export(initSmallEM)
export(modelSelection)
export(selectVariables)
+export(valse)
importFrom(methods,new)
importFrom(stats,cutree)
importFrom(stats,dist)
pi = list()
llh = list()
- for (lambda in 1:L){
+ out = lapply( seq_along(selected), function(lambda)
+ {
sel.lambda = selected[[lambda]]
col.sel = which(colSums(sel.lambda)!=0)
res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[,col.sel],Y,tau)
det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
}
llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
- rho[[lambda]] = rhoLambda
- phi[[lambda]] = phiLambda
- pi[[lambda]] = piLambda
- llh[[lambda]] = llhLambda
+ list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
}
+ )
+ return(out)
}
- return(list("phi"=phi, "rho"=rho, "pi"=pi, "llh" = llh))
}
#' @param X matrix of covariates (of size n*p)
#' @param Y matrix of responses (of size n*m)
#' @param procedure among 'LassoMLE' or 'LassoRank'
-#' @param selecMod method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'
+#' @param selecMod method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC'
#' @param gamma integer for the power in the penaly, by default = 1
#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
#' @return a list with estimators of parameters
#' @export
#-----------------------------------------------------------------------
-valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'BIC',gamma = 1,mini = 10,
- maxi = 100,eps = 1e-4,kmin = 2,kmax = 5,
+valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'DDSE',gamma = 1,mini = 10,
+ maxi = 100,eps = 1e-4,kmin = 2,kmax = 3,
rang.min = 1,rang.max = 10) {
##################################
#core workflow: compute all models
##################################
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
+ p = dim(X)[2]
+ m = dim(Y)[2]
n = dim(X)[1]
- tableauRecap = array(, dim=c(1000,4))
+ model = list()
+ tableauRecap = array(0, dim=c(1000,4))
cpt = 0
print("main loop: over all k and all lambda")
-
-for (k in kmin:kmax){
+
+ for (k in kmin:kmax){
print(k)
print("Parameters initialization")
#smallEM initializes parameters by k-means and regression model in each component,
piInit <<- init$piInit
gamInit <<- init$gamInit
source('~/valse/pkg/R/gridLambda.R')
- gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
+ grid_lambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
print("Compute relevant parameters")
#select variables according to each regularization parameter
#from the grid: A1 corresponding to selected variables, and
#A2 corresponding to unselected variables.
- params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
- params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
+ params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda,X,Y,1e-8,eps)
+ #params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda[seq(1,length(grid_lambda), by=3)],X,Y,1e-8,eps)
## etrange : params et params 2 sont différents ...
selected <<- params$selected
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
- LLH = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)]
- D = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)+1]
+ llh = matrix(ncol = 2)
+ for (l in seq_along(model[[k]])){
+ llh = rbind(llh, model[[k]][[l]]$llh)
+ }
+ LLH = llh[-1,1]
+ D = llh[-1,2]
} else {
print('run the procedure Lasso-Rank')
#compute parameter estimations, with the Low Rank
cpt = cpt+length(model[[k]])
}
print('Model selection')
-
- tableauRecap = array(dim = c())
- if (selecMod == 'SlopeHeuristic') {
-
+ tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
+ tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
+ data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
+ require(capushe)
+ modSel = capushe(data, n)
+ if (selecMod == 'DDSE') {
+ indModSel = as.numeric(modSel@DDSE@model)
+ } else if (selecMod == 'Djump') {
+ indModSel = as.numeric(modSel@Djump@model)
} else if (selecMod == 'BIC') {
- BIC = -2*tableauRecap[,1]+log(n)*tableauRecap[,2]
- indMinBIC = which.min(BIC)
- return(model[[tableauRecap[indMinBIC,3]]][[tableauRecap[indMinBIC,4]]])
+ indModSel = modSel@BIC_capushe$model
} else if (selecMod == 'AIC') {
-
+ indModSel = modSel@AIC_capushe$model
}
+ return(model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]])
}
generateXY(meanX, covX, covY, pi, beta, n)
}
\arguments{
-\item{meanX}{matrix of group means for covariates (of size p*K)}
+\item{meanX}{matrix of group means for covariates (of size p)}
-\item{covX}{covariance for covariates (of size p*p*K)}
+\item{covX}{covariance for covariates (of size p*p)}
\item{covY}{covariance for the response vector (of size m*m*K)}
\item{pi}{proportion for each cluster}
-\item{beta}{regression matrix}
+\item{beta}{regression matrix, of size p*m*k}
\item{n}{sample size}
}
% Please edit documentation in R/selectVariables.R
\name{selectVariables}
\alias{selectVariables}
-\title{selectVaribles
-It is a function which construct, for a given lambda, the sets of
-relevant variables and irrelevant variables.}
+\title{selectVariables}
\usage{
selectVariables(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, glambda,
- X, Y, thres, tau)
+ X, Y, thresh, tau, ncores = 1)
}
\arguments{
\item{phiInit}{an initial estimator for phi (size: p*m*k)}
\item{Y}{matrix of responses}
-\item{thres}{threshold to consider a coefficient to be equal to 0}
-
\item{tau}{threshold to say that EM algorithm has converged}
+
+\item{thres}{threshold to consider a coefficient to be equal to 0}
}
\value{
-TODO
+a list of outputs, for each lambda in grid: selected,Rho,Pi
}
\description{
-selectVaribles
-It is a function which construct, for a given lambda, the sets of
-relevant variables and irrelevant variables.
+It is a function which construct, for a given lambda, the sets of relevant variables.
}
\examples{
TODO
--- /dev/null
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/valse.R
+\name{valse}
+\alias{valse}
+\title{Main function}
+\usage{
+valse(X, Y, procedure = "LassoMLE", selecMod = "BIC", gamma = 1,
+ mini = 10, maxi = 100, eps = 1e-04, kmin = 2, kmax = 5,
+ rang.min = 1, rang.max = 10)
+}
+\arguments{
+\item{X}{matrix of covariates (of size n*p)}
+
+\item{Y}{matrix of responses (of size n*m)}
+
+\item{procedure}{among 'LassoMLE' or 'LassoRank'}
+
+\item{selecMod}{method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'}
+
+\item{gamma}{integer for the power in the penaly, by default = 1}
+
+\item{mini}{integer, minimum number of iterations in the EM algorithm, by default = 10}
+
+\item{maxi}{integer, maximum number of iterations in the EM algorithm, by default = 100}
+
+\item{eps}{real, threshold to say the EM algorithm converges, by default = 1e-4}
+
+\item{kmin}{integer, minimum number of clusters, by default = 2}
+
+\item{kmax}{integer, maximum number of clusters, by default = 10}
+
+\item{rang.min}{integer, minimum rank in the low rank procedure, by default = 1}
+
+\item{rang.max}{integer, maximum rank in the}
+}
+\value{
+a list with estimators of parameters
+}
+\description{
+Main function
+}
+
--- /dev/null
+niveauColores
+
+for L=ind
+ for r=1:max(b(:,L))
+ betaLassoMLE(:,:,r)=inv(rhoLassoMLE(:,:,r,L)) * phiLassoMLE(:,:,r,L);
+ end
+end
+dessinRho = zeros(max(K),size(rhoLassoMLE,1),max(ind));
+cpt=1;
+gr = gray(64);
+
+ for L=ind
+ k=size(find(piLassoMLE(:,L)~=0),1)
+ figure(1)
+ %for r=1:k
+ r=1
+ subplot(1,k+1,r)
+ cpt=cpt+1;
+ colormap(gr(end:-1:1,:));
+ imagesc(abs(betaLassoMLE(:,:,r)))
+ set(gca,'xtick',[],'ytick',[])
+ set(gca, 'FontSize', 30)
+ title(['$\hat{\underline{\beta}_1}$'],'FontSize', 45,'Interpreter','latex')
+ h=colorbar;
+ set(gca, 'FontSize', 30)
+ r=2
+ subplot(1,k+1,r)
+ cpt=cpt+1;
+ colormap(gr(end:-1:1,:));
+ imagesc(abs(betaLassoMLE(:,:,r)))
+ set(gca,'xtick',[],'ytick',[])
+ set(gca, 'FontSize', 30)
+ title(['$\hat{\underline{\beta}_2}$'],'FontSize', 45,'Interpreter','latex')
+ h=colorbar;
+ set(gca, 'FontSize', 30)
+
+ %end
+ subplot(1,k+1,k+1)
+ gr = gray(64);
+ colormap(gr(end:-1:1,:));
+ imagesc(abs(betaLassoMLE(:,:,1)-betaLassoMLE(:,:,2)))
+ set(gca,'xtick',[],'ytick',[])
+ title('$|\hat{\underline{\beta}_1}-\hat{\underline{\beta}}_2|$','FontSize',45,'Interpreter','latex')
+ h=colorbar;
+ set(gca, 'FontSize', 30)
+ figure(L+k+1)
+ for r=1:k
+ for z=1:size(rhoLassoMLE,1)
+ dessinRho(r,z,L) = inv(rhoLassoMLE(z,z,r,L)^2);
+ end
+ end
+
+ %for r=1:k
+ r=1
+ subplot(k,1,r)
+% if r==1
+% colormap(cmapRouge(end:-1:1,:));
+% else if r==2
+% colormap(cmapBleu(end:-1:1,:));
+% else colormap(cmapVert(end:-1:1,:));
+% end
+% end
+ imagesc(dessinRho(r,:,L))
+ set(gca,'xtick',[],'ytick',[])
+ xlabh = get(gca,'XLabel');
+ set(xlabh,'Position',get(xlabh,'Position') + [0 .15 0])
+ title('$\hat{\Sigma}_1$','FontSize', 55,'Interpreter','latex')
+ xlabel('d_4[1] d_4[2] d_4[3] d_3[1] d_3[2] d_3[3] d_3[4] d_3[5] d_3[6]','FontSize', 30)
+ set(gca, 'FontSize', 45)
+ set(gca,'Position',[0.01 0.1+0.5*(2-r) 0.9 0.2])
+
+ r=2
+ subplot(k,1,r)
+% if r==1
+% colormap(cmapRouge(end:-1:1,:));
+% else if r==2
+% colormap(cmapBleu(end:-1:1,:));
+% else colormap(cmapVert(end:-1:1,:));
+% end
+% end
+ imagesc(dessinRho(r,:,L))
+ set(gca,'xtick',[],'ytick',[])
+ xlabh = get(gca,'XLabel');
+ set(xlabh,'Position',get(xlabh,'Position') + [0 .15 0])
+ title('$\hat{\Sigma}_2$','FontSize', 55,'Interpreter','latex')
+ xlabel('d_4[1] d_4[2] d_4[3] d_3[1] d_3[2] d_3[3] d_3[4] d_3[5] d_3[6]','FontSize', 30)
+ set(gca, 'FontSize', 45)
+ set(gca,'Position',[0.01 0.1+0.5*(2-r) 0.9 0.2])
+ %end
+ %h=colorbar;
+ gr = gray(64);
+ colormap(gr(end:-1:1,:));
+ axes('Position', [0.14 0.1 0.8 0.8], 'Visible', 'off');
+% c=colorbar;
+% set(gca, 'FontSize', 40)
+% caxis([0 max(max(dessinRho(:,:,L)))])
+end
\ No newline at end of file
--- /dev/null
+%niveau de rouge
+cmapRouge = ones(100,3);
+for i=1:100
+ cmapRouge(i,2) = i/100;
+ cmapRouge(i,3) = i/100;
+end
+%niveau de vert
+cmapVert = ones(100,3);
+for i=1:100
+ cmapVert(i,1) = i/100;
+ cmapVert(i,3) = i/100;
+end
+%niveau de bleu
+cmapBleu = ones(100,3);
+for i=1:100
+ cmapBleu(i,1) = i/100;
+ cmapBleu(i,2) = i/100;
+end
\ No newline at end of file
--- /dev/null
+%Dessin ondelettes
+%script
+figure(1)
+courbe = X2(:,1)-mean(X2(:,1));
+[C,L] = wavedec(courbe, 4,'haar');
+
+subplot(6,1,1)
+plot(1:7,courbe(1:7),'b','LineWidth',2)
+axis([0 48 -1.6 1.6])
+hold on
+plot(8:48,courbe(8:48),'b','LineWidth',2)
+%plot(waverec([C(1:12)',zeros(1,36)],L,'haar'),'b--','LineWidth',2)
+%courbe2 = X2(:,1);
+%[C2,L2] = wavedec(courbe2,4,'haar');
+%plot(waverec([zeros(1,3),C2(4:12)',zeros(1,36)],L2,'haar'),'b-.','LineWidth',2)
+hold off
+ylabel('z','FontSize', 30)
+set(gca, 'FontSize', 20)
+subplot(6,1,2)
+A4=zeros(1,48);
+Coeff = zeros(5,48);
+for r=1:16
+ A4(r)=C(1)/power(2,5/2);
+ A4(r+16)=C(2)/power(2,5/2);
+ A4(r+32) = C(3)/power(2,5/2);
+end
+Coeff(5,8)=C(1);
+Coeff(5,24) = C(2);
+Coeff(5,40) = C(3);
+stairs(A4,'b','LineWidth',2)
+axis([0 48 -0.5 0.5])
+ylabel('A_4','FontSize', 30)
+set(gca, 'FontSize', 20)
+subplot(6,1,3)
+D4=zeros(1,48);
+for r=1:8
+ D4(r) = C(4)/power(2,4/2);
+ D4(r+8) = -C(4)/power(2,4/2);
+ D4(r+16) = C(5)/power(2,4/2);
+ D4(r+24) = -C(5)/power(2,4/2);
+ D4(r+32) = C(6)/power(2,4/2);
+ D4(r+40) = -C(6)/power(2,4/2);
+end
+Coeff(4,8) = C(4);
+Coeff(4,24) = C(5);
+Coeff(4,40) = C(6);
+stairs(D4,'b','LineWidth',2)
+axis([0 48 -0.9 0.9])
+ylabel('D_4','FontSize', 30)
+ set(gca, 'FontSize', 20)
+subplot(6,1,4)
+D3=zeros(1,48);
+for k=1:12
+ for r=1:4
+ D3(r+4*(k-1)) = (-1)^(k+1) *C(7+floor((k-1)/2))/power(2,3/2);
+ end
+end
+stairs(D3,'b','LineWidth',2)
+ylabel('D_3','FontSize', 30)
+axis([0 48 -0.5 0.5])
+ set(gca, 'FontSize', 20)
+subplot(6,1,5)
+D2=zeros(1,48);
+for k=1:24
+ for r=1:2
+ D2(r+2*(k-1)) = (-1)^(k+1) *C(13+floor((k-1)/2))/power(2,2/2);
+ end
+end
+stairs(D2,'b','LineWidth',2)
+ylabel('D_2','FontSize', 30)
+axis([0 48 -0.8 0.8])
+ set(gca, 'FontSize', 20)
+subplot(6,1,6)
+D1=zeros(1,48);
+for k=1:48
+ for r=1
+ D1(r+1*(k-1)) = (-1)^(k+1) *C(25+floor((k-1)/2))/power(2,1/2);
+ end
+end
+plot(D1,'b','LineWidth',2);
+axis([0 48 -0.9 0.9])
+ylabel('D_1','FontSize', 30)
+set(gca, 'FontSize', 20)
--- /dev/null
+ondelettes
+niveauColores
+figure(2)
+subplot(6,1,1)
+plot(1:7,courbe(1:7),'b','LineWidth',2)
+hold on
+plot(8:48,courbe(8:48),'b','LineWidth',2)
+set(gca,'ytick',[])
+ylabel('z','FontSize', 30)
+set(gca, 'FontSize', 20)
+axis([0 48 -1.6 1.6])
+set(B, 'Position', [.91 .11 .03 .8150])
+subplot(6,1,2:6)
+colormap([cmapVert([1:1:end],:);cmapBleu(end:-1:1,:)]);
+ %colormap(gr(end:-1:1,:));
+indice = [3,3,6,12,24];
+indice2=[0,cumsum(indice)];
+beta = zeros(5,48);
+for j=1:indice(1)
+ for l=1:2^4
+ beta(1,(j-1)*2^4+l) = C(indice2(1)+j);
+ end
+end
+for r=2:5
+ for j=1:indice(r)
+ for l=1:2^(5-(r-1))
+ beta(r,(j-1)*2^(5-(r-1))+l) = C(indice2(r)+j);
+ end
+ end
+end
+
+
+imagesc(beta)
+ylabel('d_1 d_2 d_3 d_4 a_4','FontSize', 30)
+set(gca, 'FontSize', 20)
+%gr = gray(64);
+
+
+
+set(gca,'xtick',[],'ytick',[])
+set(gca, 'FontSize', 20)
+
+% axes('Position', [0.05 0.05 0.9 0.9], 'Visible', 'off');
+% c=colorbar ('FontSize',18);
+B=colorbar;
+set(B, 'Position', [.91 .11 .03 .8150])
+%set(gca,'Position',[.1 0.1 0.9 0.9])
+set(gca, 'FontSize', 15)
+% figure(3)
+% subplot(6,1,1)
+% plot(courbe,'LineWidth',2)
+% axis([0 48 -0.7 0.7])
+% subplot(6,1,2)
+% plot([8,24,40],C(1:3),'+','LineWidth',2)
+% axis([0 48 -1.6 1.6])
+% subplot(6,1,3)
+% plot([8:16:48],C(4:6),'+','LineWidth',2)
+% axis([0 48 -0.6 0.6])
+% subplot(6,1,4)
+% plot([4:8:48],C(7:12),'+','LineWidth',2)
+% axis([0 48 -0.4 0.4])
+% subplot(6,1,5)
+% plot([2:4:48],C(13:24),'+','LineWidth',2)
+% axis([0 48 -0.2 0.2])
+% subplot(6,1,6)
+% plot([1:2:48],C(25:48),'+','LineWidth',2)
+% axis([0 48 -0.2 0.2])
\ No newline at end of file
--- /dev/null
+%Dessins prétraitements
+script
+
+XC=X2(:,1)-mean(X2(:,1));
+
+[C1,L1] = wavedec(X2(:,1),4,'haar');
+xProj1=C(4:12);
+
+[C2,L2]=wavedec(XC',4,'haar');
+xProj2=C(1:12);
+
+Xrecon1 = waverec([zeros(1,3),xProj1',zeros(1,36)],L1,'haar');
+Xrecon2 = waverec([xProj2',zeros(1,36)],L2,'haar');
+
+plot(X2(:,1),'LineWidth',2)
+hold on
+plot(Xrecon1,'r-o','LineWidth',2)
+plot(Xrecon2,'g-s','LineWidth',2)
+xlabel('Instant of the day','FontSize', 30)
+ylabel(['Load consumption and its reconstructions' sprintf('\n') ' after projecting and preprocessing'],'FontSize', 30)
+set(gca, 'FontSize', 20, 'fontName','Times');
+legend('Original signal','Reconstruction after projection and preprocessing 1','Reconstruction after projection and preprocessing 2','Location','NorthWest')
\ No newline at end of file
--- /dev/null
+ychap=zeros(n,m,k,94);
+%Ychap=zeros(n,m,l,94);
+
+for LL=1:10
+ for i=1:n
+ for r=1:2
+ ychap(i,:,r,LL)=x(i,:)*(phitrue(:,:,r,LL)*inv(rhotrue(:,:,r,LL)))';
+ Ychap(i,:,r,LL)=waverec(ychap(i,:,r,LL)',L,'sym4');
+ end
+ end
+end
+for i=1:n
+ for r=1:2
+ for LL=1:10
+ RMSE(i,LL,r)=sqrt(sum((donneesC(78:96,i)-Ychap(i,:,r,LL)').^2));
+ end
+ end
+end
+LL=88;
+plot(Ychap(89,:,2,LL),'r')
+hold on
+plot(donnees(49:96,89)/100,'b')
+hold off
+
+
+for LL=1:10
+ Z(LL,:)=principeMAP(Y,X,phiLassoMLE(:,:,:,LL),rhoLassoMLE(:,:,:,LL),piLassoMLE(:,LL),3.14);
+ sum((Z(1:100)==1)+(Z(101:200)==2))
+end
\ No newline at end of file
--- /dev/null
+beta = betaLassoMLE(:,:,1:2,ind);
+for i=1:338
+ for r=1:2
+ YChap(:,i,r) = X(i,:) * beta(:,:,r);
+ yChap(:,i,r) = waverec([zeros(1,3),YChap(:,i,r)',zeros(1,36)],L,'haar');
+ end
+end
--- /dev/null
+ind=indiceLassoMLE([2,3,4,160,165])
+cpt=0;
+for L2=ind
+ cpt = cpt+1
+% figure(L2)
+% hold on
+% for r=1:max(b(:,L2))
+% subplot(2,ceil(max(b(:,L2))/2),r)
+% plot(X2Final(:,b(:,L2)==r),c(r))
+% end
+% hold off
+ figure(1)
+ subplot(2,3,cpt)
+ hold on
+ for r=1:max(b(:,L2))
+ plot([mean(X2Final(:,b(:,L2)==r),2);mean(Y2Final(:,(b(:,L2)==r)),2)],c(r))
+ title('signal moyen de chaque classe, pour chaque modele')
+ end
+ hold off
+
+end
\ No newline at end of file
--- /dev/null
+%Saut de dimension
+figure(1)
+vec=[0,10:100:1210];
+
+for r=vec
+ tableauBis(:,:,r+1) = tableauLassoMLE;
+ [aaaa,bbbb]= min(tableauLassoMLE(:,4)+r*tableauLassoMLE(:,2));
+ tableauBis(:,4,r+1) = tableauLassoMLE(:,4)+r*tableauLassoMLE(:,2);
+ reponse(r+1)=tableauLassoMLE(bbbb,3);
+end
+
+[x1,y1] = stairs(vec,reponse(vec+1))
+plot(x1,y1,'LineWidth',2)
+xlabel('$\kappa$','Interpreter','LaTex','FontSize', 45)
+ylabel('Model dimension','FontSize', 45)
+set(gca, 'FontSize', 40, 'fontName','Times');
+[Cx1,IA,IC] = unique(y1,'stable')
+hold on
+plot([x1(IA(2))-0.001,x1(IA(2))],[0,Cx1(2)],'--','LineWidth',2)
+plot([2*x1(IA(2))-0.001,2*x1(IA(2))],[0,Cx1(3)],'--','LineWidth',2)
+%plot([0,2*x1(IA(2))],[Cx1(3),Cx1(3)],'--','LineWidth',2)
+set(gca,'Box','off')
+set(gca,'YTick',unique([0:800:700]))
+set(gca,'XTick',unique([0:1300:1200]))
+text(x1(IA(2))-20,-20,'$\hat{\kappa}$','Interpreter','LaTex','FontSize',45)
+text(2*x1(IA(2))-20,-20,'$2 \hat{\kappa}$','Interpreter','LaTex','FontSize',45)
+%text(-80,Cx1(3),'$\hat{m}$','Interpreter','LaTex','FontSize',45)
+axis([0 1200 0 650])
+hold off
+
+%LLF pénalisé
+
+% figure(2)
+% plot(tableauLassoMLE(:,3),tableauLassoMLE(:,4)+0*tableauLassoMLE(:,2),'.','markersize', 20)
+% xlabel('Model dimension','FontSize', 30)
+% ylabel('Log-likelihood','Interpreter','LaTex','FontSize', 30)
+% set(gca, 'FontSize', 20, 'fontName','Times');
+figure(3)
+ind = find(tableauLassoMLE(:,4) >4000);
+tableauLassoMLE(ind,:)=[];
+plot(tableauLassoMLE(2:end,3),tableauLassoMLE(2:end,4)+1100*tableauLassoMLE(2:end,2),'.','markersize', 30)
+indiceInt = find(tableauLassoMLE(:,4)+1100*tableauLassoMLE(:,2)<-4401);
+indiceBis = find(tableauLassoMLE(:,3)==53);
+hold on
+plot(tableauLassoMLE(indiceInt,3),tableauLassoMLE(indiceInt,4)+1100*tableauLassoMLE(indiceInt,2),'sr','markersize', 30,'linewidth',5)
+plot(tableauLassoMLE(indiceBis,3),tableauLassoMLE(indiceBis,4)+1100*tableauLassoMLE(indiceBis,2),'dg','markersize', 30,'linewidth',5)
+xlabel('Model dimension','Interpreter','LaTex','FontSize', 45)
+ylabel('Penalized log-likelihood for $2 \hat{\kappa}$','Interpreter','LaTex','FontSize', 45)
+set(gca, 'FontSize', 30, 'fontName','Times');
+set(gcf,'Units','normal')
+set(gca,'Position',[.1 .1 .88 .85])
+hold off
+% figure(4)
+% plot(tableauLassoMLE(:,3),tableauLassoMLE(:,4)+3000*tableauLassoMLE(:,2),'.','markersize', 20)
+% xlabel('Model dimension','FontSize', 30)
+% ylabel('Penalized log-likelihood for $\kappa$ = 3000','Interpreter','LaTex','FontSize', 30)
+% set(gca, 'FontSize', 20, 'fontName','Times');
+%
+% figure(5)
+% n2 = find(piLassoMLE(3,:)~=0,1)-1;
+% n3 = find(piLassoMLE(4,:)~=0,1)-1;
+% plot(tableauLassoMLE(:,3),tableauLassoMLE(:,4)+0*tableauLassoMLE(:,2),'.','markersize', 20)
+% xlabel('Model dimension','FontSize', 30)
+% ylabel('Log-likelihood','Interpreter','LaTex','FontSize', 30)
+% set(gca, 'FontSize', 20, 'fontName','Times');
\ No newline at end of file
--- /dev/null
+%clear all
+load donneesSelec.mat
+%donnees=[ind100,res100];
+donnees = BB;
+Res=mean(donnees,2);
+for i=1:349
+ X2(:,i)=Res(48*(i-1)+1:48*i);
+end
+for i=1:348
+ signal(:,i)=[X2(:,i);X2(:,i+1)];
+end
+
+[p1,n1]=size(X2);
+for i=1:n1
+ for j=1:p1
+ XC(i,j)=X2(j,i)-mean(X2(:,i));
+ end
+
+ [C,L]=wavedec(XC(i,:)',4,'haar');
+ xProj(i,:)=C(1:12);
+end
+
+X=xProj(1:end-2,:);
+Y=xProj(2:end-1,:);
+for i=1:n1
+ Xrecon(i,:)=waverec([xProj(i,:),zeros(1,36)],L,'haar');
+end
+for i=1:348
+ signal3(:,i)=[Xrecon(i,:),Xrecon(i+1,:)];
+end
--- /dev/null
+%clear all
+load donneesSelec.mat
+donnees=BB;
+
+Res=mean(donnees,2);
+%Ind=mean(donnees(:,1:100),2);
+for i=1:340
+ X2(:,i)=Res(48*(i-1)+1:48*i);
+end
+for i=1:339
+ signal(:,i)=[X2(:,i);X2(:,i+1)];
+end
+
+[p1,n1]=size(X2);
+for i=1:n1
+ [C,L]=wavedec(X2(:,i)',4,'haar');
+ xProj(i,:)=C(4:12);
+end
+
+X=xProj(1:end-2,:);
+Y=xProj(2:end-1,:);
+for i=1:n1
+ Xrecon(i,:)=waverec([zeros(1,3),xProj(i,:),zeros(1,36)],L,'haar');
+end
+
+for i=1:339
+ signal2(:,i)=[Xrecon(i,:),Xrecon(i+1,:)];
+end
--- /dev/null
+for i = 1:100
+ courbeX(i,:) = data(((i-1)*96+1):((i-1)*96+48));
+ courbeY(i,:) = data(((i-1)*96+49):((i-1)*96+96));
+end
+plot((courbeX)')
+figure(2)
+plot((courbeY)')
+
+X2 = (courbeX)'
\ No newline at end of file
--- /dev/null
+library("mclust")
+#library("R.matlab", lib.loc="~/R/x86_64-pc-linux-gnu-library/3.3")
+#redrawData = TRUE
+#if (redrawData==TRUE){
+ ###########
+ ## Model
+ ###########
+ K = 2
+ p = 48
+ T = seq(0,1.5,length.out = p)
+ T2 = seq(0,3, length.out = 2*p)
+ n = 100
+ x1 = cos(2*pi*T) + 0.2*cos(4*2*pi*T) +2*c(rep(0,round(length(T)/7)),rep(1,round(length(T)*(1-1/7))))
+ plot(T,x1)
+ lines(T,x1)
+
+ sigmaX = 0.085
+ sigmaY = 0.1
+ beta = list()
+ p1= 0.5
+ beta[[1]] =diag(c(rep(p1,5),rep(1,5), rep(p1,5), rep(1, p-15)))
+ p2 = 1
+ beta[[2]] = diag(c(rep(p2,5),rep(1,5), rep(p2,5), rep(1, p-15)))
+ ITE = 100
+ ARI1 = ARI2 = ARI3 = rep(0,ITE)
+ XY = array(0, dim = c(ITE, 2*p,n))
+ XYproj = array(0, dim=c(ITE, 96,n))
+
+ affec = list()
+ ###########
+ ## Iterations
+ ###########
+ for (ite in c(1:ITE)){
+ ###########
+ ##Sample
+ ###########
+ x = x1 + matrix(rnorm(n*p, 0, sigmaX), ncol = n)
+ affec[[ite]] = sample(c(1,2), n, replace = TRUE)
+ y = x
+ xy = matrix(0,ncol=n, nrow= 2*p)
+ for (i in c(1:n)){
+ y[,i] = x[,i] %*% beta[[affec[[ite]][i]]] + rnorm(p, 0, sigmaY)
+ xy[,i] = c(x[,i],y[,i])
+ XY[ite,,i] = xy[,i] - mean(xy[,i])
+ # Dx = dwt(x[,i], filter='haar')@W
+ # Dx = rev(unlist(Dx))
+ # Dx = Dx[2:(1+3+6+12+24)]
+ # Ax = dwt(x[,i], filter='haar')@V
+ # Ax = rev(unlist(Ax))
+ # Ax = Ax[2:(1+3)]
+ # Dy = dwt(y[,i], filter='haar')@W
+ # Dy = rev(unlist(Dy))
+ # Dy = Dy[2:(1+3+6+12+24)]
+ # Ay = dwt(y[,i], filter='haar')@V
+ # Ay = rev(unlist(Ay))
+ # Ay = Ay[2:(1+3)]
+ # XYproj[ite,,i] = c(Ax,Dx,Ay,Dy)
+ }
+ print(ite)
+ #
+ #
+ }
+ xy[c(7,55),] = NA
+ # write.table(XY,'data.csv', row.names=FALSE, col.names=FALSE)
+matplot(T2,xy[,affec[[ite]]==1],type='l', col='red', lty = 1)
+matplot(T2,xy[,affec[[ite]]==2],type='l', col='black', add=TRUE, lty= 1)
+abline(v = 1.5)
+text(0.75,0,'X', cex = 2 )
+text(0.75+1.5,0,'Y', cex = 2 )
+#proj = read.table('dataProj.csv')
+#}
+
+
+#matplot(T,x,type='l', col='black', xlab = '', ylab='', lwd=1.5,lty=1)
+#matplot(T,y[,affec[[ite]]==1],type='l', col='red', xlab = '', ylab='', lwd=1.5,lty=1)
+#matplot(T,y[,affec[[ite]]==2],type='l', col='black', add=TRUE,lwd=2,lty=1)
+# proj2 = array(0,dim=c(ITE,2*p,n))
+# for (ite in c(1:ITE)){
+# for (i in c(1:n)){
+# A = proj[ite,(1+(i-1)*96):(i*96)]
+# for (j in 1:96){
+# proj2[ite,j,i] = A[1,j]
+# }
+# }
+# print(ite)
+# }
+###########
+## Iterations
+###########
+Kmod2 = Kmod1 = rep(0,ITE)
+Kmod3 = rep(0,ITE)
+for (ite in c(1:ITE)){
+ print(ite)
+ ###########
+ ## k-means 1
+ ###########
+ mod1 = Mclust(t(XY[ite,,]),G = 2, mode='VII')
+ ARI1[ite] = adjustedRandIndex(mod1$classification, affec[[ite]])
+ Kmod1[ite] = mod1$G
+ # ###########
+ # ## k-means 2
+ # ###########
+ # #proj2 =
+ # mod2 = Mclust(t(XYproj[ite,,]),G = 1:8, mode='VII')
+ # ARI2[ite] = adjustedRandIndex(mod2$classification, affec[[ite]])
+ # Kmod2[ite] = mod2$G
+ # ###########
+ # ## k-means 1
+ # ###########
+ # #proj3 =
+ # mod3 = Mclust(t(XYproj[ite,c(4:12,52:60),]),G = 1:8, mode='VII')
+ # ARI3[ite] = adjustedRandIndex(mod3$classification, affec[[ite]])
+ # Kmod3[ite] = mod3$G
+}
+ARI0 = rep(1,ITE)
+par(cex.lab=1.5)
+par(cex.axis=1.5)
+boxplot(ARI0,ARI1, names = c('LassoMLE','K-means'), lwd=1.3)
+table(Kmod1)
+table(Kmod2)
-meanX = rep(0,6)
-covX = 0.1*diag(6)
+p = 10
+q = 15
+k = 2
+D = 20
+
+meanX = rep(0,p)
+covX = 0.1*diag(p)
-covY = array(dim = c(5,5,2))
-covY[,,1] = 0.1*diag(5)
-covY[,,2] = 0.2*diag(5)
+covY = array(dim = c(q,q,k))
+covY[,,1] = 0.1*diag(q)
+covY[,,2] = 0.2*diag(q)
-beta = array(dim = c(6,5,2))
-beta[,,2] = matrix(c(rep(2,12),rep(0, 18)))
-beta[,,1] = matrix(c(rep(1,12),rep(0, 18)))
+beta = array(dim = c(p,q,2))
+beta[,,2] = matrix(c(rep(2,(D)),rep(0, p*q-D)))
+beta[,,1] = matrix(c(rep(1,D),rep(0, p*q-D)))
-n = 500
+n = 100
pi = c(0.4,0.6)
-source('~/valse/R/generateSampleInputs.R')
data = generateXY(meanX,covX,covY, pi, beta, n)
X = data$X
Y = data$Y
-k = 2
-
-n = nrow(Y)
-m = ncol(Y)
-p = ncol(X)
-
-Zinit1 = array(0, dim=c(n))
-betaInit1 = array(0, dim=c(p,m,k))
-sigmaInit1 = array(0, dim = c(m,m,k))
-phiInit1 = array(0, dim = c(p,m,k))
-rhoInit1 = array(0, dim = c(m,m,k))
-Gam = matrix(0, n, k)
-piInit1 = matrix(0,k)
-gamInit1 = array(0, dim=c(n,k))
-LLFinit1 = list()
-
-require(MASS) #Moore-Penrose generalized inverse of matrix
-
- distance_clus = dist(X)
- tree_hier = hclust(distance_clus)
- Zinit1 = cutree(tree_hier, k)
- sum(Zinit1==1)
-
- for(r in 1:k)
- {
- Z = Zinit1
- Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
- if (length(Z_indice) == 1) {
- betaInit1[,,r] = ginv(crossprod(t(X[Z_indice,]))) %*%
- crossprod(t(X[Z_indice,]), Y[Z_indice,])
- } else {
- betaInit1[,,r] = ginv(crossprod(X[Z_indice,])) %*%
- crossprod(X[Z_indice,], Y[Z_indice,])
- }
- sigmaInit1[,,r] = diag(m)
- phiInit1[,,r] = betaInit1[,,r] #/ sigmaInit1[,,r]
- rhoInit1[,,r] = solve(sigmaInit1[,,r])
- piInit1[r] = mean(Z == r)
- }
-
- for(i in 1:n)
- {
- for(r in 1:k)
- {
- dotProduct = tcrossprod(Y[i,]%*%rhoInit1[,,r]-X[i,]%*%phiInit1[,,r])
- Gam[i,r] = piInit1[r]*det(rhoInit1[,,r])*exp(-0.5*dotProduct)
- }
- sumGamI = sum(Gam[i,])
- gamInit1[i,]= Gam[i,] / sumGamI
- }
-
- miniInit = 10
- maxiInit = 101
-
- new_EMG = EMGLLF(phiInit1,rhoInit1,piInit1,gamInit1,miniInit,maxiInit,1,0,X,Y,1e-6)
-
- new_EMG$phi
- new_EMG$pi
- LLFEessai = new_EMG$LLF
- LLFinit1 = LLFEessai[length(LLFEessai)]
-
-
-b = which.max(LLFinit1)
-phiInit = phiInit1[,,,b]
-rhoInit = rhoInit1[,,,b]
-piInit = piInit1[b,]
-gamInit = gamInit1[,,b]
+res_valse = valse(X,Y)
projects / valse.git / commitdiff