def _liblinear_train(self, lbls, vecs):
if MODEL_SEARCH:
# XXX: Will give BOTH c and model...
raise NotImplementedError
else:
model_type = 0
# Find C
if C_SEARCH:
c = _c_search(lbls, vecs, model_type=model_type)
if C_VERBOSE:
print >> stderr, 'Found C:', c
else:
c = 1
# Train the model
from linearutil import (train as liblinear_train,
problem as liblinear_problem,
parameter as liblinear_parameter)
self.model = liblinear_train(liblinear_problem(lbls, vecs),
liblinear_parameter(
'-q -s {0} -c {1}'.format(model_type, 2 ** c)))
def _liblinear_train(lbls, vecs, model_type, c):
from linearutil import (train as liblinear_train,
problem as liblinear_problem, parameter as liblinear_parameter)
return liblinear_train(liblinear_problem(lbls, vecs),
liblinear_parameter('-q -s {0} -c {1}'.format(model_type, c)))