def test_weighted_instance_failure_aware():
"""Checks that the CF and PICEF formulations agree on the optimal
result for an instance with weighted edges, using failure-aware solving.
"""
EPS = 0.000001
d, ndds = read_with_ndds("test-fixtures/100-random-weights")
fns = [k_ip.optimise_picef, k_ip.optimise_ccf]
for max_cycle in [0, 1, 2, 3, 4]:
for max_chain in [0, 1, 2, 3]:
cfg = k_ip.OptConfig(d,
ndds,
max_cycle,
max_chain,
edge_success_prob=0.7)
opt_result_cf = k_ip.optimise_ccf(cfg)
opt_result_picef = k_ip.optimise_picef(cfg)
opt_result_cf_relabelled = k_ip.optimise_relabelled(
k_ip.optimise_ccf, cfg)
opt_result_picef_relabelled = k_ip.optimise_relabelled(
k_ip.optimise_picef, cfg)
assert abs(opt_result_cf.total_score -
opt_result_picef.total_score) < EPS
assert abs(opt_result_cf.total_score -
opt_result_cf_relabelled.total_score) < EPS
assert abs(opt_result_cf.total_score -
opt_result_picef_relabelled.total_score) < EPS
def test_relabelled_solve():
"""Checks whether the vertex-ordering heuristic affects the
result for a weighted instance
"""
EPS = 0.000001
d, ndds = read_with_ndds("test-fixtures/100-random-weights")
for max_cycle in [0, 3]:
for max_chain in [0, 5]:
opt_result_0 = k_ip.optimise_picef(k_ip.OptConfig(d, ndds, max_cycle, max_chain))
print opt_result_0.total_score
opt_result = k_ip.optimise_relabelled(
k_ip.optimise_picef, k_ip.OptConfig(d, ndds, max_cycle, max_chain))
print " ", opt_result.total_score
assert abs(opt_result.total_score - opt_result_0.total_score) < EPS
def test_relabelled_solve():
"""Checks whether the vertex-ordering heuristic affects the
result for a weighted instance
"""
EPS = 0.000001
d, ndds = read_with_ndds("test-fixtures/100-random-weights")
for max_cycle in [0, 3]:
for max_chain in [0, 5]:
opt_result_0 = k_ip.optimise_picef(
k_ip.OptConfig(d, ndds, max_cycle, max_chain))
print(opt_result_0.total_score)
opt_result = k_ip.optimise_relabelled(
k_ip.optimise_picef,
k_ip.OptConfig(d, ndds, max_cycle, max_chain))
print(" ", opt_result.total_score)
assert abs(opt_result.total_score - opt_result_0.total_score) < EPS
def test_weighted_instance_failure_aware():
"""Checks that the CF and PICEF formulations agree on the optimal
result for an instance with weighted edges, using failure-aware solving.
"""
EPS = 0.000001
d, ndds = read_with_ndds("test-fixtures/100-random-weights")
fns = [k_ip.optimise_picef, k_ip.optimise_ccf]
for max_cycle in [0, 1, 2, 3, 4]:
for max_chain in [0, 1, 2, 3]:
cfg = k_ip.OptConfig(d, ndds, max_cycle, max_chain, edge_success_prob=0.7)
opt_result_cf = k_ip.optimise_ccf(cfg)
opt_result_picef = k_ip.optimise_picef(cfg)
opt_result_cf_relabelled = k_ip.optimise_relabelled(k_ip.optimise_ccf, cfg)
opt_result_picef_relabelled = k_ip.optimise_relabelled(k_ip.optimise_picef, cfg)
assert abs(opt_result_cf.total_score - opt_result_picef.total_score) < EPS
assert abs(opt_result_cf.total_score - opt_result_cf_relabelled.total_score) < EPS
assert abs(opt_result_cf.total_score - opt_result_picef_relabelled.total_score) < EPS
