X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;fp=pkg%2FR%2FconstructionModelesLassoMLE.R;h=0000000000000000000000000000000000000000;hp=3967dfcf623892689d186b6ab7ee8979ddde35ed;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=ca277ac5ab51fef149014eb5e4610403fdb3227b

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
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--- a/pkg/R/constructionModelesLassoMLE.R
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-#' constructionModelesLassoMLE 
-#'
-#' Construct a collection of models with the Lasso-MLE procedure.
-#' 
-#' @param phiInit an initialization for phi, get by initSmallEM.R
-#' @param rhoInit an initialization for rho, get by initSmallEM.R
-#' @param piInit an initialization for pi, get by initSmallEM.R
-#' @param gamInit an initialization for gam, get by initSmallEM.R
-#' @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 gamma integer for the power in the penaly, by default = 1
-#' @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 S output of selectVariables.R
-#' @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
-constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, 
-  maxi, gamma, X, Y, eps, S, ncores = 3, fast, verbose)
-{
-  if (ncores > 1)
-  {
-    cl <- parallel::makeCluster(ncores, outfile = "")
-    parallel::clusterExport(cl, envir = environment(), varlist = c("phiInit", 
-      "rhoInit", "gamInit", "mini", "maxi", "gamma", "X", "Y", "eps", "S", 
-      "ncores", "fast", "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]]$selected
-    # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
-    col.sel <- which(sapply(sel.lambda, length) > 0)  #if list of selected vars
-    if (length(col.sel) == 0) 
-      return(NULL)
-
-    # lambda == 0 because we compute the EMV: no penalization here
-    res <- EMGLLF(array(phiInit[col.sel, , ],dim=c(length(col.sel),m,k)), rhoInit,
-      piInit, gamInit, mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
-
-    # Eval dimension from the result + selected
-    phiLambda2 <- res$phi
-    rhoLambda <- res$rho
-    piLambda <- res$pi
-    phiLambda <- array(0, dim = c(p, m, k))
-    for (j in seq_along(col.sel))
-      phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j, sel.lambda[[j]], ]
-    dimension <- length(unlist(sel.lambda))
-
-    ## Computation of the loglikelihood
-    # Precompute det(rhoLambda[,,r]) for r in 1...k
-    detRho <- sapply(1:k, function(r) det(rhoLambda[, , r]))
-    sumLogLLH <- 0
-    for (i in 1:n)
-    {
-      # Update gam[,]; use log to avoid numerical problems
-      logGam <- sapply(1:k, function(r) {
-        log(piLambda[r]) + log(detRho[r]) - 0.5 *
-          sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
-      })
-      
-      logGam <- logGam - max(logGam) #adjust without changing proportions
-      gam[i, ] <- exp(logGam)
-      norm_fact <- sum(gam[i, ])
-      gam[i, ] <- gam[i, ] / norm_fact
-      sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
-    }
-    llhLambda <- c(sumLogLLH/n, (dimension + m + 1) * k - 1)
-    # densite <- vector("double", n)
-    # for (r in 1:k)
-    # {
-    #   if (length(col.sel) == 1)
-    #   {
-    #     delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% t(phiLambda[col.sel, , r])))
-    #   } else delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% phiLambda[col.sel, , r]))
-    #   densite <- densite + piLambda[r] * det(rhoLambda[, , r])/(sqrt(2 * base::pi))^m * 
-    #     exp(-rowSums(delta^2)/2)
-    # }
-    # llhLambda <- c(mean(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
-}