jax.numpy.minmax

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jax.numpy.minmax#

jax.numpy.minmax(a, axis=None, out=None, keepdims=False, initial=None, where=None)[source]#

Return the minimum and maximum of array elements along a given axis.

JAX implementation of numpy.minmax().

Parameters:

  • a (ArrayLike) – Input array.

  • axis (Axis) – int or array, default=None. Axis along which the minimum to be computed. If None, the minimum is computed along all the axes.

  • keepdims (bool) – bool, default=False. If true, reduced axes are left in the result with size 1.

  • initial (ArrayLike | tuple[ArrayLike, ArrayLike] | None) – int or array, or tuple of ints. Default=None. Initial value for the minimum and maximum. If given as a tuple, the first entry is the maximum value for the minimum result, and the second entry is the minimum value for the maximum result.

  • where (ArrayLike | None) – int or array, default=None. The elements to be used in the minimum. Array should be broadcast compatible to the input. initial must be specified when where is used.

  • out (None) – Unused by JAX.

Returns:

A tuple of arrays (a_min, a_max) containing the arrays of minimum and maximum values along the given axis.

Return type:

tuple[Array, Array]

See also

  • jax.numpy.min(): Compute the maximum of array elements along a given axis.

  • jax.numpy.max(): Compute the maximum of array elements along a given axis.

Examples

By default, the minimum is computed along all the axes.

>>> x = jnp.array([[2, 5, 1, 6],
...                [3, -7, -2, 4],
...                [8, -4, 1, -3]])
>>> jnp.minmax(x)
(Array(-7, dtype=int32), Array(8, dtype=int32))
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