def hanoi(disks=7,
towers=4,
num_examples=6500,
N=None,
num_actions=None,
direct=None,
stop_gradient=False,
aeclass="ConvolutionalGumbelAE",
comment=""):
for name, value in locals().items():
if value is not None:
parameters[name] = [value]
default_parameters["aeclass"] = aeclass
import latplan.puzzles.hanoi as p
p.setup()
path = os.path.join(latplan.__path__[0], "puzzles",
"-".join(map(str, ["hanoi", disks, towers])) + ".npz")
with np.load(path) as data:
pre_configs = data['pres'][:num_examples]
suc_configs = data['sucs'][:num_examples]
pres = p.generate(pre_configs, disks, towers)
sucs = p.generate(suc_configs, disks, towers)
transitions = np.array([pres, sucs])
states = np.concatenate((transitions[0], transitions[1]), axis=0)
data = np.swapaxes(transitions, 0, 1)
print(data.shape)
train = data[:int(len(data) * 0.9)]
val = data[int(len(data) * 0.9):int(len(data) * 0.95)]
test = data[int(len(data) * 0.95):]
ae = run(os.path.join("samples", sae_path), train, val, parameters)
show_summary(ae, train, test)
plot_autoencoding_image(ae, test, train)
dump_actions(ae, transitions)
dump_states(ae, states)
def hanoi(disks=7,towers=4,limit=None):
path = os.path.join(latplan.__path__[0],"puzzles","-".join(map(str,["hanoi",disks,towers]))+".npz")
import latplan.puzzles.hanoi as p
p.setup()
pres = p.generate_random_configs(disks,towers, limit)
np.random.shuffle(pres)
sucs = [ random.choice(p.successors(c1,disks,towers)) for c1 in pres ]
np.savez_compressed(path,pres=pres,sucs=sucs)
def lightsout(type='digital', size=4):
import importlib
p = importlib.import_module('latplan.puzzles.lightsout_{}'.format(type))
p.setup()
ics = reservoir_sampling(
lightsout_special(np.full(size * size, -1), steps,
lambda config: p.successors(config)), instances)
gcs = np.full((1, size * size), -1)
generate(p, ics, gcs)
def lightsout(type='digital',size=4,limit=None):
path = os.path.join(latplan.__path__[0],"puzzles","-".join(map(str,["lightsout",type,size]))+".npz")
import importlib
p = importlib.import_module('latplan.puzzles.lightsout_{}'.format(type))
p.setup()
pres = p.generate_random_configs(size, limit)
np.random.shuffle(pres)
sucs = [ random.choice(p.successors(c1)) for c1 in pres ]
np.savez_compressed(path,pres=pres,sucs=sucs)
def lightsout(type='digital', size=4):
import importlib
p = importlib.import_module('latplan.puzzles.lightsout_{}'.format(type))
p.setup()
ics = [
random_walk(np.full(size*size,-1), steps, lambda config: p.successors(config))
for i in range(instances)
]
gcs = np.full((1,size*size),-1)
generate(p, ics, gcs)
def hanoi(disks=5, towers=3):
import latplan.puzzles.hanoi as p
p.setup()
ics = [
np.zeros(disks, dtype=int), *reservoir_sampling(
dijkstra(np.full(disks, towers - 1, dtype=int), steps,
lambda config: p.successors(config, disks, towers)),
instances - 1)
]
gcs = np.full((1, disks), towers - 1, dtype=int)
generate(p, ics, gcs, disks, towers)
def hanoi(disks=5, towers=3):
import latplan.puzzles.hanoi as p
p.setup()
ics = [
np.zeros(disks, dtype=int), *[
random_walk(np.full(disks, towers - 1, dtype=int), steps,
lambda config: p.successors(config, disks, towers))
for i in range(instances - 1)
]
]
gcs = np.full((1, disks), towers - 1, dtype=int)
generate(p, ics, gcs, disks, towers)
def hanoi(disks=7,
towers=4,
num_examples=6500,
plannpz=None,
planjson=None,
allow_invalid=False):
path = os.path.join("puzzles",
"-".join(map(str, ["hanoi", disks, towers])) + ".npz")
import latplan.puzzles.hanoi as p
p.setup()
common_operation(path, p, num_examples, plannpz, planjson, allow_invalid,
disks, towers)
def lightsout(type='digital',
size=4,
num_examples=6500,
plannpz=None,
planjson=None,
allow_invalid=False):
path = os.path.join("puzzles",
"-".join(map(str, ["lightsout", type, size])) + ".npz")
import importlib
p = importlib.import_module('latplan.puzzles.lightsout_{}'.format(type))
p.setup()
common_operation(path, p, num_examples, plannpz, planjson, allow_invalid)
def lightsout(type='digital',
size=4,
num_examples=6500,
N=None,
num_actions=None,
direct=None,
stop_gradient=False,
aeclass="ConvolutionalGumbelAE",
comment="",
prob_vis=1.00):
for name, value in locals().items():
if value is not None:
parameters[name] = [value]
default_parameters["aeclass"] = aeclass
import importlib
p = importlib.import_module('latplan.puzzles.lightsout_{}'.format(type))
p.setup()
path = os.path.join(latplan.__path__[0], "puzzles",
"-".join(map(str, ["lightsout", type, size])) + ".npz")
with np.load(path) as data:
pre_configs = data['pres'][:num_examples]
suc_configs = data['sucs'][:num_examples]
# TWK: Wrap custom `add_missingness()` method around the data generators
pres = add_missingness(p.generate(pre_configs), prob_vis)
sucs = add_missingness(p.generate(suc_configs), prob_vis)
transitions = np.array([pres, sucs])
states = np.concatenate((transitions[0], transitions[1]), axis=0)
data = np.swapaxes(transitions, 0, 1)
print(data.shape)
train = data[:int(len(data) * 0.9)]
val = data[int(len(data) * 0.9):int(len(data) * 0.95)]
test = data[int(len(data) * 0.95):]
ae = run(os.path.join("samples", sae_path), train, val, parameters)
show_summary(ae, train, test)
plot_autoencoding_image(ae, test, train)
dump_actions(ae, transitions)
dump_states(ae, states)
