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)))