Package org.jblas

Class Singular

java.lang.Object
org.jblas.Singular

public class Singular extends Object
  • Constructor Details

  • Method Details

    • 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

      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*
    • fullSVD

      Compute a singular-value decomposition of 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

      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

      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*
    • fullSVD

      Compute a singular-value decomposition of 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

      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.