%% DON"T USE is ROB's MAGIC STUFF %%restoredefaultpath; matlabrc clear all clc close all disp('Startup from LOeKS directory'); cd 'E:\MATL_DIR\ANALYSIS_LOEK_HEIJNEN\LINVECMOD\' addpath( ... genpath(fullfile('E:\','MATL_DIR\ANALYSIS_LOEK_HEIJNEN\LINVECMOD\'))); cd 'E:\MATL_DIR\ANALYSIS_LOEK_HEIJNEN\LINVECMOD\' %------- CALCULATE ALL PERMUTATIONS -------% clc clear all close all %sig = 1.5; %%% .75 is a 9 unit gabor patch %sizes = 18; % pos = 6:1:(sizes-(6-1)); % MIN_DIST = 5; % height = [0.0 linspace(0.1,1.0,10)]; %%% alpha sig = 2.5; sizes = 20; pos = 6:1:(sizes-(6-1)); MIN_DIST = 5; height = [0.0 linspace(0.1,1.0,6)]; %%% alpha T1=[]; T2=[]; %%% Caculate all position combinations of T1 and T2 for i=1:1:length(pos) T1 = [ T1 repmat(pos(i), [1 , length(pos)]) ]; T2 = [ T2 pos]; end T1T2= [T1' T2']; %%% Make sure the top of T1 does not ovelap that of T2 deltaT=(T2'-T1'); dist=abs(deltaT); T1T2=T1T2(dist >= MIN_DIST,:); %%% caculate all combinations of T1 and T2 when combined with all alphas N=length(T1T2); B = height(ones(1,N),:); C = reshape(B, numel(B), 1); % gives a1 a1 a2 a2 a3 a3 %C = reshape(B', numel(B), 1); % gives a1 a2 a3 a1 a2 a3 alpha = C; T1T2= repmat(T1T2,length(height),1); T1T2=[T1T2 alpha]; T1p = T1T2(:,1); T2p = T1T2(:,2); deltaT = (T2p - T1p); alpha = T1T2(:,3); avgTp = T1p + alpha.* deltaT; difTp = (1 - alpha).* deltaT; figure(44); cla subplot(231) plot(T1p(:,1),'.'); title([ 'T1 INPUT']); subplot(232) plot(T2p(:,1),'.'); title([ 'T2 INPUT']); subplot(233) plot(deltaT(:,1),'.'); title([ 'deltaT']); subplot(235) plot(avgTp,'.'); title([ 'avgT']); subplot(236) plot(difTp,'.'); title([ 'difT']); %% %------- GENERATE INPUT / OUTPUT DATA -------% T1m=zeros(sizes); T2m=zeros(sizes); Tavgm=zeros(sizes); Tdifm=zeros(sizes); samples_length = length(T1T2); p = zeros(sizes,samples_length); p1 = zeros(sizes,samples_length); p2 = zeros(sizes,samples_length); tavg = zeros(sizes,samples_length); tdif = zeros(sizes,samples_length); for k=1:1:length(T1T2) T1m = ONE_D_gabor( sizes, sig, T1p(k) ); T2m = alpha(k) * ONE_D_gabor( sizes, sig, T2p(k) ); Tavgm = ONE_D_gabor( sizes, sig, avgTp(k) ); x_shift = abs(min(difTp))+min(pos)-4;%-2; %%%% make sure Tdifm postion is not negative Tdifm = ONE_D_gabor( sizes, sig, difTp(k) + x_shift); % INPUT % p(:,k) = reshape(T1m + T2m, numel(T1m + T2m), 1); p1(:,k) = reshape(T1m, numel(T1m), 1); p2(:,k) = reshape(T2m, numel(T2m), 1); % OUTPUT % tavg(:,k)= reshape(Tavgm, numel(Tavgm), 1); tdif(:,k)= reshape(Tdifm, numel(Tdifm), 1); end p=[p1; p2]; t=[ tavg; tdif]; [pn,stdp] = mapminmax(p); [tn,stdt] = mapminmax(t); net = newff(minmax(pn),[length(t(:,1))],{'tansig'},'trainbr'); net.trainParam.show = 3; net.trainParam.epochs = 20; net.trainParam.goal = 10/10^20; net.trainParam.mem_reduc =1; %net.trainParam.lr= 0.0001; %%net.divideFcn = ''; net.performFcn = 'msereg'; net.performParam.ratio = 2; % net.trainParam.mu= 1.5 %Initial Mu % net.trainParam.mu_dec = .9 %Mu decrease factor % net.trainParam.mu_inc = 2 %Mu increase factor % net.trainParam.mu_max = 1e10 %Maximum Mu %-- Train the network --% randn('seed',192736547); net = init(net); %VAL %TEST [TrainV,ValV,TestV] = dividevec(pn,tn,25/100,25/100); [net,tr] = train(net,TrainV.P,TrainV.T,[],[],ValV,TestV); %-- Simulate the network --% [A1,Pf,Af,E,info.train.performance] = sim(net,TrainV.P, [],[],TrainV.T); [A2,Pf,Af,E,info.validation.performance] = sim(net,ValV.P, [],[],ValV.T); [A3,Pf,Af,E,info.test.performance] = sim(net,TestV.P, [],[],TestV.T); Y1 = mapminmax('reverse',A1,stdt); Y2 = mapminmax('reverse',A2,stdt); Y3 = mapminmax('reverse',A3,stdt); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% figure(15) cla tP=t(:,TrainV.indices); %% teacher data oP=Y1; %% output simulation Q=numel(oP)/2; subplot(321) postregV2(oP(1:Q),tP(1:Q)); title('Tavg') axis square grid subplot(322) postregV1(oP(Q+1:Q*2),tP(Q+1:Q*2)); title('Tdif') axis square grid tV=t(:,ValV.indices); %% teacher data oV=Y2; %% output simulation Q=numel(oV)/2; subplot(323) postregV2(oV(1:Q),tV(1:Q)); title('Tavg') axis square grid subplot(324) postregV2(oV(Q+1:Q*2),tV(Q+1:Q*2)); title('Tdif') axis square grid tT=t(:,TestV.indices); %% teacher data oT=Y3; %% output simulation Q=numel(oT)/2; subplot(325) postregV1(oT(1:Q),tT(1:Q)); title('Tavg') axis square grid subplot(326) postregV1(oT(Q+1:Q*2),tT(Q+1:Q*2)); title('Tdif') axis square grid %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% %-- SIMULATION TRAINED DATA--% clc; figure(18); hold off; for k=1:1:length(oP) cla pt1 = p1(:,TrainV.indices); pt2 = p2(:,TrainV.indices); q = TrainV.indices(k); subplot(311) plot(pt1(:,k),'k-'); hold on; plot(pt2(:,k),'b-'); xlim([0.5 sizes+0.5]); ylim([-0.3 1.3]); title([ 'INPUT T1: ' num2str(T1p(q)) ' & T2: ' num2str(T2p(q)) ' ALPHA: ' num2str(alpha(q))]); hold off; subplot(323) plot(oP(1:sizes,k),'r-'); hold on; plot(tP(1:sizes,k),'g-'); xlim([0.5 sizes+0.5]); ylim([-0.3 1.3]); title([ 'Tavg: ' num2str(avgTp(q)) ]); hold off; subplot(324) plot(oP(sizes + 1 : sizes*2,k),'r-'); hold on; plot(tP(sizes+1:sizes*2,k),'g-'); xlim([0.5 sizes+0.5]); ylim([-0.3 1.3]); title([ 'Tdif: ' num2str( difTp(q) ) ]);%%% + abs(min(difTp)) + min(pos)-2 Q=1:2:18; set(gca,'XTick',Q); set(gca,'xTickLabel',Q - x_shift); hold off; subplot(325) postregV1(oP(1:sizes,k)',tP(1:sizes,k)') subplot(326) postregV1(oP(sizes+1:sizes*2,k)',tP(sizes+1:sizes*2,k)') pause(0.3) end