#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2020 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from ... import opcodes as OperandDef from ..array_utils import device, as_same_device from .core import TensorOutBinOp class TensorFrexp(TensorOutBinOp): _op_type_ = OperandDef.FREXP _func_name = 'frexp' def __init__(self, casting='same_kind', dtype=None, sparse=False, **kw): super().__init__(_casting=casting, _dtype=dtype, _sparse=sparse, **kw) @property def _fun(self): return np.frexp @classmethod def execute(cls, ctx, op): inputs, device_id, xp = as_same_device( [ctx[c.key] for c in op.inputs], device=op.device, ret_extra=True) with device(device_id): kw = {'casting': op.casting} inputs_iter = iter(inputs) input = next(inputs_iter) if op.out1 is not None: out1 = next(inputs_iter) else: out1 = None if op.out2 is not None: out2 = next(inputs_iter) else: out2 = None if op.where is not None: where = kw['where'] = next(inputs_iter) else: where = None kw['order'] = op.order try: args = [input] if out1 is not None: args.append(out1) if out2 is not None: args.append(out2) mantissa, exponent = xp.frexp(*args, **kw) except TypeError: if where is None: raise mantissa, exponent = xp.frexp(input) mantissa, exponent = xp.where(where, mantissa, out1), xp.where(where, exponent, out2) for c, res in zip(op.outputs, (mantissa, exponent)): ctx[c.key] = res [docs]def frexp(x, out1=None, out2=None, out=None, where=None, **kwargs): """ 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) : tuple of tensors, (float, int) `mantissa` is a float array with values between -1 and 1. `exponent` is an int array which represents the exponent of 2. See Also -------- 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.]) """ op = TensorFrexp(**kwargs) return op(x, out1=out1, out2=out2, out=out, where=where)