# mars.tensor.argmin#

mars.tensor.argmin(a, axis=None, out=None, combine_size=None)[source]#

Returns the indices of the minimum values along an axis.

Parameters
• a (array_like) – Input tensor.

• axis (int, optional) – By default, the index is into the flattened tensor, otherwise along the specified axis.

• out (Tensor, optional) – If provided, the result will be inserted into this tensor. It should be of the appropriate shape and dtype.

• combine_size (int, optional) – The number of chunks to combine.

Returns

index_array – Tensor of indices into the tensor. It has the same shape as a.shape with the dimension along axis removed.

Return type

Tensor of ints

`Tensor.argmin`, `argmax`

`amin`

The minimum value along a given axis.

`unravel_index`

Convert a flat index into an index tuple.

Notes

In case of multiple occurrences of the minimum values, the indices corresponding to the first occurrence are returned.

Examples

```>>> import mars.tensor as mt
```
```>>> a = mt.arange(6).reshape(2,3)
>>> a.execute()
array([[0, 1, 2],
[3, 4, 5]])
>>> mt.argmin(a).execute()
0
>>> mt.argmin(a, axis=0).execute()
array([0, 0, 0])
>>> mt.argmin(a, axis=1).execute()
array([0, 0])
```

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

```>>> ind = mt.unravel_index(mt.argmin(a, axis=None), a.shape)
>>> ind.execute()
(0, 0)
>>> a[ind]  # TODO(jisheng): accomplish when fancy index on tensor is supported
```
```>>> b = mt.arange(6)
>>> b = 0
>>> b.execute()
array([0, 1, 2, 3, 0, 5])
>>> mt.argmin(b).execute()  # Only the first occurrence is returned.
0
```