for line in f:
if 'average' in line:
line = line.split(' ')
it = int(line[1].strip())
r = float(line[-1].strip())
if r < best_r:
best_r = r
best_it = it
assert best_it >= 0
print 'using iter=', best_it, 'with r=', best_r
return '%s/nrange_%d_%d_iter_%d.model' % (opt['save_dir'], min_n, max_n,
best_it)
if __name__ == '__main__':
api = MvcLib(sys.argv)
opt = {}
for i in range(1, len(sys.argv), 2):
opt[sys.argv[i][1:]] = sys.argv[i + 1]
model_file = find_model_file(opt)
assert model_file is not None
print 'loading', model_file
sys.stdout.flush()
api.LoadModel(model_file)
n_test = 1000
f = open(opt['data_test'], 'rb')
frac = 0.0
with open(log_file, 'r') as f:
for line in f:
if 'average' in line:
line = line.split(' ')
it = int(line[1].strip())
r = -float(line[-1].strip())
if it > 10000 and r > best_r:
best_r = r
best_it = it
if best_it < 0:
return None
print best_it, best_r
return '%s/nrange_%d_%d_iter_%d.model' % (opt['save_dir'], n1, n2, best_it)
if __name__ == '__main__':
api = MvcLib(sys.argv)
opt = {}
for i in range(1, len(sys.argv), 2):
opt[sys.argv[i][1:]] = sys.argv[i + 1]
model_file = find_model_file(opt)
if model_file is not None:
print 'loading', model_file
sys.stdout.flush()
api.LoadModel(model_file)
PrepareValidData(opt)
# startup
gen_new_graphs(opt)
idxes = range(nx.number_of_nodes(G))
idxes = sorted(idxes, key=lambda x: len(nx.neighbors(G, x)), reverse=True)
pos = 0
while numCoveredEdges < nx.number_of_edges(G):
new_action = idxes[pos]
covered_set.add(new_action)
for neigh in nx.neighbors(G, new_action):
if neigh not in covered_set:
numCoveredEdges += 1
pos += 1
print 'done'
return len(covered_set)
if __name__ == '__main__':
api = MvcLib(sys.argv)
opt = {}
for i in range(1, len(sys.argv), 2):
opt[sys.argv[i][1:]] = sys.argv[i + 1]
g_undirected, _ = build_full_graph(
'%s/InfoNet5000Q1000NEXP.txt' % opt['data_root'], 'undirected')
print(nx.number_of_nodes(g_undirected))
print(nx.number_of_edges(g_undirected))
print greedy(g_undirected)
api.InsertGraph(g_undirected, is_test=True)
# startup
gen_new_graphs(opt)
if 'average' in line:
line = [w for w in line.split(' ') if w]
it = int(line[1].strip())
vc = line.index('vc:')
r = float(line[vc + 1].strip())
if r < best_r:
best_r = r
best_it = it
assert best_it >= 0
print 'using iter=', best_it, 'with r=', best_r
return '%s/nrange_%d_%d_iter_%d.model' % (opt['save_dir'], min_n, max_n,
best_it)
if __name__ == '__main__':
api = MvcLib(sys.argv)
opt = {}
for i in range(1, len(sys.argv), 2):
opt[sys.argv[i][1:]] = sys.argv[i + 1]
model_file = find_model_file(opt)
assert model_file is not None
print 'loading', model_file
sys.stdout.flush()
api.LoadModel(model_file)
n_test = 1000
f = open(opt['data_test'], 'rb')
frac = 0.0
frac_fastwvc = 0.0
with open(log_file, 'r') as f:
for line in f:
if 'average' in line:
line = line.split(' ')
it = int(line[1].strip())
r = -float(line[-1].strip())
if it > 10000 and r > best_r:
best_r = r
best_it = it
if best_it < 0:
return None
print best_it, best_r
return '%s/nrange_%d_%d_iter_%d.model' % (opt['save_dir'], n1, n2, best_it)
if __name__ == '__main__':
api = MvcLib(sys.argv)
opt = {}
for i in range(1, len(sys.argv), 2):
opt[sys.argv[i][1:]] = sys.argv[i + 1]
model_file = find_model_file(opt)
if model_file is not None:
print 'loading', model_file
sys.stdout.flush()
api.LoadModel(model_file)
PrepareValidData(opt)
# startup
gen_new_graphs(opt)