pkg/R/constructionModelesLassoMLE.R

   1 #' constructionModelesLassoMLE
   2 #'
   3 #' TODO: description
   4 #'
   5 #' @param ...
   6 #'
   7 #' @return ...
   8 #'
   9 #' export
  10 constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
  11                                        gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, verbose=FALSE)
  12 {
  13   if (ncores > 1)
  14   {
  15     cl = parallel::makeCluster(ncores)
  16     parallel::clusterExport( cl, envir=environment(),
  17                              varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","thresh",
  18                                        "tau","S","ncores","verbose") )
  19   }
  20
  21   # Individual model computation
  22   computeAtLambda <- function(lambda)
  23   {
  24     if (ncores > 1)
  25       require("valse") #// nodes start with an empty environment
  26
  27     if (verbose)
  28       print(paste("Computations for lambda=",lambda))
  29
  30     n = dim(X)[1]
  31     p = dim(phiInit)[1]
  32     m = dim(phiInit)[2]
  33     k = dim(phiInit)[3]
  34
  35     sel.lambda = S[[lambda]]$selected
  36     #       col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
  37     col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
  38
  39     if (length(col.sel) == 0)
  40     {return (NULL)} else {
  41
  42       # lambda == 0 because we compute the EMV: no penalization here
  43       res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
  44                       X[,col.sel],Y,tau)
  45
  46       # Eval dimension from the result + selected
  47       phiLambda2 = res_EM$phi
  48       rhoLambda = res_EM$rho
  49       piLambda = res_EM$pi
  50       phiLambda = array(0, dim = c(p,m,k))
  51       for (j in seq_along(col.sel))
  52         phiLambda[col.sel[j],,] = phiLambda2[j,,]
  53
  54       dimension = 0
  55       for (j in 1:p)
  56       {
  57         b = setdiff(1:m, sel.lambda[[j]])## je confonds un peu ligne et colonne : est-ce dans le bon sens ?
  58         ## moi pour la dimension, j'aurai juste mis length(unlist(sel.lambda)) mais je sais pas si c'est rapide
  59         if (length(b) > 0)
  60           phiLambda[j,b,] = 0.0
  61         dimension = dimension + sum(sel.lambda[[j]]!=0)
  62       }
  63
  64       # Computation of the loglikelihood
  65       densite = vector("double",n)
  66       for (r in 1:k)
  67       {
  68         delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
  69         print(max(delta))
  70         densite = densite + piLambda[r] *
  71           det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
  72       }
  73       llhLambda = c( sum(artefact^2 * log(densite)), (dimension+m+1)*k-1 )
  74       list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
  75     }
  76   }
  77
  78   # For each lambda, computation of the parameters
  79   out =
  80     if (ncores > 1)
  81       parLapply(cl, 1:length(S), computeAtLambda)
  82   else
  83     lapply(1:length(S), computeAtLambda)
  84
  85   if (ncores > 1)
  86     parallel::stopCluster(cl)
  87
  88   out
  89 }