ma.
inner
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.
If a and b are nonscalar, their last dimensions must match.
out.shape = a.shape[:-1] + b.shape[:-1]
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.
Notes
Masked values are replaced by 0.
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) 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.]])