mxnet.np.inner¶
-
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
b (a,) – If a and b are nonscalar, their last dimensions must match.
- Returns
out – out.shape = a.shape[:-1] + b.shape[:-1]
- Return type
ndarray
- Raises
ValueError – If the last dimension of a and b has different size.
See also
tensordot()
Sum products over arbitrary axes.
dot()
Generalised matrix product, using second last dimension of b.
einsum()
Einstein summation convention.
()
For vectors (1-D arrays) it computes the ordinary inner-product:: np.inner(a, b) = sum(a[:]*b[:]) More generally, if ndim(a) = r > 0 and ndim(b) = s > 0:: 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:: np.inner(a,b) = a*b
Examples
Ordinary inner product for vectors:
>>> a = np.array([1,2,3]) >>> b = np.array([0,1,0]) >>> np.inner(a, b) array(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.]])