Module « scipy.sparse »

Fonction random - module scipy.sparse

Signature de la fonction random

def random(m, n, density=0.01, format='coo', dtype=None, random_state=None, data_rvs=None) 

Description

random.__doc__

Generate a sparse matrix of the given shape and density with randomly
    distributed values.

    Parameters
    ----------
    m, n : int
        shape of the matrix
    density : real, optional
        density of the generated matrix: density equal to one means a full
        matrix, density of 0 means a matrix with no non-zero items.
    format : str, optional
        sparse matrix format.
    dtype : dtype, optional
        type of the returned matrix values.
    random_state : {None, int, `numpy.random.Generator`,
                    `numpy.random.RandomState`}, optional

        If `seed` is None (or `np.random`), the `numpy.random.RandomState`
        singleton is used.
        If `seed` is an int, a new ``RandomState`` instance is used,
        seeded with `seed`.
        If `seed` is already a ``Generator`` or ``RandomState`` instance then
        that instance is used.
        This random state will be used
        for sampling the sparsity structure, but not necessarily for sampling
        the values of the structurally nonzero entries of the matrix.
    data_rvs : callable, optional
        Samples a requested number of random values.
        This function should take a single argument specifying the length
        of the ndarray that it will return. The structurally nonzero entries
        of the sparse random matrix will be taken from the array sampled
        by this function. By default, uniform [0, 1) random values will be
        sampled using the same random state as is used for sampling
        the sparsity structure.

    Returns
    -------
    res : sparse matrix

    Notes
    -----
    Only float types are supported for now.

    Examples
    --------
    >>> from scipy.sparse import random
    >>> from scipy import stats
    >>> from numpy.random import default_rng
    >>> rng = default_rng()
    >>> rvs = stats.poisson(25, loc=10).rvs
    >>> S = random(3, 4, density=0.25, random_state=rng, data_rvs=rvs)
    >>> S.A
    array([[ 36.,   0.,  33.,   0.],   # random
           [  0.,   0.,   0.,   0.],
           [  0.,   0.,  36.,   0.]])

    >>> from scipy.sparse import random
    >>> from scipy.stats import rv_continuous
    >>> class CustomDistribution(rv_continuous):
    ...     def _rvs(self,  size=None, random_state=None):
    ...         return random_state.standard_normal(size)
    >>> X = CustomDistribution(seed=rng)
    >>> Y = X()  # get a frozen version of the distribution
    >>> S = random(3, 4, density=0.25, random_state=rng, data_rvs=Y.rvs)
    >>> S.A
    array([[ 0.        ,  0.        ,  0.        ,  0.        ],   # random
           [ 0.13569738,  1.9467163 , -0.81205367,  0.        ],
           [ 0.        ,  0.        ,  0.        ,  0.        ]])