lmlib.statespace.cost.RLSAlssmBase#
- class lmlib.statespace.cost.RLSAlssmBase(betas=None)#
Bases:
abc.ABC
Base Class for Recursive Least Square Alssm Classes
- Parameters
betas (array_like of shape=(P,) of floats, None, optional) – Segment Scalars. Factors weighting each of the P cost segments. If betas is not set, the weight is for each cost segment 1.
Methods
__init__
([betas])filter
(y[, v])Computes the intermediate parameters for subsequent squared error computations and minimizations.
set_backend
(backend)Setting the backend computations option
Attributes
Filter Parameter \(W\)
Segment scalars weights the cost function per segment
Cost Model
Filter Parameter \(\kappa\)
Filter Parameter \(\nu\)
Filter Parameter \(\xi\)
- property cost_model#
Cost Model
- Type
- filter(y, v=None)#
Computes the intermediate parameters for subsequent squared error computations and minimizations.
Computes the intermediate parameters using efficient forward- and backward recursions. The results are stored internally, ready to solve the least squares problem using e.g.,
minimize_x()
orminimize_v()
. The parameter allocationallocate()
is called internally, so a manual pre-allcation is not necessary.- Parameters
y (array_like) –
Input signal
RLSAlssm
orRLSAlssmSteadyState
Single-channel signal is of shape =(K,) for
Multi-channel signal is of shape =(K,L)RLSAlssmSet
orRLSAlssmSetSteadyState
Single-channel set signals is of shape =(K,S) for
Multi-channel set signals is of shape =(K,L,S)
Multi-channel-sets signal is of shape =(K,L,S)
v (array_like, shape=(K,), optional) – Sample weights. Weights the parameters for a time step k and is the same for all multi-channels. By default the sample weights are initialized to 1.
K : number of samples
L : output order / number of signal channels
S : number of signal sets