`numpy:vecdot`

source: /numpy/__init__.py

Summary

Vector dot product of two arrays.

Extended Summary

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}

where the sum is over the last dimension (unless axis is specified) and where $\overline{a_{i}}$ denotes the complex conjugate if $a_{i}$ is complex and the identity otherwise.

Parameters

x1, x2 : array_like: Input arrays, scalars not allowed.
out : ndarray, optional: A location into which the result is stored. If provided, it must have the broadcasted shape of x1 and x2 with the last axis removed. If not provided or None, a freshly-allocated array is used.
**kwargs: For other keyword-only arguments, see the ufunc docs <ufuncs.kwargs>.

Returns

y : ndarray: The vector dot product of the inputs. This is a scalar only when both x1, x2 are 1-d vectors.

Raises

: ValueError

If the last dimension of x1 is not the same size as the last dimension of x2.

If a scalar value is passed in.

Examples

import numpy as np

✓

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.vecdot(v, n)

`numpy:vecdot`

Summary

Extended Summary

Parameters

Returns

Raises

Examples

See also

Aliases

Referenced by

This package