dpnp.hypot¶

dpnp.hypot = <DPNPBinaryFunc 'hypot'>¶

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, tuple of ndarray}, optional

Output array to populate. Array must have the correct shape and the expected data type. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

Default: None.

order{None, "C", "F", "A", "K"}, optional

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

Default: "K".

Returns:

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

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.]])