Classe « Generator »

Méthode numpy.random.Generator.choice

Signature de la méthode choice

Description

choice.__doc__

        choice(a, size=None, replace=True, p=None, axis=0, shuffle=True)

        Generates a random sample from a given 1-D array

        Parameters
        ----------
        a : {array_like, int}
            If an ndarray, a random sample is generated from its elements.
            If an int, the random sample is generated from np.arange(a).
        size : {int, tuple[int]}, optional
            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
            ``m * n * k`` samples are drawn from the 1-d `a`. If `a` has more
            than one dimension, the `size` shape will be inserted into the
            `axis` dimension, so the output ``ndim`` will be ``a.ndim - 1 +
            len(size)``. Default is None, in which case a single value is
            returned.
        replace : bool, optional
            Whether the sample is with or without replacement
        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.
        axis : int, optional
            The axis along which the selection is performed. The default, 0,
            selects by row.
        shuffle : bool, optional
            Whether the sample is shuffled when sampling without replacement.
            Default is True, False provides a speedup.

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

        Raises
        ------
        ValueError
            If a is an int and less than zero, if p is not 1-dimensional, if
            a is array-like with a 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
        --------
        integers, shuffle, permutation

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

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

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

        >>> rng.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:

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

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

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