def from_picklable_dict(data): """ Reproduces an n-gram model that was converted to a picklable form using to_picklable_dict. """ from jazzparser.utils.nltk.storage import dict_to_object return DictionaryHmmModel(dict_to_object(data['label_dist']), dict_to_object(data['emission_dist']), data['label_dom'], data['emission_dom'])
def from_picklable_dict(cls, data, model_name="default"): """ Reproduces an n-gram model that was converted to a picklable form using to_picklable_dict. """ from jazzparser.utils.nltk.storage import dict_to_object return cls(dict_to_object(data['key_transition_dist']), dict_to_object(data['chord_transition_dist']), dict_to_object(data['emission_dist']), dict_to_object(data['chord_dist']), model_name=model_name, history=data.get('history', ''), description=data.get('description', ''), chord_set=data.get('chord_set', 'scale+dom7'))
def _load_model(name, data): obj = HalfspanPcfgModel( name = name, cutoff = data['cutoff'], cat_bins = data['cat_bins'], estimator = data['estimator'], lexical = data.get('lexical', True), chordmap = get_chord_mapping(data.get('chordmap', None)), parent_counts = dict_to_object(data['parents']), expansion_type_counts = dict_to_object(data['expansions']), head_expansion_counts = dict_to_object(data['heads']), non_head_expansion_counts = dict_to_object(data['non_heads']), lexical_counts = dict_to_object(data['words']), grammar = data['grammar'], ) return obj
def _load_model(name, data): obj = HalfspanPcfgModel( name=name, cutoff=data['cutoff'], cat_bins=data['cat_bins'], estimator=data['estimator'], lexical=data.get('lexical', True), chordmap=get_chord_mapping(data.get('chordmap', None)), parent_counts=dict_to_object(data['parents']), expansion_type_counts=dict_to_object(data['expansions']), head_expansion_counts=dict_to_object(data['heads']), non_head_expansion_counts=dict_to_object(data['non_heads']), lexical_counts=dict_to_object(data['words']), grammar=data['grammar'], ) return obj
def from_picklable_dict(cls, data): from jazzparser.utils.nltk.storage import dict_to_object if data['backoff_model'] is not None: backoff_model = cls.from_picklable_dict(data['backoff_model']) else: backoff_model = None return cls(data['order'], dict_to_object(data['point_transition_counts']), dict_to_object(data['fn_transition_counts']), dict_to_object(data['type_emission_counts']), dict_to_object(data['subst_emission_counts']), data['estimator'], backoff_model, get_chord_mapping(data['chord_map']), data['vector_dom'], data['point_dom'], history=data.get('history', ''))
def from_picklable_dict(cls, data): """ Reproduces an n-gram model that was converted to a picklable form using to_picklable_dict. """ from jazzparser.utils.nltk.storage import dict_to_object return cls(dict_to_object(data['schema_transition_dist']), dict_to_object(data['root_transition_dist']), dict_to_object(data['emission_dist']), dict_to_object(data['emission_number_dist']), dict_to_object(data['initial_state_dist']), data['schemata'], data['chord_class_mapping'], data['chord_classes'], history=data.get('history', ''), description=data.get('description', ''), illegal_transitions=data.get('illegal_transitions', []), fixed_root_transitions=data.get('fixed_root_transitions', {}))
def from_picklable_dict(cls, data): """ Reproduces an model that was converted to a picklable form using to_picklable_dict. """ from jazzparser.utils.nltk.storage import dict_to_object if data['backoff_model'] is not None: backoff_model = cls.from_picklable_dict(data['backoff_model']) else: backoff_model = None return cls(data['order'], dict_to_object(data['root_transition_counts']), dict_to_object(data['schema_transition_counts']), dict_to_object(data['emission_counts']), data['estimator'], backoff_model, data['schemata'], data['chord_vocab'], history=data.get('history', ''))
def from_picklable_dict(cls, data, name): """ Reproduces an model that was converted to a picklable form using to_picklable_dict. """ from jazzparser.utils.nltk.storage import dict_to_object return cls(dict_to_object(data['initial_key_dist']), dict_to_object(data['initial_chord_dist']), dict_to_object(data['key_transition_dist']), dict_to_object(data['chord_transition_dist']), dict_to_object(data['emission_dist']), dict_to_object(data['note_number_dist']), data['chord_vocab'], data['max_notes'], data['chord_corpus_mapping'], history=data.get('history', ''), description=data.get('description', ''), name=name)