xFitter.addObservedPoint(1, xval[i], fval[i][j]);
}
// Initial guess for the fit is zero for each coefficients (of which
// there are "xDegree" + 1).
yPolyX[j] = new PolynomialFunction(xFitter.fit(new double[xDegree + 1]));
}
// For every knot (xval[i], yval[j]) of the grid, calculate corrected
// values fval_1
final double[][] fval_1 = new double[xLen][yLen];
for (int j = 0; j < yLen; j++) {
final PolynomialFunction f = yPolyX[j];
for (int i = 0; i < xLen; i++) {
fval_1[i][j] = f.value(xval[i]);
}
}
// For each line x[i] (0 <= i < xLen), construct a polynomial, with
// respect to variable y, fitting array fval_1[i][]
final PolynomialFunction[] xPolyY = new PolynomialFunction[xLen];
for (int i = 0; i < xLen; i++) {
yFitter.clearObservations();
for (int j = 0; j < yLen; j++) {
yFitter.addObservedPoint(1, yval[j], fval_1[i][j]);
}
// Initial guess for the fit is zero for each coefficients (of which
// there are "yDegree" + 1).
xPolyY[i] = new PolynomialFunction(yFitter.fit(new double[yDegree + 1]));
}
// For every knot (xval[i], yval[j]) of the grid, calculate corrected
// values fval_2
final double[][] fval_2 = new double[xLen][yLen];
for (int i = 0; i < xLen; i++) {
final PolynomialFunction f = xPolyY[i];
for (int j = 0; j < yLen; j++) {
fval_2[i][j] = f.value(yval[j]);
}
}
return super.interpolate(xval, yval, fval_2);
}