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
def _clique(num_nodes: int,
weighted: bool = False,
rng: Optional[Generator] = None) -> NDArray:
adj_matrix = np.ones((num_nodes, num_nodes)) - np.eye(num_nodes)
weights = None
if weighted:
if rng is None:
rng = default_rng()
weights = to_undirected(rng.uniform(low=0.0, high=1.0, size=(num_nodes, num_nodes)))
return adj_matrix, weights
[docs]def clique_full(num_nodes: int,
weighted: bool = False,
rng: Optional[Generator] = None) -> NDArray:
"""
Returns a complete graph, a.k.a. a clique.
Parameters
----------
num_nodes : int
The number of nodes
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 clique in matrix representation :obj:`(num_nodes x num_nodes)`
"""
adj_matrix, weights = _clique(num_nodes=num_nodes, weighted=weighted, rng=rng)
adj_matrix = adj_matrix.astype(dtype=np.float32)
return adj_matrix * weights if weighted else adj_matrix
[docs]def clique_coo(num_nodes: int,
weighted: bool = False,
rng: Optional[Generator] = None) -> Tuple[NDArray, Optional[NDArray]]:
"""
Returns a complete graph, a.k.a. a clique.
Parameters
----------
num_nodes : int
The number of nodes
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 clique in COO representation :obj:`(num_edges x 2)`
Optional[NDArray]
Weights of the random graph.
"""
adj_matrix, weights = _clique(num_nodes=num_nodes, weighted=weighted, rng=rng)
if weighted:
weights *= adj_matrix
coo_matrix = np.argwhere(adj_matrix)
coo_weights = np.expand_dims(weights[weights.nonzero()], -1) if weights is not None else None
return coo_matrix, coo_weights