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.

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.]])