median: vertices selected for the solution
"""
N = len(dist)
median = random.sample(range(N), p)
d1 = [-1 for i in range(N)]
d2 = [-1 for i in range(N)]
r = update_assignment(dist, median, d1, d2, p , N)
if verbose: print(r)
while True:
result = next(dist, median, d1, d2, p, N)
if result[0]:
r = result[1]
if verbose: print(r)
else:
break
return r, median
if __name__ == "__main__":
import sys
sys.path.append('../networks')
from network2listmatrix import network2distancematrix
from allpairdist import allpairs
print('Problem: simple network')
a = network2distancematrix('../data/network-links', True)
allpairs(a)
teitz_bart(a, 2, True)
print('Problem: pmed1 in OR-lib')
a = network2distancematrix('../data/orlib/pmed1.orlib', False)
allpairs(a)
teitz_bart(a, 5, True)
"""
N = len(dist)
median = random.sample(range(N), p)
d1 = [-1 for i in range(N)]
d2 = [-1 for i in range(N)]
r = update_assignment(dist, median, d1, d2, p, N)
if verbose: print(r)
while True:
result = next(dist, median, d1, d2, p, N)
if result[0]:
r = result[1]
if verbose: print(r)
else:
break
return r, median
if __name__ == "__main__":
import sys
sys.path.append('../networks')
from network2listmatrix import network2distancematrix
from allpairdist import allpairs
print('Problem: simple network')
a = network2distancematrix('../data/network-links', True)
allpairs(a)
teitz_bart(a, 2, True)
print('Problem: pmed1 in OR-lib')
a = network2distancematrix('../data/orlib/pmed1.orlib', False)
allpairs(a)
teitz_bart(a, 5, True)
__author__ = "Ningchuan Xiao <*****@*****.**>"
def allpairs(a):
"""
Returns the weight/distance matrix for all pair
shortest path using the Floyd-Warshall algorithm.
Input
a: initial distance matrix where weights for
non-adjacent pairs are infinity
Output
The function directly changes the values in the input
"""
n = len(a)
for k in range(n):
for i in range(n):
for j in range(n):
if a[i][j] > a[i][k] + a[k][j]:
a[i][j] = a[i][k] + a[k][j]
if __name__ == "__main__":
from network2listmatrix import network2distancematrix
fname = '../data/network-links'
a = network2distancematrix(fname, True)
allpairs(a)
print(a[1][6])
print(a[0][7])