function [begs,ends,scores] = max_finder(x,len)
% function [begs,ends,scores] = max_finder(x,len)
%
% finds maxima in a likelihood vector. Assumes the word has length len
% if there's a conflict btw 2 overlapping words the one with better max
% wins. 

N = length(x); 

% first find all local maxima
delayed = [0 x(1:N-1)]; advanced = [x(2:N) 0]; 
ilm = find (x > delayed & x > advanced); 
lm = x(ilm); 

% no local maximum must lie between 1 and len, (otherwise we 
%  would get beg < 0)
iaux = find (ilm > len);
ilm = ilm(iaux); lm = lm(iaux); 
Nlm = length(lm); % number of local maxima

% allocate big arrays, then we'll shorten
begs = zeros (1,Nlm); ends = zeros (1,Nlm); scores = zeros (1,Nlm); 

% ok, first local max will be a candidate. 
xend = ilm(1); xbeg = xend - len; xscore = lm(1); 
cnt = 1; 
% now run the cycle
for ii=2:Nlm,
  yend = ilm(ii); ybeg = yend - len; yscore = lm(ii); 
  % if x too far, write it and make x from y; 
  if (xend < ybeg)
    begs(cnt) = xbeg; ends(cnt) = xend; scores(cnt) = xscore; cnt = cnt+1; 
    xbeg = ybeg; xend = yend; xscore = yscore; continue;
  end
  % ok, so the 2 guys overlap, or at least touch. Keep the stronger.
  % e.g. if xscore is better, let x untouched, if not, overwrite
  % with y. 
  if (yscore > xscore)
    xbeg = ybeg; xend = yend; xscore = yscore; 
  end
end
% should write the last one ... 
begs(cnt) = xbeg; ends(cnt) = xend; scores(cnt) = xscore; 

% and shorten the output matrices not to output shit.
begs = begs (1:cnt); ends = ends(1:cnt); scores = scores (1:cnt);