def Johnson(A):
printCosts(A)
N = len(A)
d = potencjal(A)
if not d:
return None
for v in range(N):
for u in range(N):
A[u][v] = A[u][v] + d[-1][u] - d[-1][v]
print
printCosts(A)
d2 = []
for u in range(N):
d2 = Dijkstra(u,A)
#for u in range(N):
# dfg = Dijkstra(u,A)
# BLAD dla u=1
#for v in range(N):
# A[u][v] = d2[v] + d[-1][u] - d[-1][v]
return A
if __name__ == '__main__':
from printCosts import *
#m = [[0, 3, 10, float('inf')],
# [3, 0, 4, 10],
# [10, 4, 0, 14],
# [float('inf'), 10, 14, 0]]
m = [[0, 5, 1, float('inf'), 10,
4, float('inf')],
[5, 0, 2, float('inf'),
float('inf'), float('inf'), 1],
[1, 2, 0, 1, float('inf'),
float('inf'), float('inf')],
[float('inf'), float('inf'), 1, 0, 4, 1,
float('inf')],
[10, float('inf'),
float('inf'), 4, 0, 1,
float('inf')], [4, float('inf'),
float('inf'), 1, 1, 0, 2],
[float('inf'), 1,
float('inf'),
float('inf'),
float('inf'), 2, 0]]
print "Original matrix:"
printCosts(m)
d = findAllShortestPaths(m)
print "Shortest distances:"
printCosts(d)
i = kr.index(1)
j = kr[(i + 1):].index(1) + i + 1
List[i].append(j)
List[j].append(i)
return List
if __name__ == '__main__':
from printCosts import *
from GraphDigital import *
#startGraf()
am = [[0, 1, 1, 0, 0], [1, 0, 0, 0, 1], [1, 0, 0, 0, 0], [0, 0, 0, 0, 0],
[0, 1, 0, 0, 0]]
printCosts(am)
al = matrixToList(am)
am = listToMatrix(al)
print '\n', 'Lista z Macierzy:', al
wyswietl(al)
print 'Macierz z Listy:'
printCosts(am)
ai = matSasToMatIn(am)
print '\n', 'Incydencji z Macierzy:', ai, '\n'
am = matInToMatSas(ai)
printCosts(am)
print '\n\n', 'Lista:', al
wyswietl(al)
return None
return [[x[0] for x in row] for row in dist]
if __name__ == '__main__':
import sys
n = 20 if len(sys.argv) < 2 else int(sys.argv[1])
l = 30 if len(sys.argv) < 2 else int(sys.argv[2])
from printCosts import *
from DIrandom import *
from DIrandomizeWeights import *
from GraphDigital2 import *
A = losowyDigraf(n, l)
wyswietl(A, False)
from DIspojne import *
from GraphSlice import *
A = GraphSlice(A, listaSpojnychSkladowych(A)[0])
A = randomizeWeights(A, -5, 20)
wyswietl(A)
print "Original matrix:"
printCosts(A)
d = findAllShortestPaths(A)
if d != None:
print "Shortest distances:"
printCosts(d)
czujnik += 1
if czujnik == 10000:
print "Wybierz inne n, l."
return None
A[i][j] = 1
A[j][i] = 1
operation += 1
return A
def H(n, p):
'''losuje graf n-wierzcholkowy z prawdopodobienstwem powstania kazdej krawedzi rownym p'''
A = [[0] * n for i in range(n)]
for i in range(n):
for j in range(i + 1, n):
if random.random() < p:
A[i][j] = 1
A[j][i] = 1
return A
if __name__ == "__main__":
from printCosts import *
from GraphDigital import *
from konwersje import *
g = G(5, 4)
printCosts(g)
wyswietl(matrixToList(g))
print("*********************************************")
printCosts(H(5, 0.7))