-#' 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
#' @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,
+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",
+ 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)
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
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) gdet(rhoLambda[, , r]))
- sumLogLLH <- 0
+ ## Affectations
+ Gam <- matrix(0, ncol = length(piLambda), nrow = n)
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 <- exp(logGam)
- norm_fact <- sum(gam)
- sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
+ for (r in 1:length(piLambda))
+ {
+ 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(sumLogLLH/n, (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
lapply(1:length(S), computeAtLambda)
}
- if (ncores > 1)
+ if (ncores > 1)
parallel::stopCluster(cl)
out