GSGBN:通过变量分组学习稀疏高斯贝叶斯网络结构-源码

The tool kit contains GSGBN's software for ICDM14 paper *"Learning Sparse Gaussian Bayesian Networks by Variable Grouping"*. #INSTALL Exract the package, then use make to compile the source code. - $ git clone https://github.com/ch11y/GSGBN/ - $ cd GSGBN - $ make Find the excutive file in ./bin/. #RUN ##Usage: GSGBN -i input.txt -l1 1.0 -l2 0.1 -num 10 -thr 0.1 ##Options: -i, an m * n matrix with m samples and n variables; -l1, the regularization parameter lambda1 for sparsity; -l2, the regularization parameter lambda2*n is used for grouping; -num, number of rounds for enumerating DAGs; -thr, threshold for filtering ##*Notes* ***The package does not contain the process of cross validation.*** ##Contact: Jie Yang (Email: jyang2@cs.hku.hk), Department of Computer Science, The University of Hong Kong. #Reference Jie Yang, Henry C.M. Leung, S.M. Yiu, Yunpeng Cai, Francis Y.L. Chin, *Learning Sparse Gaussian Bayesian Networks by Variable Grouping*, The IEEE International Conference on Data Mining (ICDM 2014).