Source code for mars.tensor.random.random_integers

#!/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
# limitations under the License.

import numpy as np

from ... import opcodes as OperandDef
from ...serialization.serializables import Int64Field
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, TensorSimpleRandomData

class TensorRandomIntegers(TensorSimpleRandomData, TensorRandomOperandMixin):
    _op_type_ = OperandDef.RAND_RANDOM_INTEGERS

    _fields_ = "low", "high", "size"
    low = Int64Field("low")
    high = Int64Field("high")
    _func_name = "random_integers"

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

[docs]def random_integers(random_state, low, high=None, size=None, chunk_size=None, gpu=None): """ Random integers of type between `low` and `high`, inclusive. Return random integers of type from the "discrete uniform" distribution in the closed interval [`low`, `high`]. If `high` is None (the default), then results are from [1, `low`]. The type translates to the C long type used by Python 2 for "short" integers and its precision is platform dependent. This function has been deprecated. Use randint instead. Parameters ---------- low : int Lowest (signed) integer to be drawn from the distribution (unless ``high=None``, in which case this parameter is the *highest* such integer). high : int, optional If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if ``high=None``). 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 Returns ------- out : int or Tensor of ints `size`-shaped array of random integers from the appropriate distribution, or a single such random int if `size` not provided. See Also -------- random.randint : Similar to `random_integers`, only for the half-open interval [`low`, `high`), and 0 is the lowest value if `high` is omitted. Notes ----- To sample from N evenly spaced floating-point numbers between a and b, use:: a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.) Examples -------- >>> import mars.tensor as mt >>> mt.random.random_integers(5).execute() 4 >>> type(mt.random.random_integers(5).execute()) <type 'int'> >>> mt.random.random_integers(5, size=(3,2)).execute() array([[5, 4], [3, 3], [4, 5]]) Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (*i.e.*, from the set :math:`{0, 5/8, 10/8, 15/8, 20/8}`): >>> (2.5 * (mt.random.random_integers(5, size=(5,)) - 1) / 4.).execute() array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ]) Roll two six sided dice 1000 times and sum the results: >>> d1 = mt.random.random_integers(1, 6, 1000) >>> d2 = mt.random.random_integers(1, 6, 1000) >>> dsums = d1 + d2 Display results as a histogram: >>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(dsums.execute(), 11, normed=True) >>> """ size = random_state._handle_size(size) seed = gen_random_seeds(1, random_state.to_numpy())[0] op = TensorRandomIntegers( seed=seed, size=size, dtype=np.dtype(int), low=low, high=high, gpu=gpu ) return op(chunk_size=chunk_size)