def create_networks(self):
network = BayesianModel([('goal_left', 'motor_left'),
('goal_right', 'motor_right'),
('obstacle_left', 'motor_left'),
('obstacle_right', 'motor_right'),
('obstacle_left', 'vision_1'),
('obstacle_left', 'vision_2'),
('obstacle_left', 'vision_3'),
('obstacle_right', 'vision_4'),
('obstacle_right', 'vision_5'),
('obstacle_right', 'vision_6')])
cpd_1 = double_hypo_cpd('motor_left', 'goal_left', 'obstacle_left')
cpd_2 = double_hypo_cpd('motor_right', 'goal_right', 'obstacle_right')
cpd_3 = wandering_hypo_cpd('goal_left')
cpd_4 = wandering_hypo_cpd('goal_right')
cpd_5 = obstacle_hypo_cpd('obstacle_left')
cpd_6 = obstacle_hypo_cpd('obstacle_right')
cpd_7 = single_hypo_cpd('vision_1', 'obstacle_left')
cpd_8 = single_hypo_cpd('vision_2', 'obstacle_left')
cpd_9 = single_hypo_cpd('vision_3', 'obstacle_left')
cpd_10 = single_hypo_cpd('vision_4', 'obstacle_right')
cpd_11 = single_hypo_cpd('vision_5', 'obstacle_right')
cpd_12 = single_hypo_cpd('vision_6', 'obstacle_right')
network.add_cpds(cpd_1, cpd_2, cpd_3, cpd_4, cpd_5, cpd_6, cpd_7,
cpd_8, cpd_9, cpd_10, cpd_11, cpd_12)
network.check_model()
return {
'main': GenerativeModel(SensoryInputVirtualPeepo(self), network)
}
def model_for_sorted(coin_set):
network = BayesianModel([('previous', 'current')])
cpd_a = TabularCPD(variable='previous', variable_card=2, values=[[0.9, 0.1]])
cpd_b = TabularCPD(variable='current', variable_card=2, values=[[0.99, 0.01],
[0.01, 0.99]], evidence=['previous'],
evidence_card=[2])
network.add_cpds(cpd_a, cpd_b)
network.check_model()
model = GenerativeModel(SensoryInputCoin(coin_set), network)
return model
def default_model(coin_set):
network = BayesianModel([('hypo', 'coin')])
cpd_a = TabularCPD(variable='hypo', variable_card=2, values=[[0.5, 0.5]])
cpd_b = TabularCPD(variable='coin', variable_card=2, values=[[0.99, 0.01],
[0.01, 0.99]], evidence=['hypo'],
evidence_card=[2])
network.add_cpds(cpd_a, cpd_b)
network.check_model()
model = GenerativeModel(SensoryInputCoin(coin_set), network)
return model
def create_network():
network = BayesianModel([('A', 'B')])
cpd_a = TabularCPD(variable='A', variable_card=2, values=[[0.2, 0.8]])
cpd_b = TabularCPD(variable='B', variable_card=2,
values=[[0.1, 0.7],
[0.9, 0.3]],
evidence=['A'],
evidence_card=[2])
network.add_cpds(cpd_a, cpd_b)
network.check_model()
return GenerativeModel(TestSensoryInput(), network)
def generic_model(coin_set):
network = BayesianModel([('A', 'D'), ('B', 'D'), ('C', 'D')])
cpd_a = TabularCPD(variable='A', variable_card=2, values=[[0.9, 0.1]])
cpd_b = TabularCPD(variable='B', variable_card=2, values=[[0.1, 0.9]])
cpd_c = TabularCPD(variable='C', variable_card=2, values=[[0.1, 0.9]])
cpd_d = TabularCPD(variable='D', variable_card=2, values=[[0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.9, 0.9],
[0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.1, 0.1]],
evidence=['A', 'B', 'C'], evidence_card=[2, 2, 2])
network.add_cpds(cpd_a, cpd_b, cpd_c, cpd_d)
network.check_model()
model = GenerativeModel(SensoryInputCoin(coin_set), network)
return model
def create_network_for_add_edge():
""" Note: adding an edge where the values are duplicated from the existing connecting nodes won't change
predictions at all. Since it will just take on the value of the pre-existing state, given that we duplicate
the values... Maybe go back to 0.5 0.5 defaults?
"""
network = BayesianModel([('A', 'B'), ('C', 'D')])
cpd_a = TabularCPD(variable='A', variable_card=2, values=[[0.4, 0.6]])
cpd_b = TabularCPD(variable='B', variable_card=2,
values=[[0.1, 0.9],
[0.9, 0.1]],
evidence=['A'],
evidence_card=[2])
cpd_c = TabularCPD(variable='C', variable_card=2, values=[[0.6, 0.4]])
cpd_d = TabularCPD(variable='D', variable_card=2,
values=[[0.8, 0.9],
[0.2, 0.1]],
evidence=['C'],
evidence_card=[2])
network.add_cpds(cpd_a, cpd_b, cpd_c, cpd_d)
network.check_model()
return GenerativeModel(TestSensoryInput(), network)
logging.basicConfig()
logging.getLogger().setLevel(logging.DEBUG)
logging.info('logging initialized')
bot = PeepoBot()
logging.info('bot initialized')
network = BayesianModel([('hypo', 'infrared'), ('hypo', 'motor')])
cpd_a = TabularCPD(variable='hypo', variable_card=2, values=[[0.7, 0.3]])
cpd_b = TabularCPD(variable='infrared',
variable_card=2,
values=[[0.9, 0.1], [0.1, 0.9]],
evidence=['hypo'],
evidence_card=[2])
cpd_c = TabularCPD(variable='motor',
variable_card=2,
values=[[0.9, 0.1], [0.1, 0.9]],
evidence=['hypo'],
evidence_card=[2])
network.add_cpds(cpd_a, cpd_b, cpd_c)
network.check_model()
model = GenerativeModel(SensoryInput(bot), network)
logging.info('starting predictive processing')
while True:
model.process()