franc.filtering.lms

Least Mean Squares filter

Classes

LMSFilter

LMS filter implementation

Functions

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

Module Contents

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

Bases: franc.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 franc as fnc
>>> n_filter = 128
>>> witness, target = fnc.evaluation.TestDataGenerator(0.1).generate(int(1e5))
>>> filt = fnc.filtering.LMSFilter(1, n_filter, 0)
>>> filt.condition(witness, target)
>>> prediction = filt.apply(witness, target) # check on the data used for conditioning
>>> residual_rms = fnc.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 multiple sequences of input data.

Similar to apply() but expects multiple sequences. First index to the given data objects indicates the sequence. The last index indicates the time within a single sequence. Sequences must not have the same length.

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]