Thanks for your answer, now, i get the correct distances, nice!
I was benchmarking some Floyd APSP source-code.
Some results (in seconds) on a random graph with 800 nodes and abouts 10000
edges:
Networkx : 82.66
Python with array : 38.97
Numpy from Networkx : 1.67 (no predecessors calculation)
graph-tool : 0.91
Scipy (Cython) : 0.50
Pure C code : 0.35
Below the code I used for Graph-tool test:
# -------------------
import graph_tool as gt
from graph_tool.topology import shortest_distance
import numpy as np
from random import randrange
def random_edges(n, density, max_weight=100):
M=n*(n-1)//2
m=int(density*M)
edges=set()
wedges=[]
while len(edges)<m:
L=(randrange(n),randrange(n))
if L[0]!=L[1] and L not in edges:
w=float(randrange(max_weight))
wedges.append(L+(w,))
edges.add(L)
return wedges
n=800
density=0.33
wedges=random_edges(n, density)
start=clock()
g= gt.Graph(directed=True)
weight = g.new_edge_property("double")
g.add_edge_list(np.array(wedges), eprops=[weight])
dist = shortest_distance(g, weights=weight, dense=True)
end=clock()
print("graph-tool \t: %.2f" %(end-start))
# -------------------
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