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FRparaSkewNormal_test.m
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FRparaSkewNormal_test.m
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% v24112016
%
%
% This script takes binding a feature condition for faces stimulus during
% retrieval stage and calculate MI using Skewness technique.
% The last 512 samples are selected for the analysis.
% Thi script works toghether with Skew fit R script
%
% It is prepeared for n electrodes analysis using random electrodes
% or pre defined electrodes
% It can do Normal, trial shuffle or data shuffle run
%
% Adapted by Fabricio Baglivo 2016 from Sergio Lew script.
clear all
close all
clc
electrode_run_type='test'; %'random_electrodes'; %selected_electrodes
data_run_type= 'data_shuffle'; %'normal';%trial_shuffle %data_shuffle
electrode_number=90;
electrode_selected_number=10;
electrodes=[1 11 17 21 30 45 52]; %Case of selected electrodes
stage='PostRetention'
load(['P12/' stage '.mat']);
BindinERPs_RED=cond(1).data(:,512:end,:);
FeaturesERPs_RED=cond(2).data(:,512:end,:);
nUnits=size(BindinERPs_RED,1);
setenv('PATH', [getenv('PATH') ';C:\Program Files\R\R-3.3.2\bin\']);
system('rm CSV/*');
%%
trials=size(BindinERPs_RED,3);
for t=1
u_high=0.4; %Features
b_high=0.3;
d_high=0.3;
u_low=0.1; %Binding
u_bkp(t)=u_low;
% u_low=0.1;
b_low=0.3;
d_low=0.3;
switch electrode_run_type
case 'random_electrodes'
switch data_run_type
case 'normal'
ok=0;
while ok==0
vector=randperm(electrode_number);
selection=vector(1:electrode_selected_number);
for iter=1:trials
Bind(iter,:)=squeeze(mean(BindinERPs_RED(selection,:,iter),2));
Feat(iter,:)=squeeze(mean(FeaturesERPs_RED(selection,:,iter),2));
end
%Test independence bewteen signals
if (rank(Bind)==electrode_selected_number) && (rank(Feat)==electrode_selected_number);ok=1;end;
end
case 'trial_shuffle'
ok=0;
while ok==0
vector=randperm(electrode_number);
selection=vector(1:electrode_selected_number);
for iter=1:trials
Bind(iter,:)=squeeze(mean(BindinERPs_RED(selection,:,iter),2));
Feat(iter,:)=squeeze(mean(FeaturesERPs_RED(selection,:,iter),2));
end
if (rank(Bind)==10) && (rank(Feat)==10);ok=1;end;
end
%mix codition: Trial permutation
Complete=[Bind' Feat']';
idx=randperm(trials);
Complete_Shuffle=Complete(idx,:);
Bind=Complete(1:trials/2,:);
Feat=Complete(trials/2+1:trials,:);
case 'data_shuffle'
%data shuffle
shufflevect=randperm(size(cond(1).data,2));
B=cond(1).data(:,shufflevect,:);
shufflevect=randperm(size(cond(2).data,2));
F=cond(1).data(:,shufflevect,:);
BindinERPs_RED=B(:,512:end,:);
FeaturesERPs_RED=F(:,512:end,:);
ok=0;
while ok==0
vector=randperm(electrode_number);
selection=vector(1:electrode_selected_number);
for iter=1:trials
Bind(iter,:)=squeeze(mean(BindinERPs_RED(selection,:,iter),2));
Feat(iter,:)=squeeze(mean(FeaturesERPs_RED(selection,:,iter),2));
end
%Test independence bewteen signals
if (rank(Bind)==electrode_selected_number) && (rank(Feat)==electrode_selected_number);ok=1;end;
end
end
case 'selected_electrodes'
electrode_selected_number=size(electrodes,2)
selection=electrodes;
switch data_run_type
case 'normal'
for iter=1:trials
Bind(iter,:)=squeeze(mean(BindinERPs_RED(selection,:,iter),2));
Feat(iter,:)=squeeze(mean(FeaturesERPs_RED(selection,:,iter),2));
end
%Test independence bewteen signals
if (rank(Bind)==size(Bind,2)) && (rank(Feat)==size(Feat,2))
ok=1;
else
error('Non-independent data matrix');
end
case 'trial_shuffle'
for iter=1:trials
Bind(iter,:)=squeeze(mean(BindinERPs_RED(selection,:,iter),2));
Feat(iter,:)=squeeze(mean(FeaturesERPs_RED(selection,:,iter),2));
end
if (rank(Bind)==size(Bind,2)) && (rank(Feat)==size(Feat,2))
ok=1;
else
error('Non-independent data matrix');
end
%mix codition: Trial permutation
Complete=[Bind' Feat']';
idx=randperm(trials);
Complete_Shuffle=Complete(idx,:);
Bind=Complete(1:trials/2,:);
Feat=Complete(trials/2+1:trials,:);
case 'data_shuffle'
%data shuffle
shufflevect=randperm(size(cond(1).data,2));
B=cond(1).data(:,shufflevect,:);
shufflevect=randperm(size(cond(2).data,2));
F=cond(1).data(:,shufflevect,:);
BindinERPs_RED=B(:,512:end,:);
FeaturesERPs_RED=F(:,512:end,:);
for iter=1:trials
Bind(iter,:)=squeeze(mean(BindinERPs_RED(selection,:,iter),2));
Feat(iter,:)=squeeze(mean(FeaturesERPs_RED(selection,:,iter),2));
end
if (rank(Bind)==size(Bind,2)) && (rank(Feat)==size(Feat,2))
ok=1;
else
error('Non-independent data matrix');
end
end
case 'test'
clear BindinERPs_RED FeaturesERPs_RED
electrode_number=2;
electrode_selected_number=2;
%Bind
tipo='low_conenction';
u=u_low;
b=b_low;d=d_low; % symetrical system
signal=henongen_func(u,b,d,tipo);
signal(:,1)=signal(:,1)-mean(signal(:,1));
signal(:,2)=signal(:,2)-mean(signal(:,2));
signal_low=signal;
for i=1:1000%ceil(size(signal,1)/512)-1
BindinERPs_RED(:,:,i)=signal((i-1)*32+1:i*32,:)';
Bind(i,:)=squeeze(mean(BindinERPs_RED(:,:,i),2));
end
% Bind(:,1)=randn(65,1);
% Bind(:,2)=randn(65,1);
%FEAT
tipo='hig_conenction';
u=u_high;
b=b_high;d=d_high; % Symetrical system
signal=henongen_func(u,b,d,tipo);
signal(:,1)=signal(:,1)-mean(signal(:,1));
signal(:,2)=signal(:,2)-mean(signal(:,2));
for i=1:1000%ceil(size(signal,1)/512)-1
FeaturesERPs_RED(:,:,i)=signal((i-1)*32+1:i*32,:)';
% FeaturesERPs_RED(:,:,i)=signal(1:512,:)';
Feat(i,:)=squeeze(mean(FeaturesERPs_RED(:,:,i),2));
end
% Feat(:,1)=rand(65,1);
% Feat(:,2)=rand(65,1);
end
% FIT vector:
%
% n -> mean values
% sum([1:1:electrode_selected_number]) -> Omega Matrix
% n -> Aplha vector
% 1 -> likelihood
fit_size=2*electrode_selected_number+sum([1:1:electrode_selected_number])+1;
%%
csvwrite(['CSV/Binding.csv'],Bind);
csvwrite(['CSV/Features.csv'],Feat);
Complete=[Bind' Feat'];
csvwrite(['CSV/Complete.csv'],Complete');
csvwrite(['CSV/FitSize.csv'],fit_size);
%%
%!unset DYLD_LIBRARY_PATH; Rscript skewNromalFitBind.R
!Rscript skewNromalFitBind.R
snParamBind = csvread('skewNromalFitedDataBind.csv');
% snParam(snParam==-999999)=NaN;
!Rscript skewNromalFitFeat.R
snParamFeat = csvread('skewNromalFitedDataFeat.csv');
% snParamBind(snParam==-999999)=NaN;
!Rscript skewNromalFit.R
snParam = csvread('skewNromalFitedData.csv');
% snParamFeat(snParamGO==-999999)=NaN;
%%
for n=1:size(snParam,1)
alfainit=electrode_selected_number+sum([1:1:electrode_selected_number])+1;
alfaend=alfainit+electrode_selected_number-1;
alfa=snParam(n,alfainit:alfaend);
likelihood(t)=snParam(end);
init=electrode_selected_number+1;
for i=1:electrode_selected_number %for 10 channels
l=electrode_selected_number-i ;% #components
omega(i,i:electrode_selected_number)=snParam(1,init:(init+l));
omega(i:electrode_selected_number,i)=snParam(1,init:(init+l));
init=(init+l+1);
end
M=100000;
a=randn(100000,1);
b=randn(100000,1);
W(find(sqrt(alfa*alfa')*a>b))=a(find(sqrt(alfa*alfa')*a>b));
W(find(sqrt(alfa*alfa')*a<=b))=-a(find(sqrt(alfa*alfa')*a<=b));
H(n) = 1/2*log((det(omega))) + 1 + log(2*pi) - mean(2*log(normcdf(sqrt(alfa*alfa')*W)));
%BIND
alfaBind=snParamBind(n,alfainit:alfaend);
likelihoodbind(t)=snParamBind(end);
init=electrode_selected_number+1;
for i=1:electrode_selected_number
l=electrode_selected_number-i ;% #components
omegaBind(i,i:electrode_selected_number)=snParamBind(1,init:(init+l));
omegaBind(i:electrode_selected_number,i)=snParamBind(1,init:(init+l));
init=(init+l+1);
end
M=100000;
a=randn(100000,1);
b=randn(100000,1);
W(find(sqrt(alfaBind*alfaBind')*a>b))=a(find(sqrt(alfaBind*alfaBind')*a>b));
W(find(sqrt(alfaBind*alfaBind')*a<=b))=-a(find(sqrt(alfaBind*alfaBind')*a<=b));
HBind(n) = 1/2*log((det(omegaBind))) + 1 + log(2*pi) - mean(2*log(normcdf(sqrt(alfaBind*alfaBind')*W)));
%FEAT
alfaFeat=snParamFeat(n,alfainit:alfaend);
likelihoodFeat(t)=snParamFeat(end);
init=electrode_selected_number+1;
for i=1:electrode_selected_number %for 10 channels
l=electrode_selected_number-i ;% #components
omegaFeat(i,i:electrode_selected_number)=snParamFeat(1,init:(init+l));
omegaFeat(i:electrode_selected_number,i)=snParamFeat(1,init:(init+l));
init=(init+l+1);
end
M=100000;
a=randn(100000,1);
b=randn(100000,1);
W(find(sqrt(alfaFeat*alfaFeat')*a>b))=a(find(sqrt(alfaFeat*alfaFeat')*a>b));
W(find(sqrt(alfaFeat*alfaFeat')*a<=b))=-a(find(sqrt(alfaFeat*alfaFeat')*a<=b));
HFeat(n) = 1/2*log((det(omegaFeat))) + 1 + log(2*pi) - mean(2*log(normcdf(sqrt(alfaFeat*alfaFeat')*W)));
end
MI(t)=H - 1/2*HFeat - 1/2*HBind
HH(t)=H;
HHf(t)=HFeat;
HHb(t)=HBind;
end
%%
save('Mat/Aasimetric.mat','HH','HHf','HHb');
%%
figure
subplot(1,2,1)
bar([H HBind HFeat],0.5)
set(gca,'xticklabels',{'H';'Hnc';'Hc'})
grid on
subplot(1,2,2)
bar(MI)
set(gca,'xticklabels',{'MI'})
figure;plot(Bind);xlabel('Low Connection')
figure;plot(Feat);xlabel('High Connection')
%%
figure
for i=1:9
bar([HH(i) HHb(i) HHf(i)],0.5)
set(gca,'xticklabels',{'H';'Hnc';'Hc'})
grid on
pause(1)
end