'update'
[valse.git] / CCC.R
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08f4604c
BA
1#' constructionModelesLassoMLE
2#'
3#' TODO: description
4#'
5#' @param ...
6#'
7#' @return ...
8#'
9#' export
10constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
11 gamma, X, Y, seuil, tau, selected, ncores=3, verbose=FALSE)
12{
13 if (ncores > 1)
14 {
15 cl = parallel::makeCluster(ncores)
16 parallel::clusterExport( cl, envir=environment(),
17 varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","seuil",
18 "tau","selected","ncores","verbose") )
19 }
20
21 # Individual model computation
22 computeAtLambda <- function(lambda)
23 {
24 if (ncores > 1)
25 require("valse") #// nodes start with an ampty environment
26
27 if (verbose)
28 print(paste("Computations for lambda=",lambda))
29
30 n = dim(X)[1]
31 p = dim(phiInit)[1]
32 m = dim(phiInit)[2]
33 k = dim(phiInit)[3]
34
35 sel.lambda = selected[[lambda]]
36# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
37 col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
38
39 if (length(col.sel) == 0)
40 return (NULL)
41
42 # lambda == 0 because we compute the EMV: no penalization here
43 res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
44 X[,col.sel],Y,tau)
45
46 # Eval dimension from the result + selected
47 phiLambda2 = res_EM$phi
48 rhoLambda = res_EM$rho
49 piLambda = res_EM$pi
50 phiLambda = array(0, dim = c(p,m,k))
51 for (j in seq_along(col.sel))
52 phiLambda[col.sel[j],,] = phiLambda2[j,,]
53
54 dimension = 0
55 for (j in 1:p)
56 {
57 b = setdiff(1:m, sel.lambda[,j])
58 if (length(b) > 0)
59 phiLambda[j,b,] = 0.0
60 dimension = dimension + sum(sel.lambda[,j]!=0)
61 }
62
63 # on veut calculer la vraisemblance avec toutes nos estimations
64 densite = vector("double",n)
65 for (r in 1:k)
66 {
67 delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
68 densite = densite + piLambda[r] *
69 det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
70 }
71 llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
72 list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
73 }
74
75 #Pour chaque lambda de la grille, on calcule les coefficients
76 out =
77 if (ncores > 1)
78 parLapply(cl, glambda, computeAtLambda)
79 else
80 lapply(glambda, computeAtLambda)
81
82 if (ncores > 1)
83 parallel::stopCluster(cl)
84
85 out
86}