mindspore.numpy.correlate

mindspore.numpy.correlate(a, v, mode='valid')[source]

Cross-correlation of two 1-dimensional sequences.

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.

Note

  • correlate is currently only used in mindscience scientific computing scenarios and dose not support other usage scenarios.

  • correlate is not supported on Windows platform yet.

Parameters

  • a (Union[list, tuple, Tensor]) – First input sequence.

  • v (Union[list, tuple, Tensor]) – Second input sequence.

  • mode (str, optional) –

    Specifies padding mode. The optional values are "same" , "valid" and "full" . Default: "valid" .

    • "same": it returns output of length \(max(M, N)\). Boundary effects are still visible.

    • "valid": it returns output of length \(max(M, N) - min(M, N) + 1\). The convolution product is only given for points where the signals overlap completely. Values outside the signal boundary have no effect.

    • "full": it returns the convolution at each point of overlap, with an output shape of \((N + M - 1,)\).At the end-points of the convolution, the signals do not overlap completely, and boundary effects may be seen.

Returns

Tensor, Discrete cross-correlation of a and v.

Raises

  • TypeError – If a or v is not a tensor.

  • TypeError – If a and v is of different dtype.

  • ValueError – If a and v are empty or have wrong dimensions

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore.numpy as mnp
>>> from mindspore import Tensor
>>> output = mnp.correlate(Tensor([1., 2., 3.]), Tensor([0., 1., 0.5]))
>>> print(output)
[3.5]
>>> output = mnp.correlate(Tensor([1., 2., 3.]), Tensor([0., 1., 0.5]), mode="same")
>>> print(output)
[2.  3.5 3. ]
>>> output = mnp.correlate(Tensor([1., 2., 3., 4., 5.]), Tensor([1., 2.]), mode="full")
>>> print(output)
[ 2.  5.  8. 11. 14.  5.]