xorbits._mars.tensor.random.random_sample 源代码
# Copyright 2022-2023 XProbe Inc.
# derived from 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
#
# 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.
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import numpy as np
from ... import opcodes as OperandDef
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, TensorSimpleRandomData
class TensorRandomSample(TensorSimpleRandomData, TensorRandomOperandMixin):
_op_type_ = OperandDef.RAND_RANDOM_SAMPLE
_fields_ = ("size",)
_func_name = "random_sample"
def __call__(self, chunk_size):
return self.new_tensor(None, None, raw_chunk_size=chunk_size)
[文档]def random_sample(random_state, size=None, chunk_size=None, gpu=None, dtype=None):
"""
Return random floats in the half-open interval [0.0, 1.0).
Results are from the "continuous uniform" distribution over the
stated interval. To sample :math:`Unif[a, b), b > a` multiply
the output of `random_sample` by `(b-a)` and add `a`::
(b - a) * random_sample() + a
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 of floats
Array of random floats of shape `size` (unless ``size=None``, in which
case a single float is returned).
Examples
--------
>>> import mars.tensor as mt
>>> mt.random.random_sample().execute()
0.47108547995356098
>>> type(mt.random.random_sample().execute())
<type 'float'>
>>> mt.random.random_sample((5,)).execute()
array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])
Three-by-two array of random numbers from [-5, 0):
>>> (5 * mt.random.random_sample((3, 2)) - 5).execute()
array([[-3.99149989, -0.52338984],
[-2.99091858, -0.79479508],
[-1.23204345, -1.75224494]])
"""
if dtype is None:
dtype = np.dtype("f8")
size = random_state._handle_size(size)
seed = gen_random_seeds(1, random_state.to_numpy())[0]
op = TensorRandomSample(seed=seed, size=size, gpu=gpu, dtype=dtype)
return op(chunk_size=chunk_size)