X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=R%2Fmain.R;h=42852d368f19592ab4a0ca7565dfc99a63b6f36b;hp=b9b8d5bb88a0127b566da1f94a8514b8e61e0f8d;hb=31463ab809c0195273ff2760606ea65361d721ab;hpb=8e92c49c15bdacebf46190e7c8279bd227873928 diff --git a/R/main.R b/R/main.R index b9b8d5b..42852d3 100644 --- a/R/main.R +++ b/R/main.R @@ -1,3 +1,5 @@ +#' @useDynLib valse + Valse = setRefClass( Class = "Valse", @@ -6,53 +8,54 @@ Valse = setRefClass( # regression data (size n*p, where n is the number of observations, # and p is the number of regressors) - X = "numeric", + X = "matrix", # response data (size n*m, where n is the number of observations, # and m is the number of responses) - Y = "numeric", + Y = "matrix", # Optionally user defined (some default values) # power in the penalty - gamma = "double", + 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 = "double", + eps = "numeric", # minimum number of components in the mixture kmin = "integer", # maximum number of components in the mixture kmax = "integer", - rangmin = "integer", - rangmax = "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, + phiInit = "numeric", # initialisation for the reparametrized variance parameter - rhoInit, + rhoInit = "numeric", # initialisation for the proportions - piInit, + piInit = "numeric", # initialisation for the allocations probabilities in each component - tauInit, + tauInit = "numeric", # values for the regularization parameter grid - gridLambda = c(), + 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, - A2, + A1 = "integer", + A2 = "integer", # collection of estimations for the reparametrized conditional mean parameters - Phi, + Phi = "numeric", # collection of estimations for the reparametrized variance parameters - Rho, + Rho = "numeric", # collection of estimations for the proportions parameters - Pi, + Pi = "numeric", - #immutable - seuil = 1e-15 + #immutable (TODO:?) + thresh = "numeric" ), methods = list( @@ -73,8 +76,9 @@ Valse = setRefClass( eps <<- ifelse (hasArg("eps"), eps, 1e-6) kmin <<- ifelse (hasArg("kmin"), kmin, as.integer(2)) kmax <<- ifelse (hasArg("kmax"), kmax, as.integer(3)) - rangmin <<- ifelse (hasArg("rangmin"), rangmin, as.integer(2)) - rangmax <<- ifelse (hasArg("rangmax"), rangmax, 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:?) }, ################################## @@ -111,7 +115,7 @@ Valse = setRefClass( #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,seuil,eps) + phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps) A1 <<- params$A1 A2 <<- params$A2 Rho <<- params$Rho @@ -125,7 +129,7 @@ Valse = setRefClass( #compute parameter estimations, with the Maximum Likelihood #Estimator, restricted on selected variables. return ( constructionModelesLassoMLE( - phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,seuil,eps,A1,A2) ) + phiInit,rhoInit,piInit,tauInit,mini,maxi,gamma,gridLambda,X,Y,thresh,eps,A1,A2) ) }, runProcedure2 = function() @@ -135,14 +139,14 @@ Valse = setRefClass( #compute parameter estimations, with the Low Rank #Estimator, restricted on selected variables. return ( constructionModelesLassoRank(Pi,Rho,mini,maxi,X,Y,eps, - A1,rangmin,rangmax) ) + A1,rank.min,rank.max) ) }, run = function() { "main loop: over all k and all lambda" - # Run the all procedure, 1 with the + # Run the whole procedure, 1 with the #maximum likelihood refitting, and 2 with the Low Rank refitting. p = dim(phiInit)[1] m = dim(phiInit)[2] @@ -160,22 +164,22 @@ Valse = setRefClass( Pi2 = Pi p = ncol(X) m = ncol(Y) - if size(Phi2) == 0 + if (is.null(dim(Phi2))) #test was: size(Phi2) == 0 { - Phi[,,1:k] = r1$phi - Rho[,,1:k] = r1$rho - Pi[1:k,] = r1$pi + 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$pi + Phi <<- array(0., dim=c(p,m,kmax,dim(Phi2)[4]+dim(r1$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 { @@ -183,14 +187,12 @@ Valse = setRefClass( Phi2 = Phi if (dim(Phi2)[1] == 0) { - Phi(:,:,1:k,:) = phi + Phi[,,1:k,] <<- phi } else { - size(Phi2) - Phi = zeros(p,m,kmax,size(Phi2,4)+size(phi,4)) - size(Phi) - Phi(:,:,1:size(Phi2,3),1:size(Phi2,4)) = Phi2 - Phi(:,:,1:k,size(Phi2,4)+1:end) = phi + 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 } } } @@ -204,6 +206,7 @@ Valse = setRefClass( # #TODO # #model = odel(...) # end + # Give at least the slope heuristic and BIC, and AIC ? ) )