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.");
}
}
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.");
}
}
