garun.m%

王小平《遗传算法——理论、应用与软件实现》随书光盘

%
% garun.m
%
% Runs the GA filter optimising program
%

%
% Clear the stuff out from the old screen
%
clear slider text but line check

%
% Initialise the ga_win screen
%
figure(ga_win);
clg
set(ga_win,'position',[20 20 600 400],'name','Optimising filters...','color',[0 0 0]);
drawnow

%
% Draw a couple of graphs
%
ax1=axes(...
	'Units','pixels',...
	'Position',[60 200 500 160],...
	'Box','on',...
	'Color',[0 0 0],...
	'Visible','on');
text(1)=da_text(ga_win,60,370,200,20,'Linear prediction of output:',[1 1 1],[0 0 0]);

ax2=axes(...
	'Units','pixels',...
	'Position',[60 40 230 100],...
	'Box','on',...
	'Color',[0 0 0],...
	'Visible','on');
text(2)=da_text(ga_win,60,150,200,20,'Model error (SSE):',[1 1 1],[0 0 0]);

%
% Percentage done bars
%
box(1)=uicontrol(ga_win,...
	'style','frame',...
	'position',[370 20 200 30],...
	'backgroundcolor',[0 0 0],...
	'foregroundcolor',[1 1 1]);

box(2)=uicontrol(ga_win,...
	'style','frame',...
	'position',[380 25 1 20],...
	'foregroundcolor',[0 0 1],...
	'backgroundcolor',[0 0 1]);

box(3)=uicontrol(ga_win,...
	'style','frame',...
	'position',[370 60 200 30],...
	'backgroundcolor',[0 0 0],...
	'foregroundcolor',[1 1 1]);

box(4)=uicontrol(ga_win,...
	'style','frame',...
	'position',[380 65 1 20],...
	'foregroundcolor',[0 0 1],...
	'backgroundcolor',[0 0 1]);

text(3)=da_text(ga_win,300,63,70,20,'Decoded:',[1 1 1],[0 0 0]);
text(4)=da_text(ga_win,300,28,70,20,'Complete:',[1 1 1],[0 0 0]);
drawnow;

%
% Load the data file
%
eval(['cd ' num2str(df_path1)]);
eval(['load ' num2str(df_name1)]);
[D L]=size(df_name1);
eval(['data=' num2str(df_name1(:,1:L-4)) ';']);
eval(['clear ' num2str(df_name1(:,1:L-4)) ';']);
eval(['cd ' num2str(working_directory)]);

%
% Initialise the x and y data
%
[meas vars]=size(data);
x_data=ones(1,vars);
x_data(out_col)=0;
x=data(:,x_data);
y=data(:,out_col);
clear data;

%
% Scale the data from 0-->1 if this has been selected
%
if scale_data==1;
	[x minx maxx]=mceng2(x);
	[y miny maxy]=mceng2(y);
end

%
% Initialise the genetic description
%
d=[];
for i=1:vars-1
	d=[d ; 0 0.01 1];
end

%
% Generate the population - with 20 members
%
[o d b]=decode(0,d,0);
bits=sum(b);
members=20;
%chrom=newpop(members,bits);
chrom=zeros(members,bits);
results=[];

%
% Main generation loop
%
for i=1:max_gen
	
	%
	% Cross and mutate
	%
	for j = 1 : 2 :members
	chrom(j,:)=mutate(chrom(j,:),0.02);
	chrom(j+1,:)=mutate(chrom(j+1,:),0.02);
	[chrom(j,:) chrom(j+1,:)]=crover(chrom(j,:),chrom(j+1,:),0.02);
	end

	%
	% Select the best
	% 
	[chrom coefs pin]=select4(chrom,d,x,y,box(2),box(4));
	set(box(2),'position',[380 25 1 20]);
	set(box(4),'position',[380 65 1 20]);

	%
	% Decode this popultaion and plot results
	%
	results(i,:)=[min(pin) mean(pin) max(pin)];
	[D L]=size(coefs);
	[D1 L1]=size(x);
	[c,in]=min(pin);
	f=coefs(in,:);
	x2=fil(x,f);
	c=pinv(x2'*x2)*x2'*y;
	yest=x2*c;
	[c2,in]=max(pin);
	
	axes(ax1);
	if scale_data==1
		plot([resclg(y,miny,maxy) resclg(yest,miny,maxy)]);
	else
		plot([y yest]);
	end

	xlabel('Sample number');
	ylabel('Actual & Predicted');
	axes(ax2);
	plot(results(:,1));
	xlabel('Generation number');
	ylabel('Error');
	drawnow;
end

if save_data==1
	if scale_data==0
		sd=[y x];
	else
		sd=[resclg(y,miny,maxy) resclg(x,minx,maxx)];
	end
	
	eval(['save ' num2str(save_data_name) ' sd -ascii']);
end

%
% Display the filter constants and coefficients
%
disp('Filter Constants:');
f
disp('Regression Coefficients:');
disp(c);