def __init__(self,
map_=None,
web_interface_topic='python',
false_alarm_prob=0.2):
self.update_rate = None
self.has_physical_dimensions = False
self.speed_model = speed_model()
self.false_alarm_prob = false_alarm_prob
super(Human, self).__init__(self.update_rate,
self.has_physical_dimensions)
# self.certainties = ['think', 'know']
self.certainties = ['know']
self.positivities = ['is not',
'is'] # <>TODO: oh god wtf why does order matter
self.relations = {
'object': [
'behind',
'in front of',
'left of',
'right of',
'near',
],
'area': ['inside', 'near', 'outside']
}
self.movement_types = ['moving', 'stopped']
self.movement_qualities = ['slowly', 'moderately', 'quickly']
self.groundings = {}
self.groundings['area'] = map_.areas
self.groundings['object'] = {}
for cop_name, cop in map_.cops.iteritems():
# if cop.has_relations:
self.groundings['object'][cop_name] = cop
for object_name, obj in map_.objects.iteritems():
if obj.has_relations:
self.groundings['object'][object_name] = obj
self.target_names = ['nothing', 'a robot'] + map_.robbers.keys()
self.utterance = ''
self.new_update = False
# Set up the VB fusion parameters
self.vb = VariationalBayes()
if web_interface_topic != 'python':
# Subscribe to web interface
print web_interface_topic
import rospy
from std_msgs.msg import String
rospy.Subscriber(web_interface_topic, String, self.callback)
def __init__(self, map_=None, web_interface_topic='python',
false_alarm_prob=0.2):
self.update_rate = None
self.has_physical_dimensions = False
self.speed_model = speed_model()
self.false_alarm_prob = false_alarm_prob
super(Human, self).__init__(self.update_rate,
self.has_physical_dimensions)
# self.certainties = ['think', 'know']
self.certainties = ['know']
self.positivities = ['is not', 'is'] # <>TODO: oh god wtf why does order matter
self.relations = {'object': ['behind',
'in front of',
'left of',
'right of',
'near',
],
'area': ['inside',
'near',
'outside'
]}
self.movement_types = ['moving', 'stopped']
self.movement_qualities = ['slowly', 'moderately', 'quickly']
self.groundings = {}
self.groundings['area'] = map_.areas
self.groundings['object'] = {}
for cop_name, cop in map_.cops.iteritems():
# if cop.has_relations:
self.groundings['object'][cop_name] = cop
for object_name, obj in map_.objects.iteritems():
if obj.has_relations:
self.groundings['object'][object_name] = obj
self.target_names = ['nothing', 'a robot'] + map_.robbers.keys()
self.utterance = ''
self.new_update = False
# Set up the VB fusion parameters
self.vb = VariationalBayes()
if web_interface_topic != 'python':
# Subscribe to web interface
print web_interface_topic
import rospy
from std_msgs.msg import String
rospy.Subscriber(web_interface_topic, String, self.callback)
def comparison_1d():
# Define prior
prior_mean, prior_var = 0.3, 0.01
min_x, max_x = -5, 5
res = 10000
prior = GaussianMixture(1, prior_mean, prior_var)
x_space = np.linspace(min_x, max_x, res)
# Define sensor likelihood
sm = speed_model()
measurement = 'Slow'
measurement_i = sm.class_labels.index(measurement)
# Do a VB update
init_mean, init_var = 0, 1
init_alpha, init_xi = 0.5, np.ones(4)
vb = VariationalBayes()
vb_mean, vb_var, _ = vb.vb_update(measurement, sm, prior, init_mean,
init_var, init_alpha, init_xi)
vb_posterior = GaussianMixture(1, vb_mean, vb_var)
nisar_vb_mean = 0.131005297841171
nisar_vb_var = 6.43335516254277e-05
diff_vb_mean = vb_mean - nisar_vb_mean
diff_vb_var = vb_var - nisar_vb_var
logging.info(
'Nisar\'s VB update had mean difference {} and var difference {}\n'.
format(diff_vb_mean, diff_vb_var))
# Do a VBIS update
vbis_mean, vbis_var, _ = vb.vbis_update(measurement, sm, prior, init_mean,
init_var, init_alpha, init_xi)
vbis_posterior = GaussianMixture(1, vbis_mean, vbis_var)
nisar_vbis_mean = 0.154223416817080
nisar_vbis_var = 0.00346064073274943
diff_vbis_mean = vbis_mean - nisar_vbis_mean
diff_vbis_var = vbis_var - nisar_vbis_var
logging.info(
'Nisar\'s VBIS update had mean difference {} and var difference {}\n'.
format(diff_vbis_mean, diff_vbis_var))
# Plot results
likelihood_label = 'Likelihood of \'{}\''.format(measurement)
fig = plt.figure()
ax = fig.add_subplot(111)
sm.classes[measurement].plot(ax=ax,
fill_between=False,
label=likelihood_label,
ls='--')
ax.plot(x_space,
prior.pdf(x_space),
lw=1,
label='prior pdf',
c='grey',
ls='--')
ax.plot(x_space,
vb_posterior.pdf(x_space),
lw=2,
label='VB posterior',
c='r')
ax.fill_between(x_space,
0,
vb_posterior.pdf(x_space),
alpha=0.2,
facecolor='r')
ax.plot(x_space,
vbis_posterior.pdf(x_space),
lw=2,
label='VBIS Posterior',
c='g')
ax.fill_between(x_space,
0,
vbis_posterior.pdf(x_space),
alpha=0.2,
facecolor='g')
ax.set_title('VBIS Update')
ax.legend()
ax.set_xlim([0, 0.4])
ax.set_ylim([0, 7])
plt.show()
