Source code for mars.tensor.random.standard_exponential

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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import numpy as np

from ... import opcodes as OperandDef
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, TensorDistribution

class TensorStandardExponential(TensorDistribution, TensorRandomOperandMixin):
    _op_type_ = OperandDef.RAND_STANDARD_EXPONENTIAL
    _func_name = "standard_exponential"
    _fields_ = ("size",)

    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) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorStandardExponential(size=size, seed=seed, gpu=gpu, dtype=dtype) return op(chunk_size=chunk_size)