dpnp.maximum

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

Compares two input arrays x1 and x2 and returns a new array containing the element-wise maxima.

For full documentation refer to numpy.maximum.

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray}) – First input array, expected to have numeric data type.

  • x2 ({dpnp.ndarray, usm_ndarray}) – Second input array, also expected to have numeric data type.

  • out ({None, dpnp.ndarray}, optional) – Output array to populate. Array must have the correct shape and the expected data type.

  • order ({"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 maxima. 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.minimum

Element-wise minimum of two arrays, propagates NaNs.

dpnp.fmax

Element-wise maximum of two arrays, ignores NaNs.

dpnp.max

The maximum value of an array along a given axis, propagates NaNs.

dpnp.nanmax

The maximum value of an array along a given axis, ignores NaNs.

dpnp.fmin

Element-wise minimum of two arrays, ignores NaNs.

dpnp.min

The minimum value of an array along a given axis, propagates NaNs.

dpnp.nanmin

The minimum value of an array along a given axis, ignores NaNs.

Examples

>>> import dpnp as np
>>> x1 = np.array([2, 3, 4])
>>> x2 = np.array([1, 5, 2])
>>> np.maximum(x1, x2)
array([2, 5, 4])

>>> x1 = np.eye(2)
>>> x2 = np.array([0.5, 2])
>>> np.maximum(x1, x2) # broadcasting
array([[1. , 2. ],
       [0.5, 2. ]])

>>> x1 = np.array([np.nan, 0, np.nan])
>>> x2 = np.array([0, np.nan, np.nan])
>>> np.maximum(x1, x2)
array([nan, nan, nan])

>>> np.maximum(np.array(np.Inf), 1)
array(inf)