if (!fast)
{
# Function in R
- return (.EMGLLF_R(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau))
+ return (.EMGLLF_R(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,eps))
}
# Function in C
m = ncol(Y) #taille de Y (multivarié)
k = length(piInit) #nombre de composantes dans le mélange
.Call("EMGLLF",
- phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, X, Y, tau,
+ phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, X, Y, eps,
phi=double(p*m*k), rho=double(m*m*k), pi=double(k), LLF=double(maxi),
S=double(p*m*k), affec=integer(n),
n, p, m, k,
}
# R version - slow but easy to read
-.EMGLLF_R = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X2,Y,tau)
+.EMGLLF_R = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X2,Y,eps)
{
# Matrix dimensions
n = dim(Y)[1]
Dist3 = max( (abs(pi-Pi)) / (1+abs(Pi)) )
dist2 = max(Dist1,Dist2,Dist3)
- if (ite >= mini && (dist >= tau || dist2 >= sqrt(tau)))
+ if (ite >= mini && (dist >= eps || dist2 >= sqrt(eps)))
break
}
print(tableauRecap)
tableauRecap = tableauRecap[which(tableauRecap[,4]!= Inf),]
- return(tableauRecap)
-
- # modSel = capushe::capushe(tableauRecap, n)
- # indModSel <-
- # if (selecMod == 'DDSE')
- # as.numeric(modSel@DDSE@model)
- # else if (selecMod == 'Djump')
- # as.numeric(modSel@Djump@model)
- # else if (selecMod == 'BIC')
- # modSel@BIC_capushe$model
- # else if (selecMod == 'AIC')
- # modSel@AIC_capushe$model
- #
- # mod = as.character(tableauRecap[indModSel,1])
- # listMod = as.integer(unlist(strsplit(mod, "[.]")))
- # modelSel = models_list[[listMod[1]]][[listMod[2]]]
- #
- # ##Affectations
- # Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
- # for (i in 1:n){
- # for (r in 1:length(modelSel$pi)){
- # sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
- # Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
- # }
- # }
- # Gam = Gam/rowSums(Gam)
- # modelSel$affec = apply(Gam, 1,which.max)
- # modelSel$proba = Gam
- #
- # if (plot){
- # print(plot_valse(X,Y,modelSel,n))
- # }
- #
- # return(modelSel)
+ modSel = capushe::capushe(tableauRecap, n)
+ indModSel <-
+ if (selecMod == 'DDSE')
+ as.numeric(modSel@DDSE@model)
+ else if (selecMod == 'Djump')
+ as.numeric(modSel@Djump@model)
+ else if (selecMod == 'BIC')
+ modSel@BIC_capushe$model
+ else if (selecMod == 'AIC')
+ modSel@AIC_capushe$model
+
+ mod = as.character(tableauRecap[indModSel,1])
+ listMod = as.integer(unlist(strsplit(mod, "[.]")))
+ modelSel = models_list[[listMod[1]]][[listMod[2]]]
+
+ ##Affectations
+ Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
+ for (i in 1:n){
+ for (r in 1:length(modelSel$pi)){
+ sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
+ Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
+ }
+ }
+ Gam = Gam/rowSums(Gam)
+ modelSel$affec = apply(Gam, 1,which.max)
+ modelSel$proba = Gam
+
+ if (plot){
+ print(plot_valse(X,Y,modelSel,n))
+ }
+
+ return(modelSel)
}