# mars.tensor.choose#

mars.tensor.choose(a, choices, out=None, mode='raise')[source]#

Construct a tensor from an index tensor and a set of tensors to choose from.

First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = mt.lib.index_tricks):

`mt.choose(a,c) == mt.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])`.

But this omits some subtleties. Here is a fully general summary:

Given an “index” tensor (a) of integers and a sequence of n tensors (choices), a and each choice tensor are first broadcast, as necessary, to tensors of a common shape; calling these Ba and Bchoices[i], i = 0,…,n-1 we have that, necessarily, `Ba.shape == Bchoices[i].shape` for each i. Then, a new array with shape `Ba.shape` is created as follows:

• if `mode=raise` (the default), then, first of all, each element of a (and thus Ba) must be in the range [0, n-1]; now, suppose that i (in that range) is the value at the (j0, j1, …, jm) position in Ba - then the value at the same position in the new array is the value in Bchoices[i] at that same position;

• if `mode=wrap`, values in a (and thus Ba) may be any (signed) integer; modular arithmetic is used to map integers outside the range [0, n-1] back into that range; and then the new array is constructed as above;

• if `mode=clip`, values in a (and thus Ba) may be any (signed) integer; negative integers are mapped to 0; values greater than n-1 are mapped to n-1; and then the new tensor is constructed as above.

Parameters
• a (int tensor) – This tensor must contain integers in [0, n-1], where n is the number of choices, unless `mode=wrap` or `mode=clip`, in which cases any integers are permissible.

• choices (sequence of tensors) – Choice tensors. a and all of the choices must be broadcastable to the same shape. If choices is itself a tensor (not recommended), then its outermost dimension (i.e., the one corresponding to `choices.shape[0]`) is taken as defining the “sequence”.

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

• mode ({'raise' (default), 'wrap', 'clip'}, optional) –

Specifies how indices outside [0, n-1] will be treated:

• ’raise’ : an exception is raised

• ’wrap’ : value becomes value mod n

• ’clip’ : values < 0 are mapped to 0, values > n-1 are mapped to n-1

Returns

merged_array – The merged result.

Return type

Tensor

Raises

ValueError – shape mismatch: If a and each choice tensor are not all broadcastable to the same shape.

`Tensor.choose`

equivalent method

Notes

To reduce the chance of misinterpretation, even though the following “abuse” is nominally supported, choices should neither be, nor be thought of as, a single tensor, i.e., the outermost sequence-like container should be either a list or a tuple.

Examples

```>>> import mars.tensor as mt
```
```>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
...   [20, 21, 22, 23], [30, 31, 32, 33]]
>>> mt.choose([2, 3, 1, 0], choices
... # the first element of the result will be the first element of the
... # third (2+1) "array" in choices, namely, 20; the second element
... # will be the second element of the fourth (3+1) choice array, i.e.,
... # 31, etc.
... ).execute()
array([20, 31, 12,  3])
>>> mt.choose([2, 4, 1, 0], choices, mode='clip').execute() # 4 goes to 3 (4-1)
array([20, 31, 12,  3])
>>> # because there are 4 choice arrays
>>> mt.choose([2, 4, 1, 0], choices, mode='wrap').execute() # 4 goes to (4 mod 4)
array([20,  1, 12,  3])
>>> # i.e., 0
```

A couple examples illustrating how choose broadcasts:

```>>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
>>> choices = [-10, 10]
>>> mt.choose(a, choices).execute()
array([[ 10, -10,  10],
[-10,  10, -10],
[ 10, -10,  10]])
```
```>>> # With thanks to Anne Archibald
>>> a = mt.array([0, 1]).reshape((2,1,1))
>>> c1 = mt.array([1, 2, 3]).reshape((1,3,1))
>>> c2 = mt.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
>>> mt.choose(a, (c1, c2)).execute() # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
array([[[ 1,  1,  1,  1,  1],
[ 2,  2,  2,  2,  2],
[ 3,  3,  3,  3,  3]],
[[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5]]])
```