def describe_info(sec, actions, info):
Distance = info['Distance']
Distance_to_inverse = info['Distance_to_inverse']
np.fill_diagonal(Distance, np.nan)
np.fill_diagonal(Distance_to_inverse, np.nan)
max_value = max(np.nanmax(Distance), np.nanmax(Distance_to_inverse))
min_value = max(np.nanmin(Distance), np.nanmin(Distance_to_inverse))
params = dict(filter='scale', filter_params={'min_value': min_value / 1.5,
'max_value': max_value
})
sec.array_as_image('Distance', zoom(Distance), **params)
sec.array_as_image('DistanceInv', zoom(Distance_to_inverse), **params)
n = len(actions)
actual_inverse = np.zeros((n, n))
for i, j in itertools.product(range(n), range(n)):
u_i = actions[i].original_cmd
u_j = actions[j].original_cmd
actual = np.allclose(u_i, -u_j)
actual_inverse[i, j] = 1 if actual else 0
sec.array_as_image('Actual', zoom(actual_inverse))
sec.text('DistanceS', formatm('distance', Distance, format_str='%.3f'))
sec.text('DistanceInvS',
formatm('distance_inv', Distance_to_inverse, format_str='%.3f'))
s = ""
for action in actions:
s += '%s\n' % action
sec.text('list_of_actions', s)
def describe_info(sec, actions, info):
Distance = info['Distance']
Distance_to_inverse = info['Distance_to_inverse']
np.fill_diagonal(Distance, np.nan)
np.fill_diagonal(Distance_to_inverse, np.nan)
max_value = max(np.nanmax(Distance), np.nanmax(Distance_to_inverse))
min_value = max(np.nanmin(Distance), np.nanmin(Distance_to_inverse))
params = dict(filter='scale',
filter_params={
'min_value': min_value / 1.5,
'max_value': max_value
})
sec.array_as_image('Distance', zoom(Distance), **params)
sec.array_as_image('DistanceInv', zoom(Distance_to_inverse), **params)
n = len(actions)
actual_inverse = np.zeros((n, n))
for i, j in itertools.product(range(n), range(n)):
u_i = actions[i].original_cmd
u_j = actions[j].original_cmd
actual = np.allclose(u_i, -u_j)
actual_inverse[i, j] = 1 if actual else 0
sec.array_as_image('Actual', zoom(actual_inverse))
sec.text('DistanceS', formatm('distance', Distance, format_str='%.3f'))
sec.text('DistanceInvS',
formatm('distance_inv', Distance_to_inverse, format_str='%.3f'))
s = ""
for action in actions:
s += '%s\n' % action
sec.text('list_of_actions', s)
def __init__(self,
id_dds,
dds,
ds,
collapse_threshold,
min_visibility=0.5,
debug_it=1000,
max_it=1000):
'''
:param id_dds:
:param dds:
:param ds:
:param collapse_threshold: info distance for two plans to be the same node
:param debug_it: Number of iterations for drawing debug graph.
'''
WithInternalLog.__init__(self)
self.collapse_threshold = collapse_threshold
self.dds = dds
self.ds = ds
self.id_dds = id_dds
self.debug_graph_iterations = debug_it
self.max_it = max_it
self.min_visibility = min_visibility
from geometry import formatm
self.info('Min_visibility: %s' % min_visibility)
self.info('collapse_threshold: %s' % collapse_threshold)
self.info(
formatm('samew', self.ds.samew, 'oppow', self.ds.oppositew,
'swapw', self.ds.swappablew))
GenericGraphSearch.__init__(self)
def __init__(self, id_dds, dds, ds, collapse_threshold, min_visibility=0.5,
debug_it=1000, max_it=1000):
'''
:param id_dds:
:param dds:
:param ds:
:param collapse_threshold: info distance for two plans to be the same node
:param debug_it: Number of iterations for drawing debug graph.
'''
WithInternalLog.__init__(self)
self.collapse_threshold = collapse_threshold
self.dds = dds
self.ds = ds
self.id_dds = id_dds
self.debug_graph_iterations = debug_it
self.max_it = max_it
self.min_visibility = min_visibility
from geometry import formatm
self.info('Min_visibility: %s' % min_visibility)
self.info('collapse_threshold: %s' % collapse_threshold)
self.info(formatm('samew', self.ds.samew,
'oppow', self.ds.oppositew,
'swapw', self.ds.swappablew))
GenericGraphSearch.__init__(self)
def from_matrices(labels, commute, inverse, same):
logger.info('Creating PlanReducer from matrices:\n' +
formatm('same', same, 'oppo', inverse, 'swap', commute))
pr = PlanReducer()
for i, j in iterate_indices(commute.shape):
li = labels[i]
lj = labels[j]
if commute[i, j]:
assert commute[j, i]
pr.set_commute(li, lj)
if same[i, j]:
assert same[j, i]
pr.set_same(li, lj)
if inverse[i, j]:
assert inverse[j, i]
pr.set_inverse(li, lj)
return pr
def from_matrices(labels, commute, inverse, same):
logger.info('Creating PlanReducer from matrices:\n' +
formatm('same', same, 'oppo', inverse, 'swap', commute))
pr = PlanReducer()
for i, j in iterate_indices(commute.shape):
li = labels[i]
lj = labels[j]
if commute[i, j]:
assert commute[j, i]
pr.set_commute(li, lj)
if same[i, j]:
assert same[j, i]
pr.set_same(li, lj)
if inverse[i, j]:
assert inverse[j, i]
pr.set_inverse(li, lj)
return pr
def description(self):
s = ""
for i in range(self.k):
s += formatm('M^s_v%d' % i, self.M[:, :, i])
s += formatm('N^s_%d' % i, self.N[:, i])
return s
