说明：  核主成分分析法，使用python实现。应对非线性数据，先使用核技巧映射高维使之线性可分，之后再用PCA方法将高维降到低维，理论上可从无穷维降到一维或二维，将数据变为线性可分。此程序中既包含了手工制作的KPCA全过程，也有直接从sklearn调用包直接实现。里面有详细的代码注释，核分块注释，可以截取自己需要的部分。直接套用的话，使用最前面一段代码替换数据即可
(Kernel principal component analysis is implemented by python. To deal with non-linear data, first use kernel technique to map the high dimension to make it linearly separable, then use PCA method to reduce the high dimension to the low dimension, theoretically from infinite dimension to one or two dimension, and change the data into linearly separable. This program not only includes the whole process of KPCA which is made by hand, but also directly realized by calling package from sklearn. There are detailed code comments, core block comments, which can intercept the parts you need. If it is applied directly, replace the data with the first code)