flopscope.numpy.min
fnp.min(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[flopscope source][numpy source]
Return the minimum of an array or minimum along an axis.
Adapted from NumPy docs np.min
Minimum value of array.
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.
If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before.
- 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.
If the default value is passed, then
keepdimswill not be passed through to the min method of sub-classes ofndarray, however any non-default value will be. If the sub-class' method does not implementkeepdimsany exceptions will be raised.- initial:scalar, optional
The maximum value of an output element. Must be present to allow computation on empty slice. See reduce for details.
- where:array_like of bool, optional
Elements to compare for the minimum. See reduce for details.
Returns
- min:ndarray or scalar
Minimum of
a. Ifaxisis None, the result is a scalar value. Ifaxisis an int, the result is an array of dimensiona.ndim - 1. Ifaxisis a tuple, the result is an array of dimensiona.ndim - len(axis).
See also
- we.flops.max The maximum value of an array along a given axis, propagating any NaNs.
- we.flops.nanmin The minimum value of an array along a given axis, ignoring any NaNs.
- we.flops.minimum Element-wise minimum of two arrays, propagating any NaNs.
- we.flops.fmin Element-wise minimum of two arrays, ignoring any NaNs.
- we.flops.argmin Return the indices of the minimum values.
- we.flops.nanmax
- we.flops.maximum
- we.flops.fmax
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 min for element-wise comparison of 2 arrays; when
a.shape[0] is 2, minimum(a[0], a[1]) is faster than
min(a, axis=0).
Examples
>>> import flopscope.numpy as fnp
>>> a = flops.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> flops.min(a) # Minimum of the flattened array
0
>>> flops.min(a, axis=0) # Minima along the first axis
array([0, 1])
>>> flops.min(a, axis=1) # Minima along the second axis
array([0, 2])
>>> flops.min(a, where=[False, True], initial=10, axis=0)
array([10, 1])>>> b = flops.arange(5, dtype=float)
>>> b[2] = flops.nan
>>> flops.min(b)
flops.float64(nan)
>>> flops.min(b, where=~flops.isnan(b), initial=10)
0.0
>>> flops.nanmin(b)
0.0>>> flops.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.
>>> flops.min([6], initial=5)
5
>>> min([6], default=5)
6