numpy.linalg.outer — NumPy v2.2 Manual (original) (raw)

linalg.outer(x1, x2, /)[source]#

Compute the outer product of two vectors.

This function is Array API compatible. Compared to np.outerit accepts 1-dimensional inputs only.

Parameters:

x1(M,) array_like

One-dimensional input array of size N. Must have a numeric data type.

x2(N,) array_like

One-dimensional input array of size M. Must have a numeric data type.

Returns:

out(M, N) ndarray

out[i, j] = a[i] * b[j]

Examples

Make a (very coarse) grid for computing a Mandelbrot set:

rl = np.linalg.outer(np.ones((5,)), np.linspace(-2, 2, 5)) rl array([[-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.], [-2., -1., 0., 1., 2.]]) im = np.linalg.outer(1j*np.linspace(2, -2, 5), np.ones((5,))) im array([[0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j], [0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j], [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], [0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j], [0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]]) grid = rl + im grid array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j], [-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j], [-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j], [-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j], [-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]])

An example using a “vector” of letters:

x = np.array(['a', 'b', 'c'], dtype=object) np.linalg.outer(x, [1, 2, 3]) array([['a', 'aa', 'aaa'], ['b', 'bb', 'bbb'], ['c', 'cc', 'ccc']], dtype=object)