# jax.numpy.argminΒΆ

jax.numpy.argmin(a, axis=None)[source]ΒΆ

Returns the indices of the minimum values along an axis.

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

Parameters
• a (array_like) β Input array.

• axis (int, optional) β By default, the index is into the flattened array, otherwise along the specified axis.

Returns

index_array β Array of indices into the array. It has the same shape as a.shape with the dimension along axis removed.

Return type

ndarray of ints

ndarray.argmin(), argmax()

amin()

The minimum value along a given axis.

unravel_index()

Convert a flat index into an index tuple.

take_along_axis()

Apply np.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

>>> a = np.arange(6).reshape(2,3) + 10
>>> a
array([[10, 11, 12],
[13, 14, 15]])
>>> np.argmin(a)
0
>>> np.argmin(a, axis=0)
array([0, 0, 0])
>>> np.argmin(a, axis=1)
array([0, 0])


Indices of the minimum elements of a N-dimensional array:

>>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape)
>>> ind
(0, 0)
>>> a[ind]
10

>>> b = np.arange(6) + 10
>>> b[4] = 10
>>> b
array([10, 11, 12, 13, 10, 15])
>>> np.argmin(b)  # Only the first occurrence is returned.
0

>>> x = np.array([[4,2,3], [1,0,3]])
>>> index_array = np.argmin(x, axis=-1)
>>> # Same as np.min(x, axis=-1, keepdims=True)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1)
array([[2],
[0]])
>>> # Same as np.max(x, axis=-1)
>>> np.take_along_axis(x, np.expand_dims(index_array, axis=-1), axis=-1).squeeze(axis=-1)
array([2, 0])