scipy.sparse.rand#
- scipy.sparse.rand(m, n, density=0.01, format='coo', dtype=None, random_state=None)[source]#
Generate a sparse matrix of the given shape and density with uniformly 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
}, optionalIf seed is None (or np.random), the
numpy.random.RandomState
singleton is used. If seed is an int, a newRandomState
instance is used, seeded with seed. If seed is already aGenerator
orRandomState
instance then that instance is used.
- Returns
- ressparse 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.toarray() array([[0.05641158, 0. , 0. , 0.65088847], [0. , 0. , 0. , 0.14286682], [0. , 0. , 0. , 0. ]])