B. P. Lathi, Zhi Ding - Modern Digital and Analog Communication Systems-Oxford University Press (2009)

792 INTRODUCTION TO INFORMATION THEORY
minx=min (x) ;
maxx=max (x) ;
deltax= (maxx-minx) /(length (x) -1) ;
lowerx=minx-deltax/2;
upperx=maxx+deltax/ 2;
ncellx=ceil(length(x) ~(l/3) );
miny=min (y) ;
maxy=max (y) ;
deltay= (maxy-miny) /(length (y) -1) ;
lowery=miny-deltay/ 2;
uppery=maxy+deltay/2 ;
ncelly=ncellx ;
rout (l:ncellx, l:ncelly) =O ;
xx=round ( (x-lowerx) /(upperx-lowerx} *ncellx + 1/2 ) ;
yy=round ( (y-lowery) /(uppery-lowery) *ncelly + 1/2 ) ;
for n=l : length (x)
indexx=xx (n) ;
indexy=yy (n) ;
if indexx >= 1 & indexx <= ncellx & indexy >= 1 & indexy <= ncelly
rout ( indexx ,indexy) =rout ( indexx ,indexy) +l;
end ;
end ;
h=rout ;
estimate=O;
sigma=O;
count=O ;
% determine row and column sums
hy=sum(h) ;
hx=sum(h');
for nx=l :ncellx
for ny=l :ncelly
if h(nx ,ny) -=0
logf=log (h(nx,ny) /hx (nx) /hy (ny) );
else
logf=O ;
end ;
count=count+h (nx,ny ) ;
estimate=estimate+h (nx,ny ) *logf;
sigma=sigma+h (nx,ny ) *logf~2;
end;
end;
% biased estimate
estimate=estimate/count ;
sigma =sqrt ( (sigma/count-estimate~2) /(count-1) ) ;