updatestate.m

遗传规划的matlab工具箱

function [state,pop]=updatestate(params,state,data,pop);
%UPDATESTATE    Updates the GPLAB algorithm state variables.
%   UPDATESTATE(PARAMS,STATE,POP) returns the state variables updated
%   with the latest measures of rank, fitness and level history.
%
%   [STATE,POP]=UPDATESTATE(PARAMS,STATE,POP) also returns some
%   additional population info that may have been needed.
%
%   Input arguments:
%      PARAMS - the running parameters (struct)
%      STATE - the state before the update (struct)
%      DATA - the dataset for the algorithm to use (struct)
%      POP - current population (array)
%   Output arguments:
%      STATE - the state after the update (struct)
%      POP - current population, updated info (array)
%
%   Copyright (C) 2003-2004 Sara Silva ([e-mail])
%   Acknowledgements: SINTEF ([e-mail],[e-mail],[e-mail])
%   This file is part of the GPLAB Toolbox

% ranking:
if params.lowerisbetter
   state.popranking=ranking(state.popfitness,0);
else
   state.popranking=ranking(state.popfitness,1);
end
% (ranking.m is used instead of sort so we can get rankings like 1,2,2,3,4,4,5,...)
state.popnormfitness=[];
state.popexpected=[];

if strcmp(params.calccomplexity,'1')
	% average nodes, average introns, and average level (history):
	for i=1:state.popsize
   	if isempty(pop(i).level)
      	pop(i).level=treelevel(pop(i).tree);
   	end
   	if isempty(pop(i).nodes)
      	pop(i).nodes=nodes(pop(i).tree);
   	end
   	if isempty(pop(i).introns)
         [pop(i).introns,state]=intronnodes(pop(i).tree,params,data,state);
   	end
	end
   
   poplevels=[pop.level];
   popnodes=[pop.nodes];
   
	state.avglevelhistory(end+1)=mean(poplevels);
	state.avgnodeshistory(end+1)=mean(popnodes);
   state.avgintronshistory(end+1)=mean([pop.introns]);
   
   % tree fill percentage:
   mlevel=max(poplevels);
   avgkids=mean(state.arity(find(state.arity>0)));
   avgkids=repmat(avgkids,state.popsize,mlevel);
   alllevels=repmat(0:1:mlevel-1,state.popsize,1);
   poplevels=repmat(poplevels',1,size(alllevels,2));
   avgkids(find(alllevels>=poplevels))=0;
   sumparts=avgkids.^alllevels;
   sumlines=sum(sumparts,2);
   fillratio=popnodes'./sumlines;
   state.avgtreefillhistory(end+1)=100*mean(fillratio);
end % if strcmp(params.calccomplexity,'1')

% bestsofar and bestsofarhistory:
bestindex=find(state.popranking==1);
if length(bestindex)>1 % there may be more than one, in each case we choose the simpler
   minlevelindex=[];
   for i=1:length(bestindex)
      if strcmp(params.depthnodes,'1')
      	if isempty(pop(bestindex(i)).level)
         	pop(bestindex(i)).level=treelevel(pop(bestindex(i)).tree);
      	end
      	if isempty(minlevelindex) | pop(bestindex(i)).level<pop(minlevelindex).level
         	minlevelindex=bestindex(i);
         end
      else
         if isempty(pop(bestindex(i)).nodes)
         	pop(bestindex(i)).nodes=nodes(pop(bestindex(i)).tree);
      	end
      	if isempty(minlevelindex) | pop(bestindex(i)).nodes<pop(minlevelindex).nodes
         	minlevelindex=bestindex(i);
         end
      end
   end
   bestindex=minlevelindex;
end
if isempty(state.bestsofar) | ((params.lowerisbetter & pop(bestindex).fitness<state.bestsofar.fitness) | (~params.lowerisbetter & pop(bestindex).fitness>state.bestsofar.fitness))
   state.bestsofar=pop(bestindex);
end
% cross validation (and save bestsofar.fitness and eventually bestsofar.testfitness):
if params.usetestdata
   % make a temporary 'varsvals', because the real one contains training data:
   for t=1:params.numvars
		% for all variables (which are first in list of inputs), ie, X1,X2,X3,...
   	tmpstate.varsvals{t}=mat2str(data.test.example(:,t));
	end
	testfitness=feval(params.calcfitness,state.bestsofar.str,params,data.test,state.terminals,tmpstate.varsvals);
   state.bestsofar.testfitness=testfitness;
   state.bestfithistory(end+1,:)=[state.bestsofar.fitness state.bestsofar.testfitness];
else   
   state.bestfithistory(end+1)=state.bestsofar.fitness;
end
if isempty(state.bestsofar.nodes)
	state.bestsofar.nodes=nodes(state.bestsofar.tree);
end
if isempty(state.bestsofar.introns)
   [nintrons,state]=intronnodes(state.bestsofar.tree,params,data,state);
   state.bestsofar.introns=nintrons;
end
if isempty(state.bestsofar.level)
	state.bestsofar.level=treelevel(state.bestsofar.tree);
end
if isempty(state.bestsofarhistory) | (state.bestsofar.id~=state.bestsofarhistory{end,2}.id)
	state.bestsofarhistory{end+1,1}=state.generation;
   state.bestsofarhistory{end,2}=state.bestsofar;
end

% get maximum, minimum, average, median, stddev fitness:
if params.lowerisbetter
	state.maxfitness=min(state.popfitness);
   state.minfitness=max(state.popfitness);
else
   state.maxfitness=max(state.popfitness);
   state.minfitness=min(state.popfitness);
end
state.avgfitness=mean(state.popfitness);
state.medianfitness=median(state.popfitness);
state.stdfitness=std(state.popfitness);

% save fitness measures in fithistory:
g=state.generation+1; % +1 because generation 0 is also saved
state.fithistory(g,1)=state.maxfitness;
state.fithistory(g,2)=state.minfitness;
state.fithistory(g,3)=state.avgfitness;
state.fithistory(g,4)=state.medianfitness;
state.fithistory(g,5)=state.stdfitness;

% save level, best level, best introns, op and op freqs history:
state.levelhistory(end+1)=state.maxlevel;
state.bestlevelhistory(end+1)=state.bestsofar.level;
state.bestnodeshistory(end+1)=state.bestsofar.nodes;
state.bestintronshistory(end+1)=state.bestsofar.introns;
state.ophistory(end+1,:)=state.operatorprobs;
state.opfreqhistory(end+1,:)=state.operatorfreqs;
state.reproductionhistory(end+1)=state.reproductions;
state.cloninghistory(end+1,:)=state.clonings;
state.reproductions=0;
state.clonings=zeros(1,length(params.operatornames));

% diversity:
for i=1:length(params.calcdiversity)
   newdiversity = feval(params.calcdiversity{i},params,state,data,pop);
   eval(['state.diversityhistory.' params.calcdiversity{i} '(end+1)=newdiversity;']);
end

	% just to remind,
   % the simple measure from population genetics:
   
   %popindividuals={pop.str};
   %uniqueindividuals=unique(popindividuals);
   %reppopindividuals=repmat(popindividuals',1,length(uniqueindividuals));
   %repuniqueindividuals=repmat(uniqueindividuals,1,length(popindividuals))';
   %uniquematch=strcmp(reppopindividuals,repuniqueindividuals);
   %uniquefreqs=sum(uniquematch,1)/state.popsize;
   %state.diversityhistory(end+1)=1-(sum(uniquefreqs.*uniquefreqs));