X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=af0506112f31e45fad18b56439c7fd75d419951b;hp=93f8e3f3b444cf3d2597ab8bb2e87071f83a6622;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=b9b0b72a2c8f7f0d1a3216528aefcec0a92c6c99

diff --git a/pkg/R/main.R b/pkg/R/main.R
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-#' valse
-#'
-#' Main function
-#'
-#' @param X matrix of covariates (of size n*p)
-#' @param Y matrix of responses (of size n*m)
-#' @param procedure among 'LassoMLE' or 'LassoRank'
-#' @param selecMod method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC'
-#' @param gamma integer for the power in the penaly, by default = 1
-#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
-#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
-#' @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
-#'
-#' @return a list with estimators of parameters
-#'
-#' @examples
-#' #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)
-{
-  p = dim(X)[2]
-  m = dim(Y)[2]
-  n = dim(X)[1]
-
-  if (verbose)
-		print("main loop: over all k and all lambda")
-
-	if (ncores_outer > 1)
-	{
-		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") )
-	}
-
-	# Compute models with k components
-	computeModels <- function(k)
-	{
-		if (ncores_outer > 1)
-			require("valse") #nodes start with an empty environment
-
-		if (verbose)
-			print(paste("Parameters initialization for k =",k))
-		#smallEM initializes parameters by k-means and regression model in each component,
-    #doing this 20 times, and keeping the values maximizing the likelihood after 10
-    #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)
-    if (length(grid_lambda)>size_coll_mod)
-      grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
-
-		if (verbose)
-			print("Compute relevant parameters")
-    #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$gamInit, mini, maxi, gamma,
-			grid_lambda, X, Y, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
-    
-    if (procedure == 'LassoMLE')
-		{
-      if (verbose)
-				print('run the procedure Lasso-MLE')
-      #compute parameter estimations, with the Maximum Likelihood
-      #Estimator, restricted on selected variables.
-      models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
-				mini, maxi, gamma, X, Y, thresh, eps, S, ncores_inner, artefact = 1e3, verbose)
-    }
-		else
-		{
-      if (verbose)
-				print('run the procedure Lasso-Rank')
-      #compute parameter estimations, with the Low Rank
-      #Estimator, restricted on selected variables.
-      models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
-				rank.min, rank.max, ncores_inner, verbose)
-    }
-		#attention certains modeles sont NULL après selectVariables
-		models = models[sapply(models, function(cell) !is.null(cell))]
-    models
-  }
-
-	# List (index k) of lists (index lambda) of models
-	models_list <-
-	  if (ncores_outer > 1)
-			parLapply(cl, kmin:kmax, computeModels)
-		else
-			lapply(kmin:kmax, computeModels)
-	if (ncores_outer > 1)
-		parallel::stopCluster(cl)
-
-	if (! requireNamespace("capushe", quietly=TRUE))
-	{
-		warning("'capushe' not available: returning all models")
-		return (models_list)
-	}
-
-	# Get summary "tableauRecap" from models
-	tableauRecap = do.call( rbind, lapply( models_list, function(models) {
-		#Pour un groupe de modeles (même k, différents lambda):
-		llh = matrix(ncol = 2)
-		for (l in seq_along(models))
-			llh = rbind(llh, models[[l]]$llh) #TODO: LLF? harmonize between EMGLLF and EMGrank?
-		LLH = llh[-1,1]
-		D = llh[-1,2]
-		k = length(models[[1]]$pi)
-		cbind(LLH, D, rep(k, length(models)), 1:length(models))
-	} ) )
-	tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
-  tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
-  data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
-browser()
-  modSel = capushe::capushe(data, n)
-  indModSel <-
-		if (selecMod == 'DDSE')
-			as.numeric(modSel@DDSE@model)
-		else if (selecMod == 'Djump')
-			as.numeric(modSel@Djump@model)
-		else if (selecMod == 'BIC')
-			modSel@BIC_capushe$model
-		else if (selecMod == 'AIC')
-			modSel@AIC_capushe$model
-  models_list[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
-}