dpnp.hypot

dpnp.hypot(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)

Computes the square root of the sum of squares for each element \(x1_i\) of the input array x1 with the respective element \(x2_i\) of the input array x2.

For full documentation refer to numpy.hypot.

Parameters:

• x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have a real-valued floating-point data type.

• x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have a real-valued floating-point data type.

• out ({None, dpnp.ndarray, usm_ndarray}, optional) --

  Output array to populate. Array must have the correct shape and the expected data type.

  Default: None.

• order ({None, "C", "F", "A", "K"}, optional) --

  Memory layout of the newly output array, if parameter out is None.

  Default: "K".

Returns:

out -- An array containing the element-wise hypotenuse. The data type of the returned array is determined by the Type Promotion Rules.

Return type:

dpnp.ndarray

Limitations

Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise NotImplementedError exception will be raised.

See also

dpnp.reduce_hypot

The square root of the sum of squares of elements in the input array.

Notes

At least one of x1 or x2 must be an array.

If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

This function is equivalent to \(\sqrt{x1^2 + x2^2}\), element-wise.

Examples

>>> import dpnp as np
>>> x1 = 3 * np.ones((3, 3))
>>> x2 = 4 * np.ones((3, 3))
>>> np.hypot(x1, x2)
array([[5., 5., 5.],
       [5., 5., 5.],
       [5., 5., 5.]])

Example showing broadcast of scalar argument:

>>> np.hypot(x1, 4)
array([[ 5.,  5.,  5.],
       [ 5.,  5.,  5.],
       [ 5.,  5.,  5.]])