def main():
from bootstrapping_olympics.extra.reprep import ReprepPublisher
usage = ""
parser = OptionParser(usage=usage)
parser.disable_interspersed_args()
parser.add_option("-o",
dest='outdir',
default='diffeo_analysis',
help="Output directory [%default].")
parser.add_option("-p", dest='pickle', help="Saved agent state")
parser.add_option("-t", dest='templates', help="Directory with templates")
(options, args) = parser.parse_args()
if args:
raise Exception('Extra args')
print('Loading %r' % options.pickle)
with open(options.pickle) as f:
data = pickle.load(f)
print('(done)')
state = data.agent_state
confid = '%s-%s' % (data.id_robot, data.id_agent)
publisher = ReprepPublisher(confid)
dd = state['diffeo_dynamics']
actions = []
for cmd_index, de in dd.commands2dynamics.items():
original_cmd = dd.commands2label[cmd_index]
print('Summarizing %s' % original_cmd)
diffeo = de.summarize()
a = Action(
diffeo=diffeo,
label="u%s" % cmd_index, # index=cmd_index,
invertible=False,
primitive=True,
original_cmd=original_cmd)
actions.append(a)
# actions = [actions[1], actions[4]]
templates = list(load_templates(options.templates, shape=diffeo.d.shape))
for template in templates[:1]:
image = template.image
name = template.name
for action in actions:
section_name = '%s-%s_%s_%s' % (name, action, action.label,
action.original_cmd)
print(section_name)
S = publisher.section(section_name)
compute_effects(S, action, image)
filename = os.path.join(options.outdir, "%s-preds.html" % confid)
publisher.r.to_html(filename)
def main():
from bootstrapping_olympics.extra.reprep import ReprepPublisher
usage = ""
parser = OptionParser(usage=usage)
parser.disable_interspersed_args()
parser.add_option("-o", dest='outdir', default='diffeo_analysis',
help="Output directory [%default].")
parser.add_option("-p", dest='pickle',
help="Saved agent state")
parser.add_option("-t", dest='templates', help="Directory with templates")
(options, args) = parser.parse_args()
if args:
raise Exception('Extra args')
print('Loading %r' % options.pickle)
with open(options.pickle) as f:
data = pickle.load(f)
print('(done)')
state = data.agent_state
confid = '%s-%s' % (data.id_robot, data.id_agent)
publisher = ReprepPublisher(confid)
dd = state['diffeo_dynamics']
actions = []
for cmd_index, de in dd.commands2dynamics.items():
original_cmd = dd.commands2label[cmd_index]
print('Summarizing %s' % original_cmd)
diffeo = de.summarize()
a = Action(diffeo=diffeo, label="u%s" % cmd_index, # index=cmd_index,
invertible=False, primitive=True, original_cmd=original_cmd)
actions.append(a)
# actions = [actions[1], actions[4]]
templates = list(load_templates(options.templates, shape=diffeo.d.shape))
for template in templates[:1]:
image = template.image
name = template.name
for action in actions:
section_name = '%s-%s_%s_%s' % (name, action,
action.label, action.original_cmd)
print(section_name)
S = publisher.section(section_name)
compute_effects(S, action, image)
filename = os.path.join(options.outdir, "%s-preds.html" % confid)
publisher.r.to_html(filename)
def actions_analysis(args):
from bootstrapping_olympics.extra.reprep import ReprepPublisher
np.seterr(all='raise')
usage = ""
parser = OptionParser(usage=usage)
parser.disable_interspersed_args()
parser.add_option("-o", dest='outdir')
parser.add_option("--actions", dest='actions', help="Saved actions")
(options, args) = parser.parse_args(args=args)
if not args:
msg = ('Please supply command. Available: %s' %
", ".join(commands.keys()))
raise Exception(msg)
if options.outdir is None:
raise Exception('Please supply "outdir".')
if options.actions is None:
raise Exception('Please supply "actions".')
cmd = args[0]
cmd_options = args[1:]
if not cmd in commands:
msg = ('Unknown command %r. Available: %s.' %
(cmd, ", ".join(commands.keys())))
raise Exception(msg)
print('Loading %r' % options.actions)
with open(options.actions) as f:
data = pickle.load(f)
print('(done)')
id_robot = data['id_robot']
id_agent = data['id_agent']
actions = data['actions']
print('id_robot: %s' % id_robot)
print('id_agent: %s' % id_agent)
for action in actions:
print('* %s' % action)
confid = '%s-%s' % (id_robot, id_agent)
publisher = ReprepPublisher(confid)
data['publisher'] = publisher
commands[cmd](options, data, cmd_options)
filename = os.path.join(options.outdir, cmd, "%s.html" % confid)
logger.info('writing to file %r' % filename)
publisher.r.to_html(filename)
def main():
from bootstrapping_olympics.extra.reprep import ReprepPublisher
N = 20
K = 2
# M = np.random.rand(N, N, K)
M = np.zeros((K, N, N))
for i, j in itertools.product(range(N), range(N)):
if i <= j:
M[:, i, j] = 0
else:
M[:, i, j] = np.random.rand(K)
M[:, :, :] = 0
M[0, 5, 10] = 1
M[1, 15, 10] = -1
bds = BDSEstimator2()
y_mean = np.random.rand(N)
A = np.random.randn(N, N)
T = 10000
Au = np.random.randn(K, K)
error_M = []
error_M2 = []
for t in range(T):
u = np.dot(Au, np.random.randn(K))
# if (T - 1) % 100 == 0:
# u = 0 * u
y = y_mean + np.dot(A, np.random.randn(N))
y_dot = bds_dynamics(M, y, u)
dt = 0.1
bds.update(y=y, y_dot=y_dot, u=u, dt=dt)
if t > T / 2:
bds.fits1.reset()
bds.fits2.reset()
if t % 100 == 0:
Mest = bds.get_M()
e = np.abs(M - Mest).mean()
error_M.append(e)
Mest2, M2info = bds.get_M2() #@UnusedVariable
e2 = np.abs(M - Mest2).mean()
error_M2.append(e2)
sys.stderr.write('%8d/%d %-10.5g %-10.5g \n' % (t, T, e, e2))
pass
rp = ReprepPublisher('bds_estimator_test_learn')
Mest = bds.get_M()
Merr = np.abs(M - Mest)
bds.publish(rp)
for i in range(K):
rp.array_as_image('M%s' % i, M[i, :, :])
for i in range(K):
rp.array_as_image('est_M%s' % i, Mest[i, :, :])
for i in range(K):
rp.array_as_image('Merr%s' % i, Merr[i, :, :])
with rp.plot('error') as pylab:
pylab.semilogy(np.array(error_M), 'x')
pylab.semilogy(np.array(error_M2), 'ro')
pylab.axis((0, len(error_M), 0, max(error_M) * 1.1))
rp.r.to_html('bds_estimator_test_learn.html')