#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
#' @param kmin integer, minimum number of clusters, by default = 2
#' @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 rank.min integer, minimum rank in the low rank procedure, by default = 1
+#' @param rank.max integer, maximum rank in the low rank procedure, by default = 5
#' @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 thresh real, threshold to say a variable is relevant, by default = 1e-8
#' @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
#' #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=2, rank.min=1, rank.max=10, ncores_outer=1, ncores_inner=1,
+ eps=1e-4, kmin=2, kmax=3, rank.min=1, rank.max=5, ncores_outer=1, ncores_inner=1,
+ thresh=1e-8,
size_coll_mod=10, fast=TRUE, verbose=FALSE, plot = TRUE)
{
p = dim(X)[2]
{
cl = parallel::makeCluster(ncores_outer, outfile='')
parallel::clusterExport( cl=cl, envir=environment(), varlist=c("X","Y","procedure",
- "selecMod","gamma","mini","maxi","eps","kmin","kmax","rang.min","rang.max",
- "ncores_outer","ncores_inner","verbose","p","m") )
+ "selecMod","gamma","mini","maxi","eps","kmin","kmax","rank.min","rank.max",
+ "ncores_outer","ncores_inner","thresh","size_coll_mod","verbose","p","m") )
}
# Compute models with k components
#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$gamInit, mini, maxi, gamma,
- grid_lambda, X, Y, 1e-8, eps, ncores_inner, fast) #TODO: 1e-8 as arg?! eps?
+ grid_lambda, X, Y, thresh, eps, ncores_inner, fast)
if (procedure == 'LassoMLE')
{
print('run the procedure Lasso-MLE')
#compute parameter estimations, with the Maximum Likelihood
#Estimator, restricted on selected variables.
- models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
- mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, fast, verbose)
+ models <- constructionModelesLassoMLE( P$phiInit, P$rhoInit, P$piInit, P$gamInit,
+ mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose)
+
}
else
{
print('run the procedure Lasso-Rank')
#compute parameter estimations, with the Low Rank
#Estimator, restricted on selected variables.
- models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, S,
+ models <- constructionModelesLassoRank(S, k, mini, maxi, X, Y, eps,
rank.min, rank.max, ncores_inner, fast, verbose)
}
#warning! Some models are NULL after running selectVariables
print(tableauRecap)
tableauRecap = tableauRecap[which(tableauRecap[,4]!= Inf),]
- modSel = capushe::capushe(tableauRecap, n)
- indModSel <-
- if (selecMod == 'DDSE')
- as.numeric(modSel@DDSE@model)
- else if (selecMod == 'Djump')
- as.numeric(modSel@Djump@model)
- else if (selecMod == 'BIC')
- modSel@BIC_capushe$model
- else if (selecMod == 'AIC')
- modSel@AIC_capushe$model
- mod = as.character(tableauRecap[indModSel,1])
- listMod = as.integer(unlist(strsplit(mod, "[.]")))
- modelSel = models_list[[listMod[1]]][[listMod[2]]]
+ return(tableauRecap)
- ##Affectations
- Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
- for (i in 1:n){
- for (r in 1:length(modelSel$pi)){
- sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
- Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
- }
- }
- Gam = Gam/rowSums(Gam)
- modelSel$affec = apply(Gam, 1,which.max)
- modelSel$proba = Gam
-
- if (plot){
- print(plot_valse(modelSel,n))
- }
-
- return(modelSel)
+ # modSel = capushe::capushe(tableauRecap, n)
+ # indModSel <-
+ # if (selecMod == 'DDSE')
+ # as.numeric(modSel@DDSE@model)
+ # else if (selecMod == 'Djump')
+ # as.numeric(modSel@Djump@model)
+ # else if (selecMod == 'BIC')
+ # modSel@BIC_capushe$model
+ # else if (selecMod == 'AIC')
+ # modSel@AIC_capushe$model
+ #
+ # mod = as.character(tableauRecap[indModSel,1])
+ # listMod = as.integer(unlist(strsplit(mod, "[.]")))
+ # modelSel = models_list[[listMod[1]]][[listMod[2]]]
+ #
+ # ##Affectations
+ # Gam = matrix(0, ncol = length(modelSel$pi), nrow = n)
+ # for (i in 1:n){
+ # for (r in 1:length(modelSel$pi)){
+ # sqNorm2 = sum( (Y[i,]%*%modelSel$rho[,,r]-X[i,]%*%modelSel$phi[,,r])^2 )
+ # Gam[i,r] = modelSel$pi[r] * exp(-0.5*sqNorm2)* det(modelSel$rho[,,r])
+ # }
+ # }
+ # Gam = Gam/rowSums(Gam)
+ # modelSel$affec = apply(Gam, 1,which.max)
+ # modelSel$proba = Gam
+ #
+ # if (plot){
+ # print(plot_valse(X,Y,modelSel,n))
+ # }
+ #
+ # return(modelSel)
}
projects / valse.git / blobdiff