X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=af0506112f31e45fad18b56439c7fd75d419951b;hp=7b78a154f72ea653a0254b39e79c32ee621b3603;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=4cc632c9a1e1d93e9a43a402d1361f23afc50e5e

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 = 2,
-                 rang.min = 1,rang.max = 10, ncores_k=1, ncores_lambda=3, verbose=FALSE)
-{
-  p = dim(X)[2]
-  m = dim(Y)[2]
-  n = dim(X)[1]
-
-  tableauRecap = list()
-  if (verbose)
-		print("main loop: over all k and all lambda")
-
-	if (ncores_k > 1)
-	{
-		cl = parallel::makeCluster(ncores_k)
-		parallel::clusterExport( cl=cl, envir=environment(), varlist=c("X","Y","procedure",
-			"selecMod","gamma","mini","maxi","eps","kmin","kmax","rang.min","rang.max",
-			"ncores_k","ncores_lambda","verbose","p","m","k","tableauRecap") )
-	}
-
-	# Compute model with k components
-	computeModel <- function(k)
-	{
-		if (ncores_k > 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)
-
-		# TODO: 100 = magic number
-    if (length(grid_lambda)>100)
-      grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
-
-		if (verbose)
-			print("Compute relevant parameters")
-    #select variables according to each regularization parameter
-    #from the grid: A1 corresponding to selected variables, and
-    #A2 corresponding to unselected variables.
-    S = selectVariables(P$phiInit,P$rhoInit,P$piInit,P$gamInit,mini,maxi,gamma,
-			grid_lambda,X,Y,1e-8,eps,ncores_lambda)
-
-    if (procedure == 'LassoMLE')
-		{
-      if (verbose)
-				print('run the procedure Lasso-MLE')
-      #compute parameter estimations, with the Maximum Likelihood
-      #Estimator, restricted on selected variables.
-      model = constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
-				maxi, gamma, X, Y, thresh, eps, S$selected)
-      llh = matrix(ncol = 2)
-      for (l in seq_along(model[[k]]))
-        llh = rbind(llh, model[[k]][[l]]$llh)
-      LLH = llh[-1,1]
-      D = llh[-1,2]
-    }
-		else
-		{
-      if (verbose)
-				print('run the procedure Lasso-Rank')
-      #compute parameter estimations, with the Low Rank
-      #Estimator, restricted on selected variables.
-      model = constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
-				rank.min, rank.max)
-
-      ################################################
-      ### Regarder la SUITE  
-      phi = runProcedure2()$phi
-      Phi2 = Phi
-      if (dim(Phi2)[1] == 0)
-        Phi[, , 1:k,] <- phi
-      else
-      {
-        Phi <- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
-        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
-        Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
-      }
-    }
-    tableauRecap[[k]] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4))
-  }
-
-	model <-
-		if (ncores_k > 1)
-			parLapply(cl, kmin:kmax, computeModel)
-		else
-			lapply(kmin:kmax, computeModel)
-	if (ncores_k > 1)
-		parallel::stopCluster(cl)
-
-	if (verbose)
-		print('Model selection')
-	tableauRecap = do.call( rbind, tableaurecap ) #stack list cells into a matrix
-  tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
-  tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
-  data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
-
-	require(capushe)
-  modSel = capushe(data, n)
-  indModSel <-
-		if (selecMod == 'DDSE')
-			as.numeric(modSel@DDSE@model)
-		else if (selecMod == 'Djump')
-			as.numeric(modSel@Djump@model)
-		else if (selecMod == 'BIC')
-			modSel@BIC_capushe$model
-		else if (selecMod == 'AIC')
-			modSel@AIC_capushe$model
-  model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]]
-}