# 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{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.        ]])

scipy.sparse.rand scipy.sparse.save_npz