From ca277ac5ab51fef149014eb5e4610403fdb3227b Mon Sep 17 00:00:00 2001
From: emilie <emilie@devijver.org>
Date: Fri, 21 Apr 2017 15:37:19 +0200
Subject: [PATCH] fix few things for the LLF

---
 pkg/R/EMGLLF.R                      |   2 +-
 pkg/R/computeGridLambda.R           |  10 ++++----
 pkg/R/constructionModelesLassoMLE.R |  38 ++++++++++++++++++++--------
 pkg/R/main.R                        |  17 ++++++++++---
 pkg/R/selectVariables.R             |  14 +++++-----
 pkg/data/data2.RData                | Bin 0 -> 18385 bytes
 pkg/data/script_data.R              |  12 +++++++++
 7 files changed, 66 insertions(+), 27 deletions(-)
 create mode 100644 pkg/data/data2.RData
 create mode 100644 pkg/data/script_data.R

diff --git a/pkg/R/EMGLLF.R b/pkg/R/EMGLLF.R
index 6ee7ba7..f944f98 100644
--- a/pkg/R/EMGLLF.R
+++ b/pkg/R/EMGLLF.R
@@ -174,7 +174,7 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
 
     sumPen <- sum(pi^gamma * b)
     last_llh <- llh
-    llh <- -sumLogLLH/n + lambda * sumPen
+    llh <- -sumLogLLH/n #+ lambda * sumPen
     dist <- ifelse(ite == 1, llh, (llh - last_llh)/(1 + abs(llh)))
     Dist1 <- max((abs(phi - Phi))/(1 + abs(phi)))
     Dist2 <- max((abs(rho - Rho))/(1 + abs(rho)))
diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index c2e9c8c..597d5c8 100644
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -3,8 +3,8 @@
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #'
 #' @param phiInit value for phi
-#' @param rhoInit\tvalue for rho
-#' @param piInit\tvalue for pi
+#' @param rhoInit  for rho
+#' @param piInit  for pi
 #' @param gamInit value for gamma
 #' @param X matrix of covariates (of size n*p)
 #' @param Y matrix of responses (of size n*m)
@@ -27,10 +27,10 @@ computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mi
   list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0, 
     X, Y, tau, fast)
   grid <- array(0, dim = c(p, m, k))
-  for (i in 1:p)
+  for (j in 1:p)
   {
-    for (j in 1:m)
-      grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
+    for (mm in 1:m)
+      grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma)
   }
   sort(unique(grid))
 }
diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 90d0a2a..3967dfc 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -63,18 +63,36 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j, sel.lambda[[j]], ]
     dimension <- length(unlist(sel.lambda))
 
-    # Computation of the loglikelihood
-    densite <- vector("double", n)
-    for (r in 1:k)
+    ## Computation of the loglikelihood
+    # Precompute det(rhoLambda[,,r]) for r in 1...k
+    detRho <- sapply(1:k, function(r) det(rhoLambda[, , r]))
+    sumLogLLH <- 0
+    for (i in 1:n)
     {
-      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(-diag(tcrossprod(delta))/2)
+      # Update gam[,]; use log to avoid numerical problems
+      logGam <- sapply(1:k, function(r) {
+        log(piLambda[r]) + log(detRho[r]) - 0.5 *
+          sum((Y[i, ] %*% rhoLambda[, , r] - X[i, ] %*% phiLambda[, , r])^2)
+      })
+      
+      logGam <- logGam - max(logGam) #adjust without changing proportions
+      gam[i, ] <- exp(logGam)
+      norm_fact <- sum(gam[i, ])
+      gam[i, ] <- gam[i, ] / norm_fact
+      sumLogLLH <- sumLogLLH + log(norm_fact) - log((2 * base::pi)^(m/2))
     }
-    llhLambda <- c(sum(log(densite)), (dimension + m + 1) * k - 1)
+    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)
   }
 
diff --git a/pkg/R/main.R b/pkg/R/main.R
index fecf519..af05061 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -17,6 +17,8 @@
 #' @param ncores_outer Number of cores for the outer loop on k
 #' @param ncores_inner Number of cores for the inner loop on lambda
 #' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
+#' @param compute_grid_lambda, TRUE to compute the grid, FALSE if known (in arguments)
+#' @param grid_lambda, a vector with regularization parameters if known, by default 0
 #' @param size_coll_mod (Maximum) size of a collection of models
 #' @param fast TRUE to use compiled C code, FALSE for R code only
 #' @param verbose TRUE to show some execution traces
@@ -28,7 +30,7 @@
 #' @export
 valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10, 
   maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1, 
-  ncores_inner = 1, thresh = 1e-08, size_coll_mod = 10, fast = TRUE, verbose = FALSE, 
+  ncores_inner = 1, thresh = 1e-08, compute_grid_lambda = TRUE, grid_lambda = 0, size_coll_mod = 10, fast = TRUE, verbose = FALSE, 
   plot = TRUE)
 {
   p <- dim(X)[2]
@@ -58,8 +60,11 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
     # component, doing this 20 times, and keeping the values maximizing the
     # likelihood after 10 iterations of the EM algorithm.
     P <- initSmallEM(k, X, Y, fast)
-    grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
-      X, Y, gamma, mini, maxi, eps, fast)
+    if (compute_grid_lambda == TRUE)
+    {
+      grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, 
+                                       X, Y, gamma, mini, maxi, eps, fast)
+    }
     if (length(grid_lambda) > size_coll_mod) 
       grid_lambda <- grid_lambda[seq(1, length(grid_lambda), length.out = size_coll_mod)]
 
@@ -119,7 +124,10 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
       complexity = sumPen, contrast = -LLH)
   }))
   tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
-
+  if (verbose == TRUE)
+  {
+    print(tableauRecap)
+  }
   modSel <- capushe::capushe(tableauRecap, n)
   indModSel <- if (selecMod == "DDSE") 
     as.numeric(modSel@DDSE@model) else if (selecMod == "Djump") 
@@ -144,6 +152,7 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
   Gam <- Gam/rowSums(Gam)
   modelSel$affec <- apply(Gam, 1, which.max)
   modelSel$proba <- Gam
+  modelSel$tableau <- tableauRecap
 
   if (plot)
     print(plot_valse(X, Y, modelSel, n))
diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index bfe4042..cdc0ec0 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -4,16 +4,16 @@
 #'
 #' @param phiInit an initial estimator for phi (size: p*m*k)
 #' @param rhoInit an initial estimator for rho (size: m*m*k)
-#' @param piInit\tan initial estimator for pi (size : k)
+#' @param piInit an initial estimator for pi (size : k)
 #' @param gamInit an initial estimator for gamma
-#' @param mini\t\tminimum number of iterations in EM algorithm
-#' @param maxi\t\tmaximum number of iterations in EM algorithm
-#' @param gamma\t power in the penalty
+#' @param mini  minimum number of iterations in EM algorithm
+#' @param maxi  maximum number of iterations in EM algorithm
+#' @param gamma  power in the penalty
 #' @param glambda grid of regularization parameters
-#' @param X\t\t\t matrix of regressors
-#' @param Y\t\t\t matrix of responses
+#' @param X    matrix of regressors
+#' @param Y    matrix of responses
 #' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
-#' @param eps\t\t threshold to say that EM algorithm has converged
+#' @param eps   threshold to say that EM algorithm has converged
 #' @param ncores Number or cores for parallel execution (1 to disable)
 #'
 #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
diff --git a/pkg/data/data2.RData b/pkg/data/data2.RData
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HcmV?d00001

diff --git a/pkg/data/script_data.R b/pkg/data/script_data.R
new file mode 100644
index 0000000..7e5c036
--- /dev/null
+++ b/pkg/data/script_data.R
@@ -0,0 +1,12 @@
+m=11
+p=10
+
+covY = array(0,dim = c(m,m,2))
+covY[,,1] = diag(m)
+covY[,,2] = diag(m)
+
+Beta = array(0, dim = c(p, p, 2))
+Beta[1:4,1:4,1] = 3*diag(4)
+Beta[1:4,1:4,2] = -2*diag(4)
+
+Data = generateXY(100, c(0.5,0.5), rep(0,p), Beta, diag(m), covY)
-- 
2.44.0