numpy.ma.innerproduct(a, b)
[source]
Inner product of two arrays.
Ordinary inner product of vectors for 1D arrays (without complex conjugation), in higher dimensions a sum product over the last axes.
Parameters: 


Returns: 

Raises: 

See also
tensordot
dot
b
.einsum
Masked values are replaced by 0.
For vectors (1D arrays) it computes the ordinary innerproduct:
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,...,ir1,j0,...,js1] = sum(a[i0,...,ir1,:]*b[j0,...,js1,:])
In addition a
or b
may be scalars, in which case:
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.]])
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https://docs.scipy.org/doc/numpy1.17.0/reference/generated/numpy.ma.innerproduct.html