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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
andb
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
andb
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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numpy.ma.innerproduct()
2017-01-10 18:15:24
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