essai fusion

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

diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
deleted file mode 100644 (file)
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--- a/pkg/R/constructionModelesLassoRank.R
+++ /dev/null
@@ -1,95 +0,0 @@
-#' constructionModelesLassoRank
-#'
-#' Construct a collection of models with the Lasso-Rank procedure.
-#'
-#' @param S output of selectVariables.R
-#' @param k number of components
-#' @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
-#' @param X matrix of covariates (of size n*p)
-#' @param Y matrix of responses (of size n*m)
-#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
-#' @param rank.min integer, minimum rank in the low rank procedure, by default = 1
-#' @param rank.max integer, maximum rank in the low rank procedure, by default = 5
-#' @param ncores Number of cores, by default = 3
-#' @param fast TRUE to use compiled C code, FALSE for R code only
-#' @param verbose TRUE to show some execution traces
-#'
-#' @return a list with several models, defined by phi, rho, pi, llh
-#'
-#' @export
-constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min, rank.max, 
-  ncores, fast, verbose)
-{
-  n <- nrow(X)
-  p <- ncol(X)
-  m <- ncol(Y)
-  L <- length(S)
-
-  # Possible interesting ranks
-  deltaRank <- rank.max - rank.min + 1
-  Size <- deltaRank^k
-  RankLambda <- matrix(0, nrow = Size * L, ncol = k + 1)
-  for (r in 1:k)
-  {
-    # On veut le tableau de toutes les combinaisons de rangs possibles, et des
-    # lambdas Dans la première colonne : on répète (rank.max-rank.min)^(k-1) chaque
-    # chiffre : ça remplit la colonne Dans la deuxieme : on répète
-    # (rank.max-rank.min)^(k-2) chaque chiffre, et on fait ça (rank.max-rank.min)^2
-    # fois ...  Dans la dernière, on répète chaque chiffre une fois, et on fait ça
-    # (rank.min-rank.max)^(k-1) fois.
-    RankLambda[, r] <- rep(rank.min + rep(0:(deltaRank - 1), deltaRank^(r - 1), 
-      each = deltaRank^(k - r)), each = L)
-  }
-  RankLambda[, k + 1] <- rep(1:L, times = Size)
-
-  if (ncores > 1)
-  {
-    cl <- parallel::makeCluster(ncores, outfile = "")
-    parallel::clusterExport(cl, envir = environment(), varlist = c("A1", "Size", 
-      "Pi", "Rho", "mini", "maxi", "X", "Y", "eps", "Rank", "m", "phi", "ncores", 
-      "verbose"))
-  }
-
-  computeAtLambda <- function(index)
-  {
-    lambdaIndex <- RankLambda[index, k + 1]
-    rankIndex <- RankLambda[index, 1:k]
-    if (ncores > 1) 
-      require("valse")  #workers start with an empty environment
-
-    # 'relevant' will be the set of relevant columns
-    selected <- S[[lambdaIndex]]$selected
-    relevant <- c()
-    for (j in 1:p)
-    {
-      if (length(selected[[j]]) > 0)
-        relevant <- c(relevant, j)
-    }
-    if (max(rankIndex) < length(relevant))
-    {
-      phi <- array(0, dim = c(p, m, k))
-      if (length(relevant) > 0)
-      {
-        res <- EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi, 
-          X[, relevant], Y, eps, rankIndex, fast)
-        llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + m)))
-        phi[relevant, , ] <- res$phi
-      }
-      list(llh = llh, phi = phi, pi = S[[lambdaIndex]]$Pi, rho = S[[lambdaIndex]]$Rho)
-    }
-  }
-
-  # For each lambda in the grid we compute the estimators
-  out <-
-    if (ncores > 1) {
-      parLapply(cl, seq_len(length(S) * Size), computeAtLambda)
-    } else {
-      lapply(seq_len(length(S) * Size), computeAtLambda)
-    }
-
-  if (ncores > 1) 
-    parallel::stopCluster(cl)
-
-  out
-}