfReal Matrix_and_Categories_to_TableOfReal (Matrix me, Categories thee) {
try {
if (thy size != my ny) {
Melder_throw (U"Number of rows and number of categories must be equal.");
}
autoTableOfReal him = TableOfReal_create (my ny, my nx);
TableOfReal_setSequentialColumnLabels (him.peek(), 0, 0, nullptr, 1, 1);
for (long i = 1; i <= my ny; i ++) {
his rowLabels [i] = Melder_dup (OrderedOfString_itemAtIndex_c (thee, i));
}
for (long i = 1; i <= my ny; i ++) {
for (long j = 1; j <= my nx; j ++) {
his data [i] [j] = my z [i] [j];
}
}
return him;
} catch (MelderError) {
Melder_throw (U"TableOfReal not created from Matrix & Categories.");
}
}
autoTableOfReal PCA_and_TableOfReal_to_TableOfReal_zscores (PCA me, TableOfReal thee, long numberOfDimensions) {
try {
if (numberOfDimensions == 0 || numberOfDimensions > my numberOfEigenvalues) {
numberOfDimensions = my numberOfEigenvalues;
}
autoTableOfReal him = TableOfReal_create (thy numberOfRows, numberOfDimensions);
for (long i = 1; i <= thy numberOfRows; i++) { /* row */
for (long j = 1; j <= numberOfDimensions; j++) {
double r = 0, sigma = sqrt (my eigenvalues[j]);
for (long k = 1; k <= my dimension; k++) {
// eigenvector in row, data in row
r += my eigenvectors[j][k] * (thy data[i][k] - my centroid[k]) / sigma;
}
his data[i][j] = r;
}
}
NUMstrings_copyElements (thy rowLabels, his rowLabels, 1, thy numberOfRows);
TableOfReal_setSequentialColumnLabels (him.get(), 0, 0, U"pc", 1, 1);
return him;
} catch (MelderError) {
Melder_throw (U"TableOfReal (zscores) not created from PCA & TableOfReal.");
}
}
TableOfReal Matrix_and_Categories_to_TableOfReal (I, Categories thee)
{
iam (Matrix); TableOfReal him; long i, j;
if (thy size != my ny) return Melder_errorp1 (L"Matrix_and_Categories_to_TableOfReal: "
"number of rows and number of categories must be equal.");
if (! (him = TableOfReal_create (my ny, my nx)) ||
! TableOfReal_setSequentialColumnLabels (him, 0, 0, NULL, 1, 1)) goto end;
for (i = 1; i <= my ny; i++)
{
if (! (his rowLabels[i] = Melder_wcsdup_e (OrderedOfString_itemAtIndex_c (thee, i)))) goto end;
}
for (i = 1; i <= my ny; i++)
{
for (j = 1; j <= my nx; j++) his data[i][j] = my z[i][j];
}
end:
if (Melder_hasError()) forget (him);
return him;
}
autoDiscriminant TableOfReal_to_Discriminant (TableOfReal me) {
try {
autoDiscriminant thee = Thing_new (Discriminant);
long dimension = my numberOfColumns;
if (! NUMdmatrix_hasFiniteElements(my data, 1, my numberOfRows, 1, my numberOfColumns)) {
Melder_throw (U"At least one of the table's elements is not finite or undefined.");
}
if (! TableOfReal_hasRowLabels (me)) {
Melder_throw (U"At least one of the rows has no label.");
}
autoTableOfReal mew = TableOfReal_sortOnlyByRowLabels (me);
if (! TableOfReal_hasColumnLabels (mew.get())) {
TableOfReal_setSequentialColumnLabels (mew.get(), 0, 0, U"c", 1, 1);
}
thy groups = TableOfReal_to_SSCPList_byLabel (mew.get());
thy total = TableOfReal_to_SSCP (mew.get(), 0, 0, 0, 0);
if ((thy numberOfGroups = thy groups -> size) < 2) {
Melder_throw (U"Number of groups must be greater than one.");
}
TableOfReal_centreColumns_byRowLabel (mew.get());
// Overall centroid and apriori probabilities and costs.
autoNUMvector<double> centroid (1, dimension);
autoNUMmatrix<double> between (1, thy numberOfGroups, 1, dimension);
thy aprioriProbabilities = NUMvector<double> (1, thy numberOfGroups);
thy costs = NUMmatrix<double> (1, thy numberOfGroups, 1, thy numberOfGroups);
double sum = 0, scale;
for (long k = 1; k <= thy numberOfGroups; k ++) {
SSCP m = thy groups->at [k];
sum += scale = SSCP_getNumberOfObservations (m);
for (long j = 1; j <= dimension; j ++) {
centroid [j] += scale * m -> centroid [j];
}
}
for (long j = 1; j <= dimension; j ++) {
centroid [j] /= sum;
}
for (long k = 1; k <= thy numberOfGroups; k ++) {
SSCP m = thy groups->at [k];
scale = SSCP_getNumberOfObservations (m);
thy aprioriProbabilities[k] = scale / my numberOfRows;
for (long j = 1; j <= dimension; j ++) {
between [k] [j] = sqrt (scale) * (m -> centroid [j] - centroid [j]);
}
}
// We need to solve B'B.x = lambda W'W.x, where B'B and W'W are the between and within covariance matrices.
// We do not calculate these covariance matrices directly from the data but instead use the GSVD to solve for
// the eigenvalues and eigenvectors of the equation.
thy eigen = Thing_new (Eigen);
Eigen_initFromSquareRootPair (thy eigen.get(), between.peek(), thy numberOfGroups, dimension, mew -> data, my numberOfRows);
// Default priors and costs
for (long igroup = 1; igroup <= thy numberOfGroups; igroup ++) {
for (long jgroup = igroup + 1; jgroup <= thy numberOfGroups; jgroup ++) {
thy costs [igroup] [jgroup] = thy costs [jgroup] [igroup] = 1.0;
}
}
return thee;
} catch (MelderError) {
Melder_throw (me, U": Discriminant not created.");
}
}
