/**
* @param weight Weight matrix.
* @throws NonSquareMatrixException if the argument is not a square matrix.
*/
public Weight(RealMatrix weight) {
if (weight.getColumnDimension() != weight.getRowDimension()) {
throw new NonSquareMatrixException(weight.getColumnDimension(), weight.getRowDimension());
}
weightMatrix = weight.copy();
}
/**
* Applies rank transform to each of the columns of <code>matrix</code> using the current <code>
* rankingAlgorithm</code>
*
* @param matrix matrix to transform
* @return a rank-transformed matrix
*/
private RealMatrix rankTransform(final RealMatrix matrix) {
RealMatrix transformed = null;
if (rankingAlgorithm instanceof NaturalRanking
&& ((NaturalRanking) rankingAlgorithm).getNanStrategy() == NaNStrategy.REMOVED) {
final Set<Integer> nanPositions = new HashSet<Integer>();
for (int i = 0; i < matrix.getColumnDimension(); i++) {
nanPositions.addAll(getNaNPositions(matrix.getColumn(i)));
}
// if we have found NaN values, we have to update the matrix size
if (!nanPositions.isEmpty()) {
transformed =
new BlockRealMatrix(
matrix.getRowDimension() - nanPositions.size(), matrix.getColumnDimension());
for (int i = 0; i < transformed.getColumnDimension(); i++) {
transformed.setColumn(i, removeValues(matrix.getColumn(i), nanPositions));
}
}
}
if (transformed == null) {
transformed = matrix.copy();
}
for (int i = 0; i < transformed.getColumnDimension(); i++) {
transformed.setColumn(i, rankingAlgorithm.rank(transformed.getColumn(i)));
}
return transformed;
}
public WeightedLeastSquaresMethod(RealMatrix R, int nFactors, RotationMethod rotationMethod) {
this.nVariables = R.getColumnDimension();
this.nParam = nVariables;
this.nFactors = nFactors;
this.rotationMethod = rotationMethod;
this.R = R;
this.R2 = R.copy();
}
/**
* Gets the initial guess.
*
* @return the initial guess.
*/
public RealMatrix getWeight() {
return weightMatrix.copy();
}
