def visualize(self, save_eps=True):
"""
Show the internal state of the daisychain in a pictorial format
"""
tools.visualize_array(self.expected_cable_activities,
label=self.name + '_exp_cable_act')
#tools.visualize_array(self.post_uncertainty,
# label=self.name + '_post_uncert')
tools.visualize_array(np.log(self.count + 1.),
label=self.name + '_count')
def visualize(self, save_eps=True):
""" Show the internal state of the daisychain in a pictorial format """
tools.visualize_array(self.reward_value, label=self.name + '_reward')
#tools.visualize_array(self.reward_uncertainty,
# label=self.name + '_reward_uncertainty')
tools.visualize_array(np.log(self.count + 1.),
label=self.name + '_count')
#tools.visualize_daisychain(self, self.num_primitives,
# self.num_actions, 10)
return
def visualize(self, save_eps=False):
print self.name, '0', np.nonzero(np.nan_to_num(np.copy(
self.bundle_map)))[0]
print self.name, '1', np.nonzero(np.nan_to_num(np.copy(
self.bundle_map)))[1]
print self.max_num_bundles, 'bundles maximum'
tools.visualize_array(self.bundle_map, label=self.name + '_bundle_map')
tools.visualize_array(self.agglomeration_energy,
label=self.name + '_agg_energy')
pass
def visualize(self, save_eps=True):
""" Show the internal state of the daisychain in a pictorial format """
tools.visualize_array(self.reward_value,
label=self.name + '_reward')
#tools.visualize_array(self.reward_uncertainty,
# label=self.name + '_reward_uncertainty')
tools.visualize_array(np.log(self.count + 1.),
label=self.name + '_count')
#tools.visualize_daisychain(self, self.num_primitives,
# self.num_actions, 10)
return
def visualize(self, save_eps=True):
""" Show the internal state of the daisychain in a pictorial format """
tools.visualize_array(self.expected_cable_activities,
label=self.name + '_exp_cable_act')
tools.visualize_array(self.post_uncertainty,
label=self.name + '_post_uncert')
tools.visualize_array(np.log(self.count + 1.),
label=self.name + '_count')
def visualize(self):
"""
Turn the state of the ``ZipTie`` into an image.
"""
print ' '.join(['ziptie', str(self.level)])
# First list the bundles andthe cables in each.
i_bundles = self.bundle_map_rows[:self.n_map_entries]
i_cables = self.bundle_map_cols[:self.n_map_entries]
i_bundles_unique = np.unique(i_bundles)
if i_bundles_unique is not None:
for i_bundle in i_bundles_unique:
b_cables = list(
np.sort(i_cables[np.where(i_bundles == i_bundle)[0]]))
print ' '.join(
[' bundle',
str(i_bundle), 'cables:',
str(b_cables)])
plot = False
if plot:
if self.n_map_entries > 0:
# Render the bundle map.
bundle_map = np.zeros(
(self.max_num_cables, self.max_num_bundles))
nb.set_dense_val(bundle_map,
self.bundle_map_rows[:self.n_map_entries],
self.bundle_map_cols[:self.n_map_entries], 1.)
tools.visualize_array(bundle_map,
label=self.name + '_bundle_map')
# Render the agglomeration energy.
label = '_'.join([self.name, 'agg_energy'])
tools.visualize_array(self.agglomeration_energy, label=label)
plt.xlabel(str(np.max(self.agglomeration_energy)))
# Render the nucleation energy.
label = '_'.join([self.name, 'nuc_energy'])
tools.visualize_array(self.nucleation_energy, label=label)
plt.xlabel(str(np.max(self.nucleation_energy)))
def visualize(self):
"""
Turn the state of the ``ZipTie`` into an image.
"""
print ' '.join(['ziptie', str(self.level)])
# First list the bundles andthe cables in each.
i_bundles = self.bundle_map_rows[:self.n_map_entries]
i_cables = self.bundle_map_cols[:self.n_map_entries]
i_bundles_unique = np.unique(i_bundles)
if i_bundles_unique is not None:
for i_bundle in i_bundles_unique:
b_cables = list(np.sort(i_cables[np.where(
i_bundles == i_bundle)[0]]))
print ' '.join([' bundle', str(i_bundle),
'cables:', str(b_cables)])
plot = False
if plot:
if self.n_map_entries > 0:
# Render the bundle map.
bundle_map = np.zeros((self.max_num_cables,
self.max_num_bundles))
nb.set_dense_val(bundle_map,
self.bundle_map_rows[:self.n_map_entries],
self.bundle_map_cols[:self.n_map_entries], 1.)
tools.visualize_array(bundle_map,
label=self.name + '_bundle_map')
# Render the agglomeration energy.
label = '_'.join([self.name, 'agg_energy'])
tools.visualize_array(self.agglomeration_energy, label=label)
plt.xlabel( str(np.max(self.agglomeration_energy)) )
# Render the nucleation energy.
label = '_'.join([self.name, 'nuc_energy'])
tools.visualize_array(self.nucleation_energy, label=label)
plt.xlabel( str(np.max(self.nucleation_energy)) )
