saftig.filtering.lms_c

faster LMS filter implemented in c

Classes

LMSFilterC

LMS filter implementation in C (faster but harder to adjust)

Module Contents

class saftig.filtering.lms_c.LMSFilterC(n_filter, idx_target, n_channel, step_scale=0.1, normalized=True, coefficient_clipping=None)

Bases: saftig.filtering.common.FilterBase

LMS filter implementation in C (faster but harder to adjust)

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

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

  • coefficient_clipping (float | None) – 

>>> import saftig as sg
>>> n_filter = 128
>>> witness, target = sg.evaluation.TestDataGenerator(0.1).generate(int(1e5))
>>> filt = sg.filtering.LMSFilterC(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_name: str = 'LMS_C'
filter
static supports_saving_loading()

Indicates whether saving and loading is supported.

reset()

reset the filter coefficients to zero

Return type:

None

condition(witness, target)

Use an input dataset to condition the filter

Parameters:

  • witness (collections.abc.Sequence[float] | collections.abc.Sequence[collections.abc.Sequence[float]]) – Witness sensor data

  • target (collections.abc.Sequence[float]) – Target sensor data

apply(witness, target, 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) – Target sensor data (is ignored)

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

  • update_state (bool) –

Returns:

prediction

Return type:

numpy.typing.NDArray