def __init__(
self,
location_set,
nested_structure=None,
stratum=None,
model_name=None,
short_name=None,
sampler="opus_core.samplers.stratified_sampler",
utilities="opus_core.hierarchical_linear_utilities",
probabilities="opus_core.nl_probabilities",
**kwargs
):
AgentLocationChoiceModel.__init__(
self,
location_set=location_set,
model_name=model_name,
short_name=short_name,
sampler=sampler,
utilities=utilities,
probabilities=probabilities,
**kwargs
)
HierarchicalChoiceModel.create_nested_and_tree_structure(self, nested_structure, stratum, **kwargs)
self.model_interaction = ModelInteractionHierLCM(
self, kwargs.get("interaction_pkg", "urbansim"), self.choice_set
)
def __init__(self, location_set, nested_structure=None, stratum=None, model_name=None, short_name=None,
sampler="opus_core.samplers.stratified_sampler",
utilities="opus_core.hierarchical_linear_utilities",
probabilities="opus_core.nl_probabilities", **kwargs):
AgentLocationChoiceModel.__init__(self, location_set=location_set, model_name=model_name,
short_name=short_name, sampler=sampler, utilities=utilities,
probabilities=probabilities, **kwargs)
HierarchicalChoiceModel.create_nested_and_tree_structure(self, nested_structure, stratum, **kwargs)
self.model_interaction = ModelInteractionHierLCM(self, kwargs.get('interaction_pkg',"urbansim"), self.choice_set)
def get_number_of_elemental_alternatives(self):
return HierarchicalChoiceModel.get_number_of_elemental_alternatives(self)
def set_choice_set_size(self):
HierarchicalChoiceModel.set_choice_set_size(self)
def estimate_step(self):
self.set_correct_for_sampling()
result = AgentLocationChoiceModel.estimate_step(self)
HierarchicalChoiceModel.add_logsum_to_coefficients(self, result)
return result
def estimate(self, specification, *args, **kwargs):
HierarchicalChoiceModel.init_membership_in_nests(self)
HierarchicalChoiceModel.delete_logsum_from_specification(self, specification)
return AgentLocationChoiceModel.estimate(self, specification, *args, **kwargs)
def run_chunk(self, agents_index, agent_set, specification, coefficients):
HierarchicalChoiceModel.add_logsum_to_specification(self, specification, coefficients)
HierarchicalChoiceModel.init_membership_in_nests(self)
return AgentLocationChoiceModel.run_chunk(self, agents_index, agent_set, specification, coefficients)
def get_number_of_elemental_alternatives(self):
return HierarchicalChoiceModel.get_number_of_elemental_alternatives(self)
def set_choice_set_size(self):
HierarchicalChoiceModel.set_choice_set_size(self)
def estimate_step(self):
self.set_correct_for_sampling()
result = AgentLocationChoiceModel.estimate_step(self)
HierarchicalChoiceModel.add_logsum_to_coefficients(self, result)
return result
def estimate(self, specification, *args, **kwargs):
HierarchicalChoiceModel.init_membership_in_nests(self)
HierarchicalChoiceModel.delete_logsum_from_specification(self, specification)
return AgentLocationChoiceModel.estimate(self, specification, *args, **kwargs)
def run_chunk(self, agents_index, agent_set, specification, coefficients):
HierarchicalChoiceModel.add_logsum_to_specification(self, specification, coefficients)
HierarchicalChoiceModel.init_membership_in_nests(self)
return AgentLocationChoiceModel.run_chunk(self, agents_index, agent_set, specification, coefficients)
def simulate_chunk(self):
return HierarchicalChoiceModel.simulate_chunk(self)
def set_choice_set_size(self, **kwargs):
HierarchicalChoiceModel.set_choice_set_size(self, **kwargs)
