Adjustments for CRAN upload

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

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 1275ca3..692fbe1 100644 (file)
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -1,7 +1,7 @@
-#' constructionModelesLassoMLE 
+#' 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
@@ -16,28 +16,31 @@
 #' @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
+#'
+#' @return a list with several models, defined by phi (the regression parameter reparametrized),
+#' rho (the covariance parameter reparametrized), pi (the proportion parameter is the mixture model), llh
+#' (the value of the loglikelihood function for this estimator on the training dataset). The list is given
+#' for several levels of sparsity, given by several regularization parameters computed automatically.
 #'
 #' @export
-constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, 
-  maxi, gamma, X, Y, eps, S, ncores = 3, fast, verbose)
+constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
+  maxi, gamma, X, Y, eps, S, ncores, 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", 
+    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) 
+    if (ncores > 1)
       require("valse")  #nodes start with an empty environment
 
-    if (verbose) 
+    if (verbose)
       print(paste("Computations for lambda=", lambda))
 
     n <- nrow(X)
@@ -47,12 +50,13 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
     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) 
+    if (length(col.sel) == 0)
       return(NULL)
 
     # lambda == 0 because we compute the EMV: no penalization here
-    res <- EMGLLF(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit,
-      mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
+    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
@@ -63,30 +67,50 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j, sel.lambda[[j]], ]
     dimension <- length(unlist(sel.lambda))
 
-    # Computation of the loglikelihood
-    densite <- vector("double", n)
-    for (r in 1:k)
+    ## Affectations
+    Gam <- matrix(0, ncol = length(piLambda), nrow = n)
+    for (i in 1:n)
     {
-      if (length(col.sel) == 1)
+      for (r in 1:length(piLambda))
       {
-        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] * gdet(rhoLambda[, , r])/(sqrt(2 * base::pi))^m * 
-        exp(-diag(tcrossprod(delta))/2)
+        sqNorm2 <- sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
+        Gam[i, r] <- piLambda[r] * exp(-0.5 * sqNorm2) * det(rhoLambda[, , r])
+      }
     }
-    llhLambda <- c(sum(log(densite)), (dimension + m + 1) * k - 1)
-    list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
+    Gam2 <- Gam/rowSums(Gam)
+    affec <- apply(Gam2, 1, which.max)
+    proba <- Gam2
+    LLH <- c(sum(log(apply(Gam,1,sum))), (dimension + m + 1) * k - 1)
+    # ## Computation of the loglikelihood
+    # # Precompute det(rhoLambda[,,r]) for r in 1...k
+    # detRho <- sapply(1:k, function(r) gdet(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 -> change the LLH
+    #   gam <- exp(logGam)
+    #   norm_fact <- sum(gam)
+    #   sumLogLLH <- sumLogLLH + log(norm_fact) - m/2* log(2 * base::pi)
+    # }
+    #llhLambda <- c(-sumLogLLH/n, (dimension + m + 1) * k - 1)
+    list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = LLH, affec = affec, proba = proba)
   }
 
   # For each lambda, computation of the parameters
   out <-
     if (ncores > 1) {
-      parLapply(cl, 1:length(S), computeAtLambda)
+      parallel::parLapply(cl, 1:length(S), computeAtLambda)
     } else {
       lapply(1:length(S), computeAtLambda)
     }
 
-  if (ncores > 1) 
+  if (ncores > 1)
     parallel::stopCluster(cl)
 
   out