def get_instances(self, n_instances):
i = 0
while i < n_instances:
if i % 2 == 0:
yield CNF.read_dimacs(self.filenames[self.index][0])
else:
yield CNF.read_dimacs(self.filenames[self.index][1])
self.index += 1
#end if-else
i += 1
def get_instances(self, n_instances):
for i in range(n_instances):
yield CNF.read_dimacs(self.filenames[self.index])
if self.index + 1 < len(self.filenames):
self.index += 1
else:
self.reset()
def get_instances(self, n_instances):
for i in range(n_instances):
yield CNF.read_dimacs(self.filenames[self.index])
self.index += 1
with open("test-64.log", "w") as f:
f.write(
"file epoch batchnumber nvertices nedges loss sat cn neurosat_cnpred pred\n"
)
for e, filename in enumerate(os.listdir(test_folder)):
if filename.endswith(".graph"):
Ma, _, cn, diff_edge = read_graph(test_folder + "/" +
filename)
#first iterate without the diff edge and with cn
for j in range(2, cn + 5):
nv = Ma.shape[0]
ne = len(np.nonzero(Ma)[0])
parse_glucose(Ma, j, "test-tmp", "temp.cnf")
batch = create_batchCNF(
[CNF.read_dimacs("test-tmp/temp.cnf")])
l, a, p = run_test_batch(sess, solver, batch,
time_steps)
if p == 1:
f.write(
"{filename} {epoch} {batch} {nv} {ne} {loss:.4f} {accuracy:.4f} {cn} {cnpred} {avg_pred:.4f}\n"
.format(
filename=filename,
epoch=e,
batch=0,
nv=nv,
ne=ne,
loss=l,
accuracy=a,
cn=cn,
cnpred=j,
