"""Derived module from dmdbase.py for forward/backward dmd."""
from .dmd import DMD
[docs]class FbDMD(DMD):
"""
Forward/backward DMD class.
:param svd_rank: the rank for the truncation; If 0, the method computes the
optimal rank and uses it for truncation; if positive interger, the
method uses the argument for the truncation; if float between 0 and 1,
the rank is the number of the biggest singular values that are needed
to reach the 'energy' specified by `svd_rank`; if -1, the method does
not compute truncation.
:type svd_rank: int or float
:param int tlsq_rank: rank truncation computing Total Least Square. Default
is 0, that means no truncation.
:param bool exact: flag to compute either exact DMD or projected DMD.
Default is False.
:param opt: argument to control the computation of DMD modes amplitudes.
See :class:`DMDBase`. Default is False.
:type opt: bool or int
:param rescale_mode: Scale Atilde as shown in
10.1016/j.jneumeth.2015.10.010 (section 2.4) before computing its
eigendecomposition. None means no rescaling, 'auto' means automatic
rescaling using singular values, otherwise the scaling factors.
:type rescale_mode: {'auto'} or None or numpy.ndarray
:param sorted_eigs: Sort eigenvalues (and modes/dynamics accordingly) by
magnitude if `sorted_eigs='abs'`, by real part (and then by imaginary
part to break ties) if `sorted_eigs='real'`. Default: False.
:type sorted_eigs: {'real', 'abs'} or False
Reference: Dawson et al. https://arxiv.org/abs/1507.02264
"""
def __init__(
self,
svd_rank=0,
tlsq_rank=0,
exact=False,
opt=False,
rescale_mode=None,
sorted_eigs=False,
):
super().__init__(
svd_rank=svd_rank,
tlsq_rank=tlsq_rank,
exact=exact,
opt=opt,
rescale_mode=rescale_mode,
forward_backward=True,
sorted_eigs=sorted_eigs,
)