相关疑难解决方法(0)

这是一个简单的代码工作实现,我在Python的scikit-learn中使用高斯过程回归(GPR),使用二维输入(即网格结束x1和x2)和1维输出(y).

import numpy as np
from matplotlib import pyplot as plt 
from sklearn.gaussian_process import GaussianProcessRegressor 
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
from mpl_toolkits.mplot3d import Axes3D

#  Example independent variable (observations)
X = np.array([[0.,0.], [1.,0.], [2.,0.], [3.,0.], [4.,0.], 
                [5.,0.], [6.,0.], [7.,0.], [8.,0.], [9.,0.], [10.,0.], 
                [11.,0.], [12.,0.], [13.,0.], [14.,0.],
                [0.,1.], [1.,1.], [2.,1.], [3.,1.], [4.,1.], 
                [5.,1.], [6.,1.], [7.,1.], [8.,1.], [9.,1.], [10.,1.], 
                [11.,1.], [12.,1.], [13.,1.], [14.,1.],
                [0.,2.], [1.,2.], [2.,2.], [3.,2.], [4.,2.], 
                [5.,2.], [6.,2.], [7.,2.], [8.,2.], [9.,2.], [10.,2.], …