Interface SingularValue<N extends java.lang.Comparable<N>>

    • Method Detail

      • getCondition

        double getCondition()
        The condition number.
        Specified by:
        getCondition in interface Provider2D.Condition
        Returns:
        The largest singular value divided by the smallest singular value.
      • getCovariance

        MatrixStore<N> getCovariance()
        Returns:
        [[A]T[A]]-1 Where [A] is the original matrix.
      • getD

        MatrixStore<N> getD()
        Returns:
        The diagonal matrix of singular values.
      • getFrobeniusNorm

        double getFrobeniusNorm()
        Sometimes also called the Schatten 2-norm or Hilbert-Schmidt norm.
        Returns:
        The square root of the sum of squares of the singular values.
      • getKyFanNorm

        double getKyFanNorm​(int k)

        Ky Fan k-norm.

        The first Ky Fan k-norm is the operator norm (the largest singular value), and the last is called the trace norm (the sum of all singular values).

        Parameters:
        k - The number of singular values to add up.
        Returns:
        The sum of the k largest singular values.
      • getOperatorNorm

        double getOperatorNorm()
        Returns:
        2-norm
      • getSingularValues

        Array1D<java.lang.Double> getSingularValues()
        Returns:
        The singular values ordered in descending order.
      • getSingularValues

        default void getSingularValues​(double[] values)
        Parameters:
        values - An array that will receive the singular values
      • getTraceNorm

        double getTraceNorm()
      • getU

        MatrixStore<N> getU()
        If [A] is m-by-n and its rank is r, then:
        • The first r columns of [U] span the column space, range or image of [A].
        • The last m-r columns of [U] span the left nullspace or cokernel of [A].
        Calculating the QR decomposition of [A] is a faster alternative.
      • getV

        MatrixStore<N> getV()
        If [A] is m-by-n and its rank is r, then:
        • The first r columns of [V] span the row space or coimage of [A].
        • The last n-r columns of [V] span the nullspace or kernel of [A].
        Calculating the QR decomposition of [A]T is a faster alternative.
      • reconstruct

        default MatrixStore<N> reconstruct​(int k)