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.
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, 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
random
Similar function allowing a custom random data sampler
random_array
Similar to random() but returns a sparse array
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 <Compressed Sparse Row sparse matrix of dtype 'float64' with 3 stored elements and shape (3, 4)> >>> matrix.toarray() array([[0.05641158, 0. , 0. , 0.65088847], # random [0. , 0. , 0. , 0.14286682], [0. , 0. , 0. , 0. ]])