def add_primitive_interaction(self, experiment, result, valence):
label = experiment.get_label() + result.get_label()
if label not in self.INTERACTIONS:
interaction = Interaction(label, valence)
interaction.set_experiment(experiment)
interaction.set_result(result)
interaction.set_valence(valence)
self.INTERACTIONS[label] = interaction
def add_primitive_interaction(self, experiment, result, valence):
label = experiment.get_label() + result.get_label()
if label not in self.INTERACTIONS:
interaction = Interaction(label, valence)
interaction.set_experiment(experiment)
interaction.set_result(result)
interaction.set_valence(valence)
self.INTERACTIONS[label] = interaction
def addget_primitive_interaction(self,
experiment,
result,
valence=None,
meaning=None):
label = experiment.get_label() + result.get_label()
if label not in self.INTERACTIONS:
interaction = Interaction(label)
interaction.set_experiment(experiment)
interaction.set_result(result)
interaction.set_valence(valence)
interaction.set_meaning(meaning)
self.INTERACTIONS[label] = interaction
return self.INTERACTIONS[label]
def learn_composite_interaction(self, context_interaction, enacted_interaction):
if context_interaction is not None:
label = context_interaction.get_label() + enacted_interaction.get_label()
if label not in self.INTERACTIONS:
valence = context_interaction.get_valence() + enacted_interaction.get_valence()
interaction = Interaction(label, valence)
interaction.set_pre_interaction(context_interaction)
interaction.set_post_interaction(enacted_interaction)
interaction.set_valence(valence)
self.INTERACTIONS[label] = interaction
print "Learn " + label
else:
interaction = self.INTERACTIONS[label]
print 'Incrementing weight for ' + interaction.__repr__()
interaction.increment_weight()
def initialize_interactions(self, primitive_interactions):
for interaction in primitive_interactions:
meaning = interaction
experiment_label = primitive_interactions[interaction][0]
result_label = primitive_interactions[interaction][1]
valence = primitive_interactions[interaction][2]
experiment = self.addget_abstract_experiment(experiment_label)
result = self.addget_result(result_label)
self.addget_primitive_interaction(experiment, result, valence, meaning)
for experiment in self.EXPERIMENTS.values():
interaction = Interaction(experiment.get_label() + "r2")
interaction.set_valence(1)
interaction.set_experiment(experiment)
experiment.set_intended_interaction(interaction)
def initialize_interactions(self, primitive_interactions):
for interaction in primitive_interactions:
meaning = interaction
experiment_label = primitive_interactions[interaction][0]
result_label = primitive_interactions[interaction][1]
valence = primitive_interactions[interaction][2]
experiment = self.addget_abstract_experiment(experiment_label)
result = self.addget_result(result_label)
self.addget_primitive_interaction(experiment, result, valence,
meaning)
for experiment in self.EXPERIMENTS.values():
interaction = Interaction(experiment.get_label() + "r2")
interaction.set_valence(1)
interaction.set_experiment(experiment)
experiment.set_intended_interaction(interaction)
def learn_composite_interaction(self, context_interaction,
enacted_interaction):
if context_interaction is not None:
label = context_interaction.get_label(
) + enacted_interaction.get_label()
if label not in self.INTERACTIONS:
valence = context_interaction.get_valence(
) + enacted_interaction.get_valence()
interaction = Interaction(label)
interaction.set_pre_interaction(context_interaction)
interaction.set_post_interaction(enacted_interaction)
interaction.set_valence(valence)
self.INTERACTIONS[label] = interaction
print "Learn " + label
else:
interaction = self.INTERACTIONS[label]
print 'Incrementing weight for ' + interaction.__repr__()
interaction.increment_weight()
def addget_primitive_interaction(self, experiment, result, valence=None, meaning=None):
"""
If a primitive interaction is not in the INTERACTIONS dictionary, add it. Otherwise just return it.
:param experiment: (str) primitive experiment
:param result: (str) primitive result
:param valence: (int) valence of the interaction
:param meaning: (str) observer's meaning of the interaction
:return: (interaction) primitive interaction from the INTERACTIONS dictionary
"""
label = experiment.get_label() + result.get_label()
if label not in self.INTERACTIONS:
interaction = Interaction(label)
interaction.set_experiment(experiment)
interaction.set_result(result)
interaction.set_valence(valence)
interaction.set_meaning(meaning)
self.INTERACTIONS[label] = interaction
return self.INTERACTIONS[label]
def learn_composite_interaction(self, context_interaction, enacted_interaction):
"""
Learn a new composite interaction or reinforce it if already known.
:param context_interaction: (Interaction) at time t-1
:param enacted_interaction: (Interaction) just performed
"""
if context_interaction is not None:
label = context_interaction.get_label() + enacted_interaction.get_label()
if label not in self.INTERACTIONS:
# valence is a sum of primitive interactions
valence = context_interaction.get_valence() + enacted_interaction.get_valence()
interaction = Interaction(label)
interaction.set_pre_interaction(context_interaction)
interaction.set_post_interaction(enacted_interaction)
interaction.set_valence(valence)
self.INTERACTIONS[label] = interaction
print "Learn " + label
else:
interaction = self.INTERACTIONS[label]
print 'Incrementing weight for ', interaction
interaction.increment_weight()
def addget_primitive_interaction(self,
experiment,
result,
valence=None,
meaning=None):
"""
If a primitive interaction is not in the INTERACTIONS dictionary, add it. Otherwise just return it.
:param experiment: (str) primitive experiment
:param result: (str) primitive result
:param valence: (int) valence of the interaction
:param meaning: (str) observer's meaning of the interaction
:return: (interaction) primitive interaction from the INTERACTIONS dictionary
"""
label = experiment.get_label() + result.get_label()
if label not in self.INTERACTIONS:
interaction = Interaction(label)
interaction.set_experiment(experiment)
interaction.set_result(result)
interaction.set_valence(valence)
interaction.set_meaning(meaning)
self.INTERACTIONS[label] = interaction
return self.INTERACTIONS[label]
def __init__(self, primitive_interactions, environment):
"""Initialize existence with a set of primitive interactions provided as a dictionary:
{(str) interaction meaning: ((str) experiment, (str) result, (int) valence)"""
self.context_interaction = None
self.context_pair_interaction = None # context at previous two steps (t-2, t-1)
self.mood = None
self.previous_experiment = None
self.penultimate_experiment = None
self.environment = environment
for interaction in primitive_interactions:
meaning = interaction
experiment_label = primitive_interactions[interaction][0]
result_label = primitive_interactions[interaction][1]
valence = primitive_interactions[interaction][2]
experiment = self.addget_rexperiment(experiment_label)
result = self.addget_result(result_label)
pi = self.addget_primitive_interaction(experiment, result, valence,
meaning)
for experiment in self.EXPERIMENTS.values():
interaction = Interaction(experiment.get_label() + "r2")
interaction.set_valence(1)
experiment.set_intended_interaction(interaction)
def learn_composite_interaction(self, context_interaction,
enacted_interaction):
"""
Learn a new composite interaction or reinforce it if already known.
:param context_interaction: (Interaction) at time t-1
:param enacted_interaction: (Interaction) just performed
"""
if context_interaction is not None:
label = context_interaction.get_label(
) + enacted_interaction.get_label()
if label not in self.INTERACTIONS:
# valence is a sum of primitive interactions
valence = context_interaction.get_valence(
) + enacted_interaction.get_valence()
interaction = Interaction(label)
interaction.set_pre_interaction(context_interaction)
interaction.set_post_interaction(enacted_interaction)
interaction.set_valence(valence)
self.INTERACTIONS[label] = interaction
print "Learn " + label
else:
interaction = self.INTERACTIONS[label]
print 'Incrementing weight for ', interaction
interaction.increment_weight()
