numpy.inner()

numpy.inner(a, b)

Inner product of two arrays.

Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.

Parameters:

Parameters:	a, b : array_like If `a` and `b` are nonscalar, their last dimensions must match.
Returns:	out : ndarray `out.shape = a.shape[:-1] + b.shape[:-1]`
Raises:	ValueError If the last dimension of `a` and `b` has different size.

a, b : array_like

If a and b are nonscalar, their last dimensions must match.

Returns:

out : ndarray

out.shape = a.shape[:-1] + b.shape[:-1]

Raises:

ValueError

If the last dimension of a and b has different size.

For vectors (1-D arrays) it computes the ordinary inner-product:

1	`np.inner(a, b)` `=` `sum(a[:]*b[:])`

More generally, if ndim(a) = r > 0 and ndim(b) = s > 0:

1	`np.inner(a, b)` `=` `np.tensordot(a, b, axes=(-1,-1))`

or explicitly:

np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
     = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])

In addition a or b may be scalars, in which case:

1	`np.inner(a,b)` `=` `a*b`

Ordinary inner product for vectors:

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

A multidimensional example:

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

An example where b is a scalar:

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

Links:

doc_NumPy

2025-01-10 15:47:30

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