rand(m, n, density=0.01, format='coo', dtype=None, random_state=None)
Only float types are supported for now.
shape of the matrix
density of the generated matrix: density equal to one means a full matrix, density of 0 means a matrix with no non-zero items.
sparse matrix format.
type of the returned matrix values.
numpy.random.RandomState}, optional
If seed
is None (or :None:None:`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.
Generate a sparse matrix of the given shape and density with uniformly distributed values.
scipy.sparse.random
Similar function that allows a user-specified random data source.
>>> 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. ]])See :
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.sparse._construct.rand
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them