def test_ye_ratio(self):
# TODO: use ye curve from examples ?
good_proto = Proto(dim=2, div=5, cubes=[
(0, 0), (0, 1), (1, 1), (1, 0), (2, 0),
(2, 1), (2, 2), (1, 2), (0, 2), (0, 3),
(0, 4), (1, 4), (1, 3), (2, 3), (2, 4),
(3, 4), (4, 4), (4, 3), (3, 3), (3, 2),
(4, 2), (4, 1), (3, 1), (3, 0), (4, 0),
])
# in new version we have (0,0)->(0,1) gate
good_proto = BaseMap.parse('ji') * good_proto
paths_gen = PathsGenerator(dim=2, div=5, hdist=1, max_cdist=1)
for paths in paths_gen.generate_paths():
if paths[0].proto == good_proto:
path0 = paths[0]
break
path0 = CurvePath(path0.proto, path0.portals) # legacy
pcurve = PathFuzzyCurve.init_from_paths([path0])
estimator = Estimator(utils.ratio_l2_squared)
curve = estimator.estimate_ratio(pcurve, rel_tol_inv=10000, verbose=False)['curve']
ratio = estimator.estimate_ratio(curve, rel_tol_inv=10000, use_vertex_brkline=True, verbose=False, max_depth=5)
assert ratio['lo'] == (Rational(408, 73) ** 2)
def test_polycurve_ratio(self):
known_bounds = [
{
'curve': get_beta_omega_curve(),
'ratio': { 'l2': 5, 'l1': 9, 'linf': 5},
},
{
'curve': get_neptunus_curve(),
'ratio': { 'l2': [18.2, 18.4], 'linf': [9.44, 9.46]},
},
]
for data in known_bounds:
curve = data['curve']
for metric, ratio in data['ratio'].items():
if isinstance(ratio, list):
ratio_lo, ratio_up = ratio
else:
ratio_lo = ratio * 0.999
ratio_up = ratio * 1.001
if metric == 'l2':
func = utils.ratio_l2_squared
ratio_lo, ratio_up = ratio_lo**2, ratio_up**2
elif metric == 'l1':
func = utils.ratio_l1
elif metric == 'linf':
func = utils.ratio_linf
res = Estimator(func).estimate_ratio(curve, rel_tol_inv=10**4)
assert float(res['up']) <= ratio_up, 'metric {} up failed: {} > {}'.format(metric, res['up'], ratio_up)
assert float(res['lo']) >= ratio_lo, 'metric {} lo failed: {} < {}'.format(metric, res['lo'], ratio_lo)
def test_55_ratio(self):
good_proto = Proto(dim=2,
div=5,
cubes=[
(0, 0),
(0, 1),
(1, 1),
(1, 0),
(2, 0),
(2, 1),
(2, 2),
(1, 2),
(0, 2),
(0, 3),
(0, 4),
(1, 4),
(1, 3),
(2, 3),
(2, 4),
(3, 4),
(4, 4),
(4, 3),
(3, 3),
(3, 2),
(4, 2),
(4, 1),
(3, 1),
(3, 0),
(4, 0),
])
# in new version we have (0,0)->(0,1) gate
good_proto = BaseMap.from_basis('ji') * good_proto
paths_gen = PathsGenerator(dim=2,
div=5,
hdist=1,
max_cdist=1,
verbose=1)
for paths in paths_gen.generate_paths():
if paths[0].proto == good_proto:
path0 = paths[0]
break
pcurve = PathFuzzyCurve.init_from_paths([path0])
estimator = Estimator(utils.ratio_l2_squared)
curve = estimator.estimate_ratio(pcurve,
rel_tol_inv=10000,
verbose=False)['curve']
ratio = estimator.estimate_ratio(curve,
rel_tol_inv=10000,
use_vertex_brkline=True,
verbose=False)
assert ratio['lo'] == (FastFraction(408, 73)**2)
def main():
curve_gen = paths.PathsGenerator(dim=2,
div=5,
hdist=1,
max_cdist=1,
verbose=1)
pcurves = list(
PathFuzzyCurve.init_from_paths([path])
for path in curve_gen.generate_paths(uniq=True))
estimator = Estimator(ratio_l2_squared)
res = estimator.estimate_ratio_sequence(
pcurves,
rel_tol_inv=1000000,
rel_tol_inv_mult=2,
sat_strategy={
'type': 'geometric',
'multiplier': 1.5
},
)
print(res)
print(estimator.stats)
process = psutil.Process(os.getpid())
print('RSS:', process.memory_info().rss) # in bytes
def meta_perebor(dim, div, pattern_count, ratio_func, rel_tol_inv, rel_tol_inv_mult):
estimator = Estimator(ratio_func, cache_max_size=2**16)
result = {}
for gates in gen_possible_gates(dim=dim, div=div, pattern_count=pattern_count):
paths_gen = PathsGenerator(dim=dim, div=div, gates=gates)
paths_list = list(paths_gen.generate_paths(uniq=True))
logging.warning('gates: %s', [str(g) for g in gates])
logging.warning('paths: %d', len(paths_list))
pcurves = (PathFuzzyCurve.init_from_paths(paths) for paths in paths_list)
res = estimator.estimate_ratio_sequence(
pcurves,
rel_tol_inv=rel_tol_inv,
rel_tol_inv_mult=rel_tol_inv_mult,
sat_strategy={'type': 'geometric', 'multiplier': 1.3},
)
result[tuple(gates)] = {'paths': paths_list, 'estimate': res}
for gates in sorted(result.keys()):
gates_result = result[gates]
print('Result for gates:', [str(g) for g in gates])
print('paths:', len(gates_result['paths']))
print('lower bound:', float(gates_result['estimate']['lo']))
print('upper bound:', float(gates_result['estimate']['up']))
print('')
def test_curve_ratio(self):
known_bounds = [
{
'curve': get_hilbert_curve(),
'ratio': { 'l2': 6, 'l1': 9, 'linf': 6},
},
{
'curve': get_peano_curve(),
'ratio': {'l2': 8, 'l1': 32/3, 'linf': 8},
},
{
'curve': get_tokarev_curve(),
'ratio': {'l1': [98.2, 98.4], 'l2': [26.1, 26.3], 'linf': [24.1, 24.3]},
},
{
'curve': get_scepin_bauman_curve(),
'ratio': {'l1': (10 + 2/3), 'l2': (5 + 2/3), 'linf': (5 + 1/3)},
},
{
'curve': get_meurthe_curve(),
'ratio': {'l1': (10 + 2/3), 'l2': (5 + 2/3), 'linf': (5 + 1/3)},
},
{
'curve': get_serpentine_curve(),
'ratio': {'l1': 10, 'l2': 6.25, 'linf': 5.625},
},
{
'curve': get_coil_curve(),
'ratio': {'l1': (10 + 2/3), 'l2': (6 + 2/3), 'linf': (6 + 2/3)},
},
{
'curve': get_r_curve(),
'ratio': {'l1': (10 + 2/3), 'l2': (6 + 2/3), 'linf': (6 + 2/3)},
},
{
'curve': get_haverkort_curve_a26(),
'ratio': {'l1': (99 + 5/9), 'l2': [22.7,22.9], 'linf': (12 + 4/9)},
},
{
'curve': get_haverkort_curve_f(),
'ratio': {'l1': [89.7, 89.8], 'l2': [18,19], 'linf': 14},
},
{
'curve': get_ye_curve(),
'ratio': {'l2': [5.588, 5.59]},
},
{
'curve': get_17_curve(),
'ratio': {'l2': [16.9, 17.0]},
},
]
for data in known_bounds:
curve = data['curve']
ratio_dict = data['ratio']
for metric in sorted(ratio_dict.keys()):
ratio = ratio_dict[metric]
if isinstance(ratio, list):
ratio_lo, ratio_up = ratio
else:
ratio_lo = ratio * 0.999
ratio_up = ratio * 1.001
if metric == 'l2':
func = utils.ratio_l2_squared
ratio_lo, ratio_up = ratio_lo**2, ratio_up**2
elif metric == 'l1':
func = utils.ratio_l1
elif metric == 'linf':
func = utils.ratio_linf
res = Estimator(func).estimate_ratio(curve, rel_tol_inv=10**5, verbose=False)
print(res)
assert float(res['up']) <= ratio_up, 'metric {} up failed: {} > {}'.format(metric, res['up'], ratio_up)
assert float(res['lo']) >= ratio_lo, 'metric {} lo failed: {} < {}'.format(metric, res['lo'], ratio_lo)
def run_estimator(
dim,
div,
pattern_count,
ratio_func=None,
rel_tol_inv=None,
rel_tol_inv_mult=None,
gate_list=None,
hyper=False,
max_cdist=None,
upper_bound=None,
output_gates=False,
output_stats=False,
shuffle=False,
):
if gate_list is not None:
gates_generator = gate_list
else:
gates_generator = GatesGenerator(dim, div, pattern_count,
hyper=hyper).gen_gates()
def gen_pcurves():
for gates_idx, gates in enumerate(gates_generator):
if output_gates:
yield gates
continue
logging.info('processing gates: %d', gates_idx + 1)
paths_gen = PathsGenerator(dim=dim,
div=div,
portals=gates,
max_cdist=max_cdist)
if output_stats:
counts = []
for gate in gates:
gcnt = len(
list(
paths_gen.generate_paths_generic(portal=gate,
std=True)))
counts.append(gcnt)
yield counts
continue
paths_list = list(paths_gen.generate_paths(std=True))
logging.warning('gates: %s', [str(g) for g in gates])
logging.warning('paths: %d', len(paths_list))
for paths_idx, paths in enumerate(paths_list):
logging.info('processing gate_paths: %d of %d', paths_idx + 1,
len(paths_list))
paths = tuple(
CurvePath(path.proto, path.portals) for path in paths)
yield PathFuzzyCurve.init_from_paths(paths)
pcurves_generator = gen_pcurves()
if output_stats:
scnt = 0
for counts in pcurves_generator:
prod = 1
for cnt in counts:
prod *= cnt
print(prod)
scnt += prod
print('TOTAL:', scnt)
return
if output_gates:
for gates in pcurves_generator:
print(' | '.join([str(gate) for gate in gates]))
return
if shuffle:
pcurves_generator = list(pcurves_generator)
random.shuffle(pcurves_generator)
estimator = Estimator(ratio_func, cache_max_size=2**16)
result = estimator.estimate_ratio_sequence(
pcurves_generator,
rel_tol_inv=rel_tol_inv,
rel_tol_inv_mult=rel_tol_inv_mult,
sat_strategy={
'type': 'geometric',
'multiplier': 1.3
},
upper_bound=upper_bound,
)
print(result)
print('======')
print('lower bound:', float(result['lo']))
print('upper bound:', float(result['up']))
def perebor(conf, start_idx=None, end_idx=None):
logging.warning('CONF: %s', conf)
funcs = {
'l1': utils.ratio_l1,
'l2': utils.ratio_l2,
'l2_squared': utils.ratio_l2_squared,
'linf': utils.ratio_linf,
}
ratio_func = funcs[conf['ratio_func_name']]
dim, div = conf['dim'], conf['div']
gates = None
if 'gate_strs' in conf:
gates = [get_gate(g) for g in conf['gate_strs']]
elif 'gates' in conf:
gates = conf['gates']
paths_gen = PathsGenerator(
dim=dim,
div=div,
hdist=conf.get('hdist'),
gates=gates,
max_cdist=conf.get('max_cdist'),
)
paths_list = list(paths_gen.generate_paths(uniq=True))
logging.warning('paths: %d', len(paths_list))
if end_idx is not None:
# should be checked first!
paths = list(paths)
paths = paths[:end_idx]
if start_idx is not None:
paths = list(paths)
paths = paths[start_idx:]
estimator = Estimator(ratio_func, cache_max_size=2**16)
#logging.warning('sort by paths ratio ...')
#path_ratio = {path: estimator.estimate_path(path) for path in paths}
#paths.sort(key=lambda path: path_ratio[path])
pcurves = (PathFuzzyCurve.init_from_paths(paths) for paths in paths_list)
res = estimator.estimate_ratio_sequence(
pcurves,
rel_tol_inv=conf['rel_tol_inv'],
rel_tol_inv_mult=conf.get('rel_tol_inv_mult', 2),
sat_strategy={
'type': 'geometric',
'multiplier': 1.3
},
upper_bound=conf.get('upper_bound'),
)
if res is None:
res = {'lo': 0, 'up': float('inf')}
res['paths_count'] = len(paths_list)
res['lo_float'] = float(res['lo'])
res['up_float'] = float(res['up'])
print('CONFIG:', conf)
print('BOUNDS: {:.6f} <= r <= {:.6f}'.format(res['lo_float'],
res['up_float']))
print('RESULT:', res, flush=True)
def run_estimator(
dim,
div,
pcount,
finish_max_count=None,
ratio_func=None,
rel_tol_inv=None,
rel_tol_inv_mult=None,
gate_list=None,
hyper=False,
max_cdist=None,
upper_bound=None,
group_by_gates=False,
output_gates=False,
output_stats=False,
output_examples=None,
):
if gate_list is not None:
gates_generator = gate_list
else:
gates_generator = GatesGenerator(dim, div, pcount,
hyper=hyper).gen_gates()
def gen_pcurves(gates_iterable):
for gates_idx, gates in enumerate(gates_iterable):
if output_gates:
yield gates
continue
logging.info('processing gates: %d', gates_idx + 1)
paths_gen = PathsGenerator(dim=dim,
div=div,
portals=gates,
max_cdist=max_cdist)
if output_stats:
counts = []
for gate in gates:
gcnt = len(
list(
paths_gen.generate_paths_generic(portal=gate,
std=True)))
counts.append(gcnt)
yield counts
continue
kw = {}
if finish_max_count is not None:
kw['finish_max_count'] = finish_max_count
paths_list = list(paths_gen.generate_paths(std=True, **kw))
logging.warning('gates: %s', [str(g) for g in gates])
logging.warning('paths: %d', len(paths_list))
for paths_idx, paths in enumerate(paths_list):
logging.info('processing gate_paths: %d of %d', paths_idx + 1,
len(paths_list))
paths = tuple(
CurvePath(path.proto, path.portals) for path in paths)
yield PathFuzzyCurve.init_from_paths(paths)
if output_stats:
scnt = 0
for counts in gen_pcurves(gates_generator):
prod = 1
for cnt in counts:
prod *= cnt
print(prod)
scnt += prod
print('TOTAL:', scnt)
return
if output_gates:
for gates in gates_generator:
print(' | '.join([str(gate) for gate in gates]))
return
estimator = Estimator(ratio_func, cache_max_size=2**16)
if group_by_gates:
pcurve_gens = [(gates, gen_pcurves([gates]))
for gates in gates_generator]
else:
pcurve_generator = gen_pcurves(gates_generator)
pcurve_gens = [('all_gates', pcurve_generator)]
for gen_id, pcurves_generator in pcurve_gens:
result = estimator.estimate_ratio_sequence(
pcurves_generator,
rel_tol_inv=rel_tol_inv,
rel_tol_inv_mult=rel_tol_inv_mult,
sat_strategy={
'type': 'geometric',
'multiplier': 1.3
},
upper_bound=upper_bound,
)
print('======')
print('GENERATOR:', gen_id)
if not result:
print('NOT FOUND!')
else:
print(result)
print('lower bound:', float(result['lo']))
print('upper bound:', float(result['up']))
if output_examples:
for curve in result['examples']:
print(curve)
print('')
print('', flush=True)