# mars.tensor.frexp¶

mars.tensor.frexp(x, out1=None, out2=None, out=None, where=None, **kwargs)[source]

Decompose the elements of x into mantissa and twos exponent.

Returns (mantissa, exponent), where x = mantissa * 2**exponent`. The mantissa is lies in the open interval(-1, 1), while the twos exponent is a signed integer.

Parameters
• x (array_like) – Tensor of numbers to be decomposed.

• out1 (Tensor, optional) – Output tensor for the mantissa. Must have the same shape as x.

• out2 (Tensor, optional) – Output tensor for the exponent. Must have the same shape as x.

• out (Tensor, None, or tuple of Tensor and None, optional) – A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated tensor is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

• where (array_like, optional) – Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone.

• **kwargs

Returns

(mantissa, exponent)mantissa is a float array with values between -1 and 1. exponent is an int array which represents the exponent of 2.

Return type

tuple of tensors, (float, int)

`ldexp`

Compute `y = x1 * 2**x2`, the inverse of frexp.

Notes

Complex dtypes are not supported, they will raise a TypeError.

Examples

```>>> import mars.tensor as mt
```
```>>> x = mt.arange(9)
>>> y1, y2 = mt.frexp(x)
```
```>>> y1_result, y2_result = mt.ExecutableTuple([y1, y2]).execute()
>>> y1_result
array([ 0.   ,  0.5  ,  0.5  ,  0.75 ,  0.5  ,  0.625,  0.75 ,  0.875,
0.5  ])
>>> y2_result
array([0, 1, 2, 2, 3, 3, 3, 3, 4])
>>> (y1 * 2**y2).execute()
array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.])
```