R 埃尔曼网络
我不熟悉R和神经网络。所以我训练并预测了一个elman网络,就像这样:R 埃尔曼网络,r,R,我不熟悉R和神经网络。所以我训练并预测了一个elman网络,就像这样: require ( RSNNS ) mydata = read.csv("mydata.csv",header = TRUE) mydata.train = mydata[1000:7000,] mydata.test = mydata[800:999,] fit <- elman ( mydata.train[,2:19],mydata.train[,1], size =100 learnFuncPar
require ( RSNNS )
mydata = read.csv("mydata.csv",header = TRUE)
mydata.train = mydata[1000:7000,]
mydata.test = mydata[800:999,]
fit <- elman ( mydata.train[,2:19],mydata.train[,1], size =100
learnFuncParams =c (0.1) , maxit =1000)
pred <-predict (fit , mydata.test[,2:19])
require(RSNNS)
mydata=read.csv(“mydata.csv”,header=TRUE)
mydata.train=mydata[1000:7000,]
mydata.test=mydata[800:999,]
fit在调用elman
函数之前,调用
set.seed(0)
这将初始化随机数生成器,如果多次遵循相同的命令序列,将导致相同的结果。对于不同的初始化,请调用set.seed(1)
等
require ( RSNNS )
mydata <- read.csv("mydata.csv",header = TRUE)
mydata.train <- mydata[1000:7000,]
mydata.test <- mydata[800:999,]
set.seed(0)
fit <- elman ( mydata.train[,2:19], mydata.train[,1], size=100,
learnFuncParams=c(0.1) , maxit=1000 )
pred <- predict ( fit , mydata.test[,2:19] )
require(RSNNS)
mydata在调用elman
函数之前,调用
set.seed(0)
这将初始化随机数生成器,如果多次遵循相同的命令序列,将导致相同的结果。对于不同的初始化,请调用set.seed(1)
等
require ( RSNNS )
mydata <- read.csv("mydata.csv",header = TRUE)
mydata.train <- mydata[1000:7000,]
mydata.test <- mydata[800:999,]
set.seed(0)
fit <- elman ( mydata.train[,2:19], mydata.train[,1], size=100,
learnFuncParams=c(0.1) , maxit=1000 )
pred <- predict ( fit , mydata.test[,2:19] )
require(RSNNS)
mydataElman网络根据输入加上前一时间步中一组隐藏单元的状态预测输出。因此,在使用predict之前,网络的“记忆”与使用predict之后不同
这是一个“及时”重置网络内存的技巧,使用训练样本的输入预测(训练)目标
require ( RSNNS )
mydata = read.csv("mydata.csv",header = TRUE)
mydata.train = mydata[1000:7000,]
mydata.test = mydata[800:999,]
fit <- elman ( mydata.train[,2:19],mydata.train[,1], size =100
learnFuncParams =c (0.1) , maxit =1000)
pred_1 <-predict (fit , mydata.test[,2:19])
resetNet <- predict(fit, mydata.train[,2:19])
pred_2 <-predict (fit , mydata.test[,2:19])
require(RSNNS)
mydata=read.csv(“mydata.csv”,header=TRUE)
mydata.train=mydata[1000:7000,]
mydata.test=mydata[800:999,]
拟合埃尔曼网络根据输入加上前一时间步中一组隐藏单元的状态预测输出。因此,在使用predict之前,网络的“记忆”与使用predict之后不同
这是一个“及时”重置网络内存的技巧,使用训练样本的输入预测(训练)目标
require ( RSNNS )
mydata = read.csv("mydata.csv",header = TRUE)
mydata.train = mydata[1000:7000,]
mydata.test = mydata[800:999,]
fit <- elman ( mydata.train[,2:19],mydata.train[,1], size =100
learnFuncParams =c (0.1) , maxit =1000)
pred_1 <-predict (fit , mydata.test[,2:19])
resetNet <- predict(fit, mydata.train[,2:19])
pred_2 <-predict (fit , mydata.test[,2:19])
require(RSNNS)
mydata=read.csv(“mydata.csv”,header=TRUE)
mydata.train=mydata[1000:7000,]
mydata.test=mydata[800:999,]
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