+++ /dev/null
-#' @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 ?
-
- )
-)
projects / valse.git / blobdiff