def load_dir(path, no_sat=False):
data = []
for filename in os.listdir(path):
name, ext = os.path.splitext(filename)
if ext != '.cnf':
continue
f = CNF.from_file(os.path.join(path, filename))
sat = int(not name.startswith('uu')) if not no_sat else -1
data.append(DataSample(filename, f, adj(f), sat))
return data
def load_dir(path):
data = []
for filename in os.listdir(path):
name, ext = os.path.splitext(filename)
if ext != '.cnf':
continue
f = CNF.from_file(os.path.join(path, filename))
if name.startswith('uu'):
continue
data.append(DataSample(filename, f, adj(f), None))
return data
import sys
from graphviz import Graph
from cnf import CNF
from util import adj
f = CNF.from_file(sys.argv[1])
a_pos, a_neg = adj(f)
a_pos = a_pos.todense()
a_neg = a_neg.todense()
n, m = a_pos.shape
g = Graph('G', filename=sys.argv[2], format='pdf', engine='neato')
for x in range(1, n + 1):
g.node(f'x{x}', label='x', color='blue', fillcolor='blue', shape='point')
for c in range(1, m + 1):
g.node(f'c{c}', label='y', color='red', fillcolor='red', shape='point')
pw = sys.argv[3]
for x in range(n):
for c in range(m):
var = f'x{x + 1}'
cl = f'c{c + 1}'
if a_pos[x, c] == 1:
# g.edge(var, cl, color='#ff0000', penwidth='0.001')
g.edge(var, cl, color='#ff0000', penwidth=pw)
def main(args):
if args.seed > -1:
random.seed(args.seed)
if args.dir_path:
med_flips = []
avg_flips = []
max_flips = []
solved = []
med_backflips = []
avg_backflips = []
max_backflips = []
unsats = []
mean_delta = []
downwards = []
upwards = []
sideways = []
for i, filename in enumerate(os.listdir(args.dir_path)):
if i >= args.samples:
break
formula = CNF.from_file(os.path.join(args.dir_path, filename))
walksat = WalkSAT(args.max_tries, args.max_flips, args.p)
walksat.run(formula)
flips = walksat.flips_to_solution
backflips = walksat.backflips
unsats = walksat.unsat_clauses
diffs = [np.diff(u) for u in unsats]
diff_a = np.array(diffs)
upwards.append(np.mean([np.sum(a > 0) for a in diff_a]))
downwards.append(np.mean([np.sum(a < 0) for a in diff_a]))
sideways.append(np.mean([np.sum(a == 0) for a in diff_a]))
mean_delta.append(np.mean([np.mean(d) for d in diffs]))
med_backflips.append(np.median(backflips))
avg_backflips.append(np.mean(backflips))
max_backflips.append(np.max(backflips))
med = np.median(flips)
med_flips.append(med)
avg_flips.append(np.mean(flips))
max_flips.append(np.max(flips))
solved.append(int(med < args.max_flips))
# print(med_flips)
# print(avg_flips)
# print(max_flips)
logging.info(
'Acc: {:.4f}, Med: {:.4f}, Mean: {:.4f}, Max: {:.4f}'.format(
100 * np.mean(solved), np.median(med_flips),
np.mean(avg_flips), np.mean(max_flips)))
logging.info(
'(Backflips) Med: {:.4f}, Mean: {:.4f}, Max: {:.4f}'.format(
np.median(med_backflips), np.mean(avg_backflips),
np.mean(max_backflips)))
logging.info('(Delta) Mean: {:.4f}'.format(np.mean(mean_delta)))
logging.info(
'(Movement) Up: {:.4f}, Side: {:.4f}, Down: {:.4f}'.format(
np.mean(upwards), np.mean(sideways), np.mean(downwards)))
elif args.file_path:
formula = CNF.from_file(args.file_path)
walksat = WalkSAT(args.max_tries, args.max_flips, args.p)
walksat.run(formula)
logging.info('Number of solutions found: {}\n'
'Avg, std of flips to solution: {:.4f}, {:.4f}'.format(
len(walksat.flips_to_solution),
np.mean(walksat.flips_to_solution),
np.std(walksat.flips_to_solution),
))