flopscope.numpy.argmin
fnp.argmin(a, axis=None, out=None, *, keepdims=<no value>)[flopscope source][numpy source]
Returns the indices of the minimum values along an axis.
Adapted from NumPy docs np.argmin
Index of minimum value.
Parameters
- a:array_like
Input array.
- axis:int, optional
By default, the index is into the flattened array, otherwise along the specified axis.
- out:array, optional
If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype.
- 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 array.
Added in version 1.22.0.
Returns
- index_array:ndarray of ints
Array of indices into the array. It has the same shape as
a.shapewith the dimension alongaxisremoved. Ifkeepdimsis set to True, then the size ofaxiswill be 1 with the resulting array having same shape asa.shape.
See also
- ndarray.argmin
- we.flops.argmax
- we.flops.min The minimum value along a given axis.
- we.flops.unravel_index Convert a flat index into an index tuple.
- we.flops.take_along_axis Apply flops.expand_dims(index_array, axis) from argmin to an array as if by calling min.
Notes
In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.
Examples
>>> import flopscope.numpy as fnp
>>> a = flops.arange(6).reshape(2,3) + 10
>>> a
array([[10, 11, 12],
[13, 14, 15]])
>>> flops.argmin(a)
0
>>> flops.argmin(a, axis=0)
array([0, 0, 0])
>>> flops.argmin(a, axis=1)
array([0, 0])Indices of the minimum elements of a N-dimensional array:
>>> ind = flops.unravel_index(flops.argmin(a, axis=None), a.shape)
>>> ind
(0, 0)
>>> a[ind]
10>>> b = flops.arange(6) + 10
>>> b[4] = 10
>>> b
array([10, 11, 12, 13, 10, 15])
>>> flops.argmin(b) # Only the first occurrence is returned.
0>>> x = flops.array([[4,2,3], [1,0,3]])
>>> index_array = flops.argmin(x, axis=-1)
>>> # Same as flops.amin(x, axis=-1, keepdims=True)
>>> flops.take_along_axis(x, flops.expand_dims(index_array, axis=-1), axis=-1)
array([[2],
[0]])
>>> # Same as flops.amax(x, axis=-1)
>>> flops.take_along_axis(x, flops.expand_dims(index_array, axis=-1),
... axis=-1).squeeze(axis=-1)
array([2, 0])Setting keepdims to True,
>>> x = flops.arange(24).reshape((2, 3, 4))
>>> res = flops.argmin(x, axis=1, keepdims=True)
>>> res.shape
(2, 1, 4)