def __init__(self, grammar, forward_model=None, data=None, ll_params=None, spatial_model=None, initial_tree=None):
"""
Initializes ShapeGrammarState
grammar: Probabilistic context free shape grammar definition. PCFG instance.
forward_model: Forward model(s) used in likelihood calculation
ll_params: parameters for likelihood calculation
spatial_model: Spatial model for the state. Initialized randomly if initial_tree is not
provided
initial_tree (optional): Parse tree for the state. If not provided, state is initialized
randomly.
"""
self.grammar = grammar
self.spatial_model = spatial_model
self.forward_model = forward_model
if initial_tree is None:
self.tree = self._get_random_tree(start=self.grammar.start_symbol, max_depth=self.MAXIMUM_DEPTH)
# initialize spatial model
self.spatial_model.update(self.tree, self.grammar)
else:
self.tree = initial_tree
# if a subclass does not define moves, we define it here and add subtree move
if hasattr(self, 'moves') is False:
self.moves = [self.subtree_proposal]
# IMPORTANT: we call base class init after we initialize spatial model,
# because prior, ll, deriv_prob calculation is done in init and
# spatial_model should be available to calculate them
PCFGTree.__init__(self, grammar=grammar, data=data, ll_params=ll_params, initial_tree=self.tree)
def _prior(self):
"""
Prior probability for state
Product of probability for tree and spatial layout
"""
prior = PCFGTree._prior(self) * self.spatial_model.probability()
return prior
def _prior(self):
"""
Prior probability for state
Product of probability for tree and spatial layout
"""
prior = PCFGTree._prior(self) * self.spatial_model.probability()
return prior
def __init__(self,
grammar,
forward_model=None,
data=None,
ll_params=None,
spatial_model=None,
initial_tree=None):
"""
Initializes ShapeGrammarState
grammar: Probabilistic context free shape grammar definition. PCFG instance.
forward_model: Forward model(s) used in likelihood calculation
ll_params: parameters for likelihood calculation
spatial_model: Spatial model for the state. Initialized randomly if initial_tree is not
provided
initial_tree (optional): Parse tree for the state. If not provided, state is initialized
randomly.
"""
self.grammar = grammar
self.spatial_model = spatial_model
self.forward_model = forward_model
if initial_tree is None:
self.tree = self._get_random_tree(start=self.grammar.start_symbol,
max_depth=self.MAXIMUM_DEPTH)
# initialize spatial model
self.spatial_model.update(self.tree, self.grammar)
else:
self.tree = initial_tree
# if a subclass does not define moves, we define it here and add subtree move
if hasattr(self, 'moves') is False:
self.moves = [self.subtree_proposal]
# IMPORTANT: we call base class init after we initialize spatial model,
# because prior, ll, deriv_prob calculation is done in init and
# spatial_model should be available to calculate them
PCFGTree.__init__(self,
grammar=grammar,
data=data,
ll_params=ll_params,
initial_tree=self.tree)
def subtree_proposal(self):
"""
Proposes a new state based on current state using subtree move
Uses base class PCFGTree's subtree_proposal_propose_tree to generate
new tree, then samples positions for new added parts
"""
pcfg_proposal = PCFGTree.subtree_proposal_propose_tree(self)
# get a new spatial model based on proposed tree
proposed_spatial_model = self.spatial_model.propose(pcfg_proposal, self.grammar)
proposal = self.__class__(forward_model=self.forward_model, data=self.data, ll_params=self.ll_params,
spatial_model=proposed_spatial_model, initial_tree=pcfg_proposal)
acc_prob = self._subtree_acceptance_probability(proposal)
return proposal, acc_prob
def subtree_proposal(self):
"""
Proposes a new state based on current state using subtree move
Uses base class PCFGTree's subtree_proposal_propose_tree to generate
new tree, then samples positions for new added parts
"""
pcfg_proposal = PCFGTree.subtree_proposal_propose_tree(self)
# get a new spatial model based on proposed tree
proposed_spatial_model = self.spatial_model.propose(
pcfg_proposal, self.grammar)
proposal = self.__class__(forward_model=self.forward_model,
data=self.data,
ll_params=self.ll_params,
spatial_model=proposed_spatial_model,
initial_tree=pcfg_proposal)
acc_prob = self._subtree_acceptance_probability(proposal)
return proposal, acc_prob
def __str__(self):
"""
Override in super class for more informative string representations
"""
return PCFGTree.__str__(self)
def __str__(self):
"""
Override in super class for more informative string representations
"""
return PCFGTree.__str__(self)
