scipy.sparse.random#

scipy.sparse.random(m, n, density=0.01, format='coo', dtype=None, random_state=None, data_rvs=None)[source]#

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

Parameters

m, nint: shape of the matrix
densityreal, 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.
formatstr, optional: sparse matrix format.
dtypedtype, 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_rvscallable, 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

ressparse 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.        ]])