jax.numpy.correlate¶

jax.numpy.correlate(a, v, mode='valid', *, precision=None)[source]¶

Cross-correlation of two 1-dimensional sequences.

LAX-backend implementation of 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 to None, which means default precision for the backend, a lax.Precision enum value (Precision.DEFAULT, Precision.HIGH or Precision.HIGHEST) or a tuple of two lax.Precision enums indicating separate precision for each argument.

Original docstring below.

This function computes the correlation as generally defined in signal processing texts:

c_{av}[k] = sum_n a[n+k] * conj(v[n])

with a and v sequences being zero-padded where necessary and conj being the conjugate.

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’.

  • old_behavior (bool) – old_behavior was removed in NumPy 1.10. If you need the old behavior, use multiarray.correlate.

Returns

out – Discrete cross-correlation of a and v.

Return type

ndarray