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Rubens Zimbres -‐ 2016
closeAllConnections() rm(list=ls()) setwd("/Volumes/16 DOS/R_nbs") train<-‐read.csv("mnist_train2.csv",sep=",",header=FALSE) test<-‐read.csv("mnist_test2.csv",sep=",",header=FALSE) t<-‐1 p<-‐c() r<-‐c() while(t<51){ zz<-‐for(i in 1:2000){p[[i]]<-‐sum(abs(t(test)[2:785,t]-‐t(train)[2:785,i]))} r[[t]]<-‐p t<-‐t+1} o<-‐lapply(r,function(x) which(x==min(x))) op<-‐as.numeric(o) l<-‐c() for(j in 1:2000){l[[j]]<-‐t(test)[1,][[j]]-‐t(train)[1,op[[j]]]} #GRAPHICS part<-‐op[[19]] a<-‐t(train) b<-‐a[2:785,part] c<-‐matrix(b,nrow=28,ncol=28) rotate <-‐ function(c) t(apply(c, 2, rev)) d<-‐rotate(rotate(rotate(c))) e<-‐apply(d, -‐2, rev) f<-‐rotate(rotate(as.matrix(rev(as.data.frame(e))))) a2<-‐t(test) b2<-‐a2[2:785,19] c2<-‐matrix(b2,nrow=28,ncol=28) rotate <-‐ function(c2) t(apply(c2, 2, rev)) d2<-‐rotate(rotate(rotate(c2))) e2<-‐apply(d2, -‐2, rev) f2<-‐rotate(rotate(as.matrix(rev(as.data.frame(e2))))) part3<-‐op[[13]] a3<-‐t(train) b3<-‐a3[2:785,part3] c3<-‐matrix(b3,nrow=28,ncol=28) rotate3 <-‐ function(c3) t(apply(c3, 2, rev)) d3<-‐rotate3(rotate3(rotate3(c3))) e3<-‐apply(d3, -‐2, rev) f3<-‐rotate3(rotate3(as.matrix(rev(as.data.frame(e3))))) a4<-‐t(test) b4<-‐a4[2:785,13]
Rubens Zimbres -‐ 2016
c4<-‐matrix(b4,nrow=28,ncol=28) rotate <-‐ function(c4) t(apply(c4, 2, rev)) d4<-‐rotate(rotate(rotate(c4))) e4<-‐apply(d4, -‐2, rev) f4<-‐rotate(rotate(as.matrix(rev(as.data.frame(e4))))) library(lattice) attach(mtcars) par(mfrow=c(2,2)) image(f,col=c("yellow","blue"),main="MNIST Task Train")+grid(lty=1,col="black",nx=14,ny=14) image(f2,col=c("yellow","blue"),main="MNIST Task Test")+grid(lty=1,col="black",nx=14,ny=14) image(f3,col=c("yellow","blue"),main="MNIST Task Train")+grid(lty=1,col="black",nx=14,ny=14) image(f4,col=c("yellow","blue"),main="MNIST Task Test")+grid(lty=1,col="black",nx=14,ny=14) acc<-‐length(which(l==0))/length(l) print(c("ACCURACY=",acc)) table(t(test)[1,1:50],t(train)[1,op])
