essai fusion

[valse.git] / pkg / R / constructionModelesLassoMLE.R

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
deleted file mode 100644 (file)
index a49529c..0000000
--- a/pkg/R/constructionModelesLassoMLE.R
+++ /dev/null
@@ -1,89 +0,0 @@
-#' constructionModelesLassoMLE
-#'
-#' TODO: description
-#'
-#' @param ...
-#'
-#' @return ...
-#'
-#' export
-constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-                                       gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, verbose=FALSE)
-{
-  if (ncores > 1)
-  {
-    cl = parallel::makeCluster(ncores)
-    parallel::clusterExport( cl, envir=environment(),
-                             varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","thresh",
-                                       "tau","S","ncores","verbose") )
-  }
-  
-  # Individual model computation
-  computeAtLambda <- function(lambda)
-  {
-    if (ncores > 1)
-      require("valse") #// nodes start with an empty environment
-    
-    if (verbose)
-      print(paste("Computations for lambda=",lambda))
-    
-    n = dim(X)[1]
-    p = dim(phiInit)[1]
-    m = dim(phiInit)[2]
-    k = dim(phiInit)[3]
-    
-    sel.lambda = S[[lambda]]$selected
-    #          col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
-    col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-    
-    if (length(col.sel) == 0)
-    {return (NULL)} else {
-      
-      # lambda == 0 because we compute the EMV: no penalization here
-      res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
-                      X[,col.sel],Y,tau)
-      
-      # Eval dimension from the result + selected
-      phiLambda2 = res_EM$phi
-      rhoLambda = res_EM$rho
-      piLambda = res_EM$pi
-      phiLambda = array(0, dim = c(p,m,k))
-      for (j in seq_along(col.sel))
-        phiLambda[col.sel[j],,] = phiLambda2[j,,]
-      
-      dimension = 0
-      for (j in 1:p)
-      {
-        b = setdiff(1:m, sel.lambda[[j]])## je confonds un peu ligne et colonne : est-ce dans le bon sens ? 
-        ## moi pour la dimension, j'aurai juste mis length(unlist(sel.lambda)) mais je sais pas si c'est rapide
-        if (length(b) > 0)
-          phiLambda[j,b,] = 0.0
-        dimension = dimension + sum(sel.lambda[[j]]!=0)
-      }
-      
-      # Computation of the loglikelihood
-      densite = vector("double",n)
-      for (r in 1:k)
-      {
-        delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
-        print(max(delta))
-        densite = densite + piLambda[r] *
-          det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
-      }
-      llhLambda = c( sum(artefact^2 * log(densite)), (dimension+m+1)*k-1 )
-      list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
-    }
-  }
-  
-  # For each lambda, computation of the parameters
-  out =
-    if (ncores > 1)
-      parLapply(cl, 1:length(S), computeAtLambda)
-  else
-    lapply(1:length(S), computeAtLambda)
-  
-  if (ncores > 1)
-    parallel::stopCluster(cl)
-  
-  out
-}