lmlib.statespace.cost.RLSAlssmSet#

class lmlib.statespace.cost.RLSAlssmSet(cost_model, kappa_diag=True)#

Bases: lmlib.statespace.cost.RLSAlssmBase

Filter and Data container for Recursive Least Sqaure Alssm Filters using Sets (multichannel parallel processing)

This class is the same as RLSAlssm except that the signal y has an additional last dimension. The signals in these dimensions are processed simultaneously, as in a normal RLSAlssm called multiple times.

Parameters

  • cost_model (CostSegment, CompositeCost) – Cost Model

  • kappa_diag (bool) – If set to False, kappa will be computed as a matrix (outer product of each signal energy) else its diagonal will saved

  • **kwargs – Forwarded to RLSAlssmBase

Methods

__init__(cost_model[, kappa_diag])

eval_errors(xs[, ks])

Evaluation of the squared error for multiple state vectors xs.

filter(y[, v])

Computes the intermediate parameters for subsequent squared error computations and minimizations.

filter_minimize_x(y[, v, H, h])

Combination of RLSAlssmSet.filter() and RLSAlssmSet.minimize_x().

minimize_v([H, h, broadcast_h, ...])

Returns the vector v of the squared error minimization with linear constraints

minimize_x([H, h, broadcast_h])

Returns the state vector x of the squared error minimization with linear constraints

set_backend(backend)

Setting the backend computations option

set_kappa_diag(b)

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

eval_errors(xs, ks=None)#

Evaluation of the squared error for multiple state vectors xs.

The return value is the squared error

\[J(x) = x^{\mathsf{T}}W_kx -2*x^{\mathsf{T}}\xi_k + \kappa_k\]

for each state vector \(x\) from the list xs.

Parameters

  • xs (array_like of shape=(K, N, S)) – List of state vectors \(x\)

  • ks (None, array_like of int of shape=(XS,)) – List of indices where to evaluate the error

Returns

J – Squared Error for each state vector

Return type

np.ndarray of shape=(XS, S [,S])

K : number of samples
XS : number of state vectors in a list
N : ALSSM system order, corresponding to the number of state variables

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

filter_minimize_v(y, v=None, H=None, h=None, **kwargs)#

Combination of RLSAlssmSet.filter() and RLSAlssmSet.minimize_v().

This method has the same output as calling the methods

rls.filter(y)
xs = rls.minimize_v()

See also

RLSAlssmSet.filter(), RLSAlssmSet.minimize_v()

filter_minimize_x(y, v=None, H=None, h=None, **kwargs)#

Combination of RLSAlssmSet.filter() and RLSAlssmSet.minimize_x().

This method has the same output as calling the methods

rls.filter(y)
xs = rls.minimize_x()

See also

RLSAlssmSet.filter(), RLSAlssmSet.minimize_x()

property kappa#

Filter Parameter \(\kappa\)

Type

ndarray

minimize_v(H=None, h=None, broadcast_h=True, return_constrains=False)#

Returns the vector v of the squared error minimization with linear constraints

Minimizes the squared error over the vector v with linear constraints with an (optional) offset [Wildhaber2018] [TABLE V].

Constraint:

  • Linear Scalar : \(x=Hv,\,v\in\mathbb{R}\)

    known : \(H \in \mathbb{R}^{N \times 1}\)

    \(\hat{v}_k = \frac{\xi_k^{\mathsf{T}}H}{H^{\mathsf{T}}W_k H}\)

  • Linear Combination With Offset : \(x=Hv +h,\,v\in\mathbb{M}\)

    known : \(H \in \mathbb{R}^{N \times M},\,h\in\mathbb{R}^N\)

    \(\hat{v}_k = \big(H^{\mathsf{T}}W_k H\big)^{-1} H^\mathsf{T}\big(\xi_k - W_k h\big)\)

Parameters

  • H (array_like, shape=(N, M)) – Matrix for linear constraining \(H\)

  • h (array_like, shape=(N, [S]), optional) – Offset vector for linear constraining \(h\)

  • broadcast_h (bool) – if True each channel has the same h vectore else h needs same shape as x.

  • return_constrains (bool) – If set to True, the output is extened by H and h

Returns

  • v (ndarray, shape = (K, M, S),) – Least square state vector estimate for each time index. The shape of one state vector x[k] is (N, [S]), where k is the time index of K samples, N the ALSSM order.

  • |def_K|

  • |def_S|

  • |def_N|

minimize_x(H=None, h=None, broadcast_h=True)#

Returns the state vector x of the squared error minimization with linear constraints

Minimizes the squared error over the state vector x. If needed its possible to apply linear constraints with an (optional) offset. [Wildhaber2018] [TABLE V].

Constraint:

  • Linear Scalar : \(x=Hv,\,v\in\mathbb{R}\)

    known : \(H \in \mathbb{R}^{N \times 1}\)

  • Linear Combination With Offset : \(x=Hv +h,\,v\in\mathbb{M}\)

    known : \(H \in \mathbb{R}^{N \times M},\,h\in\mathbb{R}^N\)

See also minimize_v()

Parameters

  • H (array_like, shape=(N, M), optional) – Matrix for linear constraining \(H\)

  • h (array_like, shape=(N, [S]), optional) – Offset vector for linear constraining \(h\)

  • broadcast_h (bool) – if True each channel has the same h vectore else h needs same shape as x.

Returns

xs – Least square state vector estimate for each time index. The shape of one state vector x[k] is (N, S), where k is the time index of K samples, N the ALSSM order.

Return type

ndarray of shape = (K, N, S)

K : number of samples
S : number of signal sets
N : ALSSM system order, corresponding to the number of state variables

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