X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=af0506112f31e45fad18b56439c7fd75d419951b;hp=1908021cd740e91f472d78abed85d4b57997e361;hb=e32621012b1660204434a56acc8cf73eac42f477;hpb=f33f35efc9a01f93bb61959522d90ee6a76b892e diff --git a/pkg/R/main.R b/pkg/R/main.R deleted file mode 100644 index 1908021..0000000 --- a/pkg/R/main.R +++ /dev/null @@ -1,212 +0,0 @@ -#' @useDynLib valse - -Valse = setRefClass( - Class = "Valse", - - fields = c( - # User defined - - # regression data (size n*p, where n is the number of observations, - # and p is the number of regressors) - X = "matrix", - # response data (size n*m, where n is the number of observations, - # and m is the number of responses) - Y = "matrix", - - # Optionally user defined (some default values) - - # power in the penalty - gamma = "numeric", - # minimum number of iterations for EM algorithm - mini = "integer", - # maximum number of iterations for EM algorithm - maxi = "integer", - # threshold for stopping EM algorithm - eps = "numeric", - # minimum number of components in the mixture - kmin = "integer", - # maximum number of components in the mixture - kmax = "integer", - # ranks for the Lasso-Rank procedure - rank.min = "integer", - rank.max = "integer", - - # Computed through the workflow - - # initialisation for the reparametrized conditional mean parameter - phiInit = "numeric", - # initialisation for the reparametrized variance parameter - rhoInit = "numeric", - # initialisation for the proportions - piInit = "numeric", - # initialisation for the allocations probabilities in each component - tauInit = "numeric", - # values for the regularization parameter grid - gridLambda = "numeric", - # je ne crois pas vraiment qu'il faille les mettre en sortie, d'autant plus qu'on construit - # une matrice A1 et A2 pour chaque k, et elles sont grandes, donc ca coute un peu cher ... - A1 = "integer", - A2 = "integer", - # collection of estimations for the reparametrized conditional mean parameters - Phi = "numeric", - # collection of estimations for the reparametrized variance parameters - Rho = "numeric", - # collection of estimations for the proportions parameters - Pi = "numeric", - - #immutable (TODO:?) - thresh = "numeric" - ), - - methods = list( - ####################### - #initialize main object - ####################### - initialize = function(X,Y,...) - { - "Initialize Valse object" - - callSuper(...) - - X <<- X - Y <<- Y - gamma <<- ifelse (hasArg("gamma"), gamma, 1.) - mini <<- ifelse (hasArg("mini"), mini, as.integer(5)) - maxi <<- ifelse (hasArg("maxi"), maxi, as.integer(10)) - eps <<- ifelse (hasArg("eps"), eps, 1e-6) - kmin <<- ifelse (hasArg("kmin"), kmin, as.integer(2)) - kmax <<- ifelse (hasArg("kmax"), kmax, as.integer(3)) - rank.min <<- ifelse (hasArg("rank.min"), rank.min, as.integer(2)) - rank.max <<- ifelse (hasArg("rank.max"), rank.max, as.integer(3)) - thresh <<- 1e-15 #immutable (TODO:?) - }, - - ################################## - #core workflow: compute all models - ################################## - - initParameters = function(k) - { - "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$phi0 - rhoInit <<- init$rho0 - piInit <<- init$pi0 - tauInit <<- init$tau0 - }, - - computeGridLambda = function() - { - "computation of the regularization grid" - #(according to explicit formula given by EM algorithm) - - gridLambda <<- gridLambda(phiInit,rhoInit,piInit,tauInit,X,Y,gamma,mini,maxi,eps) - }, - - computeRelevantParameters = function() - { - "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,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps) - A1 <<- params$A1 - A2 <<- params$A2 - Rho <<- params$Rho - Pi <<- params$Pi - }, - - runProcedure1 = function() - { - "Run procedure 1 [EMGLLF]" - - #compute parameter estimations, with the Maximum Likelihood - #Estimator, restricted on selected variables. - return ( constructionModelesLassoMLE( - phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps,A1,A2) ) - }, - - runProcedure2 = function() - { - "Run procedure 2 [EMGrank]" - - #compute parameter estimations, with the Low Rank - #Estimator, restricted on selected variables. - return ( constructionModelesLassoRank(Pi,Rho,mini,maxi,X,Y,eps, - A1,rank.min,rank.max) ) - }, - - run = function() - { - "main loop: over all k and all lambda" - - # Run the whole procedure, 1 with the - #maximum likelihood refitting, and 2 with the Low Rank refitting. - p = dim(phiInit)[1] - m = dim(phiInit)[2] - for (k in kmin:kmax) - { - print(k) - initParameters(k) - computeGridLambda() - computeRelevantParameters() - if (procedure == 1) - { - r1 = runProcedure1() - Phi2 = Phi - Rho2 = Rho - Pi2 = Pi - p = ncol(X) - m = ncol(Y) - 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$pi - } - } else - { - phi = runProcedure2()$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 - } - } - } - } - - ################################################## - #TODO: pruning: select only one (or a few best ?!) model - ################################################## - # - # function[model] selectModel( - # #TODO - # #model = odel(...) - # end - # Give at least the slope heuristic and BIC, and AIC ? - - ) -)