NaiveNormalize¶

class torch_ecg._preprocessors.NaiveNormalize(mean: Real | ndarray = 0.0, std: Real | ndarray = 1.0, per_channel: bool = False, **kwargs: Any)[source]¶

Bases: Normalize

Naive normalization.

Naive normalization defined as

\[\frac{sig - m}{s}\]

Parameters:

mean (numbers.Real or numpy.ndarray, default 0.0) – Value(s) to be subtracted.
std (numbers.Real or numpy.ndarray, default 1.0) – Value(s) to be divided.
per_channel (bool, default False) – If True, normalization will be done per channel.

Examples

from torch_ecg.cfg import DEFAULTS
sig = DEFAULTS.RNG.randn(1000)
pp = NaiveNormalize()
sig, _ = pp(sig, 500)

extra_repr_keys() → List[str][source]¶: Extra keys for __repr__() and __str__().