one dimensional testing

GP_test1D.py

def Test_1D(X):
    """1D Test Function"""
    
    y = (X*6-2)**2*np.sin(X*12-4)
    
    return y

# Training data
X_train = np.array([0.0, 0.1, 0.2, 0.4, 0.5, 0.6, 0.8, 1], ndmin=2).T
y_train = Test_1D(X_train)

# Testing data
X_test = np.linspace(0.0, 1, 100).reshape(-1,1)
y_test = Test_1D(X_test)

# GP model training
GP = GaussianProcess(n_restarts=10, optimizer='L-BFGS-B')
GP.fit(X_train, y_train)

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