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

W	Filter Parameter \(W\)
betas	Segment scalars weights the cost function per segment
cost_model	Cost Model
kappa	Filter Parameter \(\kappa\)
nu	Filter Parameter \(\nu\)
xi	Filter Parameter \(\xi\)

property W

Filter Parameter \(W\)

Type

ndarray

property betas

Segment scalars weights the cost function per segment

Type

ndarray

property cost_model

Cost Model

Type

CostSegment, CompositeCost

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() or minimize_v(). The parameter allocation allocate() is called internally, so a manual pre-allcation is not necessary.

Parameters

• y (array_like) –

  Input signal
  RLSAlssm or RLSAlssmSteadyState

  Single-channel signal is of shape =(K,) for
  Multi-channel signal is of shape =(K,L)
  

  RLSAlssmSet or RLSAlssmSetSteadyState

  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

property kappa

Filter Parameter \(\kappa\)

Type

ndarray

property nu

Filter Parameter \(\nu\)

Type

ndarray

set_backend(backend)

Setting the backend computations option

Parameters

backend (str) – ‘py’, for python backend, ‘jit’ for Just in Time backend

property xi

Filter Parameter \(\xi\)

Type

ndarray