def predict(predictor, examples, param):
"""
make prediction on examples using trained predictor
@param predictor: trained predictor
@type predictor: SVM object
@param examples: list of examples
@type examples: list
"""
#shogun data
feat = create_hashed_features_wdk(param.flags, examples)
#predict
svm_out = predictor.classify(feat).get_labels()
return svm_out
def train_splice_predictor(examples, labels, param):
"""
@param examples: list of strings
@param labels: list of integers {-1,1}
"""
##########################
# build classifier
##########################
feat_train = create_hashed_features_wdk(param.flags, examples)
lab = create_labels(labels)
svm = create_svm(param, feat_train, lab)
svm.train()
return svm
def init_predictor(examples, labels, param, w):
"""
@param examples: list of strings
@param labels: list of integers {-1,1}
@param w: weight vector of trained svm
"""
##########################
# build classifier
##########################
feat_train = create_hashed_features_wdk(param.flags, examples)
lab = create_labels(labels)
svm = create_svm(param, feat_train, lab)
svm.set_w(w)
return svm
