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,