Boost to tackle nonlinearity

shiyiming

From oloolo's blog on SasProgramming


<p><a href="http://feedads.g.doubleclick.net/~a/vxjN9NmrYX5QuayULzfxI_qWna8/0/da"><img src="http://feedads.g.doubleclick.net/~a/vxjN9NmrYX5QuayULzfxI_qWna8/0/di" border="0" ismap="true"></img></a><br/>
<a href="http://feedads.g.doubleclick.net/~a/vxjN9NmrYX5QuayULzfxI_qWna8/1/da"><img src="http://feedads.g.doubleclick.net/~a/vxjN9NmrYX5QuayULzfxI_qWna8/1/di" border="0" ismap="true"></img></a></p><pre style="background-color: #ebebeb; border-bottom: #999999 1px dashed; border-left: #999999 1px dashed; border-right: #999999 1px dashed; border-top: #999999 1px dashed; color: #000001; font-family: Andale Mono, Lucida Console, Monaco, fixed, monospace; font-size: 12px; line-height: 14px; overflow: auto; padding-bottom: 5px; padding-left: 5px; padding-right: 5px; padding-top: 5px; width: 100%;"><code>
data nonlinear;
     do x=1 to 627;
     p=(sin(x/100)+1)*0.45;
     do j=1 to 100;
     x1=x+(j-1)/100;
     if ranuni(8655645)&lt;=p then y=1; else y=0;
     output; drop p j;
  end;
  end;
run;

proc rank data=nonlinear  out=nonlinearrank groups=20;
     var x1;
  ranks rank1;
run;

proc means data=nonlinearrank noprint;
     class rank1;
  var y x1;
  output out=_mean(where=(_type_=1))  mean(y)=y  mean(x1)=x1;
run;

%inc "C:\Documents and Settings\lxie\Desktop\SAS Prog and Docs\Boost macro2 ver3.0.sas";
%inc "C:\Documents and Settings\lxie\Desktop\SAS Prog and Docs\predict macro.sas";

%boost2(nonlinear, 1, outputds,  outwts, 100, 3);

%macro pred(niter);
%do i=1 %to &amp;niter;
  %predict(nonlinear, 1, outputds, outlogds, out_pred, sumpred&amp;i, 3, &amp;i);
%end;
data sumpred_all;
     merge %do i=1 %to &amp;niter;
            sumpred&amp;i.(rename=(sum_pred=sum_pred&amp;i))
     %end;;
run;
%mend;

%pred(100);

proc datasets library=work nolist;
     delete sumpred1-sumpred100;
quit;

options nosource;
proc export data=sumpred_all  outfile="c:\sumpred.csv"  dbms=csv replace; run;
options source;
</code></pre><br />
In R, use this code piece to recover the animation:<br />
<pre style="background-color: #ebebeb; border-bottom: #999999 1px dashed; border-left: #999999 1px dashed; border-right: #999999 1px dashed; border-top: #999999 1px dashed; color: #000001; font-family: Andale Mono, Lucida Console, Monaco, fixed, monospace; font-size: 12px; line-height: 14px; overflow: auto; padding-bottom: 5px; padding-left: 5px; padding-right: 5px; padding-top: 5px; width: 100%;"><code>
library(caTools);
test&lt;-read.csv('c:/sumpred.csv', header=T)
minmax&lt;-quantile(as.matrix(test), c(0,1))
sz=314; p=100;
y&lt;-sort(sample(1:62700, size=sz))
image=array(0, c(sz, p, 100))
for (i in (1:100)){
  pic=matrix(0, ncol=p, nrow=sz)
  trace=round((test[y,i]-minmax[1])/(minmax[2]-minmax[1])*p);
  for(j in (1:sz)){
     image[j, trace[j], i]=1
  }
}
write.gif(image, "c:/boost.gif", col=gray(1:2/2))
im = read.gif("c:/boost.gif")
for(i in 1:100){
   image(im$image[,,i], col=(im$col),
         main=paste('Iter', i), y=1:100, x=1:314,
         xlab="Index", ylab="Percentage of 1")
}

test2&lt;-test[,-1]-test[-100]
test2&lt;-cbind(test[,1], test2)

par(mfrow=c(2,1))
for (i in (1:100)){
    plot(test2[,i], type='l', ylim=c(-0.5, 1),
         main=paste('Iteration', i, 'Delta'),
         ylab='Delta')
    plot(test[,i], type='l', ylim=minmax,
         main=paste('Iteration', i, 'Score'),
         ylab='Score')
    Sys.sleep(0.1)
}

</code></pre><br />
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