def main(n=150,
block_size=60,
float_type="d",
logq=40,
verbose=False,
seed=0xdeadbeef):
print "= n: %3d, β: %2d, bits: %3d, float_type: %s, seed: 0x%08x =" % (
n, block_size, logq, float_type, seed)
print
set_random_seed(seed)
A = IntegerMatrix.random(n, "qary", k=n // 2, bits=logq)
A = LLL.reduction(A)
params = BKZ.Param(block_size=block_size,
max_loops=4,
strategies=BKZ.DEFAULT_STRATEGY,
flags=BKZ.MAX_LOOPS | BKZ.VERBOSE)
bkz = BKZReduction(GSO.Mat(copy.copy(A), float_type=float_type))
bkz(params)
print bkz.trace
bkz2 = BKZ2(GSO.Mat(copy.copy(A), float_type=float_type))
bkz2(params)
print bkz2.trace
if verbose:
print
print bkz.trace.report()
def test_bkz_init():
for cls in (SimpleBKZ, SimpleDualBKZ, BKZ, BKZ2):
for n in dimensions:
set_random_seed(2 ** 10 + n)
A = make_integer_matrix(n)
B = cls(copy(A))
del B
def test_bkz_init():
for cls in (SimpleBKZ, SimpleDualBKZ, BKZ, BKZ2):
for n in dimensions:
set_random_seed(2**10 + n)
A = make_integer_matrix(n)
B = cls(copy(A))
del B
def test_bkz_call(block_size=10):
params = fplll_bkz.Param(block_size=block_size, flags=fplll_bkz.VERBOSE | fplll_bkz.GH_BND)
for cls in (BKZ, BKZ2):
for n in dimensions:
set_random_seed(n)
A = make_integer_matrix(n)
B = copy(A)
cls(B)(params=params)
def test_bkz_call(block_size=10):
params = fplll_bkz.Param(block_size=block_size, flags=fplll_bkz.VERBOSE|fplll_bkz.GH_BND)
for cls in (BKZ, BKZ2):
for n in dimensions:
set_random_seed(n)
A = make_integer_matrix(n)
B = copy(A)
cls(B)(params=params)
def test_simple_bkz_reduction(block_size=10):
for n in dimensions:
set_random_seed(n)
A = make_integer_matrix(n)
LLL.reduction(A)
B = copy(A)
BKZ.reduction(B, BKZ.Param(block_size=block_size))
C = copy(A)
SimpleBKZ(C)(block_size=block_size)
assert abs(C[0].norm() - B[0].norm()) < 0.1
assert abs(C[0].norm() < A[0].norm())
def load_prebkz(n, s=0, blocksize=40):
"""
"""
filename = "qarychallenge/prebkz-%02d-dim-%03d-seed-%02d.txt" % (blocksize,
n, s)
if not os.path.isdir("qarychallenge"):
os.mkdir("qarychallenge")
if os.path.isfile(filename) is False:
set_random_seed(s)
A = IntegerMatrix.random(n, "qary", q=2**30, k=n // 2)
print "Did not find '{filename}'. Creating and reducing".format(
filename=filename)
print "created, ",
sys.stdout.flush()
A = LLL.reduction(A)
print "LLLed, ",
sys.stdout.flush()
if A.nrows >= 160:
float_type = "long double"
elif A.nrows >= 200:
float_type = "dd"
else:
float_type = "double"
M = GSO.Mat(A, float_type=float_type, flags=GSO.ROW_EXPO)
bkz = BKZReduction(M)
for b in range(10, blocksize + 1):
print "\r created, LLLed, BKZed %d" % b,
sys.stdout.flush()
par = fplll_bkz.Param(b,
strategies=fplll_bkz.DEFAULT_STRATEGY,
max_loops=1,
flags=fplll_bkz.MAX_LOOPS)
bkz(par)
print
fn = open(filename, "w")
fn.write(str(A))
fn.close()
return load_matrix_file(filename, randomize=False)
def test_gh():
for n in dimensions:
set_random_seed(n)
A = make_integer_matrix(n)
M = GSO.Mat(A, float_type="ld")
M.discover_all_rows()
M.update_gso()
radius = M.get_r(0, 0)
root_det = M.get_root_det(0, n)
gh_radius, ge = adjust_radius_to_gh_bound(2000*radius, 0, n, root_det, 1.0)
gh1 = gh_radius * 2**ge
r = dump_r(M, 0, n)
gh2 = gaussian_heuristic(r)
assert abs(gh1/gh2 -1) < 0.01
def test_gh():
for n in dimensions:
set_random_seed(n)
A = make_integer_matrix(n)
M = GSO.Mat(A, float_type="ld")
M.discover_all_rows()
M.update_gso()
radius = M.get_r(0, 0)
root_det = M.get_root_det(0, n)
gh_radius, ge = adjust_radius_to_gh_bound(2000 * radius, 0, n,
root_det, 1.0)
gh1 = gh_radius * 2**ge
r = dump_r(M, 0, n)
gh2 = gaussian_heuristic(r)
assert abs(gh1 / gh2 - 1) < 0.01
def test_gh():
try:
from fpylll.numpy import dump_r
except ImportError:
return
for n in dimensions:
set_random_seed(n)
A = make_integer_matrix(n)
try:
M = GSO.Mat(A, float_type="ld")
except ValueError:
M = GSO.Mat(A, float_type="d")
M.discover_all_rows()
M.update_gso()
radius = M.get_r(0, 0)
root_det = M.get_root_det(0, n)
gh_radius, ge = adjust_radius_to_gh_bound(2000*radius, 0, n, root_det, 1.0)
gh1 = gh_radius * 2**ge
r = dump_r(M, 0, n)
gh2 = gaussian_heuristic(r)
assert abs(gh1/gh2 -1) < 0.01
def test_simple_bkz_call(block_size=10):
for cls in (SimpleBKZ, SimpleDualBKZ):
for n in dimensions:
set_random_seed(n)
A = make_integer_matrix(n)
cls(A)(block_size=block_size)
def test_simple_bkz_call(block_size=10):
for cls in (SimpleBKZ, SimpleDualBKZ):
for n in dimensions:
set_random_seed(n)
A = make_integer_matrix(n)
cls(A)(block_size=block_size)
def test_simple_bkz_init():
for n in dimensions:
set_random_seed(2**10 + n)
A = make_integer_matrix(n)
B = SimpleBKZ(copy(A))
del B
