numpy.matvec#

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)\(\mathbf{A}\) inx1 anda vector (or stack of vectors)\(\mathbf{v}\) inx2, thematrix-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 inx1 andx2(unlessaxes is specified). (For a matrix-vector product with thevector conjugated, usenp.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 havethe broadcasted shape ofx1 andx2 with the summation axisremoved. 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 ofx1 andx2 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.]])
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