y_predict = predict_with_last_action(clf, X_test, onehot) clf = LinearCRF(feature_names=feature_names, label_names=labels, addone=True, regularization=None, lmbd=0.01, sigma=100, transition_weighting=False) # a single chain: clf.fit(X_train, y_train, X_test, y_test) y_predict = clf.predict(X_test) # one chain per person clf.batch_fit(X_train_pers, y_train_pers, X_test_pers, y_test_pers) y_predict = np.concatenate(clf.batch_predict(X_test_pers)) clf = LinearCRF(sigma=10) X_train_svm, X_test_svm = SVM_feature_extraction(X_train, y_train, X_test) clf = LinearCRF(addone=True, sigma=100) clf.fit(X_train, y_train, X_test, y_test) clf.fit(X_train_svm, y_train, X_test_svm, y_test) y_predict = clf.predict(X_test_svm) clf.fit(X_train, y_train) print "predicting test data" y_predict = clf.predict(X_test)