From: Benjamin Auder <benjamin.auder@somewhere>
Date: Fri, 14 Apr 2017 16:43:47 +0000 (+0200)
Subject: remove pre-commit hook; fix weird formatting from formatR package
X-Git-Url: https://git.auder.net/doc/html/css/scripts/%24%7BgetWhatsApp%28link%29%7D?a=commitdiff_plain;h=1b698c1619dbcf5b3a0608dc894d249945d2bce3;p=valse.git

remove pre-commit hook; fix weird formatting from formatR package
---

diff --git a/hooks/pre-commit b/hooks/pre-commit.bad
similarity index 100%
rename from hooks/pre-commit
rename to hooks/pre-commit.bad
diff --git a/pkg/R/EMGLLF.R b/pkg/R/EMGLLF.R
index ee7a4fc..0a279f0 100644
--- a/pkg/R/EMGLLF.R
+++ b/pkg/R/EMGLLF.R
@@ -24,14 +24,14 @@
 #' @export
 EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
   X, Y, eps, fast = TRUE)
-  {
+{
   if (!fast)
   {
     # Function in R
     return(.EMGLLF_R(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
       X, Y, eps))
   }
-  
+
   # Function in C
   n <- nrow(X)  #nombre d'echantillons
   p <- ncol(X)  #nombre de covariables
@@ -46,22 +46,21 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
 # R version - slow but easy to read
 .EMGLLF_R <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
   X2, Y, eps)
-  {
+{
   # Matrix dimensions
   n <- dim(Y)[1]
-  if (length(dim(phiInit)) == 2)
-  {
+  if (length(dim(phiInit)) == 2) {
     p <- 1
     m <- dim(phiInit)[1]
     k <- dim(phiInit)[2]
-  } else
-  {
+  } else {
     p <- dim(phiInit)[1]
     m <- dim(phiInit)[2]
     k <- dim(phiInit)[3]
   }
   X <- matrix(nrow = n, ncol = p)
   X[1:n, 1:p] <- X2
+
   # Outputs
   phi <- array(NA, dim = c(p, m, k))
   phi[1:p, , ] <- phiInit
@@ -69,7 +68,7 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
   pi <- piInit
   llh <- -Inf
   S <- array(0, dim = c(p, m, k))
-  
+
   # Algorithm variables
   gam <- gamInit
   Gram2 <- array(0, dim = c(p, p, k))
@@ -77,33 +76,37 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
   X2 <- array(0, dim = c(n, p, k))
   Y2 <- array(0, dim = c(n, m, k))
   EPS <- 1e-15
-  
+
   for (ite in 1:maxi)
   {
     # Remember last pi,rho,phi values for exit condition in the end of loop
     Phi <- phi
     Rho <- rho
     Pi <- pi
-    
+
     # Computations associated to X and Y
     for (r in 1:k)
     {
-      for (mm in 1:m) Y2[, mm, r] <- sqrt(gam[, r]) * Y[, mm]
-      for (i in 1:n) X2[i, , r] <- sqrt(gam[i, r]) * X[i, ]
-      for (mm in 1:m) ps2[, mm, r] <- crossprod(X2[, , r], Y2[, mm, r])
+      for (mm in 1:m)
+        Y2[, mm, r] <- sqrt(gam[, r]) * Y[, mm]
+      for (i in 1:n)
+        X2[i, , r] <- sqrt(gam[i, r]) * X[i, ]
+      for (mm in 1:m)
+        ps2[, mm, r] <- crossprod(X2[, , r], Y2[, mm, r])
       for (j in 1:p)
       {
-        for (s in 1:p) Gram2[j, s, r] <- crossprod(X2[, j, r], X2[, s, r])
+        for (s in 1:p)
+          Gram2[j, s, r] <- crossprod(X2[, j, r], X2[, s, r])
       }
     }
-    
-    ######### M step #
-    
+
+    ## M step
+
     # For pi
     b <- sapply(1:k, function(r) sum(abs(phi[, , r])))
     gam2 <- colSums(gam)
     a <- sum(gam %*% log(pi))
-    
+
     # While the proportions are nonpositive
     kk <- 0
     pi2AllPositive <- FALSE
@@ -113,66 +116,64 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
       pi2AllPositive <- all(pi2 >= 0)
       kk <- kk + 1
     }
-    
+
     # t(m) is the largest value in the grid O.1^k such that it is nonincreasing
-    while (kk < 1000 && -a/n + lambda * sum(pi^gamma * b) < -sum(gam2 * log(pi2))/n + 
-      lambda * sum(pi2^gamma * b))
-      {
+    while (kk < 1000 && -a/n + lambda * sum(pi^gamma * b) <
+      -sum(gam2 * log(pi2))/n + lambda * sum(pi2^gamma * b))
+    {
       pi2 <- pi + 0.1^kk * (1/n * gam2 - pi)
       kk <- kk + 1
     }
     t <- 0.1^kk
     pi <- (pi + t * (pi2 - pi))/sum(pi + t * (pi2 - pi))
-    
+
     # For phi and rho
     for (r in 1:k)
     {
       for (mm in 1:m)
       {
         ps <- 0
-        for (i in 1:n) ps <- ps + Y2[i, mm, r] * sum(X2[i, , r] * phi[, mm, 
-          r])
+        for (i in 1:n)
+          ps <- ps + Y2[i, mm, r] * sum(X2[i, , r] * phi[, mm, r])
         nY2 <- sum(Y2[, mm, r]^2)
         rho[mm, mm, r] <- (ps + sqrt(ps^2 + 4 * nY2 * gam2[r]))/(2 * nY2)
       }
     }
-    
+
     for (r in 1:k)
     {
       for (j in 1:p)
       {
         for (mm in 1:m)
         {
-          S[j, mm, r] <- -rho[mm, mm, r] * ps2[j, mm, r] + sum(phi[-j, mm, 
-          r] * Gram2[j, -j, r])
-          if (abs(S[j, mm, r]) <= n * lambda * (pi[r]^gamma))
-          {
-          phi[j, mm, r] <- 0
-          } else if (S[j, mm, r] > n * lambda * (pi[r]^gamma))
-          {
-          phi[j, mm, r] <- (n * lambda * (pi[r]^gamma) - S[j, mm, r])/Gram2[j, 
-            j, r]
-          } else
-          {
-          phi[j, mm, r] <- -(n * lambda * (pi[r]^gamma) + S[j, mm, r])/Gram2[j, 
-            j, r]
+          S[j, mm, r] <- -rho[mm, mm, r] * ps2[j, mm, r]
+            + sum(phi[-j, mm, r] * Gram2[j, -j, r])
+          if (abs(S[j, mm, r]) <= n * lambda * (pi[r]^gamma)) {
+            phi[j, mm, r] <- 0
+          } else if (S[j, mm, r] > n * lambda * (pi[r]^gamma)) {
+            phi[j, mm, r] <- (n * lambda * (pi[r]^gamma) - S[j, mm, r])/Gram2[j, j, r]
+          } else {
+            phi[j, mm, r] <- -(n * lambda * (pi[r]^gamma) + S[j, mm, r])/Gram2[j, j, r]
           }
         }
       }
     }
-    
+
     ######## E step#
-    
+
     # Precompute det(rho[,,r]) for r in 1...k
     detRho <- sapply(1:k, function(r) det(rho[, , r]))
     gam1 <- matrix(0, nrow = n, ncol = k)
     for (i in 1:n)
     {
       # Update gam[,]
-      for (r in 1:k) gam1[i, r] <- pi[r] * exp(-0.5 * sum((Y[i, ] %*% rho[, 
-        , r] - X[i, ] %*% phi[, , r])^2)) * detRho[r]
+      for (r in 1:k)
+      {
+        gam1[i, r] <- pi[r] * exp(-0.5
+          * sum((Y[i, ] %*% rho[, , r] - X[i, ] %*% phi[, , r])^2)) * detRho[r]
+      }
     }
-    gam <- gam1/rowSums(gam1)
+    gam <- gam1 / rowSums(gam1)
     sumLogLLH <- sum(log(rowSums(gam)) - log((2 * base::pi)^(m/2)))
     sumPen <- sum(pi^gamma * b)
     last_llh <- llh
@@ -182,10 +183,10 @@ EMGLLF <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
     Dist2 <- max((abs(rho - Rho))/(1 + abs(rho)))
     Dist3 <- max((abs(pi - Pi))/(1 + abs(Pi)))
     dist2 <- max(Dist1, Dist2, Dist3)
-    
+
     if (ite >= mini && (dist >= eps || dist2 >= sqrt(eps))) 
       break
   }
-  
+
   list(phi = phi, rho = rho, pi = pi, llh = llh, S = S)
 }
diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
index 436b289..db40948 100644
--- a/pkg/R/EMGrank.R
+++ b/pkg/R/EMGrank.R
@@ -1,4 +1,4 @@
-#' EMGrank 
+#' EMGrank
 #'
 #' Description de EMGrank
 #'
@@ -23,7 +23,7 @@ EMGrank <- function(Pi, Rho, mini, maxi, X, Y, tau, rank, fast = TRUE)
     # Function in R
     return(.EMGrank_R(Pi, Rho, mini, maxi, X, Y, tau, rank))
   }
-  
+
   # Function in C
   n <- nrow(X)  #nombre d'echantillons
   p <- ncol(X)  #nombre de covariables
@@ -50,12 +50,12 @@ matricize <- function(X)
   p <- dim(X)[2]
   m <- dim(Rho)[2]
   k <- dim(Rho)[3]
-  
+
   # init outputs
   phi <- array(0, dim = c(p, m, k))
   Z <- rep(1, n)
   LLF <- 0
-  
+
   # local variables
   Phi <- array(0, dim = c(p, m, k))
   deltaPhi <- c()
@@ -69,12 +69,12 @@ matricize <- function(X)
     # M step: update for Beta ( and then phi)
     for (r in 1:k)
     {
-      Z_indice <- seq_len(n)[Z == r]  #indices where Z == r
+      Z_indice <- seq_len(n)[Z == r] #indices where Z == r
       if (length(Z_indice) == 0) 
         next
       # U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-      s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ]))) %*% crossprod(matricize(X[Z_indice, 
-        ]), matricize(Y[Z_indice, ])))
+      s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ])))
+        %*% crossprod(matricize(X[Z_indice, ]), matricize(Y[Z_indice, ])))
       S <- s$d
       # Set m-rank(r) singular values to zero, and recompose best rank(r) approximation
       # of the initial product
@@ -82,7 +82,7 @@ matricize <- function(X)
         S[(rank[r] + 1):length(S)] <- 0
       phi[, , r] <- s$u %*% diag(S) %*% t(s$v) %*% Rho[, , r]
     }
-    
+
     # Step E and computation of the loglikelihood
     sumLogLLF2 <- 0
     for (i in seq_len(n))
@@ -91,8 +91,7 @@ matricize <- function(X)
       maxLogGamIR <- -Inf
       for (r in seq_len(k))
       {
-        dotProduct <- tcrossprod(Y[i, ] %*% Rho[, , r] - X[i, ] %*% phi[, 
-          , r])
+        dotProduct <- tcrossprod(Y[i, ] %*% Rho[, , r] - X[i, ] %*% phi[, , r])
         logGamIR <- log(Pi[r]) + log(det(Rho[, , r])) - 0.5 * dotProduct
         # Z[i] = index of max (gam[i,])
         if (logGamIR > maxLogGamIR)
@@ -104,15 +103,15 @@ matricize <- function(X)
       }
       sumLogLLF2 <- sumLogLLF2 + log(sumLLF1)
     }
-    
+
     LLF <- -1/n * sumLogLLF2
-    
+
     # update distance parameter to check algorithm convergence (delta(phi, Phi))
-    deltaPhi <- c(deltaPhi, max((abs(phi - Phi))/(1 + abs(phi))))  #TODO: explain?
+    deltaPhi <- c(deltaPhi, max((abs(phi - Phi))/(1 + abs(phi)))) #TODO: explain?
     if (length(deltaPhi) > deltaPhiBufferSize) 
       deltaPhi <- deltaPhi[2:length(deltaPhi)]
     sumDeltaPhi <- sum(abs(deltaPhi))
-    
+
     # update other local variables
     Phi <- phi
     ite <- ite + 1
diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index 4b68bcd..c34c707 100644
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -18,18 +18,19 @@
 #' @export
 computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, 
   maxi, tau, fast = TRUE)
-  {
+{
   n <- nrow(X)
   p <- dim(phiInit)[1]
   m <- dim(phiInit)[2]
   k <- dim(phiInit)[3]
-  
+
   list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0, 
     X, Y, tau, fast)
   grid <- array(0, dim = c(p, m, k))
   for (i in 1:p)
   {
-    for (j in 1:m) grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
+    for (j in 1:m)
+      grid[i, j, ] <- abs(list_EMG$S[i, j, ])/(n * list_EMG$pi^gamma)
   }
   sort(unique(grid))
 }
diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 760da40..4f23bb0 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -22,7 +22,7 @@
 #' @export
 constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, 
   maxi, gamma, X, Y, eps, S, ncores = 3, fast = TRUE, verbose = FALSE)
-  {
+{
   if (ncores > 1)
   {
     cl <- parallel::makeCluster(ncores, outfile = "")
@@ -30,16 +30,16 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       "rhoInit", "gamInit", "mini", "maxi", "gamma", "X", "Y", "eps", "S", 
       "ncores", "fast", "verbose"))
   }
-  
+
   # Individual model computation
   computeAtLambda <- function(lambda)
   {
     if (ncores > 1) 
       require("valse")  #nodes start with an empty environment
-    
+
     if (verbose) 
       print(paste("Computations for lambda=", lambda))
-    
+
     n <- dim(X)[1]
     p <- dim(phiInit)[1]
     m <- dim(phiInit)[2]
@@ -49,43 +49,45 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
     col.sel <- which(sapply(sel.lambda, length) > 0)  #if list of selected vars
     if (length(col.sel) == 0) 
       return(NULL)
-    
+
     # lambda == 0 because we compute the EMV: no penalization here
     res <- EMGLLF(phiInit[col.sel, , ], rhoInit, piInit, gamInit, mini, maxi, 
       gamma, 0, X[, col.sel], Y, eps, fast)
-    
+
     # Eval dimension from the result + selected
     phiLambda2 <- res$phi
     rhoLambda <- res$rho
     piLambda <- res$pi
     phiLambda <- array(0, dim = c(p, m, k))
-    for (j in seq_along(col.sel)) phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j, 
-      sel.lambda[[j]], ]
+    for (j in seq_along(col.sel))
+      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)
     {
       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)
+        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)
     }
     llhLambda <- c(sum(log(densite)), (dimension + m + 1) * k - 1)
     list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
   }
-  
+
   # For each lambda, computation of the parameters
-  out <- if (ncores > 1) 
-    parLapply(cl, 1:length(S), computeAtLambda) else lapply(1:length(S), computeAtLambda)
-  
+  out <-
+    if (ncores > 1) {
+      parLapply(cl, 1:length(S), computeAtLambda)
+    } else {
+      lapply(1:length(S), computeAtLambda)
+    }
+
   if (ncores > 1) 
     parallel::stopCluster(cl)
-  
+
   out
 }
diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
index b997303..5857a42 100644
--- a/pkg/R/constructionModelesLassoRank.R
+++ b/pkg/R/constructionModelesLassoRank.R
@@ -20,12 +20,12 @@
 #' @export
 constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min, rank.max, 
   ncores, fast = TRUE, verbose = FALSE)
-  {
+{
   n <- dim(X)[1]
   p <- dim(X)[2]
   m <- dim(Y)[2]
   L <- length(S)
-  
+
   # Possible interesting ranks
   deltaRank <- rank.max - rank.min + 1
   Size <- deltaRank^k
@@ -42,7 +42,7 @@ constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min,
       each = deltaRank^(k - r)), each = L)
   }
   RankLambda[, k + 1] <- rep(1:L, times = Size)
-  
+
   if (ncores > 1)
   {
     cl <- parallel::makeCluster(ncores, outfile = "")
@@ -50,23 +50,21 @@ constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min,
       "Pi", "Rho", "mini", "maxi", "X", "Y", "eps", "Rank", "m", "phi", "ncores", 
       "verbose"))
   }
-  
+
   computeAtLambda <- function(index)
   {
     lambdaIndex <- RankLambda[index, k + 1]
     rankIndex <- RankLambda[index, 1:k]
     if (ncores > 1) 
       require("valse")  #workers start with an empty environment
-    
+
     # 'relevant' will be the set of relevant columns
     selected <- S[[lambdaIndex]]$selected
     relevant <- c()
     for (j in 1:p)
     {
       if (length(selected[[j]]) > 0)
-      {
         relevant <- c(relevant, j)
-      }
     }
     if (max(rankIndex) < length(relevant))
     {
@@ -75,25 +73,23 @@ constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min,
       {
         res <- EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi, 
           X[, relevant], Y, eps, rankIndex, fast)
-        llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + 
-          m)))
+        llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + m)))
         phi[relevant, , ] <- res$phi
       }
       list(llh = llh, phi = phi, pi = S[[lambdaIndex]]$Pi, rho = S[[lambdaIndex]]$Rho)
     }
   }
-  
+
   # For each lambda in the grid we compute the estimators
-  out <- if (ncores > 1)
-  {
-    parLapply(cl, seq_len(length(S) * Size), computeAtLambda)
-  } else
-  {
-    lapply(seq_len(length(S) * Size), computeAtLambda)
-  }
-  
+  out <-
+    if (ncores > 1) {
+      parLapply(cl, seq_len(length(S) * Size), computeAtLambda)
+    } else {
+      lapply(seq_len(length(S) * Size), computeAtLambda)
+    }
+
   if (ncores > 1) 
     parallel::stopCluster(cl)
-  
+
   out
 }
diff --git a/pkg/R/generateXY.R b/pkg/R/generateXY.R
index fe86045..064b54b 100644
--- a/pkg/R/generateXY.R
+++ b/pkg/R/generateXY.R
@@ -17,14 +17,14 @@ generateXY <- function(n, π, meanX, β, covX, covY)
   p <- dim(covX)[1]
   m <- dim(covY)[1]
   k <- dim(covY)[3]
-  
+
   X <- matrix(nrow = 0, ncol = p)
   Y <- matrix(nrow = 0, ncol = m)
-  
+
   # random generation of the size of each population in X~Y (unordered)
   sizePop <- rmultinom(1, n, π)
-  class <- c()  #map i in 1:n --> index of class in 1:k
-  
+  class <- c() #map i in 1:n --> index of class in 1:k
+
   for (i in 1:k)
   {
     class <- c(class, rep(i, sizePop[i]))
@@ -33,7 +33,7 @@ generateXY <- function(n, π, meanX, β, covX, covY)
     Y <- rbind(Y, t(apply(newBlockX, 1, function(row) MASS::mvrnorm(1, row %*% 
       β[, , i], covY[, , i]))))
   }
-  
+
   shuffle <- sample(n)
   list(X = X[shuffle, ], Y = Y[shuffle, ], class = class[shuffle])
 }
diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index fafa2c4..d1ade1c 100644
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -23,24 +23,22 @@ initSmallEM <- function(k, X, Y, fast = TRUE)
   piInit1 <- matrix(0, nIte, k)
   gamInit1 <- array(0, dim = c(n, k, nIte))
   LLFinit1 <- list()
-  
+
   # require(MASS) #Moore-Penrose generalized inverse of matrix
   for (repet in 1:nIte)
   {
     distance_clus <- dist(cbind(X, Y))
     tree_hier <- hclust(distance_clus)
     Zinit1[, repet] <- cutree(tree_hier, k)
-    
+
     for (r in 1:k)
     {
       Z <- Zinit1[, repet]
       Z_indice <- seq_len(n)[Z == r]  #renvoit les indices où Z==r
-      if (length(Z_indice) == 1)
-      {
+      if (length(Z_indice) == 1) {
         betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*% 
           crossprod(t(X[Z_indice, ]), Y[Z_indice, ])
-      } else
-      {
+      } else {
         betaInit1[, , r, repet] <- MASS::ginv(crossprod(X[Z_indice, ])) %*% 
           crossprod(X[Z_indice, ], Y[Z_indice, ])
       }
@@ -49,26 +47,26 @@ initSmallEM <- function(k, X, Y, fast = TRUE)
       rhoInit1[, , r, repet] <- solve(sigmaInit1[, , r, repet])
       piInit1[repet, r] <- mean(Z == r)
     }
-    
+
     for (i in 1:n)
     {
       for (r in 1:k)
       {
-        dotProduct <- tcrossprod(Y[i, ] %*% rhoInit1[, , r, repet] - X[i, 
-          ] %*% phiInit1[, , r, repet])
-        Gam[i, r] <- piInit1[repet, r] * det(rhoInit1[, , r, repet]) * exp(-0.5 * 
-          dotProduct)
+        dotProduct <- tcrossprod(Y[i, ] %*% rhoInit1[, , r, repet]
+          - X[i, ] %*% phiInit1[, , r, repet])
+        Gam[i, r] <- piInit1[repet, r]
+          * det(rhoInit1[, , r, repet]) * exp(-0.5 * dotProduct)
       }
       sumGamI <- sum(Gam[i, ])
       gamInit1[i, , repet] <- Gam[i, ]/sumGamI
     }
-    
+
     miniInit <- 10
     maxiInit <- 11
-    
-    init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet, 
-      ], gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X, 
-      Y, eps = 1e-04, fast)
+
+    init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet, ],
+      gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X, Y,
+      eps = 1e-04, fast)
     LLFEessai <- init_EMG$LLF
     LLFinit1[repet] <- LLFEessai[length(LLFEessai)]
   }
@@ -77,6 +75,6 @@ initSmallEM <- function(k, X, Y, fast = TRUE)
   rhoInit <- rhoInit1[, , , b]
   piInit <- piInit1[b, ]
   gamInit <- gamInit1[, , b]
-  
+
   return(list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit))
 }
diff --git a/pkg/R/main.R b/pkg/R/main.R
index 3b9620d..64e0586 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -30,29 +30,28 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
   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, 
   plot = TRUE)
-  {
+{
   p <- dim(X)[2]
   m <- dim(Y)[2]
   n <- dim(X)[1]
-  
+
   if (verbose) 
     print("main loop: over all k and all lambda")
-  
-  if (ncores_outer > 1)
-  {
+
+  if (ncores_outer > 1) {
     cl <- parallel::makeCluster(ncores_outer, outfile = "")
     parallel::clusterExport(cl = cl, envir = environment(), varlist = c("X", 
       "Y", "procedure", "selecMod", "gamma", "mini", "maxi", "eps", "kmin", 
       "kmax", "rank.min", "rank.max", "ncores_outer", "ncores_inner", "thresh", 
       "size_coll_mod", "verbose", "p", "m"))
   }
-  
+
   # Compute models with k components
   computeModels <- function(k)
   {
     if (ncores_outer > 1) 
       require("valse")  #nodes start with an empty environment
-    
+
     if (verbose) 
       print(paste("Parameters initialization for k =", k))
     # smallEM initializes parameters by k-means and regression model in each
@@ -63,25 +62,22 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
       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)]
-    
+
     if (verbose) 
       print("Compute relevant parameters")
     # select variables according to each regularization parameter from the grid:
     # S$selected corresponding to selected variables
     S <- selectVariables(P$phiInit, P$rhoInit, P$piInit, P$gamInit, mini, maxi, 
       gamma, grid_lambda, X, Y, thresh, eps, ncores_inner, fast)
-    
-    if (procedure == "LassoMLE")
-    {
+
+    if (procedure == "LassoMLE") {
       if (verbose) 
         print("run the procedure Lasso-MLE")
       # compute parameter estimations, with the Maximum Likelihood Estimator,
       # restricted on selected variables.
       models <- constructionModelesLassoMLE(P$phiInit, P$rhoInit, P$piInit, 
         P$gamInit, mini, maxi, gamma, X, Y, eps, S, ncores_inner, fast, verbose)
-      
-    } else
-    {
+    } else {
       if (verbose) 
         print("run the procedure Lasso-Rank")
       # compute parameter estimations, with the Low Rank Estimator, restricted on
@@ -93,19 +89,23 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
     models <- models[sapply(models, function(cell) !is.null(cell))]
     models
   }
-  
+
   # List (index k) of lists (index lambda) of models
-  models_list <- if (ncores_outer > 1) 
-    parLapply(cl, kmin:kmax, computeModels) else lapply(kmin:kmax, computeModels)
+  models_list <-
+    if (ncores_outer > 1) {
+      parLapply(cl, kmin:kmax, computeModels)
+    } else {
+      lapply(kmin:kmax, computeModels)
+    }
   if (ncores_outer > 1) 
     parallel::stopCluster(cl)
-  
+
   if (!requireNamespace("capushe", quietly = TRUE))
   {
     warning("'capushe' not available: returning all models")
     return(models_list)
   }
-  
+
   # Get summary 'tableauRecap' from models
   tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i)
   {
@@ -118,41 +118,35 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
     data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, 
       complexity = sumPen, contrast = -LLH)
   }))
-  
-  print(tableauRecap)
   tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
-  
+
   modSel <- capushe::capushe(tableauRecap, n)
   indModSel <- if (selecMod == "DDSE") 
     as.numeric(modSel@DDSE@model) else if (selecMod == "Djump") 
     as.numeric(modSel@Djump@model) else if (selecMod == "BIC") 
     modSel@BIC_capushe$model else if (selecMod == "AIC") 
     modSel@AIC_capushe$model
-  
+
   mod <- as.character(tableauRecap[indModSel, 1])
   listMod <- as.integer(unlist(strsplit(mod, "[.]")))
   modelSel <- models_list[[listMod[1]]][[listMod[2]]]
-  
+
   ## Affectations
   Gam <- matrix(0, ncol = length(modelSel$pi), nrow = n)
   for (i in 1:n)
   {
     for (r in 1:length(modelSel$pi))
     {
-      sqNorm2 <- sum((Y[i, ] %*% modelSel$rho[, , r] - X[i, ] %*% modelSel$phi[, 
-        , r])^2)
-      Gam[i, r] <- modelSel$pi[r] * exp(-0.5 * sqNorm2) * det(modelSel$rho[, 
-        , r])
+      sqNorm2 <- sum((Y[i, ] %*% modelSel$rho[, , r] - X[i, ] %*% modelSel$phi[, , r])^2)
+      Gam[i, r] <- modelSel$pi[r] * exp(-0.5 * sqNorm2) * det(modelSel$rho[, , r])
     }
   }
   Gam <- Gam/rowSums(Gam)
   modelSel$affec <- apply(Gam, 1, which.max)
   modelSel$proba <- Gam
-  
+
   if (plot)
-  {
     print(plot_valse(X, Y, modelSel, n))
-  }
-  
+
   return(modelSel)
 }
diff --git a/pkg/R/plot_valse.R b/pkg/R/plot_valse.R
index 6207061..ec2302d 100644
--- a/pkg/R/plot_valse.R
+++ b/pkg/R/plot_valse.R
@@ -18,7 +18,7 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   require("ggplot2")
   require("reshape2")
   require("cowplot")
-  
+
   K <- length(model$pi)
   ## regression matrices
   gReg <- list()
@@ -27,51 +27,47 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
     Melt <- melt(t((model$phi[, , r])))
     gReg[[r]] <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) + 
       geom_tile() + scale_fill_gradient2(low = "blue", high = "red", mid = "white", 
-      midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster", 
-      r))
+      midpoint = 0, space = "Lab") + ggtitle(paste("Regression matrices in cluster", r))
   }
   print(gReg)
-  
+
   ## Differences between two clusters
   if (comp)
   {
     if (is.na(k1) || is.na(k))
-    {
       print("k1 and k2 must be integers, representing the clusters you want to compare")
-    }
     Melt <- melt(t(model$phi[, , k1] - model$phi[, , k2]))
-    gDiff <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value)) + geom_tile() + 
-      scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
-        space = "Lab") + ggtitle(paste("Difference between regression matrices in cluster", 
-      k1, "and", k2))
+    gDiff <- ggplot(data = Melt, aes(x = Var1, y = Var2, fill = value))
+      + geom_tile()
+      + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
+        space = "Lab")
+      + ggtitle(paste("Difference between regression matrices in cluster", 
+        k1, "and", k2))
     print(gDiff)
-    
   }
-  
+
   ### Covariance matrices
   matCov <- matrix(NA, nrow = dim(model$rho[, , 1])[1], ncol = K)
   for (r in 1:K)
-  {
     matCov[, r] <- diag(model$rho[, , r])
-  }
   MeltCov <- melt(matCov)
-  gCov <- ggplot(data = MeltCov, aes(x = Var1, y = Var2, fill = value)) + geom_tile() + 
-    scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
-      space = "Lab") + ggtitle("Covariance matrices")
+  gCov <- ggplot(data = MeltCov, aes(x = Var1, y = Var2, fill = value)) + geom_tile()
+    + scale_fill_gradient2(low = "blue", high = "red", mid = "white", midpoint = 0, 
+      space = "Lab")
+    + ggtitle("Covariance matrices")
   print(gCov)
-  
+
   ### Proportions
   gam2 <- matrix(NA, ncol = K, nrow = n)
   for (i in 1:n)
-  {
     gam2[i, ] <- c(model$proba[i, model$affec[i]], model$affec[i])
-  }
-  
-  bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2)) + 
-    geom_boxplot() + theme(legend.position = "none") + background_grid(major = "xy", 
-    minor = "none")
+
+  bp <- ggplot(data.frame(gam2), aes(x = X2, y = X1, color = X2, group = X2))
+    + geom_boxplot()
+    + theme(legend.position = "none")
+    + background_grid(major = "xy", minor = "none")
   print(bp)
-  
+
   ### Mean in each cluster
   XY <- cbind(X, Y)
   XY_class <- list()
@@ -79,18 +75,15 @@ plot_valse <- function(X, Y, model, n, comp = FALSE, k1 = NA, k2 = NA)
   for (r in 1:K)
   {
     XY_class[[r]] <- XY[model$affec == r, ]
-    if (sum(model$affec == r) == 1)
-    {
+    if (sum(model$affec == r) == 1) {
       meanPerClass[, r] <- XY_class[[r]]
-    } else
-    {
+    } else {
       meanPerClass[, r] <- apply(XY_class[[r]], 2, mean)
     }
   }
-  data <- data.frame(mean = as.vector(meanPerClass), cluster = as.character(rep(1:K, 
-    each = dim(XY)[2])), time = rep(1:dim(XY)[2], K))
+  data <- data.frame(mean = as.vector(meanPerClass),
+    cluster = as.character(rep(1:K, each = dim(XY)[2])), time = rep(1:dim(XY)[2], K))
   g <- ggplot(data, aes(x = time, y = mean, group = cluster, color = cluster))
-  print(g + geom_line(aes(linetype = cluster, color = cluster)) + geom_point(aes(color = cluster)) + 
-    ggtitle("Mean per cluster"))
-  
+  print(g + geom_line(aes(linetype = cluster, color = cluster))
+    + geom_point(aes(color = cluster)) + ggtitle("Mean per cluster"))
 }
diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index fe0688c..f717cae 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -24,35 +24,33 @@
 #'
 selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, 
   glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast = TRUE)
-  {
-  if (ncores > 1)
-  {
+{
+  if (ncores > 1) {
     cl <- parallel::makeCluster(ncores, outfile = "")
     parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit", 
       "mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
   }
-  
+
   # Computation for a fixed lambda
   computeCoefs <- function(lambda)
   {
     params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda, 
       X, Y, eps, fast)
-    
+
     p <- dim(phiInit)[1]
     m <- dim(phiInit)[2]
-    
+
     # selectedVariables: list where element j contains vector of selected variables
     # in [1,m]
-    selectedVariables <- lapply(1:p, function(j)
-    {
+    selectedVariables <- lapply(1:p, function(j) {
       # from boolean matrix mxk of selected variables obtain the corresponding boolean
       # m-vector, and finally return the corresponding indices
       seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
     })
-    
+
     list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
   }
-  
+
   # For each lambda in the grid, we compute the coefficients
   out <- if (ncores > 1) 
     parLapply(cl, glambda, computeCoefs) else lapply(glambda, computeCoefs)
@@ -67,8 +65,6 @@ selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma
   ind_uniq <- which(!ind_dup)
   out2 <- list()
   for (l in 1:length(ind_uniq))
-  {
     out2[[l]] <- out[[ind_uniq[l]]]
-  }
   out2
 }