SciPy

numpy.ma.innerproduct

numpy.ma.innerproduct(a, b)[source]

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 :

a, b : array_like

If a and b are nonscalar, their last dimensions of 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.

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.

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.]])

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