Miron Kursa mbq

## bench-plot.R
library(ggplot2)

readRDS('ans-ps.RDS')->ss
do.call(rbind,lapply(
 split(ss,paste(ss$met,ss$k)),
 function(S){
  median(S$time[S$p==1])->bl
  S$speedup<-bl/S$time
  S
 })

## relevance-and-redundancy.R
setNames(
 do.call(expand.grid,rep(list(c(T,F)),5)),
 c("A","B","N1","N2","N3")
)->X
cbind(X,AoB=with(X,A|B),AnB=with(X,A&B),nA=!X$A)->X
factor(with(X,A!=B))->Y
data.frame(lapply(X,factor))->X

greedySVM<-function(X,Y,sel=names(X),err=Inf){
 #Empty selection is not interesting

## prf.R
precision<-function(y,p) sum(y&p)/sum(p)
recall<-function(y,p) sum(y&p)/sum(y)
Fscore<-function(y,p) 2/(1+sum(y|p)/sum(y&p))


precisionS<-function(Y,P) mean(P%in%Y)
recallS<-function(Y,P) mean(Y%in%P)
FscoreS<-function(Y,P) mean(c(Y,P)%in%intersect(P,Y))


## auroc.R
#AUROC calculation code
auroc<-function(score,cls){
 n1<-sum(!cls); sum(cls)->n2;
 U<-sum(rank(score)[!cls])-n1*(n1+1)/2;
 return(1-U/n1/n2);
}

#... as a cryptic one-liner
auroc1l<-function(score,cls)
 mean(rank(score)[cls]-1:sum(cls))/sum(!cls)

## nom2.brew.svg

      
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                mbq
                / nom2.brew.svg
            
            
              Last active
              May 15, 2016 23:43
            
              
                Code to reproduce https://mbq.me/blog/nomads/
              
          
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## genToken.R
cat(sprintf("::%s\n",paste(replicate(4,paste(sample(z,4,rep=TRUE),collapse="")),collapse=":")))

## smooth9.R
s9<-function(M,iters=1){
 M*0+9->n;
 n[,1]<-n[1,]<-n[,ncol(n)]<-n[nrow(n),]<-6;
 n[1,1]<-n[1,ncol(n)]<-n[nrow(n),1]<-n[nrow(n),ncol(n)]<-4;
 for(e in 1:iters){
  s<-M+
     cbind(M[,-1],0)+
     cbind(0,M[,-ncol(M)])+
     rbind(M[-1,],0)+
     rbind(0,M[-nrow(M),])+
	library(ggplot2)

	readRDS('ans-ps.RDS')->ss
	do.call(rbind,lapply(
	split(ss,paste(ss$met,ss$k)),
	function(S){
	median(S$time[S$p==1])->bl
	S$speedup<-bl/S$time
	S
	})
	setNames(
	do.call(expand.grid,rep(list(c(T,F)),5)),
	c("A","B","N1","N2","N3")
	)->X
	cbind(X,AoB=with(X,A\|B),AnB=with(X,A&B),nA=!X$A)->X
	factor(with(X,A!=B))->Y
	data.frame(lapply(X,factor))->X

	greedySVM<-function(X,Y,sel=names(X),err=Inf){
	#Empty selection is not interesting
	precision<-function(y,p) sum(y&p)/sum(p)
	recall<-function(y,p) sum(y&p)/sum(y)
	Fscore<-function(y,p) 2/(1+sum(y\|p)/sum(y&p))


	precisionS<-function(Y,P) mean(P%in%Y)
	recallS<-function(Y,P) mean(Y%in%P)
	FscoreS<-function(Y,P) mean(c(Y,P)%in%intersect(P,Y))
	#AUROC calculation code
	auroc<-function(score,cls){
	n1<-sum(!cls); sum(cls)->n2;
	U<-sum(rank(score)[!cls])-n1*(n1+1)/2;
	return(1-U/n1/n2);
	}

	#... as a cryptic one-liner
	auroc1l<-function(score,cls)
	mean(rank(score)[cls]-1:sum(cls))/sum(!cls)
	s9<-function(M,iters=1){
	M*0+9->n;
	n[,1]<-n[1,]<-n[,ncol(n)]<-n[nrow(n),]<-6;
	n[1,1]<-n[1,ncol(n)]<-n[nrow(n),1]<-n[nrow(n),ncol(n)]<-4;
	for(e in 1:iters){
	s<-M+
	cbind(M[,-1],0)+
	cbind(0,M[,-ncol(M)])+
	rbind(M[-1,],0)+
	rbind(0,M[-nrow(M),])+