Source code for mars.tensor.random.random_sample

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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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.
# You may obtain a copy of the License at
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#      http://www.apache.org/licenses/LICENSE-2.0
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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 __init__(self, size=None, dtype=None, **kw):
        dtype = np.dtype(dtype) if dtype is not None else dtype
        super().__init__(_size=size, dtype=dtype, **kw)

    def __call__(self, chunk_size):
        return self.new_tensor(None, None, raw_chunk_size=chunk_size)


[docs]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)