From: Benjamin Auder <benjamin.auder@somewhere>
Date: Fri, 21 Apr 2017 15:14:10 +0000 (+0200)
Subject: Merge branch 'master' of auder.net:valse
X-Git-Url: https://git.auder.net/?p=valse.git;a=commitdiff_plain;h=c3bf2821bce67c75504e303fae23dd41c00f06c8;hp=-c

Merge branch 'master' of auder.net:valse
---

c3bf2821bce67c75504e303fae23dd41c00f06c8
diff --combined pkg/R/constructionModelesLassoMLE.R
index 75ae679,c55035e..78e11ad
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@@ -40,10 -40,10 +40,10 @@@ constructionModelesLassoMLE <- function
      if (verbose) 
        print(paste("Computations for lambda=", lambda))
  
 -    n <- dim(X)[1]
 -    p <- dim(phiInit)[1]
 -    m <- dim(phiInit)[2]
 -    k <- dim(phiInit)[3]
 +    n <- nrow(X)
 +    p <- ncol(X)
 +    m <- ncol(Y)
 +    k <- length(piInit)
      sel.lambda <- S[[lambda]]$selected
      # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
      col.sel <- which(sapply(sel.lambda, length) > 0)  #if list of selected vars
@@@ -51,8 -51,8 +51,8 @@@
        return(NULL)
  
      # lambda == 0 because we compute the EMV: no penalization here
 -    res <- EMGLLF(array(phiInit[col.sel, , ],dim=c(length(col.sel),m,k)), rhoInit,
 -      piInit, gamInit, mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
 +    res <- EMGLLF(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit,
 +      mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
  
      # Eval dimension from the result + selected
      phiLambda2 <- res$phi
@@@ -65,7 -65,7 +65,7 @@@
  
      ## Computation of the loglikelihood
      # Precompute det(rhoLambda[,,r]) for r in 1...k
 -    detRho <- sapply(1:k, function(r) det(rhoLambda[, , r]))
 +    detRho <- sapply(1:k, function(r) gdet(rhoLambda[, , r]))
      sumLogLLH <- 0
      for (i in 1:n)
      {
@@@ -77,11 -77,21 +77,10 @@@
        
        logGam <- logGam - max(logGam) #adjust without changing proportions
        gam <- exp(logGam)
-       print(gam)
        norm_fact <- sum(gam)
        sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
      }
      llhLambda <- c(sumLogLLH/n, (dimension + m + 1) * k - 1)
 -    # densite <- vector("double", n)
 -    # for (r in 1:k)
 -    # {
 -    #   if (length(col.sel) == 1)
 -    #   {
 -    #     delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% t(phiLambda[col.sel, , r])))
 -    #   } else delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% phiLambda[col.sel, , r]))
 -    #   densite <- densite + piLambda[r] * det(rhoLambda[, , r])/(sqrt(2 * base::pi))^m * 
 -    #     exp(-rowSums(delta^2)/2)
 -    # }
 -    # llhLambda <- c(mean(log(densite)), (dimension + m + 1) * k - 1)
      list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
    }