jax.numpy.sum

jax.numpy.sum(a, axis=None, dtype=None, out=None, keepdims=False, initial=None, where=None, promote_integers=True)

Sum of the elements of the array over a given axis.

JAX implementation of numpy.sum().

Parameters:

• a (ArrayLike) – Input array.

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

• dtype (DTypeLike | None | None) – The type of the output array. Default=None.

• out (None | None) – Unused by JAX

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

• initial (ArrayLike | None | None) – int or array, Default=None. Initial value for the sum.

• where (ArrayLike | None | None) – int or array, default=None. The elements to be used in the sum. Array should be broadcast compatible to the input.

• promote_integers (bool) – bool, default=True. If True, then integer inputs will be promoted to the widest available integer dtype, following numpy’s behavior. If False, the result will have the same dtype as the input. promote_integers is ignored if dtype is specified.

Returns:

An array of the sum along the given axis.

Return type:

Array

See also

• jax.numpy.prod(): Compute the product of array elements over a given axis.

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

• jax.numpy.min(): Compute the minimum of array elements over given axis.

Examples

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

>>> x = jnp.array([[1, 3, 4, 2],
...                [5, 2, 6, 3],
...                [8, 1, 3, 9]])
>>> jnp.sum(x)
Array(47, dtype=int32)

If axis=1, the sum is computed along axis 1.

>>> jnp.sum(x, axis=1)
Array([10, 16, 21], dtype=int32)

If keepdims=True, ndim of the output is equal to that of the input.

>>> jnp.sum(x, axis=1, keepdims=True)
Array([[10],
       [16],
       [21]], dtype=int32)

To include only specific elements in the sum, you can use where.

>>> where=jnp.array([[0, 0, 1, 0],
...                  [0, 0, 1, 1],
...                  [1, 1, 1, 0]], dtype=bool)
>>> jnp.sum(x, axis=1, keepdims=True, where=where)
Array([[ 4],
       [ 9],
       [12]], dtype=int32)
>>> where=jnp.array([[False],
...                  [False],
...                  [False]])
>>> jnp.sum(x, axis=0, keepdims=True, where=where)
Array([[0, 0, 0, 0]], dtype=int32)