def __init__(self, *args, **_):
GeneralizedPairState.__init__(self, *args)
self.dp = config.preparers[config.preparer_index][0](constants.window_size)
self.clf_fname = 'data/clf/{}{}{}.clf'.format(
PairClassifier.get_name(),
config.preparers[config.preparer_index][2],
constants.window_size,
)
self.clf = self._get_classifier()
self.annotations, self.ann_x, self.ann_y = None, None, None
self.emission_table = None
def __init__(self, *args, **_):
GeneralizedPairState.__init__(self, *args)
self.dp = config.preparers[config.preparer_index][0](
constants.window_size)
self.clf_fname = 'data/clf/{}{}{}.clf'.format(
PairClassifier.get_name(),
config.preparers[config.preparer_index][2],
constants.window_size,
)
self.clf = self._get_classifier()
self.annotations, self.ann_x, self.ann_y = None, None, None
self.emission_table = None
def __init__(self, *args, **kwargs):
ClassifierState.__init__(self, *args, **kwargs)
self.dp = self._get_preparer(0)
if config.same_classifier:
suffix = ''
else:
suffix = '_indel'
self.clf_fname = 'data/clf/{}{}{}{}.clf'.format(
PairClassifier.get_name(),
config.preparers[config.preparer_index][2],
constants.window_size,
suffix,
)
self.clf = self._get_classifier()
def __init__(self, *args, **kwargs):
ClassifierState.__init__(self, *args, **kwargs)
self.dp = self._get_preparer(0)
if config.same_classifier:
suffix = ''
else:
suffix = '_indel'
self.clf_fname = 'data/clf/{}{}{}{}.clf'.format(
PairClassifier.get_name(),
config.preparers[config.preparer_index][2],
constants.window_size,
suffix,
)
self.clf = self._get_classifier()
data = (
(
seq_x, pos, create_annotation(ann_x),
seq_y, pos, create_annotation(ann_y),
)
for seq_x in itertools.product('ACGT', repeat=window_size)
for seq_y in itertools.product('ACGT', repeat=window_size)
for ann_x in itertools.product('01', repeat=window_size)
for ann_y in itertools.product('01', repeat=window_size)
)
else:
data = (
(
seq_x, pos, create_annotation(ann_x),
seq_y, pos, create_annotation(ann_y),
)
for seq_x in 'ACGT'
for seq_y in 'ACGT'
for ann_x in '01'
for ann_y in '01'
)
s = sum(clf.multi_prepare_predict(data))
return s
if __name__ == '__main__':
dp = DataPreparer(window_size)
clf = PairClassifier(dp, filename='data/randomforest1.clf')
print('Normalization constant:', compute_norm_constant(clf))
def _get_classifier(self):
return PairClassifier(self.dp,
filename=self.clf_fname,
inverted=config.same_classifier,
use_global_classifier=config.same_classifier)
