scipy.sparse.random

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

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 : {numpy.random.RandomState, int}, optional

Random number generator or random seed. If not given, the singleton numpy.random will be 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

>>> class CustomRandomState(np.random.RandomState):
...     def randint(self, k):
...         i = np.random.randint(k)
...         return i - i % 2
>>> np.random.seed(12345)
>>> rs = CustomRandomState()
>>> rvs = stats.poisson(25, loc=10).rvs
>>> S = random(3, 4, density=0.25, random_state=rs, 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, *args, **kwargs):
...         return self._random_state.randn(*self._size)
>>> X = CustomDistribution(seed=2906)
>>> Y = X()  # get a frozen version of the distribution
>>> S = random(3, 4, density=0.25, random_state=2906, data_rvs=Y.rvs)
>>> S.A
array([[ 0.        ,  0.        ,  0.        ,  0.        ],
       [ 0.13569738,  1.9467163 , -0.81205367,  0.        ],
       [ 0.        ,  0.        ,  0.        ,  0.        ]])