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(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 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):
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 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):
"""
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)
