Train a 2D GP model

GP_test2D.py

# Training data
sample_num = 25
lb, ub = np.array([-2, -1]), np.array([2, 3])
X_train = (ub-lb)*lhs(2, samples=sample_num) + lb
y_train = Test_2D(X_train).reshape(-1,1)

# Test data
X1 = np.linspace(-2, 2, 20)
X2 = np.linspace(-1, 3, 20)
X1, X2 = np.meshgrid(X1, X2)
X_test = np.hstack((X1.reshape(-1,1), X2.reshape(-1,1)))
y_test = Test_2D(X_test)

# GP model training
pipe = Pipeline([('scaler', MinMaxScaler()), 
         ('GP', GaussianProcess(n_restarts=10, optimizer='L-BFGS-B'))])
pipe.fit(X_train, y_train)

# GP model predicting
y_pred, y_pred_SSqr = pipe.predict(X_test)

# Accuracy score
pipe.score(X_test, y_test)