Source code for xorbits._mars.tensor.arithmetic.ldexp
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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import numpy as np
from ... import opcodes as OperandDef
from ..datasource import tensor as astensor
from ..utils import infer_dtype
from .core import TensorBinOp
from .utils import arithmetic_operand
@arithmetic_operand
class TensorLdexp(TensorBinOp):
_op_type_ = OperandDef.LDEXP
_func_name = "ldexp"
@classmethod
def _is_sparse(cls, x1, x2):
if hasattr(x1, "issparse") and x1.issparse():
return True
return False
[docs]@infer_dtype(np.ldexp)
def ldexp(x1, x2, out=None, where=None, **kwargs):
"""
Returns x1 * 2**x2, element-wise.
The mantissas `x1` and twos exponents `x2` are used to construct
floating point numbers ``x1 * 2**x2``.
Parameters
----------
x1 : array_like
Tensor of multipliers.
x2 : array_like, int
Tensor of twos exponents.
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
-------
y : Tensor or scalar
The result of ``x1 * 2**x2``.
See Also
--------
frexp : Return (y1, y2) from ``x = y1 * 2**y2``, inverse to `ldexp`.
Notes
-----
Complex dtypes are not supported, they will raise a TypeError.
`ldexp` is useful as the inverse of `frexp`, if used by itself it is
more clear to simply use the expression ``x1 * 2**x2``.
Examples
--------
>>> import mars.tensor as mt
>>> mt.ldexp(5, mt.arange(4)).execute()
array([ 5., 10., 20., 40.], dtype=float32)
>>> x = mt.arange(6)
>>> mt.ldexp(*mt.frexp(x)).execute()
array([ 0., 1., 2., 3., 4., 5.])
"""
x2_dtype = astensor(x2).dtype
casting = kwargs.get("casting", "safe")
if not np.can_cast(x2_dtype, np.int64, casting=casting):
raise TypeError(
"ufunc 'ldexp' not supported for the input types, "
"and the inputs could not be safely coerced to any supported types "
f"according to the casting rule ''{casting}''"
)
op = TensorLdexp(**kwargs)
return op(x1, x2, out=out, where=where)