# numpy.ma.inner¶

numpy.ma.inner(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. out : ndarray out.shape = a.shape[:-1] + b.shape[:-1] ValueError If the last dimension of a and b has different size.

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.]])
```

#### Previous topic

numpy.ma.identity

#### Next topic

numpy.ma.innerproduct