我在R中找到了一个要迭代以获取不同值的过程。
原始过程如下所示（完全在base R中运行）：
# A minimalistic Echo State Networks demo with Mackey-Glass (delay 17) data
# in "plain" R.
# by Mantas Lukosevicius 2012-2018
# http://mantas.info
myfile <- read.table(url("https://mantas.info/wp/wp-content/uploads/simple_esn/MackeyGlass_t17.txt"))
# load the data
trainLen = 2000
testLen = 2000
initLen = 100
data = as.matrix(myfile)
# plot some of it
while( dev.cur() != 1 ) dev.off() # close all previous plots
dev.new()
plot(data[1:1000],type='l')
title(main='A sample of data')
# generate the ESN reservoir
inSize = outSize = 1
resSize = 1000
a = 0.3 # leaking rate
set.seed(42)
Win = matrix(runif(resSize*(1+inSize),-0.5,0.5),resSize)
W = matrix(runif(resSize*resSize,-0.5,0.5),resSize)
# normalizing and setting spectral radius
cat('Computing spectral radius...')
rhoW = abs(eigen(W,only.values=TRUE)$values[1])
print('done.')
W = W * 1.25 / rhoW
# allocated memory for the design (collected states) matrix
X = matrix(0,1+inSize+resSize,trainLen-initLen)
# set the corresponding target matrix directly
Yt = matrix(data[(initLen+2):(trainLen+1)],1)
# run the reservoir with the data and collect X
x = rep(0,resSize)
for (t in 1:trainLen){
u = data[t]
x = (1-a)*x + a*tanh( Win %*% rbind(1,u) + W %*% x )
if (t > initLen)
X[,t-initLen] = rbind(1,u,x)
}
# train the output
reg = 1e-8 # regularization coefficient
X_T = t(X)
Wout = Yt %*% X_T %*% solve( X %*% X_T + reg*diag(1+inSize+resSize) )
# run the trained ESN in a generative mode. no need to initialize here,
# because x is initialized with training data and we continue from there.
Y = matrix(0,outSize,testLen)
u = data[trainLen+1]
for (t in 1:testLen){
x = (1-a)*x + a*tanh( Win %*% rbind(1,u) + W %*% x )
y = Wout %*% rbind(1,u,x)
Y[,t] = y
# generative mode:
u = y
# this would be a predictive mode:
#u = data[trainLen+t+1]
}
# compute MSE for the first errorLen time steps
errorLen = 500
mse = ( sum( (data[(trainLen+2):(trainLen+errorLen+1)] - Y[1,1:errorLen])^2 )
/ errorLen )
print( paste( 'MSE = ', mse ) )
# plot some signals
dev.new()
plot( data[(trainLen+1):(trainLen+testLen+1)], type='l', col='green' )
lines( c(Y), col='blue' )
title(main=expression(paste('Target and generated signals ', bold(y)(italic(n)),
' starting at ', italic(n)==0 )))
legend('bottomleft',legend=c('Target signal', 'Free-running predicted signal'),
col=c('green','blue'), lty=1, bty='n' )
dev.new()
matplot( t(X[(1:20),(1:200)]), type='l' )
title(main=expression(paste('Some reservoir activations ', bold(x)(italic(n)))))
dev.new()
barplot( Wout )
title(main=expression(paste('Output weights ', bold(W)^{out})))
我想针对参数“ resSize”和“ a”的不同值运行此过程。经过一些研究和大量的试验和错误，我能够弄清楚如何针对3个不同的“ resSize”值和3个不同的“ a”值循环上述过程-总共9个值。见下文：
myfile <- read.table(url("https://mantas.info/wp/wp-content/uploads/simple_esn/MackeyGlass_t17.txt"))
# load the data
trainLen = 2000
testLen = 2000
initLen = 100
data = as.matrix(myfile)
# plot some of it
while( dev.cur() != 1 ) dev.off() # close all previous plots
dev.new()
plot(data[1:1000],type='l')
title(main='A sample of data')
# LOOP generate the ESN reservoir, different reservoir sizes and leakage rates
inSize = outSize = 1
for (resSize in c(100,500,1000)) {
for (a in c(0.3, 0.5, 0.7)) {
### resSize = 1000
### a = 0.3 # leaking rate
set.seed(42)
Win = matrix(runif(resSize*(1+inSize),-0.5,0.5),resSize)
W = matrix(runif(resSize*resSize,-0.5,0.5),resSize)
# normalizing and setting spectral radius
cat('Computing spectral radius...')
rhoW = abs(eigen(W,only.values=TRUE)$values[1])
print('done.')
W = W * 1.25 / rhoW
# allocated memory for the design (collected states) matrix
X = matrix(0,1+inSize+resSize,trainLen-initLen)
# set the corresponding target matrix directly
Yt = matrix(data[(initLen+2):(trainLen+1)],1)
# run the reservoir with the data and collect X
x = rep(0,resSize)
for (t in 1:trainLen){
u = data[t]
x = (1-a)*x + a*tanh( Win %*% rbind(1,u) + W %*% x )
if (t > initLen)
X[,t-initLen] = rbind(1,u,x)
}
# train the output
reg = 1e-8 # regularization coefficient
X_T = t(X)
Wout = Yt %*% X_T %*% solve( X %*% X_T + reg*diag(1+inSize+resSize) )
# run the trained ESN in a generative mode. no need to initialize here,
# because x is initialized with training data and we continue from there.
Y = matrix(0,outSize,testLen)
u = data[trainLen+1]
for (t in 1:testLen) {
x = (1-a)*x + a*tanh( Win %*% rbind(1,u) + W %*% x )
y = Wout %*% rbind(1,u,x)
Y[,t] = y
# generative mode:
u = y
# this would be a predictive mode:
#u = data[trainLen+t+1]
}
# compute MSE for the first errorLen time steps
errorLen = 500
mse = ( sum( (data[(trainLen+2):(trainLen+errorLen+1)] - Y[1,1:errorLen])^2 )
/ errorLen )
print( paste( 'Reservoir Size =',resSize))
print( paste( 'Leakage Rate =',a))
print( paste( 'MSE = ', mse ) )
# plot some signals
dev.new()
plot( data[(trainLen+1):(trainLen+testLen+1)], type='l', col='green' )
lines( c(Y), col='blue' )
title(main=expression(paste('Target and generated signals ', bold(y)(italic(n)),
' starting at ', italic(n)==0 )))
legend('bottomleft',legend=c('Target signal', 'Free-running predicted signal'),
col=c('green','blue'), lty=1, bty='n' )
dev.new()
matplot( t(X[(1:20),(1:200)]), type='l' )
title(main=expression(paste('Some reservoir activations ', bold(x)(italic(n)))))
dev.new()
barplot( Wout )
title(main=expression(paste('Output weights ', bold(W)^{out})))
}
}
这将输出9个不同的结果。我试图弄清楚如何制作这些结果的表格（3列“储层大小”，“泄漏率”和“ MSE”）。现在，我正在将R中的结果复制并粘贴到Microsoft Excel中。
有人可以告诉我一种更简单的方法吗？
谢谢
更新：G Grothendieck提供的答案，如下所示：
myfile <- read.table(url("https://mantas.info/wp/wp-content/uploads/simple_esn/MackeyGlass_t17.txt"))
mseDF <- NULL
# load the data
trainLen = 2000
testLen = 2000
initLen = 100
data = as.matrix(myfile)
# plot some of it
while( dev.cur() != 1 ) dev.off() # close all previous plots
dev.new()
plot(data[1:1000],type='l')
title(main='A sample of data')
# LOOP generate the ESN reservoir, different reservoir sizes and leakage rates
inSize = outSize = 1
for (resSize in c(100,500,1000)) {
for (a in c(0.3, 0.5, 0.7)) {
### resSize = 1000
### a = 0.3 # leaking rate
set.seed(42)
Win = matrix(runif(resSize*(1+inSize),-0.5,0.5),resSize)
W = matrix(runif(resSize*resSize,-0.5,0.5),resSize)
# normalizing and setting spectral radius
cat('Computing spectral radius...')
rhoW = abs(eigen(W,only.values=TRUE)$values[1])
print('done.')
W = W * 1.25 / rhoW
# allocated memory for the design (collected states) matrix
X = matrix(0,1+inSize+resSize,trainLen-initLen)
# set the corresponding target matrix directly
Yt = matrix(data[(initLen+2):(trainLen+1)],1)
# run the reservoir with the data and collect X
x = rep(0,resSize)
for (t in 1:trainLen){
u = data[t]
x = (1-a)*x + a*tanh( Win %*% rbind(1,u) + W %*% x )
if (t > initLen)
X[,t-initLen] = rbind(1,u,x)
}
# train the output
reg = 1e-8 # regularization coefficient
X_T = t(X)
Wout = Yt %*% X_T %*% solve( X %*% X_T + reg*diag(1+inSize+resSize) )
# run the trained ESN in a generative mode. no need to initialize here,
# because x is initialized with training data and we continue from there.
Y = matrix(0,outSize,testLen)
u = data[trainLen+1]
for (t in 1:testLen) {
x = (1-a)*x + a*tanh( Win %*% rbind(1,u) + W %*% x )
y = Wout %*% rbind(1,u,x)
Y[,t] = y
# generative mode:
u = y
# this would be a predictive mode:
#u = data[trainLen+t+1]
}
# compute MSE for the first errorLen time steps
errorLen = 500
mse = ( sum( (data[(trainLen+2):(trainLen+errorLen+1)] - Y[1,1:errorLen])^2 )
/ errorLen )
print( paste( 'Reservoir Size =',resSize))
print( paste( 'Leakage Rate =',a))
print( paste( 'MSE = ', mse ) )
# plot some signals
dev.new()
plot( data[(trainLen+1):(trainLen+testLen+1)], type='l', col='green' )
lines( c(Y), col='blue' )
title(main=expression(paste('Target and generated signals ', bold(y)(italic(n)),
' starting at ', italic(n)==0 )))
legend('bottomleft',legend=c('Target signal', 'Free-running predicted signal'),
col=c('green','blue'), lty=1, bty='n' )
dev.new()
matplot( t(X[(1:20),(1:200)]), type='l' )
title(main=expression(paste('Some reservoir activations ', bold(x)(italic(n)))))
dev.new()
barplot( Wout )
title(main=expression(paste('Output weights ', bold(W)^{out})))
mseDF <- rbind(mseDF, data.frame(resSize, a, mse)) }
}
1）在问题中使用修改后的代码，在第一次for插入此行之前进行初始化mseDF：
mseDF <- NULL
并在倒数第二个}之前插入此行以向其添加一行：
mseDF <- rbind(mseDF, data.frame(resSize, a, mse))
代码完成mseDF后将是一个数据帧，如下所示：
mseDF
## resSize a mse
## 1 100 0.3 1.652439e-02
## 2 100 0.5 Inf
## 3 100 0.7 1.237748e+05
## 4 500 0.3 2.955434e-06
## 5 500 0.5 1.083321e-05
## 6 500 0.7 1.446731e-05
## 7 1000 0.3 1.820381e-06
## 8 1000 0.5 4.680945e-06
## 9 1000 0.7 1.299191e-04
或者，如果您希望使用二维表的mse值，则可以mseDF像这样进行转换：
xtabs(mse ~., mseDF)
## a
## resSize 0.3 0.5 0.7
## 100 1.652439e-02 Inf 1.237748e+05
## 500 2.955434e-06 1.083321e-05 1.446731e-05
## 1000 1.820381e-06 4.680945e-06 1.299191e-04
2）另一种方法是创建一个mseFun函数，该函数计算每个输入的一个值的mse，然后将其应用于网格的每一行g以产生数据帧结果。在这里，我们将使用mseFunJust的简单版本进行说明：
mseFun <- function(resSize, a) {
a + resSize # replace this with your calculation of mse
}
g <- expand.grid(resSize = c(100, 500, 1000), a = c(0.3, 0.5, 0.7)) # 9x2
transform(g, mse = mapply(mseFun, resSize, a))
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