Module « numpy.random »

Fonction default_rng - module numpy.random

Signature de la fonction default_rng

Description

default_rng.__doc__

Construct a new Generator with the default BitGenerator (PCG64).

    Parameters
    ----------
    seed : {None, int, array_like[ints], SeedSequence, BitGenerator, Generator}, optional
        A seed to initialize the `BitGenerator`. If None, then fresh,
        unpredictable entropy will be pulled from the OS. If an ``int`` or
        ``array_like[ints]`` is passed, then it will be passed to
        `SeedSequence` to derive the initial `BitGenerator` state. One may also
        pass in a `SeedSequence` instance.
        Additionally, when passed a `BitGenerator`, it will be wrapped by
        `Generator`. If passed a `Generator`, it will be returned unaltered.

    Returns
    -------
    Generator
        The initialized generator object.

    Notes
    -----
    If ``seed`` is not a `BitGenerator` or a `Generator`, a new `BitGenerator`
    is instantiated. This function does not manage a default global instance.
    
    Examples
    --------
    ``default_rng`` is the reccomended constructor for the random number class
    ``Generator``. Here are several ways we can construct a random 
    number generator using ``default_rng`` and the ``Generator`` class. 
    
    Here we use ``default_rng`` to generate a random float:
 
    >>> import numpy as np
    >>> rng = np.random.default_rng(12345)
    >>> print(rng)
    Generator(PCG64)
    >>> rfloat = rng.random()
    >>> rfloat
    0.22733602246716966
    >>> type(rfloat)
    <class 'float'>
     
    Here we use ``default_rng`` to generate 3 random integers between 0 
    (inclusive) and 10 (exclusive):
        
    >>> import numpy as np
    >>> rng = np.random.default_rng(12345)
    >>> rints = rng.integers(low=0, high=10, size=3)
    >>> rints
    array([6, 2, 7])
    >>> type(rints[0])
    <class 'numpy.int64'>
    
    Here we specify a seed so that we have reproducible results:
    
    >>> import numpy as np
    >>> rng = np.random.default_rng(seed=42)
    >>> print(rng)
    Generator(PCG64)
    >>> arr1 = rng.random((3, 3))
    >>> arr1
    array([[0.77395605, 0.43887844, 0.85859792],
           [0.69736803, 0.09417735, 0.97562235],
           [0.7611397 , 0.78606431, 0.12811363]])

    If we exit and restart our Python interpreter, we'll see that we
    generate the same random numbers again:

    >>> import numpy as np
    >>> rng = np.random.default_rng(seed=42)
    >>> arr2 = rng.random((3, 3))
    >>> arr2
    array([[0.77395605, 0.43887844, 0.85859792],
           [0.69736803, 0.09417735, 0.97562235],
           [0.7611397 , 0.78606431, 0.12811363]])