As nnet is done almost entirely in compiled C, you may well find that
already most of the computation is in a compiled language.
Please look at `Writing R Extensions' and profile your code to find the
bottlenecks.
While you are looking at that manual, please also consider the section on
tidying up your code to make it readable for others.
On Mon, 11 Sep 2006, Mike Lawrence wrote:
> 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
>
>
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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