import ctypes
import networkx as nx
import numpy as np
import os
import sys
import scipy.sparse as sp
class reducelib(object):
def __init__(self):
dir_path = os.path.dirname(os.path.realpath(__file__))
self.lib = ctypes.CDLL('%s/libreduce.so' % dir_path)
def __CtypeNetworkX(self, g):
edges = g.edges()
e_list_from = (ctypes.c_int * len(edges))()
e_list_to = (ctypes.c_int * len(edges))()
if len(edges):
a, b = zip(*edges)
e_list_from[:] = a
e_list_to[:] = b
return (len(g.nodes()), len(edges), ctypes.cast(e_list_from, ctypes.c_void_p), ctypes.cast(e_list_to, ctypes.c_void_p))
def __CtypeAdj(self, adj):
adj = adj.tocoo()
num_edge = adj.nnz
num_node = adj.shape[0]
e_list_from = (ctypes.c_int * num_edge)()
e_list_to = (ctypes.c_int * num_edge)()
edges = zip(adj.col, adj.row)
if num_edge:
a, b = zip(*edges)
e_list_from[:] = a
e_list_to[:] = b
return (num_node, num_edge, ctypes.cast(e_list_from, ctypes.c_void_p), ctypes.cast(e_list_to, ctypes.c_void_p))
def reduce_graph(self, adj):
# g = nx.from_scipy_sparse_matrix(adj)
n_nodes, n_edges, e_froms, e_tos = self.__CtypeAdj(adj)
reduced_node = (ctypes.c_int * (n_nodes))()
new_n_nodes = ctypes.c_int()
new_n_edges = ctypes.c_int()
reduced_xadj = (ctypes.c_int * (n_nodes+1))()
reduced_adjncy = (ctypes.c_int * (2*n_edges))()
mapping = (ctypes.c_int * (n_nodes))()
reverse_mapping = (ctypes.c_int * (n_nodes))()
crt_is_size = self.lib.Reduce(n_nodes, n_edges, e_froms, e_tos, reduced_node,
ctypes.byref(new_n_nodes), ctypes.byref(new_n_edges),
reduced_xadj, reduced_adjncy, mapping, reverse_mapping)
# crt_is_size = self.lib.Reduce(n_nodes, n_edges, e_froms, e_tos, reduced_node)
new_n_nodes = new_n_nodes.value
new_n_edges = new_n_edges.value
reduced_node = np.asarray(reduced_node[:])
reduced_xadj = np.asarray(reduced_xadj[:])
reduced_xadj = reduced_xadj[:new_n_nodes+1]
reduced_adjncy = np.asarray(reduced_adjncy[:])
reduced_adjncy = reduced_adjncy[:new_n_edges]
mapping = np.asarray(mapping[:])
reverse_mapping = np.asarray(reverse_mapping[:])
reverse_mapping = reverse_mapping[:new_n_nodes]
reduced_adj = sp.csr_matrix((np.ones(new_n_edges), reduced_adjncy, reduced_xadj), shape=[new_n_nodes, new_n_nodes])
return reduced_node, reduced_adj, mapping, reverse_mapping, crt_is_size
# return reduced_node[:], crt_is_size
def local_search(self, adj, indset):
n_nodes, n_edges, e_froms, e_tos = self.__CtypeAdj(adj)
init_mis = (ctypes.c_int * (n_nodes))()
final_mis = (ctypes.c_int * (n_nodes))()
init_mis[:] = np.int32(indset)
init_mis = ctypes.cast(init_mis, ctypes.c_void_p)
self.lib.LocalSearch(n_nodes, n_edges, e_froms, e_tos, init_mis, final_mis)
indset = np.asarray(final_mis[:])
return indset
def test(self):
self.lib.test()
