def exactMatch(A, resolution=1e-8, cython=True):
# via tha LAPJV algorithm
A = np.array(A)
if cython:
[pi, cost, v, u, costMat] = LAPJV.fast_lapjv(A, resolution)
else:
[pi, cost, v, u, costMat] = LAPJV.lapjv(A, resolution)
N = len(pi)
edge_cost = A[xrange(N), pi]
return cost, np.array(pi), np.array(edge_cost)
def best_ss_lapjv(self,cost_matrix):
a = numpy.array(cost_matrix)
jv_results = LAPJV.lap(a)
max_val = self.max_val
multiplier = self.multiplier
wip_ss = 0
for p_idx, c_idx in enumerate(jv_results[1]):
cost = cost_matrix[p_idx][c_idx]
ss = max_val - cost
wip_ss += ss
match_ss = float(wip_ss) / float(multiplier)
return match_ss
def best_ss_lapjv(self, cost_matrix):
a = numpy.array(cost_matrix)
jv_results = LAPJV.lap(a)
max_val = self.max_val
multiplier = self.multiplier
wip_ss = 0
for p_idx, c_idx in enumerate(jv_results[1]):
cost = cost_matrix[p_idx][c_idx]
ss = max_val - cost
wip_ss += ss
match_ss = float(wip_ss) / float(multiplier)
return match_ss
#!/usr/bin/env python
## Thanks to Dr N.D. van Foreest for providing this example code. ##
"""
The cost matrix is based on Balas and Toth, 1985, Branch and bound
# methods, in Lawler, E.L, et al., The TSP, John Wiley & Sons,
Chischester, pp 361--401.
"""
import numpy
import LAPJV
inf = 1000
a = numpy.array( [[inf,2,11,10,8,7,6,5],
[6,inf,1,8,8,4,6,7],
[5,12,inf,11,8,12,3,11],
[11,9,10,inf,1,9,8,10],
[11,11,9,4,inf,2,10,9],
[12,8,5,2,11,inf,11,9],
[10,11,12,10,9,12,inf,3],
[10,10,10,10,6,3,1,inf]] )
print LAPJV.lap(a)[1]
#!/usr/bin/env python
## Thanks to Dr N.D. van Foreest for providing this example code. ##
"""
The cost matrix is based on Balas and Toth, 1985, Branch and bound
# methods, in Lawler, E.L, et al., The TSP, John Wiley & Sons,
Chischester, pp 361--401.
"""
import numpy
import LAPJV
inf = 1000
a = numpy.array( [[inf,2,11,10,8,7,6,5],
[6,inf,1,8,8,4,6,7],
[5,12,inf,11,8,12,3,11],
[11,9,10,inf,1,9,8,10],
[11,11,9,4,inf,2,10,9],
[12,8,5,2,11,inf,11,9],
[10,11,12,10,9,12,inf,3],
[10,10,10,10,6,3,1,inf]] )
print LAPJV.lap(a)[0]
