Package org.jblas

Class Singular


  • public class Singular
    extends java.lang.Object
    • Method Detail

      • fullSVD

        public static DoubleMatrix[] fullSVD​(DoubleMatrix A)
        Compute a singular-value decomposition of A.
        Returns:
        A DoubleMatrix[3] array of U, S, V such that A = U * diag(S) * V'
      • sparseSVD

        public static DoubleMatrix[] sparseSVD​(DoubleMatrix A)
        Compute a singular-value decomposition of A (sparse variant). Sparse means that the matrices U and V are not square but only have as many columns (or rows) as necessary.
        Parameters:
        A -
        Returns:
        A DoubleMatrix[3] array of U, S, V such that A = U * diag(S) * V'
      • sparseSVD

        public static ComplexDoubleMatrix[] sparseSVD​(ComplexDoubleMatrix A)
        Compute a singular-value decomposition of A (sparse variant). Sparse means that the matrices U and V are not square but only have as many columns (or rows) as necessary.
        Parameters:
        A -
        Returns:
        A ComplexDoubleMatrix[3] array of U, S, V such that A = U * diag(S) * V*
      • SVDValues

        public static DoubleMatrix SVDValues​(DoubleMatrix A)
        Compute the singular values of a matrix.
        Parameters:
        A - DoubleMatrix of dimension m * n
        Returns:
        A min(m, n) vector of singular values.
      • SVDValues

        public static DoubleMatrix SVDValues​(ComplexDoubleMatrix A)
        Compute the singular values of a complex matrix.
        Parameters:
        A - ComplexDoubleMatrix of dimension m * n
        Returns:
        A real-valued (!) min(m, n) vector of singular values.
      • fullSVD

        public static FloatMatrix[] fullSVD​(FloatMatrix A)
        Compute a singular-value decomposition of A.
        Returns:
        A FloatMatrix[3] array of U, S, V such that A = U * diag(S) * V'
      • sparseSVD

        public static FloatMatrix[] sparseSVD​(FloatMatrix A)
        Compute a singular-value decomposition of A (sparse variant). Sparse means that the matrices U and V are not square but only have as many columns (or rows) as necessary.
        Parameters:
        A -
        Returns:
        A FloatMatrix[3] array of U, S, V such that A = U * diag(S) * V'
      • sparseSVD

        public static ComplexFloatMatrix[] sparseSVD​(ComplexFloatMatrix A)
        Compute a singular-value decomposition of A (sparse variant). Sparse means that the matrices U and V are not square but only have as many columns (or rows) as necessary.
        Parameters:
        A -
        Returns:
        A ComplexFloatMatrix[3] array of U, S, V such that A = U * diag(S) * V*
      • SVDValues

        public static FloatMatrix SVDValues​(FloatMatrix A)
        Compute the singular values of a matrix.
        Parameters:
        A - FloatMatrix of dimension m * n
        Returns:
        A min(m, n) vector of singular values.
      • SVDValues

        public static FloatMatrix SVDValues​(ComplexFloatMatrix A)
        Compute the singular values of a complex matrix.
        Parameters:
        A - ComplexFloatMatrix of dimension m * n
        Returns:
        A real-valued (!) min(m, n) vector of singular values.