numpy.matvec — NumPy v2.3.dev0 Manual (original) (raw)

numpy.matvec(x1, x2, /, out=None, *, casting='same_kind', order='K', dtype=None, _subok=True_[, signature, axes, _axis_]) = <ufunc 'matvec'>#

Matrix-vector dot product of two arrays.

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

\[\mathbf{A} \cdot \mathbf{b} = \sum_{j=0}^{n-1} A_{ij} v_j\]

where the sum is over the last dimensions in x1 and x2(unless axes is specified). (For a matrix-vector product with the vector conjugated, use np.vecmat(x2, x1.mT).)

New in version 2.2.0.

Parameters:

x1, x2array_like

Input arrays, scalars not allowed.

outndarray, 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 theufunc docs.

Returns:

yndarray

The matrix-vector product of the inputs.

Raises:

ValueError

If the last dimensions of x1 and x2 are not the same size.

If a scalar value is passed in.

See also

vecdot

Vector-vector product.

vecmat

Vector-matrix product.

matmul

Matrix-matrix product.

einsum

Einstein summation convention.

Examples

Rotate a set of vectors from Y to X along Z.

a = np.array([[0., 1., 0.], ... [-1., 0., 0.], ... [0., 0., 1.]]) v = np.array([[1., 0., 0.], ... [0., 1., 0.], ... [0., 0., 1.], ... [0., 6., 8.]]) np.matvec(a, v) array([[ 0., -1., 0.], [ 1., 0., 0.], [ 0., 0., 1.], [ 6., 0., 8.]])