update to get a valse programm which could be run

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 50879c9..ed08b38 100644 (file)
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -1,37 +1,88 @@
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
-       X,Y,seuil,tau,selected)
+constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,
+                                       X,Y,seuil,tau,selected, parallel = FALSE)

 {
 {

-       #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
-       cl = parallel::makeCluster( parallel::detectCores() / 4 )
-       parallel::clusterExport(cl=cl,
-               varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","seuil","tau"),
-               envir=environment())
-       #Pour chaque lambda de la grille, on calcule les coefficients
-       out = parLapply( seq_along(glambda), function(lambdaindex)
-       {
-               n = dim(X)[1]
-               p = dim(phiInit)[1]
-               m = dim(phiInit)[2]
-               k = dim(phiInit)[3]
-
-               #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
-               #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
-    res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
-
-               #comment évaluer la dimension à partir du résultat et de [not]selected ?
-    #dimension = ...
-
-    #on veut calculer la vraisemblance avec toutes nos estimations
-               densite = vector("double",n)
-               for (r in 1:k)
-               {
-                       delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
-                       densite = densite + pi[r] *
-                               det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-               }
-               llh = c( sum(log(densite[,lambdaIndex])), (dimension+m+1)*k-1 )
-               list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
-       })
-       parallel::stopCluster(cl)
-       out
+  if (parallel) {
+    #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
+    cl = parallel::makeCluster( parallel::detectCores() / 4 )
+    parallel::clusterExport(cl=cl,
+                            varlist=c("phiInit","rhoInit","gamInit","mini","maxi","X","Y","seuil","tau"),
+                            envir=environment())
+    #Pour chaque lambda de la grille, on calcule les coefficients
+    out = parLapply( seq_along(glambda), function(lambda)
+    {
+      n = dim(X)[1]
+      p = dim(phiInit)[1]
+      m = dim(phiInit)[2]
+      k = dim(phiInit)[3]
+      
+      #TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
+      #lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
+      res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
+      
+      #comment évaluer la dimension à partir du résultat et de [not]selected ?
+      #dimension = ...
+      
+      #on veut calculer la vraisemblance avec toutes nos estimations
+      densite = vector("double",n)
+      for (r in 1:k)
+      {
+        delta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])
+        densite = densite + pi[r] *
+          det(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+      }
+      llh = c( sum(log(densite[,lambda])), (dimension+m+1)*k-1 )
+      list("phi"=res$phi, "rho"=res$rho, "pi"=res$pi, "llh" = llh)
+    })
+    parallel::stopCluster(cl)
+    out
+  }
+  else {
+    #Pour chaque lambda de la grille, on calcule les coefficients
+    n = dim(X)[1]
+    p = dim(phiInit)[1]
+    m = dim(phiInit)[2]
+    k = dim(phiInit)[3]
+    L = length(selected)
+    phi = list()
+    phiLambda = array(0, dim = c(p,m,k))
+    rho = list()
+    pi = list()
+    llh = list()
+    
+    for (lambda in 1:L){
+      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)
+      phiLambda2 = res_EM$phi
+      rhoLambda = res_EM$rho
+      piLambda = res_EM$pi
+      for (j in 1:length(col.sel)){
+        phiLambda[col.sel[j],,] = phiLambda2[j,,]
+      }
+      
+      dimension = 0
+      for (j in 1:p){
+        b = setdiff(1:m, sel.lambda[,j])
+        if (length(b) > 0){
+          phiLambda[j,b,] = 0.0
+        }
+        dimension = dimension + sum(sel.lambda[,j]!=0)
+      }
+      
+      #on veut calculer la vraisemblance avec toutes nos estimations
+      densite = vector("double",n)
+      for (r in 1:k)
+      {
+        delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
+        densite = densite + piLambda[r] *
+          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
+    }
+  }
+  return(list("phi"=phi, "rho"=rho, "pi"=pi, "llh" = llh))

 }
 }

diff --git a/pkg/R/constructionModelesLassoMLE_essai.R b/pkg/R/constructionModelesLassoMLE_essai.R
deleted file mode 100644 (file)
index e295d0c..0000000
--- a/pkg/R/constructionModelesLassoMLE_essai.R
+++ /dev/null
@@ -1,55 +0,0 @@
-constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,X,Y,seuil,tau,selected){
-  #get matrix sizes
-  n = dim(X)[1]
-  p = dim(phiInit)[1]
-  m = dim(phiInit)[2]
-  k = dim(phiInit)[3]
-  L = length(glambda)
-  
-  #output parameters
-  phi = array(0, dim = c(p,m,k,L))
-  rho = array(dim = c(m,m,k,L))
-  pi = array( dim = c(k,L))
-  lvraisemblance = array( dim = c(L,2))
-  
-  for (lambdaIndex in 1:L){
-    # Procedure Lasso-MLE  
-    a = selected[,1,lambdaIndex]
-    a(a==0) = c()
-    if (length(a) != 0){
-      res_EM = EMGLLF(phiInit[a,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,X[,a],Y,tau)
-      phiLambda = res_EM$phi
-      rhoLambda = res_EM$rho
-      piLambda = res_EM$pi
-      for (j in 1:length(a)){
-        phi[a[j],,,lambdaIndex] = phiLambda[j,,]
-      }
-      rho[,,,lambdaIndex] = rhoLambda
-      pi[,lambdaIndex] = piLambda
-      
-      dimension = 0
-      for (j in 1:p){
-        b = A2[j,2:end,lambdaIndex]
-        b[b==0] = c()
-        if (length(b) > 0){
-        phi[A2[j,1,lambdaIndex],b,,lambdaIndex] = 0.0
-        }
-        c = A1[j,2:end,lambdaIndex]
-        c[c==0] = c()
-        dimension = dimension + length(c)
-      }
-      
-      #on veut calculer l'EMV avec toutes nos estimations
-      densite = array(n,L)
-      for (i in 1:n){
-        for (r in 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*base::pi))^m*exp(-delta %*% delta/2.0)
-        }
-      }
-      
-      lvraisemblance(lambdaIndex,1) = sum(log(densite[,lambdaIndex]))
-      lvraisemblance(lambdaIndex,2) = (dimension+m+1)*k-1
-    }
-  }
-}
\ No newline at end of file

diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index ce7d3b3..e4ed179 100644 (file)
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -41,22 +41,24 @@ selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambd
                m = dim(phiInit)[2]
 
                #selectedVariables: list where element j contains vector of selected variables in [1,m]
                m = dim(phiInit)[2]
 
                #selectedVariables: list where element j contains vector of selected variables in [1,m]

-               selectedVariables = lapply(1:p, function(j) {
+               selectedVariables = sapply(1:p, function(j) { ## je me suis permise de changer le type, 
+                 ##une liste de liste ca devenait compliqué je trouve pour choper ce qui nous intéresse

                        #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
                        #and finally return the corresponding indices
                        #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
                        #and finally return the corresponding indices

-                       seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
+                       #seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
+                 c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ], 
+                   rep(0, m-length(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ] ) ))

                })
 
                })
 

-               list("selected"=selectedVariables,"Rho"=params$Rho,"Pi"=params$Pi)
+               list("selected"=selectedVariables,"Rho"=params$rho,"Pi"=params$pi)

        }
 
        # Pour chaque lambda de la grille, on calcule les coefficients
        out <-
        }
 
        # Pour chaque lambda de la grille, on calcule les coefficients
        out <-

-               if (ncores > 1)
-                       parLapply(cl, seq_along(glambda, computeCoefs)
-               else
-                       lapply(seq_along(glambda), computeCoefs)
-       if (ncores > 1)
-               parallel::stopCluster(cl)
+               if (ncores > 1){
+                       parLapply(cl, seq_along(glambda, computeCoefs))}
+               else lapply(seq_along(glambda), computeCoefs)
+       if (ncores > 1){
+               parallel::stopCluster(cl)}

        out
 }
        out
 }

diff --git a/pkg/R/selectiontotale.R b/pkg/R/selectiontotale.R
index bfb9325..25128cb 100644 (file)
--- a/pkg/R/selectiontotale.R
+++ b/pkg/R/selectiontotale.R
@@ -29,21 +29,24 @@ selectiontotale = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambd
     selectedVariables = list()
     Rho = list()
     Pi = list()
     selectedVariables = list()
     Rho = list()
     Pi = list()

+    cpt = 0

     #Pour chaque lambda de la grille, on calcule les coefficients
     for (lambdaIndex in 1:length(glambda)){
     #Pour chaque lambda de la grille, on calcule les coefficients
     for (lambdaIndex in 1:length(glambda)){

-      print(lambdaIndex)

       params = 
         EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
       p = dim(phiInit)[1]
       m = dim(phiInit)[2]
       #selectedVariables: list where element j contains vector of selected variables in [1,m]
       params = 
         EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
       p = dim(phiInit)[1]
       m = dim(phiInit)[2]
       #selectedVariables: list where element j contains vector of selected variables in [1,m]

-      selectedVariables[[lambdaIndex]] = sapply(1:p, function(j) {
+      if (sum(params$phi) != 0){
+        cpt = cpt+1
+      selectedVariables[[cpt]] = sapply(1:p, function(j) {

         #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
         #and finally return the corresponding indices
         #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
         #and finally return the corresponding indices

-        c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ], rep(0, m-length(apply( abs(params$phi[j,,]) > thresh, 1, any ) )))
+        c(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ], rep(0, m-length(seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ] ) ))

       })
       })

-      Rho[[lambdaIndex]] = params$Rho
-      Pi[[lambdaIndex]] = params$Pi
+      Rho[[cpt]] = params$rho
+      Pi[[cpt]] = params$pi
+      }

     }
     list("selected"=selectedVariables,"Rho"=Rho,"Pi"=Pi)
   }
     }
     list("selected"=selectedVariables,"Rho"=Rho,"Pi"=Pi)
   }

diff --git a/pkg/R/valse.R b/pkg/R/valse.R
index 445ea26..46a5fd0 100644 (file)
--- a/pkg/R/valse.R
+++ b/pkg/R/valse.R
@@ -15,8 +15,8 @@
 #' @return a list with estimators of parameters
 #' @export
 #-----------------------------------------------------------------------
 #' @return a list with estimators of parameters
 #' @export
 #-----------------------------------------------------------------------

-valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
-                 maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,
+valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'BIC',gamma = 1,mini = 10,
+                 maxi = 100,eps = 1e-4,kmin = 2,kmax = 5,

                  rang.min = 1,rang.max = 10) {
   ##################################
   #core workflow: compute all models
                  rang.min = 1,rang.max = 10) {
   ##################################
   #core workflow: compute all models


@@ -24,12 +24,14 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
   
   p = dim(phiInit)[1]
   m = dim(phiInit)[2]
   
   p = dim(phiInit)[1]
   m = dim(phiInit)[2]

+  n = dim(X)[1]

   
   

+  tableauRecap = array(, dim=c(1000,4))
+  cpt = 0

   print("main loop: over all k and all lambda")
   print("main loop: over all k and all lambda")

-  for (k in kmin:kmax)
-  {
+
+for (k in kmin:kmax){

     print(k)
     print(k)

-    

     print("Parameters initialization")
     #smallEM initializes parameters by k-means and regression model in each component,
     #doing this 20 times, and keeping the values maximizing the likelihood after 10
     print("Parameters initialization")
     #smallEM initializes parameters by k-means and regression model in each component,
     #doing this 20 times, and keeping the values maximizing the likelihood after 10


@@ -39,14 +41,18 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
     rhoInit <<- init$rhoInit
     piInit     <<- init$piInit
     gamInit <<- init$gamInit
     rhoInit <<- init$rhoInit
     piInit     <<- init$piInit
     gamInit <<- init$gamInit

-    
+    source('~/valse/pkg/R/gridLambda.R')

     gridLambda <<- 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.
     gridLambda <<- 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,X,Y,1e-8,eps)
+    
+    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)
+    ## etrange : params et params 2 sont différents ...
+    

     selected <<- params$selected
     Rho <<- params$Rho
     Pi <<- params$Pi
     selected <<- params$selected
     Rho <<- params$Rho
     Pi <<- params$Pi


@@ -55,36 +61,9 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
       print('run the procedure Lasso-MLE')
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.
       print('run the procedure Lasso-MLE')
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.

-      model = constructionModelesLassoMLE(
-        phiInit, rhoInit,piInit,tauInit,mini,maxi,
-        gamma,gridLambda,X,Y,thresh,eps,selected)
-      ################################################
-      ### Regarder la SUITE
-      r1 = runProcedure1()
-      Phi2 = Phi
-      Rho2 = Rho
-      Pi2 = Pi
-      
-      if (is.null(dim(Phi2)))
-        #test was: size(Phi2) == 0
-      {
-        Phi[, , 1:k] <<- r1$phi
-        Rho[, , 1:k] <<- r1$rho
-        Pi[1:k,] <<- r1$pi
-      } else
-      {
-        Phi <<-
-          array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))
-        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
-        Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi
-        Rho <<-
-          array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))
-        Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2
-        Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho
-        Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))
-        Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2
-        Pi[1:k, ncol(Pi2) + 1] <<- r1$pi
-      }
+      model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
+      LLH = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)]
+      D = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)+1]

     } else {
       print('run the procedure Lasso-Rank')
       #compute parameter estimations, with the Low Rank
     } else {
       print('run the procedure Lasso-Rank')
       #compute parameter estimations, with the Low Rank


@@ -106,12 +85,18 @@ valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
         Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
       }
     }
         Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
       }
     }

+    tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4)
+    cpt = cpt+length(model[[k]])

   }
   print('Model selection')
   }
   print('Model selection')

+  
+  tableauRecap = array(dim = c())

   if (selecMod == 'SlopeHeuristic') {
     
   } else if (selecMod == 'BIC') {
   if (selecMod == 'SlopeHeuristic') {
     
   } else if (selecMod == 'BIC') {

-    
+    BIC = -2*tableauRecap[,1]+log(n)*tableauRecap[,2]
+    indMinBIC = which.min(BIC)
+    return(model[[tableauRecap[indMinBIC,3]]][[tableauRecap[indMinBIC,4]]])

   } else if (selecMod == 'AIC') {
     
   }
   } else if (selecMod == 'AIC') {
     
   }