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# 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.

  Warning

  Since numpy 1.17, passing a `np.random.Generator` (e.g. `np.random.default_rng`) for `random_state` will lead to much faster execution times.

  A much slower implementation is used by default for backwards compatibility.

  Warning

  This function returns a sparse matrix – not a sparse array. You are encouraged to use `random_array` to take advantage of the sparse array functionality.

  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

  See also

  `random_array`

    constructs sparse arrays instead of sparse matrices

  Examples

  Passing a `np.random.Generator` instance for better performance:

      >>> import scipy as sp
      >>> import numpy as np
      >>> rng = np.random.default_rng()
      >>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng)

  Providing a sampler for the values:

      >>> rvs = sp.stats.poisson(25, loc=10).rvs
      >>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng, data_rvs=rvs)
      >>> S.toarray()
      array([[ 36.,   0.,  33.,   0.],   # random
             [  0.,   0.,   0.,   0.],
             [  0.,   0.,  36.,   0.]])

  Building a custom distribution. This example builds a squared normal from np.random:

      >>> def np_normal_squared(size=None, random_state=rng):
      ...     return random_state.standard_normal(size) ** 2
      >>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng,
      ...                      data_rvs=np_normal_squared)

  Or we can build it from sp.stats style rvs functions:

      >>> def sp_stats_normal_squared(size=None, random_state=rng):
      ...     std_normal = sp.stats.distributions.norm_gen().rvs
      ...     return std_normal(size=size, random_state=random_state) ** 2
      >>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng,
      ...                      data_rvs=sp_stats_normal_squared)

  Or we can subclass sp.stats rv_continous or rv_discrete:

      >>> class NormalSquared(sp.stats.rv_continuous):
      ...     def _rvs(self,  size=None, random_state=rng):
      ...         return random_state.standard_normal(size) ** 2
      >>> X = NormalSquared()
      >>> Y = X()  # get a frozen version of the distribution
      >>> S = sp.sparse.random(3, 4, density=0.25, random_state=rng, data_rvs=Y.rvs)