The Python code for On the Target-kernel Alignment: a Unified Analysis with Kernel Complexity is included in the submitted zip file. 
A brief code instructions are provided as follows.

In the file 'alpha.ipynb', we perform the experiment to investigate the impact of different kernels with varying alpha 
on the performance of KM and TKM. The experiment results are reported in Section 6 of the submitted manuscript.

The files 'demo_krr.ipynb', 'demo_logistic.ipynb', 'demo_quantile.ipynb', 'demo_svm.ipynb'  aim to investigate the
 influence of the sample size n  and the truncation level r under various learning tasks.  The experiment results are 
reported in Appendix G of the submitted manuscript. 

'function.py' contains all the necessary functions used in the previous five .ipynb files.


