saftig.filtering.lms

Least Mean Squares filter

Classes

LMSFilter

LMS filter implementation

Functions

_lms_loop(witness, target, n_filter, idx_target, ...)

Module Contents

saftig.filtering.lms._lms_loop(witness, target, n_filter, idx_target, filter_state, normalized, step_scale, coefficient_clipping)
Parameters:

  • witness (numpy.typing.NDArray) –

  • target (numpy.typing.NDArray) –

  • n_filter (int) –

  • idx_target (int) –

  • filter_state (numpy.typing.NDArray) –

  • normalized (bool) –

  • step_scale (float) –

  • coefficient_clipping (float) –

Return type:

tuple[numpy.typing.NDArray, numpy.typing.NDArray, int, int]

class saftig.filtering.lms.LMSFilter(n_filter, idx_target, n_channel=1, normalized=True, step_scale=0.1, coefficient_clipping=np.nan)

Bases: saftig.filtering.common.FilterBase

LMS filter implementation

Parameters:

  • n_filter (int) – Length of the FIR filter (how many samples are in the input window per output sample)

  • idx_target (int) – Position of the prediction

  • n_channel (int) – Number of witness sensor channels

  • normalized (bool) – if True: NLMS, else LMS

  • coefficient_clipping (float) – If set to a positive float, FIR filter coefficients will be limited to this value. This can increase filter stability.

  • step_scale (float) – the learning rate of the LMS filter

>>> import saftig as sg
>>> n_filter = 128
>>> witness, target = sg.evaluation.TestDataGenerator(0.1).generate(int(1e5))
>>> filt = sg.filtering.LMSFilter(n_filter, 0, 1)
>>> filt.condition(witness, target)
>>> prediction = filt.apply(witness, target) # check on the data used for conditioning
>>> residual_rms = sg.evaluation.rms(target-prediction)
>>> residual_rms > 0.05 and residual_rms < 0.15 # the expected RMS in this test scenario is 0.1
True
filter_state: numpy.typing.NDArray
normalized: bool
step_scale: float
coefficient_clipping: float
filter_name: str = 'LMS'
reset()

reset the filter coefficients to zero

condition(witness, target)

Use an input dataset to condition the filter

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) – Witness sensor data

  • target (collections.abc.Sequence | numpy.typing.NDArray) – Target sensor data

Return type:

None

condition_multi_sequence(witness, target)

Similar to condition(), but expects multiple sequences

Parameters:

  • witness (collections.abc.Sequence | collections.abc.Sequence[collections.abc.Sequence] | numpy.typing.NDArray) –

  • target (collections.abc.Sequence | numpy.typing.NDArray) –

Return type:

None

apply(witness, target=None, pad=True, update_state=False)

Apply the filter to input data

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) – Witness sensor data

  • target (collections.abc.Sequence | numpy.typing.NDArray | None) – Target sensor data (is ignored)

  • pad (bool) – if True, apply padding zeros so that the length matches the target signal

  • update_state (bool) – if True, the filter state will be changed. If false, the filter state will remain

Returns:

prediction

Return type:

numpy.typing.NDArray

apply_multi_sequence(witness, target=None, pad=True, update_state=False)

Apply the filter to input data

Similar to apply() but expects multiple sequences.

Parameters:

  • witness (collections.abc.Sequence | numpy.typing.NDArray) –

  • target (collections.abc.Sequence | numpy.typing.NDArray | None) –

  • pad (bool) –

  • update_state (bool) –

Return type:

collections.abc.Sequence[numpy.typing.NDArray]