#R語言實現神經網絡
library(neuralnet)
library(caret)
library(MASS)
library(vcd)
data(shuttle)
str(shuttle)#因變量use;
table1<-structable(wind+magn~use,shuttle)
mosaic(table1,shading=T)
mosaic(use~error+vis,shuttle)
prop.table(table(shuttle$use,shuttle$stability))#用來計算兩個變量 shuttle$use 和 shuttle$stability 之間頻率表的函數
#數據準備
#虛擬變量
dummies<-dummyVars(use~.,shuttle,fullRank=T)
#虛擬變量預測現有數據
shuttle.2=as.data.frame(predict(dummies,newdata=shuttle))
names(shuttle.2)
shuttle.2$use<-ifelse(shuttle$use=="auto",1,0)
#拆分測試集和訓練集
set.seed(123)
trainIndex<-createDataPartition(shuttle.2$use,p=0.7,list=F)
shuttleTrain<-shuttle.2[trainIndex,]
shuttleTest<-shuttle.2[-trainIndex,]
#模型構建
n<-names(shuttleTrain)
form<-as.formula(paste("use~",paste(n[!n %in% "use"],collapse = "+")))
fit<-neuralnet(form,data=shuttleTrain,hidden = c(3, 3),#第一個隱藏層有3個神經元，第二個隱藏層有3個神經元err.fct = "ce",#默認sse，因為此處是二值結果，所以選擇celinear.output = F,#是否忽略act.fctlikelihood=TRUE)
fit$result.matrix
plot(fit)
par(mfrow=c(1,2))
gwplot(fit,selected.covariate="magn.Out")
gwplot(fit,selected.covariate="wind.tail")
#預測
resultsTrain<-compute(fit,shuttleTrain[,1:10])
predTrain<-resultsTrain$net.result
predTrain<-ifelse(predTrain>=0.5,1,0)
table(predTrain,shuttleTrain$use)
#在測試集上的結果
resultsTest <- compute(fit, shuttleTest[,1:10])
predTest <- resultsTest$net.result
predTest <- ifelse(predTest >= 0.5, 1, 0)
table(predTest, shuttleTest$use)
which(predTest == 0 & shuttleTest$use ==1)

?

?

參考文獻：《精通機器學習-基于R（第二版）》