+++ /dev/null
-n = 50; m = 10; p = 5
-X = matrix(runif(n*p, -10, 10), nrow=n)
-Y = matrix(runif(n*m, -5, 15), nrow=n)
-beta = array(0, dim=c(p,m,2))
-beta[,,1] = 1
-beta[,,2] = 2
-data = generateXY(n, c(0.4,0.6), rep(0,p), beta, diag(0.5, p), diag(0.5, m))
-X = data$X
-Y = data$Y
-class = data$class
-V1 = runValse(X, Y, fast=FALSE)
-Error in while (!pi2AllPositive) { :
- missing value where TRUE/FALSE needed
-
-V2 = runValse(X, Y, fast=TRUE)
-list()
-Error in out[[ind_uniq[l]]] :
- attempt to select less than one element in get1index
-
-==> Error identified: line 61 in initSmallEM.R, division by 0
-It occurs also for smallers values of n and m, e.g.: n = 20; m = 20; p = 3
-
-=====
-
-Also:
-X <- matrix(runif(100), nrow=50)
-Y <- matrix(runif(100), nrow=50)
-(...)
-Error: cannot allocate vector of size 16.0 Gb
#'
#' @export
constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
- maxi, gamma, X, Y, eps, S, ncores = 3, fast, verbose)
+ maxi, gamma, X, Y, eps, S, ncores, fast, verbose)
{
if (ncores > 1)
{
return(NULL)
# lambda == 0 because we compute the EMV: no penalization here
- res <- EMGLLF(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit,
- mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
+ res <- EMGLLF(array(phiInit[col.sel, , ], dim=c(length(col.sel),m,k)),
+ rhoInit, piInit, gamInit, mini, maxi, gamma, 0,
+ as.matrix(X[, col.sel]), Y, eps, fast)
# Eval dimension from the result + selected
phiLambda2 <- res$phi
#' @param verbose TRUE to show some execution traces
#' @param plot TRUE to plot the selected models after run
#'
-#' @return a list with estimators of parameters
+#' @return
+#' The selected model if enough data are available to estimate it,
+#' or a list of models otherwise.
#'
#' @examples
-#' #TODO: a few examples
+#' n = 50; m = 10; p = 5
+#' beta = array(0, dim=c(p,m,2))
+#' beta[,,1] = 1
+#' beta[,,2] = 2
+#' data = generateXY(n, c(0.4,0.6), rep(0,p), beta, diag(0.5, p), diag(0.5, m))
+#' X = data$X
+#' Y = data$Y
+#' res = runValse(X, Y)
+#' X <- matrix(runif(100), nrow=50)
+#' Y <- matrix(runif(100), nrow=50)
+#' res = runValse(X, Y)
#'
#' @export
runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10,
complexity = sumPen, contrast = -LLH)
}))
tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
-
- if (verbose == TRUE)
+ if (verbose)
print(tableauRecap)
- modSel <- capushe::capushe(tableauRecap, n)
- indModSel <- if (selecMod == "DDSE")
- {
- as.numeric(modSel@DDSE@model)
- } else if (selecMod == "Djump")
- {
- as.numeric(modSel@Djump@model)
- } else if (selecMod == "BIC")
- {
- modSel@BIC_capushe$model
- } else if (selecMod == "AIC")
- {
- modSel@AIC_capushe$model
- }
- listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
- modelSel <- models_list[[listMod[1]]][[listMod[2]]]
- modelSel$tableau <- tableauRecap
-
- if (plot)
- print(plot_valse(X, Y, modelSel))
+ if (nrow(tableauRecap) > 10) {
+ modSel <- capushe::capushe(tableauRecap, n)
+ indModSel <- if (selecMod == "DDSE")
+ {
+ as.numeric(modSel@DDSE@model)
+ } else if (selecMod == "Djump")
+ {
+ as.numeric(modSel@Djump@model)
+ } else if (selecMod == "BIC")
+ {
+ modSel@BIC_capushe$model
+ } else if (selecMod == "AIC")
+ {
+ modSel@AIC_capushe$model
+ }
+ listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
+ modelSel <- models_list[[listMod[1]]][[listMod[2]]]
+ modelSel$models <- tableauRecap
- return(modelSel)
+ if (plot)
+ print(plot_valse(X, Y, modelSel))
+ return(modelSel)
+ }
+ tableauRecap
}
if (ncores > 1)
parallel::stopCluster(cl)
- print(out) #DEBUG TRACE
# Suppress models which are computed twice
# sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
selec <- lapply(out, function(model) model$selected)