#' @param kmax integer, maximum number of clusters, by default = 10
#' @param rang.min integer, minimum rank in the low rank procedure, by default = 1
#' @param rang.max integer, maximum rank in the
+#' @param ncores_outer Number of cores for the outer loop on k
+#' @param ncores_inner Number of cores for the inner loop on lambda
+#' @param size_coll_mod (Maximum) size of a collection of models
+#' @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 estimators of parameters
#'
#' #TODO: a few examples
#' @export
valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10, maxi=50,
- eps=1e-4, kmin=2, kmax=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1, size_coll_mod = 50,
- verbose=FALSE)
+ eps=1e-4, kmin=2, kmax=4, rang.min=1, rang.max=10, ncores_outer=1, ncores_inner=1,
+ size_coll_mod=50, fast=TRUE, verbose=FALSE)
{
p = dim(X)[2]
m = dim(Y)[2]
#iterations of the EM algorithm.
P = initSmallEM(k, X, Y)
grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, X, Y,
- gamma, mini, maxi, eps)
+ gamma, mini, maxi, eps, fast)
if (length(grid_lambda)>size_coll_mod)
grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
#select variables according to each regularization parameter
#from the grid: S$selected corresponding to selected variables
S = selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, gamma,
- grid_lambda, X, Y, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
+ grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps?
if (procedure == 'LassoMLE')
{
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
- mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact = 1e3, verbose)
+ mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact=1e3, fast, verbose)
}
else
{
#compute parameter estimations, with the Low Rank
#Estimator, restricted on selected variables.
models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
- rank.min, rank.max, ncores_inner, verbose)
+ rank.min, rank.max, ncores_inner, fast, verbose)
}
#attention certains modeles sont NULL après selectVariables
models = models[sapply(models, function(cell) !is.null(cell))]
}
# Get summary "tableauRecap" from models
- tableauRecap = do.call( rbind, lapply( models_list, function(models) {
+ tableauRecap = do.call( rbind, lapply( seq_along(models_list), function(i) {
+ models <- models_list[[i]]
#Pour un groupe de modeles (même k, différents lambda):
- llh = matrix(ncol = 2)
- for (l in seq_along(models))
- llh = rbind(llh, models[[l]]$llh) #TODO: LLF? harmonize between EMGLLF and EMGrank?
- LLH = llh[-1,1]
- D = llh[-1,2]
+ LLH <- sapply( models, function(model) model$llh[1] )
k = length(models[[1]]$pi)
- cbind(LLH, D, rep(k, length(models)), 1:length(models))
+ # TODO: chuis pas sûr du tout des lignes suivantes...
+ # J'ai l'impression qu'il manque des infos
+ ## C'est surtout que la pénalité est la mauvaise, la c'est celle du Lasso, nous on veut ici
+ ##celle de l'heuristique de pentes
+ #sumPen = sapply( models, function(model)
+ # sum( model$pi^gamma * sapply(1:k, function(r) sum(abs(model$phi[,,r]))) ) )
+ sumPen = sapply(models, function(model)
+ k*(dim(model$rho)[1]+sum(model$phi[,,1]!=0)+1)-1)
+ data.frame(model=paste(i,".",seq_along(models),sep=""),
+ pen=sumPen/n, complexity=sumPen, contrast=LLH)
} ) )
- tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
- tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
- data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
-browser()
- modSel = capushe::capushe(data, n)
+print(tableauRecap)
+ modSel = capushe::capushe(tableauRecap, n)
indModSel <-
if (selecMod == 'DDSE')
as.numeric(modSel@DDSE@model)
modSel@BIC_capushe$model
else if (selecMod == 'AIC')
modSel@AIC_capushe$model
- models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
+
+ mod = as.character(tableauRecap[indModSel,1])
+ listMod = as.integer(unlist(strsplit(mod, "[.]")))
+ models_list[[listMod[1]]][[listMod[2]]]
+ models_list
}