def to_picklable_dict(self): """ Produces a picklable representation of model as a dict. """ from jazzparser.utils.nltk.storage import object_to_dict if self.backoff_model is not None: backoff_model = self.backoff_model.to_picklable_dict() else: backoff_model = None return { 'order': self.order, 'root_transition_counts': object_to_dict(self.root_transition_counts), 'schema_transition_counts': object_to_dict(self.schema_transition_counts), 'emission_counts': object_to_dict(self.emission_counts), 'estimator': self._estimator, 'backoff_model': backoff_model, 'chord_vocab': self.chord_vocab, 'schemata': self.schemata, 'history': self.history, }
def to_picklable_dict(self): """ Produces a picklable representation of model as a dict. """ from jazzparser.utils.nltk.storage import object_to_dict return { 'label_dom' : self.label_dom, 'emission_dom' : self.emission_dom, 'label_dist' : object_to_dict(self.label_dist), 'emission_dist' : object_to_dict(self.emission_dist), }
def to_picklable_dict(self): """ Produces a picklable representation of model as a dict. """ from jazzparser.utils.nltk.storage import object_to_dict return { 'label_dom': self.label_dom, 'emission_dom': self.emission_dom, 'label_dist': object_to_dict(self.label_dist), 'emission_dist': object_to_dict(self.emission_dist), }
def _get_model_data(self): data = { 'parents' : object_to_dict(self._parent_counts), 'expansions' : object_to_dict(self._expansion_type_counts), 'heads' : object_to_dict(self._head_expansion_counts), 'non_heads' : object_to_dict(self._non_head_expansion_counts), 'words' : object_to_dict(self._lexical_counts), 'cutoff' : self.cutoff, 'cat_bins' : self.cat_bins, 'estimator': self._estimator, 'grammar' : self.grammar, 'lexical' : self.lexical, 'chordmap' : self.chordmap.name, } return data
def _get_model_data(self): data = { 'parents': object_to_dict(self._parent_counts), 'expansions': object_to_dict(self._expansion_type_counts), 'heads': object_to_dict(self._head_expansion_counts), 'non_heads': object_to_dict(self._non_head_expansion_counts), 'words': object_to_dict(self._lexical_counts), 'cutoff': self.cutoff, 'cat_bins': self.cat_bins, 'estimator': self._estimator, 'grammar': self.grammar, 'lexical': self.lexical, 'chordmap': self.chordmap.name, } return data
def to_picklable_dict(self): """ Produces a picklable representation of model as a dict. You can't just pickle the object directly because some of the NLTK classes can't be pickled. You can pickle this dict and reconstruct the model using NgramModel.from_picklable_dict(dict). """ from jazzparser.utils.nltk.storage import object_to_dict return { 'key_transition_dist' : object_to_dict(self.key_transition_dist), 'chord_transition_dist' : object_to_dict(self.chord_transition_dist), 'emission_dist' : object_to_dict(self.emission_dist), 'chord_dist' : object_to_dict(self.chord_dist), 'history' : self.history, 'description' : self.description, 'chord_set' : self.chord_set, }
def to_picklable_dict(self): """ Produces a picklable representation of model as a dict. You can't just pickle the object directly because some of the NLTK classes can't be pickled. You can pickle this dict and reconstruct the model using NgramModel.from_picklable_dict(dict). """ from jazzparser.utils.nltk.storage import object_to_dict return { 'key_transition_dist': object_to_dict(self.key_transition_dist), 'chord_transition_dist': object_to_dict(self.chord_transition_dist), 'emission_dist': object_to_dict(self.emission_dist), 'chord_dist': object_to_dict(self.chord_dist), 'history': self.history, 'description': self.description, 'chord_set': self.chord_set, }
def to_picklable_dict(self): """ Produces a picklable representation of model as a dict. """ from jazzparser.utils.nltk.storage import object_to_dict if self.backoff_model is not None: backoff_model = self.backoff_model.to_picklable_dict() else: backoff_model = None return { 'order' : self.order, 'root_transition_counts' : object_to_dict(self.root_transition_counts), 'schema_transition_counts' : object_to_dict(self.schema_transition_counts), 'emission_counts' : object_to_dict(self.emission_counts), 'estimator' : self._estimator, 'backoff_model' : backoff_model, 'chord_vocab' : self.chord_vocab, 'schemata' : self.schemata, 'history' : self.history, }
def to_picklable_dict(self): from jazzparser.utils.nltk.storage import object_to_dict if self.backoff_model is not None: backoff_model = self.backoff_model.to_picklable_dict() else: backoff_model = None return { 'order' : self.order, 'point_transition_counts' : object_to_dict(self.point_transition_counts), 'fn_transition_counts' : object_to_dict(self.fn_transition_counts), 'type_emission_counts' : object_to_dict(self.type_emission_counts), 'subst_emission_counts' : object_to_dict(self.subst_emission_counts), 'estimator' : self._estimator, 'backoff_model' : backoff_model, 'chord_map' : self.chord_map.name, 'vector_dom' : self.vector_dom, 'point_dom' : self.point_dom, 'history' : self.history, }
def to_picklable_dict(self): from jazzparser.utils.nltk.storage import object_to_dict if self.backoff_model is not None: backoff_model = self.backoff_model.to_picklable_dict() else: backoff_model = None return { 'order': self.order, 'point_transition_counts': object_to_dict(self.point_transition_counts), 'fn_transition_counts': object_to_dict(self.fn_transition_counts), 'type_emission_counts': object_to_dict(self.type_emission_counts), 'subst_emission_counts': object_to_dict(self.subst_emission_counts), 'estimator': self._estimator, 'backoff_model': backoff_model, 'chord_map': self.chord_map.name, 'vector_dom': self.vector_dom, 'point_dom': self.point_dom, 'history': self.history, }
def to_picklable_dict(self): """ Produces a picklable representation of model as a dict. You can't just pickle the object directly because some of the NLTK classes can't be pickled. You can pickle this dict and reconstruct the model using NgramModel.from_picklable_dict(dict). """ from jazzparser.utils.nltk.storage import object_to_dict return { 'schema_transition_dist' : object_to_dict(self.schema_transition_dist), 'root_transition_dist' : object_to_dict(self.root_transition_dist), 'emission_dist' : object_to_dict(self.emission_dist), 'emission_number_dist' : object_to_dict(self.emission_number_dist), 'initial_state_dist' : object_to_dict(self.initial_state_dist), 'schemata' : self.schemata, 'history' : self.history, 'description' : self.description, 'chord_class_mapping' : self.chord_class_mapping, 'chord_classes' : self.chord_classes, 'illegal_transitions' : self.illegal_transitions, 'fixed_root_transitions' : self.fixed_root_transitions, }
def _object_to_dict(obj): if type(obj) == MutableProbDist: # Convert to a normal dict prob dist obj = prob_dist_to_dictionary_prob_dist(obj) return object_to_dict(obj)