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Classe « RandomState »

Méthode numpy.random.RandomState.choice

Signature de la méthode choice

def choice(self, a, size=None, replace=True, p=None) 

Description

help(RandomState.choice)

        choice(a, size=None, replace=True, p=None)

        Generates a random sample from a given 1-D array

        .. note::
            New code should use the `~numpy.random.Generator.choice`
            method of a `~numpy.random.Generator` instance instead;
            please see the :ref:`random-quick-start`.

        .. warning::
            This function uses the C-long dtype, which is 32bit on windows
            and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones).
            Since NumPy 2.0, NumPy's default integer is 32bit on 32bit platforms
            and 64bit on 64bit platforms.


        Parameters
        ----------
        a : 1-D array-like or int
            If an ndarray, a random sample is generated from its elements.
            If an int, the random sample is generated as if it were ``np.arange(a)``
        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.
        replace : boolean, optional
            Whether the sample is with or without replacement. Default is True,
            meaning that a value of ``a`` can be selected multiple times.
        p : 1-D array-like, optional
            The probabilities associated with each entry in a.
            If not given, the sample assumes a uniform distribution over all
            entries in ``a``.

        Returns
        -------
        samples : single item or ndarray
            The generated random samples

        Raises
        ------
        ValueError
            If a is an int and less than zero, if a or p are not 1-dimensional,
            if a is an array-like of size 0, if p is not a vector of
            probabilities, if a and p have different lengths, or if
            replace=False and the sample size is greater than the population
            size

        See Also
        --------
        randint, shuffle, permutation
        random.Generator.choice: which should be used in new code

        Notes
        -----
        Setting user-specified probabilities through ``p`` uses a more general but less
        efficient sampler than the default. The general sampler produces a different sample
        than the optimized sampler even if each element of ``p`` is 1 / len(a).

        Sampling random rows from a 2-D array is not possible with this function,
        but is possible with `Generator.choice` through its ``axis`` keyword.

        Examples
        --------
        Generate a uniform random sample from np.arange(5) of size 3:

        >>> np.random.choice(5, 3)
        array([0, 3, 4]) # random
        >>> #This is equivalent to np.random.randint(0,5,3)

        Generate a non-uniform random sample from np.arange(5) of size 3:

        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
        array([3, 3, 0]) # random

        Generate a uniform random sample from np.arange(5) of size 3 without
        replacement:

        >>> np.random.choice(5, 3, replace=False)
        array([3,1,0]) # random
        >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]

        Generate a non-uniform random sample from np.arange(5) of size
        3 without replacement:

        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
        array([2, 3, 0]) # random

        Any of the above can be repeated with an arbitrary array-like
        instead of just integers. For instance:

        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']
        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random
              dtype='<U11')

        

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