jax.numpy.correlate#
- jax.numpy.correlate(a, v, mode='valid', *, precision=None, preferred_element_type=None)[source]#
Cross-correlation of two 1-dimensional sequences.
LAX-backend implementation of
numpy.correlate()
.In addition to the original NumPy arguments listed below, also supports
precision
for extra control over matrix-multiplication precision on supported devices.precision
may be set toNone
, which means default precision for the backend, aPrecision
enum value (Precision.DEFAULT
,Precision.HIGH
orPrecision.HIGHEST
) or a tuple of twoPrecision
enums indicating separate precision for each argument.Original docstring below.
This function computes the correlation as generally defined in signal processing texts:
\[c_k = \sum_n a_{n+k} \cdot \overline{v}_n\]with a and v sequences being zero-padded where necessary and \(\overline x\) denoting complex conjugation.
- Parameters:
a (array_like) – Input sequences.
v (array_like) – Input sequences.
mode ({'valid', 'same', 'full'}, optional) – Refer to the convolve docstring. Note that the default is ‘valid’, unlike convolve, which uses ‘full’.
preferred_element_type (dtype, optional) – If specified, accumulate results and return a result of the given data type. If not specified, the function instead follows the numpy convention of always accumulating results and returning an inexact dtype.
precision (PrecisionLike) –
- Returns:
out – Discrete cross-correlation of a and v.
- Return type:
ndarray