From: Benjamin Auder <benjamin.auder@somewhere>
Date: Sat, 14 Jan 2017 02:54:13 +0000 (+0100)
Subject: OLD_MATLAB_CODE not required anymore
X-Git-Url: https://git.auder.net/DESCRIPTION?a=commitdiff_plain;h=5deedb4ce4b94294f07f9f604ba37a5cbf3425af;p=valse.git

OLD_MATLAB_CODE not required anymore
---

diff --git a/OLD_MATLAB/InputParameters/basicInitParameters.m b/OLD_MATLAB/InputParameters/basicInitParameters.m
deleted file mode 100644
index 50410f5..0000000
--- a/OLD_MATLAB/InputParameters/basicInitParameters.m
+++ /dev/null
@@ -1,19 +0,0 @@
-function[phiInit,rhoInit,piInit,gamInit] = basicInitParameters(n,p,m,k)
-
-	phiInit = zeros(p,m,k);
-	
-	piInit = (1.0/k) * ones(1,k);
-	
-	rhoInit = zeros(m,m,k);
-	for r=1:k
-		rhoInit(:,:,r) = eye(m,m);
-	end
-	
-	gamInit = 0.1 * ones(n,k);
-	R = random('unid',k,n,1);
-	for i=1:n
-		gamInit(i,R(i)) = 0.9;
-	end
-	gamInit = gamInit / (sum(gamInit(1,:)));
-
-end
diff --git a/OLD_MATLAB/InputParameters/compileMex.m b/OLD_MATLAB/InputParameters/compileMex.m
deleted file mode 100644
index 9e5f64b..0000000
--- a/OLD_MATLAB/InputParameters/compileMex.m
+++ /dev/null
@@ -1,7 +0,0 @@
-%compile C code (for MATLAB or Octave)
-if exist('octave_config_info')
-	setenv('CFLAGS','-O2 -std=gnu99 -fopenmp')
-	mkoctfile --mex -DOctave -I../Util -I../ProcLassoMLE selectiontotale.c selectiontotale_interface.c ../Util/ioutils.c ../ProcLassoMLE/EMGLLF.c -o selectiontotale -lm -lgsl -lgslcblas -lgomp
-else
-	mex CFLAGS="\$CFLAGS -O2 -std=gnu99 -fopenmp" -I../Util -I../ProcLassoMLE selectiontotale.c selectiontotale_interface.c ../Util/ioutils.c ../ProcLassoMLE/EMGLLF.c -output selectiontotale -lm -lgsl -lgslcblas -lgomp
-end
diff --git a/OLD_MATLAB/InputParameters/generateIO.m b/OLD_MATLAB/InputParameters/generateIO.m
deleted file mode 100644
index c677fd2..0000000
--- a/OLD_MATLAB/InputParameters/generateIO.m
+++ /dev/null
@@ -1,37 +0,0 @@
-%X is generated following a gaussian mixture \sum pi_r N(meanX_k, covX_r)
-%Y is generated then, with Y_i ~ \sum pi_r N(Beta_r.X_i, covY_r)
-function[X,Y,Z] = generateIO(meanX, covX, covY, pi, beta, n)
-
-	[p, ~, k] = size(covX);
-	[m, ~, ~] = size(covY);
-	if exist('octave_config_info')
-		%Octave statistics package	doesn't have gmdistribution()
-		X = zeros(n, p);
-		Z = zeros(n);
-		cs = cumsum(pi);
-		for i=1:n
-			%TODO: vectorize ? http://stackoverflow.com/questions/2977497/weighted-random-numbers-in-matlab
-			tmpRand01 = rand();
-			[~,Z(i)] = min(cs - tmpRand01 >= 0);
-			X(i,:) = mvnrnd(meanX(Z(i),:), covX(:,:,Z(i)), 1);
-		end
-	else
-		gmDistX = gmdistribution(meanX, covX, pi);
-		[X, Z] = random(gmDistX, n);
-	end
-	
-	Y = zeros(n, m);
-	BX = zeros(n,m,k);
-	for i=1:n
-		for r=1:k
-			%compute beta_r . X_i
-			BXir = zeros(1, m);
-			for mm=1:m
-				BXir(mm) = dot(X(i,:), beta(:,mm,r));
-			end
-			%add pi(r) * N(beta_r . X_i, covY) to Y_i
-			Y(i,:) = Y(i,:) + pi(r) * mvnrnd(BXir, covY(:,:,r), 1);
-		end
-	end
-
-end
diff --git a/OLD_MATLAB/InputParameters/generateIOdefault.m b/OLD_MATLAB/InputParameters/generateIOdefault.m
deleted file mode 100644
index f4c3c1f..0000000
--- a/OLD_MATLAB/InputParameters/generateIOdefault.m
+++ /dev/null
@@ -1,23 +0,0 @@
-%call generateIO with default parameters (random means, covariances = identity, equirepartition)
-function[X,Y,Z] = generateIOdefault(n, p, m, k)
-
-	rangeX = 100;
-	meanX = rangeX * (1 - 2*rand(k, p));
-	covX = zeros(p,p,k);
-	covY = zeros(m,m,k);
-	for r=1:k
-		covX(:,:,r) = eye(p);
-		covY(:,:,r) = eye(m);
-	end
-	pi = (1/k) * ones(1,k);
-	
-	%initialize beta to a random number of non-zero random value
-	beta = zeros(p,m,k);
-	for j=1:p
-		nonZeroCount = ceil(m*rand(1));
-		beta(j,1:nonZeroCount,:) = rand(nonZeroCount, k);
-	end
-	
-	[X,Y,Z] = generateIO(meanX, covX, covY, pi, beta, n);
-	
-end
diff --git a/OLD_MATLAB/InputParameters/grillelambda.m b/OLD_MATLAB/InputParameters/grillelambda.m
deleted file mode 100644
index 8d90665..0000000
--- a/OLD_MATLAB/InputParameters/grillelambda.m
+++ /dev/null
@@ -1,17 +0,0 @@
-function[gridLambda] = grillelambda(phiInit,rhoInit,piInit,gamInit,X,Y,gamma,mini,maxi,tau)
-
-	n = size(X, 1);
-	[p,m,k] = size(phiInit);
-	[phi,rho,pi,~,S] = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau);
-
-	gridLambda = zeros(p,m,k);
-	for j=1:p
-		for mm=1:m
-			gridLambda(j,mm,:) = abs(reshape(S(j,mm,:),[k,1])) ./ (n*pi.^gamma);
-		end
-	end
-	
-	gridLambda = unique(gridLambda);
-	gridLambda(gridLambda()>1) = [];
-
-end
diff --git a/OLD_MATLAB/InputParameters/initSmallEM.m b/OLD_MATLAB/InputParameters/initSmallEM.m
deleted file mode 100644
index 6a2ccc9..0000000
--- a/OLD_MATLAB/InputParameters/initSmallEM.m
+++ /dev/null
@@ -1,33 +0,0 @@
-function[phiInit,rhoInit,piInit,gamInit] = initSmallEM(k,x,y,tau)
-[n,m]=size(y);
-gamInit1=zeros(n,k,20);
-for repet=1:20
-    Zinit1(:,repet)=clusterdata(y,k);
-    for r=1:k
-        betaInit1(:,:,r,repet)=pinv(transpose(x(Zinit1(:,repet)==r,:))*x(Zinit1(:,repet)==r,:))*transpose(x(Zinit1(:,repet)==r,:))*y(Zinit1(:,repet)==r,:);
-        sigmaInit1(:,:,r,repet)=eye(m);
-        phiInit1(:,:,r,repet)=betaInit1(:,:,r,repet)/sigmaInit1(:,:,r,repet);
-        rhoInit1(:,:,r,repet)=inv(sigmaInit1(:,:,r,repet));
-        piInit1(repet,r)=sum(Zinit1(:,repet)==r)/n;
-    end
-    for i=1:n
-        for r=1:k
-            dotProduct = (y(i,:)*rhoInit1(:,:,r,repet)-x(i,:)*phiInit1(:,:,r,repet)) * transpose(y(i,:)*rhoInit1(:,:,r,repet)-x(i,:)*phiInit1(:,:,r,repet));
-            Gam(i,r) = piInit1(repet,r)*det(rhoInit1(:,:,r,repet))*exp(-0.5*dotProduct);
-        end
-        sumGamI = sum(Gam(i,:));
-        gamInit1(i,:,repet) = Gam(i,:) / sumGamI;
-    end
-    miniInit=int64(10);
-    maxiInit=int64(11);
-    
-    [~,~,~,LLFEssai,~] = EMGLLF(phiInit1(:,:,:,repet),rhoInit1(:,:,:,repet),piInit1(repet,:),gamInit1(:,:,repet),miniInit,maxiInit,1,0,x,y,tau);
-    LLFinit1(repet)=LLFEssai(end);
-end
-[~,b]=max(LLFinit1);
-
-phiInit=phiInit1(:,:,:,b);
-rhoInit=rhoInit1(:,:,:,b);
-piInit=piInit1(b,:);
-gamInit=gamInit1(:,:,b);
-end
\ No newline at end of file
diff --git a/OLD_MATLAB/InputParameters/selectiontotale.m b/OLD_MATLAB/InputParameters/selectiontotale.m
deleted file mode 100644
index 6c24d05..0000000
--- a/OLD_MATLAB/InputParameters/selectiontotale.m
+++ /dev/null
@@ -1,54 +0,0 @@
-function[A1,A2,Rho,Pi] = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau)
-
-	[p,m,k] = size(phiInit);
-	L = length(glambda);
-	A1 = zeros(p,m+1,L,'int64');
-	A2 = zeros(p,m+1,L,'int64');
-	Rho = zeros(m,m,k,L);
-	Pi = zeros(k,L);
-
-	%Pour chaque lambda de la grille, on calcule les coefficients
-	for lambdaIndex=1:L
-		[phi,rho,pi,~,~] = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda(lambdaIndex),X,Y,tau);
-		
-		%Si un des coefficients est supérieur au seuil, on garde cette variable
-		selectedVariables = zeros(p,m);
-		discardedVariables = zeros(p,m);
-		atLeastOneSelectedVariable = false;
-		for j=1:p
-			cpt=1;
-			cpt2=1;
-			for mm=1:m
-				if max(abs(phi(j,mm,:))) > seuil
-					selectedVariables(j,cpt) = mm;
-					cpt = cpt+1;
-					atLeastOneSelectedVariable = true;
-				else
-					discardedVariables(j,cpt2) = mm;
-					cpt2 = cpt2+1;
-				end
-			end
-		end
-		
-		%Si aucun des coefficients n'a été gardé on renvoit la matrice nulle
-		%Et si on enlevait ces colonnes de zéro ??? Indices des colonnes vides
-		if atLeastOneSelectedVariable
-			vec = [];
-			for j=1:p
-				if selectedVariables(j,1) ~= 0
-					vec = [vec;j];
-				end
-			end
-			
-			%Sinon on renvoit les numéros des coefficients utiles
-			A1(:,1,lambdaIndex) = [vec;zeros(p-length(vec),1)];
-			A1(1:length(vec),2:m+1,lambdaIndex) = selectedVariables(vec,:);
-			A2(:,1,lambdaIndex) = 1:p;
-			A2(:,2:m+1,lambdaIndex) = discardedVariables;
-			Rho(:,:,:,lambdaIndex) = rho;
-			Pi(:,lambdaIndex) = pi;
-		end
-
-	end
-
-end
diff --git a/OLD_MATLAB/ProcLassoMLE/EMGLLF.m b/OLD_MATLAB/ProcLassoMLE/EMGLLF.m
deleted file mode 100644
index 1be6ba0..0000000
--- a/OLD_MATLAB/ProcLassoMLE/EMGLLF.m
+++ /dev/null
@@ -1,174 +0,0 @@
-function[phi,rho,pi,LLF,S] = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
-
-	%Get matrices dimensions
-	PI = 4.0 * atan(1.0);
-	n = size(X, 1);
-	[p,m,k] = size(phiInit);
-	
-	%Initialize outputs
-	phi = phiInit;
-	rho = rhoInit;
-	pi = piInit;
-	LLF = zeros(maxi,1);
-	S = zeros(p,m,k);
-	
-	%Other local variables
-	%NOTE: variables order is always n,p,m,k
-	gam = gamInit;
-	Gram2 = zeros(p,p,k);
-	ps2 = zeros(p,m,k);
-	b = zeros(k,1);
-	pen = zeros(maxi,k);
-	X2 = zeros(n,p,k);
-	Y2 = zeros(n,m,k);
-	dist = 0;
-	dist2 = 0;
-	ite = 1;
-	pi2 = zeros(k,1);
-	ps = zeros(m,k);
-	nY2 = zeros(m,k);
-	ps1 = zeros(n,m,k);
-	nY21 = zeros(n,m,k);
-	Gam = zeros(n,k);
-	EPS = 1e-15;
-
-	while ite<=mini || (ite<=maxi && (dist>=tau || dist2>=sqrt(tau)))
-		
-		Phi = phi;
-		Rho = rho;
-		Pi = pi;
-		
-		%Calculs associés à Y et X
-		for r=1:k
-			for mm=1:m
-				Y2(:,mm,r) = sqrt(gam(:,r)) .* Y(:,mm);
-			end
-			for i=1:n
-				X2(i,:,r) = X(i,:) .* sqrt(gam(i,r));
-			end
-			for mm=1:m
-				ps2(:,mm,r) = transpose(X2(:,:,r)) * Y2(:,mm,r);
-			end
-			for j=1:p
-				for s=1:p
-					Gram2(j,s,r) = dot(X2(:,j,r), X2(:,s,r));
-				end
-			end
-		end
-
-		%%%%%%%%%%
-		%Etape M %
-		%%%%%%%%%%
-		
-		%Pour pi
-		for r=1:k
-			b(r) = sum(sum(abs(phi(:,:,r))));
-		end
-		gam2 = sum(gam,1);
-		a = sum(gam*transpose(log(pi)));
-		
-		%tant que les proportions sont negatives
-		kk = 0;
-		pi2AllPositive = false;
-		while ~pi2AllPositive
-			pi2 = pi + 0.1^kk * ((1/n)*gam2 - pi);
-			pi2AllPositive = true;
-			for r=1:k
-				if pi2(r) < 0
-					pi2AllPositive = false;
-					break;
-				end
-			end
-			kk = kk+1;
-		end
-		
-		%t(m) la plus grande valeur dans la grille O.1^k tel que ce soit
-		%décroissante ou constante
-		while (-1/n*a+lambda*((pi.^gamma)*b))<(-1/n*gam2*transpose(log(pi2))+lambda.*(pi2.^gamma)*b) && kk<1000
-			pi2 = pi+0.1^kk*(1/n*gam2-pi);
-			kk = kk+1;
-		end
-		t = 0.1^(kk);
-		pi = (pi+t*(pi2-pi)) / sum(pi+t*(pi2-pi));
-
-		%Pour phi et rho
-		for r=1:k
-			for mm=1:m
-				for i=1:n
-					ps1(i,mm,r) = Y2(i,mm,r) * dot(X2(i,:,r), phi(:,mm,r));
-					nY21(i,mm,r) = (Y2(i,mm,r))^2;
-				end
-				ps(mm,r) = sum(ps1(:,mm,r));
-				nY2(mm,r) = sum(nY21(:,mm,r));
-				rho(mm,mm,r) = ((ps(mm,r)+sqrt(ps(mm,r)^2+4*nY2(mm,r)*(gam2(r))))/(2*nY2(mm,r)));
-			end
-		end
-		for r=1:k
-			for j=1:p
-				for mm=1:m	
-					S(j,mm,r) = -rho(mm,mm,r)*ps2(j,mm,r) + dot(phi(1:j-1,mm,r),Gram2(j,1:j-1,r)')...
-						+ dot(phi(j+1:p,mm,r),Gram2(j,j+1:p,r)');
-					if abs(S(j,mm,r)) <= n*lambda*(pi(r)^gamma)
-						phi(j,mm,r)=0;
-					else 
-						if S(j,mm,r)> n*lambda*(pi(r)^gamma)
-							phi(j,mm,r)=(n*lambda*(pi(r)^gamma)-S(j,mm,r))/Gram2(j,j,r);
-						else
-							phi(j,mm,r)=-(n*lambda*(pi(r)^gamma)+S(j,mm,r))/Gram2(j,j,r);
-						end
-					end
-				end
-			end
-		end
-
-		%%%%%%%%%%
-		%Etape E %
-		%%%%%%%%%%
-		
-		sumLogLLF2 = 0.0;
-		for i=1:n
-			%precompute dot products to numerically adjust their values
-			dotProducts = zeros(k,1);
-			for r=1:k
-				dotProducts(r)= (Y(i,:)*rho(:,:,r)-X(i,:)*phi(:,:,r)) * transpose(Y(i,:)*rho(:,:,r)-X(i,:)*phi(:,:,r));
-			end
-			shift = 0.5*min(dotProducts);
-			
-			%compute Gam(:,:) using shift determined above
-			sumLLF1 = 0.0;
-			for r=1:k
-				Gam(i,r) = pi(r)*det(rho(:,:,r))*exp(-0.5*dotProducts(r) + shift);
-				sumLLF1 = sumLLF1 + Gam(i,r)/(2*PI)^(m/2);
-			end
-			sumLogLLF2 = sumLogLLF2 + log(sumLLF1);
-			sumGamI = sum(Gam(i,:));
-			if sumGamI > EPS
-				gam(i,:) = Gam(i,:) / sumGamI;
-			else
-				gam(i,:) = zeros(k,1);
-			end
-		end
-		
-		sumPen = 0.0;
-		for r=1:k
-			sumPen = sumPen + pi(r).^gamma .* b(r);
-		end
-		LLF(ite) = -(1/n)*sumLogLLF2 + lambda*sumPen;
-		
-		if ite == 1
-			dist = LLF(ite);
-		else 
-			dist = (LLF(ite)-LLF(ite-1))/(1+abs(LLF(ite)));
-		end
-		
-		Dist1=max(max(max((abs(phi-Phi))./(1+abs(phi)))));
-		Dist2=max(max(max((abs(rho-Rho))./(1+abs(rho)))));
-		Dist3=max(max((abs(pi-Pi))./(1+abs(Pi))));
-		dist2=max([Dist1,Dist2,Dist3]); 
-		
-		ite=ite+1;
-	end
-
-	pi = transpose(pi);
-
-end
diff --git a/OLD_MATLAB/ProcLassoMLE/compileMex.m b/OLD_MATLAB/ProcLassoMLE/compileMex.m
deleted file mode 100644
index e595409..0000000
--- a/OLD_MATLAB/ProcLassoMLE/compileMex.m
+++ /dev/null
@@ -1,9 +0,0 @@
-%compile C code (for MATLAB or Octave)
-if exist('octave_config_info')
-	setenv('CFLAGS','-O2 -std=gnu99 -fopenmp')
-	mkoctfile --mex -DOctave -I../Util EMGLLF.c EMGLLF_interface.c ../Util/ioutils.c -o EMGLLF -lm -lgsl -lgslcblas -lgomp
-    mkoctfile --mex -DOctave -I../Util constructionModelesLassoMLE.c constructionModelesLassoMLE_interface.c EMGLLF.c ../Util/ioutils.c -o constructionModelesLassoMLE -lm -lgsl -lgslcblas -lgomp
-else
-	mex CFLAGS="\$CFLAGS -O2 -std=gnu99 -fopenmp" -I../Util EMGLLF.c EMGLLF_interface.c ../Util/ioutils.c -output EMGLLF -lm -lgsl -lgslcblas -lgomp
-    mex CFLAGS="\$CFLAGS -O2 -std=gnu99 -fopenmp" -I../Util constructionModelesLassoMLE.c constructionModelesLassoMLE_interface.c EMGLLF.c ../Util/ioutils.c -output constructionModelesLassoMLE -lm -lgsl -lgslcblas -lgomp
-end
diff --git a/OLD_MATLAB/ProcLassoMLE/constructionModelesLassoMLE.m b/OLD_MATLAB/ProcLassoMLE/constructionModelesLassoMLE.m
deleted file mode 100644
index 9f977ac..0000000
--- a/OLD_MATLAB/ProcLassoMLE/constructionModelesLassoMLE.m
+++ /dev/null
@@ -1,58 +0,0 @@
-function[phi,rho,pi,lvraisemblance] = constructionModelesLassoMLE(...
-	phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau,A1,A2)
-
-	PI = 4.0 * atan(1.0);
-	
-	%get matrix sizes
-	n = size(X, 1);
-	[p,m,k] = size(phiInit);
-	L = length(glambda);
-	
-	%output parameters
-	phi = zeros(p,m,k,L);
-	rho = zeros(m,m,k,L);
-	pi = zeros(k,L);
-	lvraisemblance = zeros(L,2);
-	
-	for lambdaIndex=1:L
-		% Procedure Lasso-MLE  
-		a = A1(:,1,lambdaIndex);
-		a(a==0) = [];
-		if length(a) == 0
-			continue;
-		end
-		[phiLambda,rhoLambda,piLambda,~,~] = EMGLLF(...
-			phiInit(a,:,:),rhoInit,piInit,gamInit,mini,maxi,gamma,0,X(:,a),Y,tau);
-		
-		for j=1:length(a)
-			phi(a(j),:,:,lambdaIndex) = phiLambda(j,:,:);
-		end
-		rho(:,:,:,lambdaIndex) = rhoLambda;
-		pi(:,lambdaIndex) = piLambda;
-		
-		dimension = 0;
-		for j=1:p
-			b = A2(j,2:end,lambdaIndex);
-			b(b==0) = [];
-			if length(b) > 0
-				phi(A2(j,1,lambdaIndex),b,:,lambdaIndex) = 0.0;
-			end
-			c = A1(j,2:end,lambdaIndex);
-			c(c==0) = [];
-			dimension = dimension + length(c);
-		end
-		
-		%on veut calculer l'EMV avec toutes nos estimations
-		densite = zeros(n,L);
-		for i=1:n
-			for r=1:k
-				delta = Y(i,:)*rho(:,:,r,lambdaIndex) - (X(i,a)*(phi(a,:,r,lambdaIndex)));
-				densite(i,lambdaIndex) = densite(i,lambdaIndex) +...
-					pi(r,lambdaIndex)*det(rho(:,:,r,lambdaIndex))/(sqrt(2*PI))^m*exp(-dot(delta,delta)/2.0);
-			end
-		end
-		lvraisemblance(lambdaIndex,1) = sum(log(densite(:,lambdaIndex)));
-		lvraisemblance(lambdaIndex,2) = (dimension+m+1)*k-1;
-	end
-
-end
diff --git a/OLD_MATLAB/ProcLassoRank/EMGrank.m b/OLD_MATLAB/ProcLassoRank/EMGrank.m
deleted file mode 100644
index 6ffc313..0000000
--- a/OLD_MATLAB/ProcLassoRank/EMGrank.m
+++ /dev/null
@@ -1,69 +0,0 @@
-function[phi,LLF] = EMGrank(Pi,Rho,mini,maxi,X,Y,tau,rank)
-
-	% get matrix sizes
-	[~,m,k] = size(Rho);
-	[n,p] = size(X);
-
-	% allocate output matrices
-	phi = zeros(p,m,k);
-	Z = ones(n,1,'int64');
-	LLF = 0.0;
-
-	% local variables
-	Phi = zeros(p,m,k);
-	deltaPhi = 0.0;
-	deltaPhi = [];
-	sumDeltaPhi = 0.0;
-	deltaPhiBufferSize = 20;
-
-	%main loop (at least mini iterations)
-	ite = int64(1);
-	while ite<=mini || (ite<=maxi && sumDeltaPhi>tau)
-
-		%M step: Mise à jour de Beta (et donc phi)
-		for r=1:k
-			if (sum(Z==r) == 0)
-				continue;
-			end
-			%U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-			[U,S,V] = svd(pinv(transpose(X(Z==r,:))*X(Z==r,:))*transpose(X(Z==r,:))*Y(Z==r,:));
-			%Set m-rank(r) singular values to zero, and recompose 
-			%best rank(r) approximation of the initial product
-			S(rank(r)+1:end,:) = 0;
-			phi(:,:,r) = U * S * transpose(V) * Rho(:,:,r);
-		end
-		
-		%Etape E et calcul de LLF
-		sumLogLLF2 = 0.0;
-		for i=1:n
-			sumLLF1 = 0.0;
-			maxLogGamIR = -Inf;
-			for r=1:k
-				dotProduct = (Y(i,:)*Rho(:,:,r)-X(i,:)*phi(:,:,r)) * transpose(Y(i,:)*Rho(:,:,r)-X(i,:)*phi(:,:,r));
-				logGamIR = log(Pi(r)) + log(det(Rho(:,:,r))) - 0.5*dotProduct;
-				%Z(i) = index of max (gam(i,:))
-				if logGamIR > maxLogGamIR
-					Z(i) = r;
-					maxLogGamIR = logGamIR;
-				end
-				sumLLF1 = sumLLF1 + exp(logGamIR) / (2*pi)^(m/2);
-			end
-			sumLogLLF2 = sumLogLLF2 + log(sumLLF1);
-		end
-		
-		LLF = -1/n * sumLogLLF2;
-
-		% update distance parameter to check algorithm convergence (delta(phi, Phi))
-		deltaPhi = [ deltaPhi, max(max(max((abs(phi-Phi))./(1+abs(phi))))) ];
-		if length(deltaPhi) > deltaPhiBufferSize
-			deltaPhi = deltaPhi(2:length(deltaPhi));
-		end
-		sumDeltaPhi = sum(abs(deltaPhi));
-
-		% update other local variables
-		Phi = phi;
-		ite = ite+1;
-
-	end
-
-end
diff --git a/OLD_MATLAB/ProcLassoRank/compileMex.m b/OLD_MATLAB/ProcLassoRank/compileMex.m
deleted file mode 100644
index 044f442..0000000
--- a/OLD_MATLAB/ProcLassoRank/compileMex.m
+++ /dev/null
@@ -1,9 +0,0 @@
-%compile C code (for MATLAB or Octave)
-if exist('octave_config_info')
-	setenv('CFLAGS','-O2 -std=gnu99 -fopenmp')
-	mkoctfile --mex -DOctave -I../Util EMGrank.c EMGrank_interface.c ../Util/ioutils.c -o EMGrank -lm -lgsl -lgslcblas -lgomp
-    mkoctfile --mex -DOctave -I../Util constructionModelesLassoRank.c constructionModelesLassoRank_interface.c EMGrank.c ../Util/ioutils.c -o constructionModelesLassoRank -lm -lgsl -lgslcblas -lgomp
-else
-	mex CFLAGS="\$CFLAGS -O2 -std=gnu99 -fopenmp" -I../Util EMGrank.c EMGrank_interface.c ../Util/ioutils.c -output EMGrank -lm -lgsl -lgslcblas -lgomp
-    mex CFLAGS="\$CFLAGS -O2 -std=gnu99 -fopenmp" -I../Util constructionModelesLassoRank.c constructionModelesLassoRank_interface.c EMGrank.c ../Util/ioutils.c -output constructionModelesLassoRank -lm -lgsl -lgslcblas -lgomp
-end
diff --git a/OLD_MATLAB/ProcLassoRank/constructionModelesLassoRank.m b/OLD_MATLAB/ProcLassoRank/constructionModelesLassoRank.m
deleted file mode 100644
index ae5e34e..0000000
--- a/OLD_MATLAB/ProcLassoRank/constructionModelesLassoRank.m
+++ /dev/null
@@ -1,40 +0,0 @@
-function[phi,lvraisemblance] = constructionModelesLassoRank(Pi,Rho,mini,maxi,X,Y,tau,A1,rangmin,rangmax)
-
-	PI = 4.0 * atan(1.0);
-	
-	%get matrix sizes
-	[n,p] = size(X);
-	[~,m,k,~] = size(Rho);
-	L = size(A1, 2); %A1 est p x m+1 x L ou p x L ?!
-	
-	%On cherche les rangs possiblement intéressants
-	deltaRank = rangmax - rangmin + 1;
-	Size = deltaRank^k;
-	Rank = zeros(Size,k,'int64');
-	for r=1:k
-		%On veut le tableau de toutes les combinaisons de rangs possibles
-		%Dans la première colonne : on répète (rangmax-rangmin)^(k-1) chaque chiffre : ca remplit la colonne
-		%Dans la deuxieme : on répète (rangmax-rangmin)^(k-2) chaque chiffre, et on fait ca (rangmax-rangmin)^2 fois 
-		%...
-		%Dans la dernière, on répète chaque chiffre une fois, et on fait ca (rangmin-rangmax)^(k-1) fois.
-		Rank(:,r) = rangmin + reshape(repmat(0:(deltaRank-1), deltaRank^(k-r), deltaRank^(r-1)), Size, 1);
-	end
-	
-	%output parameters
-	phi = zeros(p,m,k,L*Size);
-	lvraisemblance = zeros(L*Size,2);
-	for lambdaIndex=1:L
-		%On ne garde que les colonnes actives
-		%active sera l'ensemble des variables informatives
-		active = A1(:,lambdaIndex);
-		active(active==0) = [];
-		if length(active) > 0
-			for j=1:Size
-				[phiLambda,LLF] = EMGrank(Pi(:,lambdaIndex),Rho(:,:,:,lambdaIndex),mini,maxi,X(:,active),Y,tau,Rank(j,:));
-				lvraisemblance((lambdaIndex-1)*Size+j,:) = [LLF, sum(Rank(j,:) .* (length(active)-Rank(j,:)+m))];
-				phi(active,:,:,(lambdaIndex-1)*Size+j) = phiLambda;
-			end
-		end
-	end
-
-end
diff --git a/OLD_MATLAB/SelectModel/selectionModelesLassoMLE.m b/OLD_MATLAB/SelectModel/selectionModelesLassoMLE.m
deleted file mode 100644
index ab39371..0000000
--- a/OLD_MATLAB/SelectModel/selectionModelesLassoMLE.m
+++ /dev/null
@@ -1,12 +0,0 @@
-%Selection de modèle dans la procédure Lasso-MLE
-% Selection de modele : voici les parametres selectionnes pour chaque
-% dimension, et l'indice associe
-[indiceLassoMLE,D1LassoMLE] = selectionmodele(vraisLassoMLE);
-
-%on veut tracer la courbe
-%figure(2)
-%plot(D1LassoMLE/n,vraisLassoMLE(indiceLassoMLE),'.')
-%on veut appliquer  Capushe : il nous faut un tableau de données
-tableauLassoMLE = enregistrerdonnees(D1LassoMLE,vraisLassoMLE(indiceLassoMLE,:),n);
-save tableauLassoMLE.mat tableauLassoMLE
-%On conclut avec Capushe !
diff --git a/OLD_MATLAB/SelectModel/selectionModelesLassoRank.m b/OLD_MATLAB/SelectModel/selectionModelesLassoRank.m
deleted file mode 100644
index aacc460..0000000
--- a/OLD_MATLAB/SelectModel/selectionModelesLassoRank.m
+++ /dev/null
@@ -1,11 +0,0 @@
-%Selection de modèle dans la procedure Lasso Rank
-vraisLassoRank=vraisLassoRank';
-% Selection de modele : voici les parametres selectionnes pour chaque
-% dimension, et l'indice associe
-[indiceLassoRank,D1LassoRank]=selectionmodele(vraisLassoRank);
-tableauLassoRank=enregistrerdonnees(D1LassoRank,vraisLassoRank(indiceLassoRank,:),n);
-%On veut tracer la courbe des pentes
-%figure(2)
-%plot(D1LassoRank/n,vraisLassoRank(indiceLassoRank),'.')
-save tableauLassoRank.mat tableauLassoRank
-%On conclut avec Capushe !
diff --git a/OLD_MATLAB/SelectModel/selectiondindice.m b/OLD_MATLAB/SelectModel/selectiondindice.m
deleted file mode 100644
index 4ef8b96..0000000
--- a/OLD_MATLAB/SelectModel/selectiondindice.m
+++ /dev/null
@@ -1,19 +0,0 @@
-%utile dans selectiontotale.m, remarque quels coefficients sont nuls et lesquels ne le sont pas
-function[A,B]=selectiondindice(phi,seuil)
-
-	[pp,m,~]=size(phi);
-	A=zeros(pp,m);
-	B=zeros(pp,m);
-	for j=1:pp
-		cpt=0;cpt2=0;
-		for mm=1:m
-			if (max(phi(j,mm,:)) > seuil)
-				cpt=cpt+1;
-				A(j,cpt)=mm;
-			else cpt2=cpt2+1;
-				 B(j,cpt2)=mm;
-			end
-		end
-	end
-
-end 
diff --git a/OLD_MATLAB/SelectModel/selectionmodele.m b/OLD_MATLAB/SelectModel/selectionmodele.m
deleted file mode 100644
index 33828ab..0000000
--- a/OLD_MATLAB/SelectModel/selectionmodele.m
+++ /dev/null
@@ -1,20 +0,0 @@
-function [indice,D1]=selectionmodele(vraisemblance)
-
-	D=vraisemblance(:,2);
-	[D1]=unique(D);
-	indice=ones(1,length(D1));
-	%On ne sélectionne que celui qui maximise : l'EMV
-	if length(D1)>2
-		for i=1:length(D1)
-			a=[];
-			for j=1:length(D)
-				if D(j)==D1(i)
-					a=[a,vraisemblance(j,1)];
-				end
-			end
-			b=max(a);
-			indice(i)=find(vraisemblance(:,1)==b,1);
-		end
-	end
-
-end
diff --git a/OLD_MATLAB/SelectModel/suppressionmodelesegaux.m b/OLD_MATLAB/SelectModel/suppressionmodelesegaux.m
deleted file mode 100644
index 3d2a3a9..0000000
--- a/OLD_MATLAB/SelectModel/suppressionmodelesegaux.m
+++ /dev/null
@@ -1,18 +0,0 @@
-function[B1,B2,glambda,ind,rho,pi]=suppressionmodelesegaux(B1,B2,glambda,rho,pi)
-
-	ind=[];
-	for l=1:length(glambda) 
-		for ll=1:l-1
-			if B1(:,:,l)==B1(:,:,ll)
-				ind=[ind l];
-			end
-		end
-	end
-	ind=unique(ind);
-	B1(:,:,ind)=[];
-	glambda(ind)=[];
-	B2(:,:,ind)=[];
-	rho(:,:,:,ind)=[];
-	pi(:,ind)=[];
-
-end
diff --git a/OLD_MATLAB/SelectModel/suppressionmodelesegaux2.m b/OLD_MATLAB/SelectModel/suppressionmodelesegaux2.m
deleted file mode 100644
index 3158d36..0000000
--- a/OLD_MATLAB/SelectModel/suppressionmodelesegaux2.m
+++ /dev/null
@@ -1,17 +0,0 @@
-function[B1,ind,rho,pi]=suppressionmodelesegaux2(B1,rho,pi)
-
-	ind=[];
-	nombreLambda=size(B1,2);
-	for l=1:nombreLambda
-		for ll=1:l-1
-			if B1(:,l)==B1(:,ll)
-				ind=[ind l];
-			end
-		end
-	end
-	ind=unique(ind);
-	B1(:,ind)=[];
-	rho(:,:,:,ind)=[];
-	pi(:,ind)=[];
-
-end