def iddp_asvd(eps, A): """ Compute SVD of a real matrix to a specified relative precision using random sampling. :param eps: Relative precision. :type eps: float :param A: Matrix. :type A: :class:`numpy.ndarray` :return: Left singular vectors. :rtype: :class:`numpy.ndarray` :return: Right singular vectors. :rtype: :class:`numpy.ndarray` :return: Singular values. :rtype: :class:`numpy.ndarray` """ A = np.asfortranarray(A) m, n = A.shape n2, winit = _id.idd_frmi(m) w = np.empty(max((min(m,n) + 1)*(3*m + 5*n + 1) + 25*min(m,n)**2, (2*n + 1)*(n2 + 1)), order='F') k, iU, iV, iS, w, ier = _id.iddp_asvd(eps, A, winit, w) if ier != 0: raise _RETCODE_ERROR U = w[iU-1:iU+m*k-1].reshape((m, k), order='F') V = w[iV-1:iV+n*k-1].reshape((n, k), order='F') S = w[iS-1:iS+k-1] return U, V, S
def iddp_asvd(eps, A): """ Compute SVD of a real matrix to a specified relative precision using random sampling. :param eps: Relative precision. :type eps: float :param A: Matrix. :type A: :class:`numpy.ndarray` :return: Left singular vectors. :rtype: :class:`numpy.ndarray` :return: Right singular vectors. :rtype: :class:`numpy.ndarray` :return: Singular values. :rtype: :class:`numpy.ndarray` """ A = np.asfortranarray(A) m, n = A.shape n2, winit = _id.idd_frmi(m) w = np.empty(max((min(m, n) + 1) * (3 * m + 5 * n + 1) + 25 * min(m, n)**2, (2 * n + 1) * (n2 + 1)), order='F') k, iU, iV, iS, w, ier = _id.iddp_asvd(eps, A, winit, w) if ier != 0: raise _RETCODE_ERROR U = w[iU - 1:iU + m * k - 1].reshape((m, k), order='F') V = w[iV - 1:iV + n * k - 1].reshape((n, k), order='F') S = w[iS - 1:iS + k - 1] return U, V, S
def idd_frmi(m): """ Initialize data for :func:`idd_frm`. :param m: Length of vector to be transformed. :type m: int :return: Greatest power-of-two integer `n` satisfying `n <= m`. :rtype: int :return: Initialization array to be used by :func:`idd_frm`. :rtype: :class:`numpy.ndarray` """ return _id.idd_frmi(m)