【deep learning学习笔记】注释yusugomori的LR代码 --- LogisticRegression.h 机器学习

【【deep learning学习笔记】注释yusugomori的LR代码 --- LogisticRegression.h】继续看yusugomori的代码，看逻辑回归。在DBN（Deep Blief Network）中，下面几层是RBM，最上层就是LR了。关于回归、二类回归、以及逻辑回归，资料就是前面转的几篇。套路就是设定目标函数（softmax损失函数），对参数求偏导数，得出权重更新公式等。
LogisticRegression.h注释如下：

class LogisticRegression { public: int N; // number of input samples int n_in; // number of input nodes int n_out; // number of output nodes double **W; // weights connecting the input nodes and the output nodes double *b; // bias of the output nodes // allocate memory and initialize the parameters LogisticRegression( int,// N int,// n_in int// n_out ); ~LogisticRegression(); public: // train the logistic regression model, update the value of W and b void train ( int*,// the input from input nodes in training set int*,// the output from output nodes in training set double // the learning rate ); // calculate the softmax for a input vector // dSoftMax = exp(d_i - Max) / sum_i( exp(d_i - Max) ) void softmax ( double* // the calculated softmax probabiltiy -- input & output ); // do prediction by calculating the softmax probability from input void predict ( int*,// the input from input nodes in testing set double* // the calculated softmax probability ); };

顺便提一句。从前RBM的那个注释，是在家用VS2008写的；现在这个，用CFree5.0，轻量级、编辑器操作贴心，赞一下！