dpnp.inner

dpnp.inner(a, b)[source]

Returns the inner product of two arrays.

For full documentation refer to numpy.inner.

Parameters:

  • a ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array. Both inputs a and b can not be scalars at the same time.

  • b ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array. Both inputs a and b can not be scalars at the same time.

Returns:

out -- If either a or b is a scalar, the shape of the returned arrays matches that of the array between a and b, whichever is an array. If a and b are both 1-D arrays then a 0-d array is returned; otherwise an array with a shape as out.shape == (*a.shape[:-1], *b.shape[:-1]) is returned.

Return type:

dpnp.ndarray

See also

dpnp.einsum

Einstein summation convention.

dpnp.dot

Generalized matrix product, using second last dimension of b.

dpnp.tensordot

Sum products over arbitrary axes.

dpnp.vecdot

Vector dot product of two arrays.

Examples

Ordinary inner product for vectors:

>>> import dpnp as np
>>> a = np.array([1, 2, 3])
>>> b = np.array([0, 1, 0])
>>> np.inner(a, b)
array(2)

Some multidimensional examples:

>>> a = np.arange(24).reshape((2, 3, 4))
>>> b = np.arange(4)
>>> c = np.inner(a, b)
>>> c.shape
(2, 3)
>>> c
array([[ 14,  38,  62],
       [86, 110, 134]])

>>> a = np.arange(2).reshape((1, 1, 2))
>>> b = np.arange(6).reshape((3, 2))
>>> c = np.inner(a, b)
>>> c.shape
(1, 1, 3)
>>> c
array([[[1, 3, 5]]])

An example where b is a scalar:

>>> np.inner(np.eye(2), 7)
array([[7., 0.],
       [0., 7.]])