aalpy.learning_algs.stochastic_passive.FPTA
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from collections import defaultdict class AlergiaPtaNode: __slots__ = ['output', 'input_frequency', 'children', 'prefix', 'state_id', 'children_prob'] def __init__(self, output): self.output = output self.input_frequency = defaultdict(int) self.children = dict() self.prefix = () # # for visualization self.state_id = None self.children_prob = None def succs(self): return list(self.children.values()) def __lt__(self, other): return len(self.prefix) < len(other.prefix) def __le__(self, other): return len(self.prefix) <= len(other.prefix) def __eq__(self, other): return self.prefix == other.prefix def create_fpta(data, automaton_type): is_iofpta = True if automaton_type != 'mc' else False seq_iter_index = 0 if automaton_type == 'smm' else 1 not_smm = automaton_type != 'smm' # NOTE: This approach with _copy is not optimal, but a big time save from doing deep copy at the end if automaton_type != 'smm': root_node, root_copy = AlergiaPtaNode(data[0][0]), AlergiaPtaNode(data[0][0]) else: root_node, root_copy = AlergiaPtaNode(None), AlergiaPtaNode(None) for seq in data: if not_smm and seq[0] != root_node.output: print('All strings should have the same initial output') assert False curr_node, curr_copy = root_node, root_copy for el in seq[seq_iter_index:]: if el not in curr_node.children.keys(): if not_smm: out = el if not is_iofpta else el[1] node, node_copy = AlergiaPtaNode(out), AlergiaPtaNode(out) else: node, node_copy = AlergiaPtaNode(None), AlergiaPtaNode(None) node.prefix = tuple(curr_node.prefix) node.prefix += (el,) node_copy.prefix = node.prefix curr_node.children[el] = node curr_copy.children[el] = node_copy curr_node.input_frequency[el] += 1 curr_node = curr_node.children[el] curr_copy.input_frequency[el] += 1 curr_copy = curr_copy.children[el] return root_node, root_copy
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class AlergiaPtaNode: __slots__ = ['output', 'input_frequency', 'children', 'prefix', 'state_id', 'children_prob'] def __init__(self, output): self.output = output self.input_frequency = defaultdict(int) self.children = dict() self.prefix = () # # for visualization self.state_id = None self.children_prob = None def succs(self): return list(self.children.values()) def __lt__(self, other): return len(self.prefix) < len(other.prefix) def __le__(self, other): return len(self.prefix) <= len(other.prefix) def __eq__(self, other): return self.prefix == other.prefix
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def __init__(self, output): self.output = output self.input_frequency = defaultdict(int) self.children = dict() self.prefix = () # # for visualization self.state_id = None self.children_prob = None
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def succs(self): return list(self.children.values())
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def create_fpta(data, automaton_type): is_iofpta = True if automaton_type != 'mc' else False seq_iter_index = 0 if automaton_type == 'smm' else 1 not_smm = automaton_type != 'smm' # NOTE: This approach with _copy is not optimal, but a big time save from doing deep copy at the end if automaton_type != 'smm': root_node, root_copy = AlergiaPtaNode(data[0][0]), AlergiaPtaNode(data[0][0]) else: root_node, root_copy = AlergiaPtaNode(None), AlergiaPtaNode(None) for seq in data: if not_smm and seq[0] != root_node.output: print('All strings should have the same initial output') assert False curr_node, curr_copy = root_node, root_copy for el in seq[seq_iter_index:]: if el not in curr_node.children.keys(): if not_smm: out = el if not is_iofpta else el[1] node, node_copy = AlergiaPtaNode(out), AlergiaPtaNode(out) else: node, node_copy = AlergiaPtaNode(None), AlergiaPtaNode(None) node.prefix = tuple(curr_node.prefix) node.prefix += (el,) node_copy.prefix = node.prefix curr_node.children[el] = node curr_copy.children[el] = node_copy curr_node.input_frequency[el] += 1 curr_node = curr_node.children[el] curr_copy.input_frequency[el] += 1 curr_copy = curr_copy.children[el] return root_node, root_copy