add automatic code formatter following https://google.github.io/styleguide/Rguide.xml

diff --git a/hooks/pre-commit b/hooks/pre-commit
new file mode 100755 (executable)
index 0000000..f9649b3
--- /dev/null
+++ b/hooks/pre-commit
@@ -0,0 +1,46 @@
+#!/bin/sh
+#
+# Hook used to indent all source files before commiting
+#
+
+# indent / format file by type
+indent() {
+       # getting against as the current commit
+       if git rev-parse --verify HEAD >/dev/null 2>&1
+       then
+               local against=HEAD
+       else
+               # Initial commit: diff against an empty tree object
+               local against=4b825dc642cb6eb9a060e54bf8d69288fbee4904
+       fi
+
+       # loop on modified files
+       git diff --cached --name-only $against |while read file;
+       do
+               local ext=$(expr "$file" : ".*\(\..*\)")
+               case $ext in
+               .R|.r)
+                       __indent_R;
+               ;;
+               esac
+       done
+}
+
+# Indent the file with `indent' if this is a R file
+__indent_R() {
+       if test ! -x "$INDENT"
+       then
+               return;
+       fi
+       if test ! -f $file
+       then
+               return;
+       fi
+
+       echo "Indenting " $file
+       echo "library(formatR);formatR::tidy_source('$file',comment=TRUE,blank=TRUE,
+               arrow=TRUE,brace.newline=TRUE,indent=2,width.cutoff=80,file='$file')" | R --slave
+       git add "$file"
+}
+
+indent

diff --git a/hooks/pre-push b/hooks/pre-push
new file mode 100755 (executable)
index 0000000..7f26c8f
--- /dev/null
+++ b/hooks/pre-push
@@ -0,0 +1,5 @@
+#!/bin/sh
+
+./.git-fat/git-fat pull
+./.git-fat/git-fat push
+git submodule update --merge

diff --git a/initialize.sh b/initialize.sh
index 59c9d17..48f0ba1 100755 (executable)
--- a/initialize.sh
+++ b/initialize.sh
@@ -3,21 +3,32 @@
 #initialize submodules, set-up .git/config and .gitattributes, and pre-push hook
 git submodule init && git submodule update --merge
 
+#filter for git-fat
+printf \
+'*.pdf filter=fat
+*.tar.xz filter=fat
+*.png filter=fat
+*.jpg filter=fat
+*.ps filter=fat\n' > .gitattributes
+
 #filter for Jupyter
 python .nbstripout/nbstripout.py --install --attributes .gitattributes
 
-#filter for git-fat [TODO: idempotent...]
-printf '*.pdf filter=fat\n*.tar.xz filter=fat\n*.png filter=fat\n*.jpg filter=fat\n*.ps filter=fat\n'  >> .gitattributes
+#pre-commit and pre-push hooks: indentation, git fat push, submodules update
+cp hooks/* .git/hooks/
 
-#pre-push hook: git fat push, submodules update
-printf '#!/bin/sh\n./.git-fat/git-fat pull\n./.git-fat/git-fat push\ngit submodule update --merge\n' > .git/hooks/pre-push
-chmod 755 .git/hooks/pre-push
+#install formatR
+echo 'if (! "formatR" %in% rownames(installed.packages()))
+       install.packages("formatR",repos="https://cloud.r-project.org")' | R --slave
 
 #.gitfat file with remote on gitfat@auder.net
 printf '[rsync]\nremote = gitfat@auder.net:~/files/valse\n' > .gitfat
 
 #manual git-fat init: with relative path to binary
-#1] remove filter if exists http://stackoverflow.com/questions/12179437/replace-3-lines-with-another-line-sed-syntax
+#1] remove filter if exists http://stackoverflow.com/a/12179641/4640434
 sed -i '1N;$!N;s/\[filter "fat"\]\n.*\n.*//;P;D' .git/config
 #2] place new filter
-printf '[filter "fat"]\n\tclean = ./.git-fat/git-fat filter-clean\n\tsmudge = ./.git-fat/git-fat filter-smudge\n' >> .git/config
+printf \
+'[filter "fat"]
+       clean = ./.git-fat/git-fat filter-clean
+       smudge = ./.git-fat/git-fat filter-smudge\n' >> .git/config

diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
index 5da26e3..fe75d2c 100644 (file)
--- a/pkg/R/constructionModelesLassoRank.R
+++ b/pkg/R/constructionModelesLassoRank.R
@@ -18,77 +18,77 @@
 #' @return a list with several models, defined by phi, rho, pi, llh
 #'
 #' @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)
+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
-  RankLambda = matrix(0, nrow=Size*L, ncol=k+1)
+  deltaRank <- rank.max - rank.min + 1
+  Size <- deltaRank^k
+  RankLambda <- matrix(0, nrow = Size * L, ncol = k + 1)
   for (r in 1:k)
   {
-    # On veut le tableau de toutes les combinaisons de rangs possibles, et des lambdas
-    # Dans la première colonne : on répète (rank.max-rank.min)^(k-1) chaque chiffre :
-    #   ça remplit la colonne
-    # Dans la deuxieme : on répète (rank.max-rank.min)^(k-2) chaque chiffre,
-    #   et on fait ça (rank.max-rank.min)^2 fois
-    # ...
-    # Dans la dernière, on répète chaque chiffre une fois,
-    #   et on fait ça (rank.min-rank.max)^(k-1) fois.
-    RankLambda[,r] = rep(rank.min + rep(0:(deltaRank-1), deltaRank^(r-1), each=deltaRank^(k-r)), each = L)
+    # On veut le tableau de toutes les combinaisons de rangs possibles, et des
+    # lambdas Dans la première colonne : on répète (rank.max-rank.min)^(k-1) chaque
+    # chiffre : ça remplit la colonne Dans la deuxieme : on répète
+    # (rank.max-rank.min)^(k-2) chaque chiffre, et on fait ça (rank.max-rank.min)^2
+    # fois ...  Dans la dernière, on répète chaque chiffre une fois, et on fait ça
+    # (rank.min-rank.max)^(k-1) fois.
+    RankLambda[, r] <- rep(rank.min + rep(0:(deltaRank - 1), deltaRank^(r - 1), 
+      each = deltaRank^(k - r)), each = L)
   }
-  RankLambda[,k+1] = rep(1:L, times = Size)
+  RankLambda[, k + 1] <- rep(1:L, times = Size)
   
   if (ncores > 1)
   {
-    cl = parallel::makeCluster(ncores, outfile='')
-    parallel::clusterExport( cl, envir=environment(),
-                             varlist=c("A1","Size","Pi","Rho","mini","maxi","X","Y","eps",
-                                       "Rank","m","phi","ncores","verbose") )
+    cl <- parallel::makeCluster(ncores, outfile = "")
+    parallel::clusterExport(cl, envir = environment(), varlist = c("A1", "Size", 
+      "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
+    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]]$selected
-    relevant = c()
-    for (j in 1:p){
-      if (length(selected[[j]])>0){
-        relevant = c(relevant,j)
+    selected <- S[[lambdaIndex]]$selected
+    relevant <- c()
+    for (j in 1:p)
+    {
+      if (length(selected[[j]]) > 0)
+      {
+        relevant <- c(relevant, j)
       }
     }
-    if (max(rankIndex)<length(relevant)){
-      phi = array(0, dim=c(p,m,k))
+    if (max(rankIndex) < length(relevant))
+    {
+      phi <- array(0, dim = c(p, m, k))
       if (length(relevant) > 0)
       {
-        res = EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi,
-                      X[,relevant], Y, eps, rankIndex, fast)
-        llh = c( res$LLF, sum(rankIndex * (length(relevant)- rankIndex + m)) ) 
-        phi[relevant,,] = res$phi
+        res <- EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi, 
+          X[, relevant], Y, eps, rankIndex, fast)
+        llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + 
+          m)))
+        phi[relevant, , ] <- res$phi
       }
-      list("llh"=llh, "phi"=phi, "pi" = S[[lambdaIndex]]$Pi, "rho" = S[[lambdaIndex]]$Rho)
+      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)
+  # 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)
   
-  if (ncores > 1)
+  if (ncores > 1) 
     parallel::stopCluster(cl)
   
   out