`numpy:vecmat`

source: /numpy/__init__.py

Summary

Vector-matrix dot product of two arrays.

Extended Summary

Given a vector (or stack of vector) $v$ in x1 and a matrix (or stack of matrices) $A$ in x2, the vector-matrix product is defined as:

v \cdot A = i = 0 \sum n - 1 \overline{v_{i}} A_{ij}

where the sum is over the last dimension of x1 and the one-but-last dimensions in x2 (unless axes is specified) and where $\overline{v_{i}}$ denotes the complex conjugate if $v$ is complex and the identity otherwise. (For a non-conjugated vector-matrix product, use np.matvec(x2.mT, x1).)

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 summation 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-matrix product of the inputs.

Raises

: ValueError

If the last dimensions of x1 and the one-but-last dimension of x2 are not the same size.

If a scalar value is passed in.

Examples

Project a vector along X and Y.

v = np.array([0., 4., 2.])
a = np.array([[1., 0., 0.],
              [0., 1., 0.],
              [0., 0., 0.]])

✓

np.vecmat(v, a)

`numpy:vecmat`

Summary

Extended Summary

Parameters

Returns

Raises

Examples

See also

Aliases

Referenced by

This package