dpnp.outer

dpnp.outer(a, b, out=None)[source]

Returns the outer product of two arrays.

For full documentation refer to numpy.outer.

Parameters:

a ({dpnp.ndarray, usm_ndarray}) -- First input vector. Input is flattened if not already 1-dimensional.
b ({dpnp.ndarray, usm_ndarray}) -- Second input vector. Input is flattened if not already 1-dimensional.
out ({None, dpnp.ndarray, usm_ndarray}, optional) -- A location where the result is stored. Default: None.

Returns:

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

Return type:

dpnp.ndarray

See also

dpnp.linalg.outer: Array API compatible version.
dpnp.einsum: Evaluates the Einstein summation convention on the operands.
dpnp.inner: Returns the inner product of two arrays.
dpnp.tensordot: dpnp.tensordot(a.ravel(), b.ravel(), axes=((), ())) is the equivalent.

Examples

>>> import dpnp as np
>>> a = np.array([1, 1, 1])
>>> b = np.array([1, 2, 3])
>>> np.outer(a, b)
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

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

>>> rl = np.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.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]])