# scipy.sparse.rand

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

  Generate a sparse matrix of the given shape and density with uniformly 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.

  Returns
    res sparse matrix

  See also

  `scipy.sparse.random`

    Similar function that allows a user-specified random data source.

  Notes

  Only float types are supported for now.

  Examples

      >>> from scipy.sparse import rand
      >>> matrix = rand(3, 4, density=0.25, format="csr", random_state=42)
      >>> matrix
      <3x4 sparse matrix of type '<class 'numpy.float64'>'
         with 3 stored elements in Compressed Sparse Row format>
      >>> matrix.todense()
      matrix([[0.05641158, 0.        , 0.        , 0.65088847],
              [0.        , 0.        , 0.        , 0.14286682],
              [0.        , 0.        , 0.        , 0.        ]])

scipy.sparse.vstack scipy.sparse.random