问题描述
我正在考虑使用此方法对我拥有的一些3D点进行插值。作为输入，我在一个区域中的各个高度具有大气浓度的气体。我的数据显示为垂直高度每隔几英尺延伸几十英尺，但水平分开数百英尺（所以列是紧密堆积的值）。
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).
假设是在任何给定时间点，垂直方向的值变化明显大于水平方向的变化。
The assumption is that values vary in the vertical direction significantly more than in the horizontal direction at any given point in time.
我想以此进行3D克里金法假设已考虑在内（作为我可以调整的参数，或者是统计定义的参数-或/或）。
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，我希望将[x_location，y_location，value]的输出与那些（或相似的）距离分开。
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，我没有很多时间一起玩，所以我只是希望在研究它之前先弄清楚在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，它是kriging /高斯过程回归的基本功能，可以使用各向异性协方差内核。
In fact, it is a basic feature of kriging/Gaussian process regression that you can use anisotropic covariance kernels.
（如下所述），您可以自己设置协方差的参数或进行估计。您可以选择使所有参数相等或全部不同。
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.
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