R神经网络相关的R包

Posted 2020-08-01 算法少年Shawn的阴谋

　　本科毕业设计涉及用机器学习的方法训练预测模型，线性回归、SVM、RF等方法表现均不理想，于是需要用简单的神经网络方法做对比实验。在对NN的优化没有深入理解的情况下，直接调用了R包提供的接口，在此略作记录，供以后反思改进。

　　主要用到了nnet、neuralnet、h2o这几个包，具体的建模、预测、优化的方法在手册中均能查到。nnet、neuralnet提供的都是单隐藏层的简单神经网络，h2o提供了DNN的方法。

 1   library(nnet)
 2   data<-read.csv("tomcat_done_1.csv",header=T)
 3   
 4   total_size <-363
 5   test_size <- 90
 6   
 7   train=sample(1:dim(data)[1],total_size-test_size)
 8   
 9   train_set<- data[train,]
10   
11   test<-data[-train,1:35]
12   
13   test_effort<- data[-train,36]
14   
15   count <-0
16   
17   
18   m<-nnet(Effort~.,train_set,size=9,decay=0.015,maxit=10,linout=T,trace=F,MaxNWts=8000)
19   
20   preds <- predict(m,test)

 

 1   library(neuralnet)
 2   data<-read.csv("tomcat_done_2.csv",header=T)
 3   
 4   total_size <-363
 5   test_size <- 90
 6   
 7   train=sample(1:dim(data)[1],total_size-test_size)
 8   
 9   train_set<- data[train,]
10   
11   test<-data[-train,1:35]
12   
13   test_effort<- data[-train,36]
14   
15   count <-0
16   
17   
18   m<-neuralnet(Effort~CountDeclClass+CountDeclClassMethod+CountDeclClassVariable
19                +CountDeclFunction+CountDeclInstanceMethod+CountDeclInstanceVariable
20                +CountDeclMethod+CountDeclMethodDefault+CountDeclMethodPrivate
21                +CountDeclMethodProtected+CountDeclMethodPublic+CountLine
22                +CountLineBlank+CountLineCode+CountLineCodeDecl+CountLineCodeExe
23                +CountLineComment+CountSemicolon+CountStmt+CountStmtDecl+CountStmtExe
24                +SumCyclomatic+SumCyclomaticModified+SumCyclomaticStrict+SumEssential
25                +MaxCyclomatic+MaxCyclomaticModified+MaxCyclomaticStrict+MaxEssential
26                +MaxNesting+AvgCyclomatic+AvgCyclomaticModified+AvgCyclomaticStrict
27                +AvgEssential+RatioCommentToCode,data = train_set,hidden = 2)
28   
29   
30   
31   preds <- compute(m,test)

数据需要按照模型的格式要求进行预处理再输入，例如某些包要求label信息映射到[0,1]。多看手册以及原始论文了解优化方法，切记！