jax.numpy.min¶

jax.numpy.min(a, axis=None, out=None, keepdims=None, initial=None, where=None)[source]

Return the minimum of an array or minimum along an axis.

LAX-backend implementation of amin(). Original docstring below.

Parameters
• a (array_like) – Input data.

• axis (None or int or tuple of ints, optional) – Axis or axes along which to operate. By default, flattened input is used.

• out (ndarray, optional) – Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See ufuncs-output-type for more details.

• keepdims (bool, optional) – If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

• initial (scalar, optional) – The maximum value of an output element. Must be present to allow computation on empty slice. See ~numpy.ufunc.reduce for details.

• where (array_like of bool, optional) – Elements to compare for the minimum. See ~numpy.ufunc.reduce for details.

Returns

amin – Minimum of a. If axis is None, the result is a scalar value. If axis is given, the result is an array of dimension a.ndim - 1.

Return type

ndarray or scalar

amax()

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

nanmin()

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

minimum()

Element-wise minimum of two arrays, propagating any NaNs.

fmin()

Element-wise minimum of two arrays, ignoring any NaNs.

argmin()

Return the indices of the minimum values.

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding min value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmin.

Don’t use amin for element-wise comparison of 2 arrays; when a.shape[0] is 2, minimum(a[0], a[1]) is faster than amin(a, axis=0).

Examples

>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.amin(a)           # Minimum of the flattened array
0
>>> np.amin(a, axis=0)   # Minima along the first axis
array([0, 1])
>>> np.amin(a, axis=1)   # Minima along the second axis
array([0, 2])
>>> np.amin(a, where=[False, True], initial=10, axis=0)
array([10,  1])

>>> b = np.arange(5, dtype=float)
>>> b[2] = np.NaN
>>> np.amin(b)
nan
>>> np.amin(b, where=~np.isnan(b), initial=10)
0.0
>>> np.nanmin(b)
0.0

>>> np.min([[-50], [10]], axis=-1, initial=0)
array([-50,   0])


Notice that the initial value is used as one of the elements for which the minimum is determined, unlike for the default argument Python’s max function, which is only used for empty iterables.

Notice that this isn’t the same as Python’s default argument.

>>> np.min([6], initial=5)
5
>>> min([6], default=5)
6