Python - scikit_learn 中的克里金法(高斯过程) [英] Python - Kriging (Gaussian Process) in scikit_learn

问题描述

我正在考虑使用这种方法来插入我拥有的一些 3D 点.作为输入，我在一个区域的不同海拔处有一种气体的大气浓度.我拥有的数据显示为每隔几英尺垂直高程几十英尺的值，但水平相隔数百英尺(因此是紧密排列的值的列").

I am considering using this method to interpolate some 3D points I have. As an input I have atmospheric concentrations of a gas at various elevations over an area. The data I have appears as values every few feet of vertical elevation for several tens of feet, but horizontally separated by many hundreds of feet (so 'columns' of tightly packed values).

假设在任何给定时间点，垂直方向的值变化明显大于水平方向的值.

The assumption is that values vary in the vertical direction significantly more than in the horizontal direction at any given point in time.

我想在考虑到该假设的情况下执行 3D 克里金法(作为我可以调整的参数或统计定义的参数 - 要么/要么).

I want to perform 3D kriging with that assumption accounted for (as a parameter I can adjust or that is statistically defined - either/or).

我相信 scikit 学习模块可以做到这一点.如果可以，我的问题是如何创建离散单元格输出?也就是说，输出到尺寸为 50 x 50 x 1 英尺的 3D 数据网格中.理想情况下，我想要一个 [x_location, y_location, value] 的输出，并分离这些(或类似的)距离.

I believe the scikit learn module can do this. If it can, my question is how do I create a discrete cell output? That is, output into a 3D grid of data with dimensions of, say, 50 x 50 x 1 feet. Ideally, I would like an output of [x_location, y_location, value] with separation of those (or similar) distances.

不幸的是，我没有很多时间来玩它，所以我只是希望在深入研究它之前弄清楚这在 Python 中是否可行.谢谢！

Unfortunately I don't have a lot of time to play around with it, so I'm just hoping to figure out if this is possible in Python before delving into it. Thanks!

推荐答案

是的，您绝对可以在 scikit_learn 中做到这一点.

Yes, you can definitely do that in scikit_learn.

实际上，可以使用各向异性协方差核是克里金/高斯过程回归的一个基本特征.

In fact, it is a basic feature of kriging/Gaussian process regression that you can use anisotropic covariance kernels.

正如手册(下面引用)您可以自己设置协方差的参数或估计它们.您可以选择让所有参数相同或全部不同.

As it is precised in the manual (cited below) ou can either set the parameters of the covariance yourself or estimate them. And you can choose either having all parameters equal or all different.

theta0 : double array_like，可选形状为 (n_features, ) 或 (1, ) 的数组.中的参数自相关模型.如果还指定了 thetaL 和 thetaU，则 theta0被认为是最大似然的起点估计最佳参数集.默认假设为各向同性具有 theta0 = 1e-1 的自相关模型.

theta0 : double array_like, optional An array with shape (n_features, ) or (1, ). The parameters in the autocorrelation model. If thetaL and thetaU are also specified, theta0 is considered as the starting point for the maximum likelihood estimation of the best set of parameters. Default assumes isotropic autocorrelation model with theta0 = 1e-1.

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