如何解决将宏的结果存储到表中
我在 R 中找到了一个我想要迭代不同值的过程。
原始过程如下所示（完全在基 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,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,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})))
我想为参数“resSize”和“a”的不同值运行此过程。经过一些研究和大量的反复试验，我能够弄清楚如何为“resSize”的 3 个不同值和“a”的 3 个不同值循环上述过程 - 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),resSize)
W = matrix(runif(resSize*resSize,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,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[,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,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,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)],col='green' )
lines( c(Y),col='blue' )
title(main=expression(paste('Target and generated signals ',italic(n)==0 )))
legend('bottomleft',bty='n' )
dev.new()
matplot( t(X[(1:20),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})))
}
}
这会输出 9 种不同的结果。我想弄清楚如何制作这些结果的表格（3 列“水库大小”、“泄漏率”和“MSE”）。现在，我正在将结果从 R 复制并粘贴到 Microsoft Excel 中。
有人可以告诉我一个更简单的方法吗？
谢谢
更新：G Grothendieck 提供的答案，见下文：
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],bold(W)^{out})))
mseDF <- rbind(mseDF,data.frame(resSize,a,mse)) }
}
解决方法
1) 使用问题中修改后的代码，在第一个for之前插入这一行来初始化mseDF：
mseDF <- NULL
并在倒数第二个}之前插入这一行以向其追加一行：
mseDF <- rbind(mseDF,data.frame(resSize,a,mse))
代码完成后，mseDF 将是一个如下所示的数据框：
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
或者如果您更喜欢 mse 值的 2d 表，则像这样转换 mseDF：
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 g 的每一行以生成数据框结果。这里我们将使用一个简单版本的 mseFun 来说明：
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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