From: emilie <emilie@devijver.org>
Date: Mon, 6 Mar 2017 12:37:22 +0000 (+0100)
Subject: add comment in constructionMdodelesLassoMLE.R
X-Git-Url: https://git.auder.net/%7B%7B%20path%28%27fos_user_profile_show%27%29%20%7D%7D?a=commitdiff_plain;h=7411013519fc6aef07280502d7d201e5ca177e0c;p=valse.git

add comment in constructionMdodelesLassoMLE.R
---

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+{
+    "collab_server" : "",
+    "contents" : "#' Main function\n#'\n#' @param X matrix of covariates (of size n*p)\n#' @param Y matrix of responses (of size n*m)\n#' @param procedure among 'LassoMLE' or 'LassoRank'\n#' @param selecMod method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'\n#' @param gamma integer for the power in the penaly, by default = 1\n#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10\n#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100\n#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4\n#' @param kmin integer, minimum number of clusters, by default = 2\n#' @param kmax integer, maximum number of clusters, by default = 10\n#' @param rang.min integer, minimum rank in the low rank procedure, by default = 1\n#' @param rang.max integer, maximum rank in the\n#' @return a list with estimators of parameters\n#' @export\n#-----------------------------------------------------------------------\nvalse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,\n                 maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,\n                 rang.min = 1,rang.max = 10) {\n  ##################################\n  #core workflow: compute all models\n  ##################################\n  \n  p = dim(phiInit)[1]\n  m = dim(phiInit)[2]\n  \n  print(\"main loop: over all k and all lambda\")\n  for (k in kmin:kmax)\n  {\n    print(k)\n    \n    print(\"Parameters initialization\")\n    #smallEM initializes parameters by k-means and regression model in each component,\n    #doing this 20 times, and keeping the values maximizing the likelihood after 10\n    #iterations of the EM algorithm.\n    init = initSmallEM(k, X, Y)\n    phiInit <<- init$phiInit\n    rhoInit <<- init$rhoInit\n    piInit\t<<- init$piInit\n    gamInit <<- init$gamInit\n    \n    gridLambda <<- gridLambda(phiInit, rhoInit, piInit, tauInit, X, Y, gamma, mini, maxi, eps)\n    \n    print(\"Compute relevant parameters\")\n    #select variables according to each regularization parameter\n    #from the grid: A1 corresponding to selected variables, and\n    #A2 corresponding to unselected variables.\n    params = selectiontotale(phiInit,rhoInit,piInit,tauInit,\n                             mini,maxi,gamma,gridLambda,\n                             X,Y,thresh,eps)\n    A1 <<- params$A1\n    A2 <<- params$A2\n    Rho <<- params$Rho\n    Pi <<- params$Pi\n    \n    if (procedure == 'LassoMLE') {\n      print('run the procedure Lasso-MLE')\n      #compute parameter estimations, with the Maximum Likelihood\n      #Estimator, restricted on selected variables.\n      model = constructionModelesLassoMLE(\n        phiInit, rhoInit,piInit,tauInit,mini,maxi,\n        gamma,gridLambda,X,Y,thresh,eps,A1,A2)\n      ################################################\n      ### Regarder la SUITE\n      r1 = runProcedure1()\n      Phi2 = Phi\n      Rho2 = Rho\n      Pi2 = Pi\n      \n      if (is.null(dim(Phi2)))\n        #test was: size(Phi2) == 0\n      {\n        Phi[, , 1:k] <<- r1$phi\n        Rho[, , 1:k] <<- r1$rho\n        Pi[1:k,] <<- r1$pi\n      } else\n      {\n        Phi <<-\n          array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))\n        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2\n        Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi\n        Rho <<-\n          array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))\n        Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2\n        Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho\n        Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))\n        Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2\n        Pi[1:k, ncol(Pi2) + 1] <<- r1$pi\n      }\n    } else {\n      print('run the procedure Lasso-Rank')\n      #compute parameter estimations, with the Low Rank\n      #Estimator, restricted on selected variables.\n      model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,\n                                           A1, rank.min, rank.max)\n      \n      ################################################\n      ### Regarder la SUITE  \n      phi = runProcedure2()$phi\n      Phi2 = Phi\n      if (dim(Phi2)[1] == 0)\n      {\n        Phi[, , 1:k,] <<- phi\n      } else\n      {\n        Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))\n        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2\n        Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi\n      }\n    }\n  }\n  print('Model selection')\n  if (selecMod == 'SlopeHeuristic') {\n    \n  } else if (selecMod == 'BIC') {\n    \n  } else if (selecMod == 'AIC') {\n    \n  }\n}\n",
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+{
+    "collab_server" : "",
+    "contents" : "# VAriable seLection with mixtureS of modEls\n\nThis code is the applied part of the PhD thesis of [Benjamin Gohehry](http://www.math.u-psud.fr/~goehry/).\n\n## Description\n\nTODO : see R package\n",
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+    "type" : "markdown"
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+{
+    "collab_server" : "",
+    "contents" : "Trouver un jeu de données (+) intéressant (que les autres) ?\nAjouter toy datasets pour les tests (ou piocher dans MASS ?)\n\nED : j'ai simulé un truc basique via 'generateXYdefault(10,5,6,2)'\n",
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+    "contents" : "constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,\n\tX,Y,seuil,tau,selected)\n{\n\t#TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)\n\tcl = parallel::makeCluster( parallel::detectCores() / 4 )\n\tparallel::clusterExport(cl=cl,\n\t\tvarlist=c(\"phiInit\",\"rhoInit\",\"gamInit\",\"mini\",\"maxi\",\"glambda\",\"X\",\"Y\",\"seuil\",\"tau\"),\n\t\tenvir=environment())\n\t#Pour chaque lambda de la grille, on calcule les coefficients\n\tout = parLapply( seq_along(glambda), function(lambdaindex)\n\t{\n\t\tn = dim(X)[1]\n\t\tp = dim(phiInit)[1]\n\t\tm = dim(phiInit)[2]\n\t\tk = dim(phiInit)[3]\n\n\t\t#TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?\n\t\t#lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus\n    res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)\n\n\t\t#comment évaluer la dimension à partir du résultat et de [not]selected ?\n    #dimension = ...\n\n    #on veut calculer la vraisemblance avec toutes nos estimations\n\t\tdensite = vector(\"double\",n)\n\t\tfor (r in 1:k)\n\t\t{\n\t\t\tdelta = Y%*%rho[,,r] - (X[selected]%*%res$phi[selected,,r])\n\t\t\tdensite = densite + pi[r] *\n\t\t\t\tdet(rho[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)\n\t\t}\n\t\tllh = c( sum(log(densite[,lambdaIndex])), (dimension+m+1)*k-1 )\n\t\tlist(\"phi\"=res$phi, \"rho\"=res$rho, \"pi\"=res$pi, \"llh\" = llh)\n\t})\n\tparallel::stopCluster(cl)\n\tout\n}\n",
+    "created" : 1488803513260.000,
+    "dirty" : false,
+    "encoding" : "UTF-8",
+    "folds" : "",
+    "hash" : "1470456796",
+    "id" : "C0098B3E",
+    "lastKnownWriteTime" : 1488803686,
+    "last_content_update" : 1488803686911,
+    "path" : "~/valse/R/constructionModelesLassoMLE.R",
+    "project_path" : "R/constructionModelesLassoMLE.R",
+    "properties" : {
+    },
+    "relative_order" : 4,
+    "source_on_save" : false,
+    "source_window" : "",
+    "type" : "r_source"
+}
\ No newline at end of file
diff --git a/.Rproj.user/40465803/sdb/s-B9318FF6/lock_file b/.Rproj.user/40465803/sdb/s-B9318FF6/lock_file
new file mode 100644
index 0000000..e69de29
diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 56843bc..0000000
--- a/.gitignore
+++ /dev/null
@@ -1,7 +0,0 @@
-.Rproj.user
-.Rhistory
-.RData
-.Ruserdata
-src/*.o
-src/*.so
-src/*.dll
diff --git a/R/constructionModelesLassoMLE.R b/R/constructionModelesLassoMLE.R
index 55e9419..50879c9 100644
--- a/R/constructionModelesLassoMLE.R
+++ b/R/constructionModelesLassoMLE.R
@@ -15,13 +15,13 @@ constructionModelesLassoMLE = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,
 		k = dim(phiInit)[3]
 
 		#TODO: phiInit[selected] et X[selected] sont bien sûr faux; par quoi remplacer ?
-		#lambda == 0 c'est normal ?
+		#lambda == 0 c'est normal ? -> ED : oui, ici on calcule le maximum de vraisembance, donc on ne pénalise plus
     res = EMGLLF(phiInit[selected],rhoInit,piInit,gamInit,mini,maxi,gamma,0.,X[selected],Y,tau)
 
 		#comment évaluer la dimension à partir du résultat et de [not]selected ?
     #dimension = ...
 
-    #on veut calculer l'EMV avec toutes nos estimations
+    #on veut calculer la vraisemblance avec toutes nos estimations
 		densite = vector("double",n)
 		for (r in 1:k)
 		{