`numpy.linalg:vecdot`

Signature

def vecdot ( x1 , x2 , / , axis = -1 )

Computes the vector dot product.

This function is restricted to arguments compatible with the Array API, contrary to numpy.vecdot.

Let $a$ be a vector in x1 and $b$ be a corresponding vector in x2. The dot product is defined as:

a \cdot b = i = 0 \sum n - 1 \overline{a_{i}} b_{i}

over the dimension specified by axis and where $\overline{a_{i}}$ denotes the complex conjugate if $a_{i}$ is complex and the identity otherwise.

x1 : array_like: First input array.
x2 : array_like: Second input array.
axis : int, optional: Axis over which to compute the dot product. Default: -1.

Get the projected size along a given normal for an array of vectors.

v = np.array([[0., 5., 0.], [0., 0., 10.], [0., 6., 8.]])
n = np.array([0., 0.6, 0.8])
np.linalg.vecdot(v, n)