#!/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 .core import TensorRandomOperandMixin, TensorDistribution class TensorStandardExponential(TensorDistribution, TensorRandomOperandMixin): __slots__ = '_size', _op_type_ = OperandDef.RAND_STANDARD_EXPONENTIAL _func_name = 'standard_exponential' def __init__(self, size=None, state=None, dtype=None, gpu=None, **kw): dtype = np.dtype(dtype) if dtype is not None else dtype super().__init__(_size=size, _state=state, _dtype=dtype, _gpu=gpu, **kw) def __call__(self, chunk_size=None): return self.new_tensor(None, None, raw_chunk_size=chunk_size) [docs]def standard_exponential(random_state, size=None, chunk_size=None, gpu=None, dtype=None): """ Draw samples from the standard exponential distribution. `standard_exponential` is identical to the exponential distribution with a scale parameter of 1. Parameters ---------- size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then ``m * n * k`` samples are drawn. Default is None, in which case a single value is returned. chunk_size : int or tuple of int or tuple of ints, optional Desired chunk size on each dimension gpu : bool, optional Allocate the tensor on GPU if True, False as default dtype : data-type, optional Data-type of the returned tensor. Returns ------- out : float or Tensor Drawn samples. Examples -------- Output a 3x8000 tensor: >>> import mars.tensor as mt >>> n = mt.random.standard_exponential((3, 8000)) """ if dtype is None: dtype = np.random.RandomState().standard_exponential(size=(0,)).dtype size = random_state._handle_size(size) op = TensorStandardExponential(size=size, state=random_state.to_numpy(), gpu=gpu, dtype=dtype) return op(chunk_size=chunk_size)