问题描述
我正在尝试将kriging（高斯过程）回归与常数项和广义指数相关模型一起使用。我可以使用函数（版本0.17.1）。当我使用它时，我会得到一条警告：
I am trying to use kriging(gaussian process) regression with a constant term and generalized exponential correlation model. I was able to do this in older version with GaussianProcess function (version 0.17.1). When I use it, I get a warning saying
但是，我找不到。我想知道我是否可以对GaussianProcessRegressor进行同样的操作，或者scikit-learn不再打算支持此功能。
However, I can't find similar options with GaussianProcessRegressor. I wonder if I can do the same with GaussianProcessRegressor or scikit-learn doesn't plan to support this functionality anymore.
推荐答案
GaussianProcess函数（版本0.17.1）基于DACE程序包，并提供两个选项：趋势项和相关项。
GaussianProcess function (version 0.17.1) is based on DACE package and provides two options: trend term and correlation term.
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克里格的趋势项可以是设置为常数或多项式，例如线性，二次方。 GaussianProcessRegressor假定一个恒定的趋势项，它是训练数据的平均值。它不支持带有趋势的克里金法。但是，这不是主要问题，因为文献表明复杂的趋势项可能无济于事。例如，请参阅（Chen，2016）。
Trend term in Kriging can be set to constant or polynomials such as linear, quadratic. GaussianProcessRegressor assumes a constant trend term which is mean of the training data. It does not support Kriging with trend. However, this is not a major issue because literature suggests that having complicated trend term might not help much. See (Chen, 2016) for example.
GaussianProcess中的相关项类似于GaussianProcessRegressor中的相关内核，后者更灵活。 RBF内核与平方指数内核相似，但形式略有不同。 （而不是用exp（-theta *（distance）^ 2 / length ^ 2代替exp（-theta *（distance）^ 2））。
Correlation term in GaussianProcess is similar to correlation kernels in GaussianProcessRegressor, latter being more flexible. RBF kernel is similar to squared exponential kernel in a slightly different form. (Instead of exp(-theta * (distance)^2) is replaced by exp(-theta*(distance)^2/length^2).
最后，GaussianProcess的过程方差是通过解析表达式获得的，而GaussianProcessRegressor中的过程方差是通过最大化似然估计器获得的。
Finally, process variance in GaussianProcess is obtained by an analytical expression whereas in GaussianProcessRegressor it is obtained by maximizing likelihood estimator.
参考文献：
SN Lophaven，HB Nielsen，J.Sondergaard，DACE-A MATLAB Kriging工具箱，
2.0版，技术代表，2002a。
S. N. Lophaven, H. B. Nielsen, J. Sondergaard, DACE-A MATLAB Kriging toolbox,version 2.0, Tech. Rep., 2002a.
H 。Chen，JL Loeppky，J。Sacks，WJ Welch等人，《计算机实验分析方法：如何评估和计数？》，统计科学31（1）（2016）40-60。
H. Chen, J. L. Loeppky, J. Sacks, W. J. Welch, et al., Analysis Methods for Computer Experiments: How to Assess and What Counts?, Statistical Science 31 (1) (2016) 40-60.
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