numpy.linalg.vecdot#
- linalg.vecdot(x1, x2, /, *, axis=-1)[source]#
Computes the vector dot product.
This function is restricted to arguments compatible with the Array API, contrary to
numpy.vecdot
.Let \(\mathbf{a}\) be a vector in
x1
and \(\mathbf{b}\) be a corresponding vector inx2
. The dot product is defined as:\[\mathbf{a} \cdot \mathbf{b} = \sum_{i=0}^{n-1} \overline{a_i}b_i\]over the dimension specified by
axis
and where \(\overline{a_i}\) denotes the complex conjugate if \(a_i\) is complex and the identity otherwise.- Parameters:
- x1array_like
First input array.
- x2array_like
Second input array.
- axisint, optional
Axis over which to compute the dot product. Default:
-1
.
- Returns:
- outputndarray
The vector dot product of the input.
See also
Examples
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) array([ 3., 8., 10.])