dpnp.minimum

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

Computes the minimum value for each element \(x1_i\) of the input array x1 relative to the respective element \(x2_i\) of the input array x2.

If one of the elements being compared is a NaN, then that element is returned. If both elements are NaNs then the first is returned. The latter distinction is important for complex NaNs, which are defined as at least one of the real or imaginary parts being a NaN. The net effect is that NaNs are propagated.

For full documentation refer to numpy.minimum.

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, may have any data type.

  • x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also may have any 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 minima. 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.maximum

Element-wise maximum of two arrays, propagates 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.

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.

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).

Examples

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

>>> x1 = np.eye(2)
>>> x2 = np.array([0.5, 2])
>>> np.minimum(x1, x2) # broadcasting
array([[0.5, 0. ],
       [0. , 1. ]]

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

>>> np.minimum(np.array(-np.inf), 1)
array(-inf)