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 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)
import latplan
import latplan.puzzles.hanoi as p
# importlib.reload(p)
import itertools
c = [c for c in itertools.islice(p.generate_configs(4, 3), 10000)]
from functools import partial
from latplan.util.timer import Timer
with Timer(
"************************* states on cpu ***************************"):
s = p.generate(c, 4, 3)
print(s[:3])
with Timer(
"************************* validate_states ***************************"
):
print("results:", np.all(p.validate_states(s)), "(should be True)")
with Timer(
"************************* validate_states with noise ***************************"
):
print(
"results:",
np.all(
p.validate_states(
#!/usr/bin/env python3
import numpy as np
import sys
sys.path.append('../../')
from latplan.puzzles.hanoi import generate_configs, successors, generate, states, transitions
from plot import plot_image, plot_grid
disks = 8
towers = 3
configs = generate_configs(disks, towers)
puzzles = generate(configs, disks, towers)
print(puzzles.shape)
print(puzzles[10])
for line in puzzles[10]:
print(line)
plot_image(puzzles[0], "hanoi.png")
plot_image(
np.clip(puzzles[0] + np.random.normal(0, 0.1, puzzles[0].shape), 0, 1),
"hanoi+noise.png")
plot_image(
np.round(
np.clip(puzzles[0] + np.random.normal(0, 0.1, puzzles[0].shape), 0,
1)), "hanoi+noise+round.png")
plot_grid(puzzles[:36], "hanois.png")
_transitions = transitions(disks, towers)
