Merge branch 'master' of auder.net:valse

[valse.git] / R / valse.R

diff --git a/R/valse.R b/R/valse.R
deleted file mode 100644 (file)
index 445ea26..0000000
--- a/R/valse.R
+++ /dev/null
@@ -1,118 +0,0 @@
-#' 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 'SlopeHeuristic', 'BIC', '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
-#' @export
-#-----------------------------------------------------------------------
-valse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,
-                 maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,
-                 rang.min = 1,rang.max = 10) {
-  ##################################
-  #core workflow: compute all models
-  ##################################
-  
-  p = dim(phiInit)[1]
-  m = dim(phiInit)[2]
-  
-  print("main loop: over all k and all lambda")
-  for (k in kmin:kmax)
-  {
-    print(k)
-    
-    print("Parameters initialization")
-    #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.
-    init = initSmallEM(k, X, Y)
-    phiInit <<- init$phiInit
-    rhoInit <<- init$rhoInit
-    piInit     <<- init$piInit
-    gamInit <<- init$gamInit
-    
-    gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
-    
-    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.
-    params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda,X,Y,1e-8,eps)
-    selected <<- params$selected
-    Rho <<- params$Rho
-    Pi <<- params$Pi
-    
-    if (procedure == 'LassoMLE') {
-      print('run the procedure Lasso-MLE')
-      #compute parameter estimations, with the Maximum Likelihood
-      #Estimator, restricted on selected variables.
-      model = constructionModelesLassoMLE(
-        phiInit, rhoInit,piInit,tauInit,mini,maxi,
-        gamma,gridLambda,X,Y,thresh,eps,selected)
-      ################################################
-      ### Regarder la SUITE
-      r1 = runProcedure1()
-      Phi2 = Phi
-      Rho2 = Rho
-      Pi2 = Pi
-      
-      if (is.null(dim(Phi2)))
-        #test was: size(Phi2) == 0
-      {
-        Phi[, , 1:k] <<- r1$phi
-        Rho[, , 1:k] <<- r1$rho
-        Pi[1:k,] <<- r1$pi
-      } else
-      {
-        Phi <<-
-          array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))
-        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
-        Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi
-        Rho <<-
-          array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))
-        Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2
-        Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho
-        Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))
-        Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2
-        Pi[1:k, ncol(Pi2) + 1] <<- r1$pi
-      }
-    } else {
-      print('run the procedure Lasso-Rank')
-      #compute parameter estimations, with the Low Rank
-      #Estimator, restricted on selected variables.
-      model = constructionModelesLassoRank(Pi, 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
-      }
-    }
-  }
-  print('Model selection')
-  if (selecMod == 'SlopeHeuristic') {
-    
-  } else if (selecMod == 'BIC') {
-    
-  } else if (selecMod == 'AIC') {
-    
-  }
-}