update to get a valse programm which could be run
[valse.git] / pkg / R / valse.R
CommitLineData
c7dab9ff 1#' Main function
2#'
3#' @param X matrix of covariates (of size n*p)
4#' @param Y matrix of responses (of size n*m)
5#' @param procedure among 'LassoMLE' or 'LassoRank'
6#' @param selecMod method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'
7#' @param gamma integer for the power in the penaly, by default = 1
8#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
9#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
10#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
11#' @param kmin integer, minimum number of clusters, by default = 2
12#' @param kmax integer, maximum number of clusters, by default = 10
13#' @param rang.min integer, minimum rank in the low rank procedure, by default = 1
14#' @param rang.max integer, maximum rank in the
15#' @return a list with estimators of parameters
16#' @export
17#-----------------------------------------------------------------------
51485a7d 18valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'BIC',gamma = 1,mini = 10,
19 maxi = 100,eps = 1e-4,kmin = 2,kmax = 5,
c7dab9ff 20 rang.min = 1,rang.max = 10) {
21 ##################################
22 #core workflow: compute all models
23 ##################################
24
25 p = dim(phiInit)[1]
26 m = dim(phiInit)[2]
51485a7d 27 n = dim(X)[1]
c7dab9ff 28
51485a7d 29 tableauRecap = array(, dim=c(1000,4))
30 cpt = 0
c7dab9ff 31 print("main loop: over all k and all lambda")
51485a7d 32
33for (k in kmin:kmax){
c7dab9ff 34 print(k)
c7dab9ff 35 print("Parameters initialization")
36 #smallEM initializes parameters by k-means and regression model in each component,
37 #doing this 20 times, and keeping the values maximizing the likelihood after 10
38 #iterations of the EM algorithm.
39 init = initSmallEM(k, X, Y)
40 phiInit <<- init$phiInit
41 rhoInit <<- init$rhoInit
42 piInit <<- init$piInit
43 gamInit <<- init$gamInit
51485a7d 44 source('~/valse/pkg/R/gridLambda.R')
e54d1bb9 45 gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
c7dab9ff 46
47 print("Compute relevant parameters")
48 #select variables according to each regularization parameter
49 #from the grid: A1 corresponding to selected variables, and
50 #A2 corresponding to unselected variables.
51485a7d 51
52 params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
53 params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda[seq(1,length(gridLambda), by=3)],X,Y,1e-8,eps)
54 ## etrange : params et params 2 sont différents ...
55
f1b0e0ab 56 selected <<- params$selected
c7dab9ff 57 Rho <<- params$Rho
58 Pi <<- params$Pi
59
60 if (procedure == 'LassoMLE') {
61 print('run the procedure Lasso-MLE')
62 #compute parameter estimations, with the Maximum Likelihood
63 #Estimator, restricted on selected variables.
51485a7d 64 model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
65 LLH = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)]
66 D = unlist(model[[k]]$llh)[seq(1,2*length(model[[k]]),2)+1]
c7dab9ff 67 } else {
68 print('run the procedure Lasso-Rank')
69 #compute parameter estimations, with the Low Rank
70 #Estimator, restricted on selected variables.
71 model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,
72 A1, rank.min, rank.max)
73
74 ################################################
75 ### Regarder la SUITE
76 phi = runProcedure2()$phi
77 Phi2 = Phi
78 if (dim(Phi2)[1] == 0)
79 {
80 Phi[, , 1:k,] <<- phi
81 } else
82 {
83 Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
84 Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
85 Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
86 }
87 }
51485a7d 88 tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4)
89 cpt = cpt+length(model[[k]])
c7dab9ff 90 }
91 print('Model selection')
51485a7d 92
93 tableauRecap = array(dim = c())
c7dab9ff 94 if (selecMod == 'SlopeHeuristic') {
95
96 } else if (selecMod == 'BIC') {
51485a7d 97 BIC = -2*tableauRecap[,1]+log(n)*tableauRecap[,2]
98 indMinBIC = which.min(BIC)
99 return(model[[tableauRecap[indMinBIC,3]]][[tableauRecap[indMinBIC,4]]])
c7dab9ff 100 } else if (selecMod == 'AIC') {
101
102 }
103}