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)
