import numpy as np
from numpy.typing import NDArray
from numpy.random import Generator, default_rng
from typing import Optional, Tuple
from numgraph.utils import to_undirected, unsorted_coalesce
def _star(num_nodes: int,
directed: bool = False,
weighted: bool = False,
rng: Optional[Generator] = None) -> NDArray:
edges = np.stack([np.zeros((num_nodes - 1, ), dtype = int), np.arange(1, num_nodes)],
axis=1)
weights = None
if weighted:
if rng is None:
rng = default_rng()
weights = rng.uniform(low=0.0, high=1.0, size=(len(edges), 1))
if not directed:
edges = to_undirected(edges)
weights = np.vstack((weights, weights)) if weights is not None else None
return unsorted_coalesce(edges, weights)
[docs]def star_full(num_nodes: int,
directed: bool = False,
weighted: bool = False,
rng: Optional[Generator] = None) -> NDArray:
"""
Returns a star graph.
Parameters
----------
num_nodes : int
The number of nodes
directed : bool, optional
If set to :obj:`True`, will return a directed graph, by default :obj:`False`
weighted : bool, optional
If set to :obj:`True`, will return a dense representation of the weighted graph, by default :obj:`False`
rng : Optional[Generator], optional
Numpy random number generator, by default :obj:`None`
Returns
-------
NDArray
The star graph in matrix representaion :obj:`(num_nodes x num_nodes)`
"""
coo_matrix, weights = _star(num_nodes=num_nodes, directed=directed, weighted=weighted, rng=rng)
# Fill adj_matrix with the weights
adj_matrix = np.zeros((num_nodes, num_nodes))
adj_matrix[coo_matrix[:, 0], coo_matrix[:, 1]] = np.squeeze(weights) if weighted else 1
return adj_matrix
[docs]def star_coo(num_nodes: int,
directed: bool = False,
weighted: bool = False,
rng: Optional[Generator] = None) -> Tuple[NDArray, Optional[NDArray]]:
"""
Returns a star graph.
Parameters
----------
num_nodes : int
The number of nodes
directed : bool, optional
If set to :obj:`True`, will return a directed graph, by default :obj:`False`
weighted : bool, optional
If set to :obj:`True`, will return a dense representation of the weighted graph, by default :obj:`False`
rng : Optional[Generator], optional
Numpy random number generator, by default :obj:`None`
Returns
-------
NDArray
The star graph in COO representation :obj:`(num_edges x 2)`
Optional[NDArray]
Weights of the random graph.
"""
return _star(num_nodes=num_nodes, directed=directed, weighted=weighted, rng=rng)