jax.numpy.in1d#

jax.numpy.in1d(ar1, ar2, assume_unique=False, invert=False)[source]#

Test whether each element of a 1-D array is also present in a second array.

LAX-backend implementation of numpy.in1d().

In the JAX version, the assume_unique argument is not referenced.

Original docstring below.

Returns a boolean array the same length as ar1 that is True where an element of ar1 is in ar2 and False otherwise.

We recommend using isin() instead of in1d for new code.

Parameters

  • ar1 ((M,) array_like) – Input array.

  • ar2 (array_like) – The values against which to test each value of ar1.

  • assume_unique (bool, optional) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.

  • invert (bool, optional) – If True, the values in the returned array are inverted (that is, False where an element of ar1 is in ar2 and True otherwise). Default is False. np.in1d(a, b, invert=True) is equivalent to (but is faster than) np.invert(in1d(a, b)).

Returns

in1d – The values ar1[in1d] are in ar2.

Return type

(M,) ndarray, bool