Hi all,
I'm running a monte carlo test of a neural network tool I've developed,
and it looks like it's going to take a very long time if I run it in R
so I'm interested in translating my code (included below) into something
faster like Fortran (which I'll have to learn from scratch). However, as
you'll see my code loads the nnet library and uses it quite a bit, and I
don't have a good sense of how this impacts the translation process;
will I have to translate all the code for the nnet library itself as well?
Any pointers would be greatly appreciated! Here's my code:
#This code replicates the simulation performed by Rouder et al (2005),
#which attempts to test the estimation of weibull distribution parameters
#from sample data. In this implementation, their HB estimation method is
#replaced by an iterative neural network approach.
library(nnet)
data.gen=function(iterations,min.sample.size,max.sample.size,min.shift,max.shift,min.scale,max.scale,min.shape,max.shape){
#set up some collection vectors
sample.size=vector(mode="numeric",length=iterations)
exp.shift=vector(mode="numeric",length=iterations)
exp.scale=vector(mode="numeric",length=iterations)
exp.shape=vector(mode="numeric",length=iterations)
for(i in 1:iterations){
#sample from the parameter space
sample.size[i]=round(runif(1,min.sample.size,max.sample.size),digits=0)
exp.shift[i]=runif(1,min.shift,max.shift)
exp.scale[i]=runif(1,min.scale,max.scale)
exp.shape[i]=runif(1,min.shape,max.shape)
#generate rt data and record summary stats
obs.rt=rweibull(sample.size[i],exp.shape[i],exp.scale[i])+exp.shift[i]
if(i==1){
obs.stats=summary(obs.rt)
}else{
obs.stats=rbind(obs.stats,summary(obs.rt))
}
}
row.names(obs.stats)=c(1:iterations)
obs.stats=as.data.frame(obs.stats)
obs=as.data.frame(cbind(obs.stats,sample.size,exp.shift,exp.scale,exp.shape))
names(obs)=c("min","q1","med","mean","q3","max","samples","exp.shift","exp.scale","exp.shape")
return(obs)
}
#set working directory
setwd("E:/Various Data/NNEst/NetWeibull/Rouder data")
stadler=read.table("bayest.par")
names(stadler)=c("exp.shift","exp.scale","exp.shape")
cell.size=20
sim.size=600
#first train initial neural nets
training.data=data.gen(1e4,cell.size,cell.size,.1,1,.1,1,1,4)
#train nn.shift with error checking
ok=F
while(ok==F){
nn1.shift=nnet(exp.shift~min+q1+med+mean+q3+max+samples,data=training.data,size=8,linout=T,rang=1e-08,maxit=500,trace=F)
cor.shift=predict(nn.shift,training.data[,c(1:7)],type="raw")
temp=hist(cor.shift,plot=F)
if(length(temp$counts[temp$counts>0])>10){
ok=T
}
}
#train nn.scale with error checking
ok=F
while(ok==F){
nn1.scale=nnet(exp.scale~min+q1+med+mean+q3+max+samples,data=training.data,size=8,linout=T,rang=1e-08,maxit=500,trace=F)
cor.scale=predict(nn.scale,training.data[,c(1:7)],type="raw")
temp=hist(cor.scale,plot=F)
if(length(temp$counts[temp$counts>0])>10){
ok=T
}
}
#train nn.shape with error checking
ok=F
while(ok==F){
nn1.shape=nnet(exp.shape~min+q1+med+mean+q3+max+samples,data=training.data,size=8,linout=T,rang=1e-08,maxit=500,trace=F)
cor.shape=predict(nn.shape,training.data[,c(1:7)],type="raw")
temp=hist(cor.shape,plot=F)
if(length(temp$counts[temp$counts>0])>10){
ok=T
}
}
#run simulation
obs.stats=matrix(0,80,7)
ind.shift.err=matrix(0,80,sim.size)
ind.scale.err=matrix(0,80,sim.size)
ind.shape.err=matrix(0,80,sim.size)
group.shift.err=vector(mode="numeric",length=sim.size)
group.scale.err=vector(mode="numeric",length=sim.size)
group.shape.err=vector(mode="numeric",length=sim.size)
for(i in 1:sim.size){
for(j in 1:80){
obs.stats[j,]=c(summary(rweibull(cell.size,stadler$exp.shape[j],stadler$exp.scale[j])+stadler$exp.shift[j]),cell.size)
}
obs.stats=as.data.frame(obs.stats)
names(obs.stats)=c("min","q1","med","mean","q3","max","samples")
#estimation iteration 1
cor.shift=predict(nn1.shift,obs.stats,type="raw")
cor.scale=predict(nn1.scale,obs.stats,type="raw")
cor.shape=predict(nn1.shape,obs.stats,type="raw")
min.obs.samples=min(obs.stats$samples)
max.obs.samples=max(obs.stats$samples)
min.shift=quantile(cor.shift,seq(0,1,.05))[2]
max.shift=quantile(cor.shift,seq(0,1,.05))[20]
min.scale=quantile(cor.scale,seq(0,1,.05))[2]
max.scale=quantile(cor.scale,seq(0,1,.05))[20]
min.shape=quantile(cor.shape,seq(0,1,.05))[2]
max.shape=quantile(cor.shape,seq(0,1,.05))[20]
#re-train nets to reduced parameter space
training.data=data.gen(1e4,min.obs.samples,max.obs.samples,min.shift,max.shift,min.scale,max.scale,min.shape,max.shape)
#train nn.shift with error checking
ok=F
while(ok==F){
nn2.shift=nnet(exp.shift~min+q1+med+mean+q3+max+samples,data=training.data,size=8,linout=T,rang=1e-08,maxit=500,trace=F)
cor.shift=predict(nn2.shift,training.data[,c(1:7)],type="raw")
temp=hist(cor.shift,plot=F)
if(length(temp$counts[temp$counts>0])>10){
ok=T
}
}
#train nn.scale with error checking
ok=F
while(ok==F){
nn2.scale=nnet(exp.scale~min+q1+med+mean+q3+max+samples,data=training.data,size=8,linout=T,rang=1e-08,maxit=500,trace=F)
cor.scale=predict(nn2.scale,training.data[,c(1:7)],type="raw")
temp=hist(cor.scale,plot=F)
if(length(temp$counts[temp$counts>0])>10){
ok=T
}
}
#train nn.shape with error checking
ok=F
while(ok==F){
nn2.shape=nnet(exp.shape~min+q1+med+mean+q3+max+samples,data=training.data,size=8,linout=T,rang=1e-08,maxit=500,trace=F)
cor.shape=predict(nn2.shape,training.data[,c(1:7)],type="raw")
temp=hist(cor.shape,plot=F)
if(length(temp$counts[temp$counts>0])>10){
ok=T
}
}
#estimation iteration 2
cor.shift=predict(nn2.shift,obs.stats,type="raw")
cor.scale=predict(nn2.scale,obs.stats,type="raw")
cor.shape=predict(nn2.shape,obs.stats,type="raw")
#record error
ind.shift.err[,i]=cor.shift-stadler$exp.shift
ind.scale.err[,i]=cor.scale-stadler$exp.scale
ind.shape.err[,i]=cor.shape-stadler$exp.shape
group.shift.err[i]=mean(cor.shift)-mean(stadler$exp.shift)
group.scale.err[i]=mean(cor.scale)-mean(stadler$exp.scale)
group.shape.err[i]=mean(cor.shape)-mean(stadler$exp.shape)
}
results=as.data.frame(rbind(cbind(sd(c(ind.shift.err[,1:162])),sd(c(ind.scale.err[,1:162])),sd(c(ind.shape.err[,1:162]))),cbind(sd(group.shift.err[1:162]),sd(group.scale.err[1:162]),sd(group.shape.err[1:162]))))
results
--
Mike Lawrence
http://arts.uwaterloo.ca/~m4lawren
"The road to wisdom? Well, it's plain and simple to express:
Err and err and err again, but less and less and less."
- Piet Hein
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