From: Benjamin Auder <benjamin.auder@somewhere>
Date: Mon, 3 Apr 2017 16:23:38 +0000 (+0200)
Subject: several modifs - pkg looks better (but untested)
X-Git-Url: https://git.auder.net/app_dev.php/js/current/img/parser.js?a=commitdiff_plain;h=0eb161e3f3d018bce7d98fc85622d14910f89d43;p=valse.git

several modifs - pkg looks better (but untested)
---

diff --git a/pkg/DESCRIPTION b/pkg/DESCRIPTION
index 5febdc0..8ec8b8a 100644
--- a/pkg/DESCRIPTION
+++ b/pkg/DESCRIPTION
@@ -19,13 +19,11 @@ Depends:
     R (>= 3.0.0)
 Imports:
     MASS,
-    methods
+		parallel
 Suggests:
-    parallel,
-    testhat,
-    devtools,
-    rmarkdown
+    capushe,
+    roxygen2,
+    testhat
 URL: http://git.auder.net/?p=valse.git
 License: MIT + file LICENSE
-VignetteBuilder: knitr
 RoxygenNote: 5.0.1
diff --git a/pkg/R/A_NAMESPACE.R b/pkg/R/A_NAMESPACE.R
index 62989ce..a1c8ce3 100644
--- a/pkg/R/A_NAMESPACE.R
+++ b/pkg/R/A_NAMESPACE.R
@@ -1,4 +1,17 @@
+#' @include generateXY.R
+#' @include EMGLLF.R
+#' @include EMGrank.R
+#' @include initSmallEM.R
+#' @include computeGridLambda.R
+#' @include constructionModelesLassoMLE.R
+#' @include constructionModelesLassoRank.R
+#' @include filterModels.R
+#' @include selectVariables.R
+#' @include main.R
+#' @include plot.R
+#'
 #' @useDynLib valse
 #'
 #' @importFrom parallel makeCluster parLapply stopCluster clusterExport
+#' @importFrom MASS ginv
 NULL
diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index f89b2a3..a051441 100644
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -16,14 +16,17 @@
 #' @return the grid of regularization parameters
 #'
 #' @export
-computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
+computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y,
+	gamma, mini, maxi, tau)
 {
 	n = nrow(X)
 	p = dim(phiInit)[1]
 	m = dim(phiInit)[2]
 	k = dim(phiInit)[3]
 
-  list_EMG = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+	# TODO: explain why gamma=1 instad of just 'gamma'?
+  list_EMG = EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi,
+		gamma=1, lamba=0, X, Y, tau)
 	grid = array(0, dim=c(p,m,k))
 	for (i in 1:p)
 	{
@@ -31,6 +34,6 @@ computeGridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, min
 			grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
 	}
 	grid = unique(grid)
-	grid = grid[grid <=1]
+	grid = grid[grid <= 1]
 	grid
 }
diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 6c37751..67fc1fc 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -75,9 +75,9 @@ constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini,
 	#Pour chaque lambda de la grille, on calcule les coefficients
   out =
 		if (ncores > 1)
-			parLapply(cl, seq_along(glambda), computeAtLambda)
+			parLapply(cl, glambda, computeAtLambda)
 		else
-			lapply(seq_along(glambda), computeAtLambda)
+			lapply(glambda, computeAtLambda)
 
 	if (ncores > 1)
     parallel::stopCluster(cl)
diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
index c219d75..71713f7 100644
--- a/pkg/R/constructionModelesLassoRank.R
+++ b/pkg/R/constructionModelesLassoRank.R
@@ -10,7 +10,6 @@
 constructionModelesLassoRank = function(pi, rho, mini, maxi, X, Y, tau, A1, rangmin,
 	rangmax, ncores, verbose=FALSE)
 {
-  #get matrix sizes
   n = dim(X)[1]
   p = dim(X)[2]
   m = dim(rho)[2]
@@ -21,7 +20,7 @@ constructionModelesLassoRank = function(pi, rho, mini, maxi, X, Y, tau, A1, rang
   deltaRank = rangmax - rangmin + 1
   Size = deltaRank^k
   Rank = matrix(0, nrow=Size, ncol=k)
-  for(r in 1:k)
+  for (r in 1:k)
 	{
 		# On veut le tableau de toutes les combinaisons de rangs possibles
 		# Dans la première colonne : on répète (rangmax-rangmin)^(k-1) chaque chiffre :
@@ -34,28 +33,52 @@ constructionModelesLassoRank = function(pi, rho, mini, maxi, X, Y, tau, A1, rang
     Rank[,r] = rangmin + rep(0:(deltaRank-1), deltaRank^(r-1), each=deltaRank^(k-r))
   }
 
-	# output parameters
-  phi = array(0, dim=c(p,m,k,L*Size))
-  llh = matrix(0, L*Size, 2) #log-likelihood
+  if (ncores > 1)
+	{
+    cl = parallel::makeCluster(ncores)
+    parallel::clusterExport( cl, envir=environment(),
+			varlist=c("A1","Size","Pi","Rho","mini","maxi","X","Y","tau",
+			"Rank","m","phi","ncores","verbose") )
+	}
 
-	# TODO: // loop
-	for(lambdaIndex in 1:L)
+	computeAtLambda <- function(lambdaIndex)
 	{
+		if (ncores > 1)
+			require("valse") #workers start with an empty environment
+
     # on ne garde que les colonnes actives
     # 'active' sera l'ensemble des variables informatives
     active = A1[,lambdaIndex]
     active = active[-(active==0)]
+		phi = array(0, dim=c(p,m,k,Size))
+		llh = matrix(0, Size, 2) #log-likelihood
     if (length(active) > 0)
 		{
       for (j in 1:Size)
 			{
         res = EMGrank(Pi[,lambdaIndex], Rho[,,,lambdaIndex], mini, maxi,
 					X[,active], Y, tau, Rank[j,])
-        llh[(lambdaIndex-1)*Size+j,] =
-					c( res$LLF, sum(Rank[j,] * (length(active)- Rank[j,] + m)) )
-        phi[active,,,(lambdaIndex-1)*Size+j] = res$phi
+        llh = rbind(llh,
+					c( res$LLF, sum(Rank[j,] * (length(active)- Rank[j,] + m)) ) )
+        phi[active,,,] = rbind(phi[active,,,], res$phi)
       }
     }
-  }
-  return (list("phi"=phi, "llh" = llh))
+		list("llh"=llh, "phi"=phi)
+	}
+
+	#Pour chaque lambda de la grille, on calcule les coefficients
+  out =
+		if (ncores > 1)
+			parLapply(cl, seq_along(glambda), computeAtLambda)
+		else
+			lapply(seq_along(glambda), computeAtLambda)
+
+	if (ncores > 1)
+    parallel::stopCluster(cl)
+
+	# TODO: this is a bit ugly. Better use bigmemory and fill llh/phi in-place
+	# (but this also adds a dependency...)
+	llh <- do.call( rbind, lapply(out, function(model) model$llh) )
+	phi <- do.call( rbind, lapply(out, function(model) model$phi) )
+	list("llh"=llh, "phi"=phi)
 }
diff --git a/pkg/R/discardSimilarModels.R b/pkg/R/discardSimilarModels.R
deleted file mode 100644
index 5f6a8c8..0000000
--- a/pkg/R/discardSimilarModels.R
+++ /dev/null
@@ -1,53 +0,0 @@
-#' Discard models which have the same relevant variables - for EMGLLF
-#'
-#' @param B1 array of relevant coefficients (of size p*m*length(gridlambda))
-#' @param B2 array of irrelevant coefficients (of size p*m*length(gridlambda))
-#' @param glambda grid of regularization parameters (vector)
-#' @param rho covariance matrix (of size m*m*K*size(gridLambda))
-#' @param pi weight parameters (of size K*size(gridLambda))
-#'
-#' @return a list with update B1, B2, glambda, rho and pi, and ind the vector of indices
-#'	of selected models.
-#' @export
-discardSimilarModels_EMGLLF = function(B1,B2,glambda,rho,pi)
-{
-	ind = c()
-	for (j in 1:length(glambda))
-	{
-		for (ll in 1:(l-1))
-		{
-			if(B1[,,l] == B1[,,ll])
-				ind = c(ind, l)
-		}
-	}
-	ind = unique(ind)
-	B1 = B1[,,-ind]
-	glambda = glambda[-ind]
-	B2 = B2[,,-ind]
-	rho = rho[,,,-ind] 
-	pi = pi[,-ind]
-
-	return (list("B1"=B1,"B2"=B2,"glambda"=glambda,"rho"=rho,"pi"=pi,"ind"=ind))
-}
-
-#' Discard models which have the same relevant variables
-#'   - for Lasso-rank procedure (focus on columns)
-#'
-#' @param B1 array of relevant coefficients (of size p*m*length(gridlambda))
-#' @param rho covariance matrix
-#' @param pi weight parameters
-#'
-#' @return a list with B1, in, rho, pi
-#' @export
-discardSimilarModels_EMGrank = function(B1,rho,pi)
-{
-	ind = c()
-	dim_B1 = dim(B1)
-	B2 = array(0,dim=c(dim_B1[1],dim_B1[2],dim_B1[3]))
-	sizeLambda=dim_B1[3]
-	glambda = rep(0,sizeLambda)
-
-	suppressmodel = discardSimilarModels_EMGLLF(B1,B2,glambda,rho,pi)
-	return (list("B1" = suppressmodel$B1, "ind" = suppressmodel$ind,
-		"rho" = suppressmodel$rho, "pi" = suppressmodel$pi))
-}
diff --git a/pkg/R/modelSelection.R b/pkg/R/filterModels.R
similarity index 85%
rename from pkg/R/modelSelection.R
rename to pkg/R/filterModels.R
index 86e2efd..2659ed4 100644
--- a/pkg/R/modelSelection.R
+++ b/pkg/R/filterModels.R
@@ -7,9 +7,9 @@
 #'
 #' @return a list with indices, a vector of indices selected models,
 #'				 and D1, a vector of corresponding dimensions
-#' @export
 #'
-modelSelection = function(LLF)
+#' @export
+filterModels = function(LLF)
 {
 	D = LLF[,2]
 	D1 = unique(D)
@@ -34,7 +34,3 @@ modelSelection = function(LLF)
 
 	return (list(indices=indices,D1=D1))
 }
-
-#TODO:
-## Programme qui sélectionne un modèle
-## proposer à l'utilisation différents critères (BIC, AIC, slope heuristic)
diff --git a/pkg/R/initSmallEM.R b/pkg/R/initSmallEM.R
index 541d7e1..bfe1d46 100644
--- a/pkg/R/initSmallEM.R
+++ b/pkg/R/initSmallEM.R
@@ -24,7 +24,7 @@ initSmallEM = function(k,X,Y)
 	gamInit1 = array(0, dim=c(n,k,20))
 	LLFinit1 = list()
 
-	require(MASS) #Moore-Penrose generalized inverse of matrix
+	#require(MASS) #Moore-Penrose generalized inverse of matrix
 	for(repet in 1:20)
 	{
 	  distance_clus = dist(X)
@@ -36,10 +36,10 @@ initSmallEM = function(k,X,Y)
 			Z = Zinit1[,repet]
 			Z_indice = seq_len(n)[Z == r] #renvoit les indices où Z==r
 			if (length(Z_indice) == 1) {
-			  betaInit1[,,r,repet] = ginv(crossprod(t(X[Z_indice,]))) %*%
+			  betaInit1[,,r,repet] = MASS::ginv(crossprod(t(X[Z_indice,]))) %*%
 			    crossprod(t(X[Z_indice,]), Y[Z_indice,])
 			} else {
-			betaInit1[,,r,repet] = ginv(crossprod(X[Z_indice,])) %*%
+			betaInit1[,,r,repet] = MASS::ginv(crossprod(X[Z_indice,])) %*%
 				crossprod(X[Z_indice,], Y[Z_indice,])
 			}
 			sigmaInit1[,,r,repet] = diag(m)
@@ -62,9 +62,8 @@ initSmallEM = function(k,X,Y)
 		miniInit = 10
 		maxiInit = 11
 		
-		#new_EMG = .Call("EMGLLF_core",phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],
-#			gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4)
-		new_EMG = EMGLLF(phiInit1[,,,repet],rhoInit1[,,,repet],piInit1[repet,],gamInit1[,,repet],miniInit,maxiInit,1,0,X,Y,1e-4)
+		new_EMG = EMGLLF(phiInit1[,,,repet], rhoInit1[,,,repet], piInit1[repet,],
+			gamInit1[,,repet], miniInit, maxiInit, gamma=1, lambda=0, X, Y, tau=1e-4)
 		LLFEessai = new_EMG$LLF
 		LLFinit1[repet] = LLFEessai[length(LLFEessai)]
 	}
diff --git a/pkg/R/main.R b/pkg/R/main.R
index 8ce5117..ab25daf 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -28,7 +28,6 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
   m = dim(Y)[2]
   n = dim(X)[1]
 
-  tableauRecap = list()
   if (verbose)
 		print("main loop: over all k and all lambda")
 
@@ -40,21 +39,20 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 			"ncores_outer","ncores_inner","verbose","p","m","k","tableauRecap") )
 	}
 
-	# Compute model with k components
-	computeModel <- function(k)
+	# 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 component,
+		#smallEM initializes parameters by k-means and regression model in each component,
     #doing this 20 times, and keeping the values maximizing the likelihood after 10
     #iterations of the EM algorithm.
     P = initSmallEM(k, X, Y)
     grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit, X, Y,
 			gamma, mini, maxi, eps)
-
 		# TODO: 100 = magic number
     if (length(grid_lambda)>100)
       grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
@@ -62,10 +60,9 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 		if (verbose)
 			print("Compute relevant parameters")
     #select variables according to each regularization parameter
-    #from the grid: A1 corresponding to selected variables, and
-    #A2 corresponding to unselected variables.
-    S = selectVariables(P$phiInit,P$rhoInit,P$piInit,P$gamInit,mini,maxi,gamma,
-			grid_lambda,X,Y,1e-8,eps,ncores_inner)
+    #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, 1e-8, eps, ncores_inner) #TODO: 1e-8 as arg?! eps?
 
     if (procedure == 'LassoMLE')
 		{
@@ -73,7 +70,7 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 				print('run the procedure Lasso-MLE')
       #compute parameter estimations, with the Maximum Likelihood
       #Estimator, restricted on selected variables.
-      model = constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
+      models <- constructionModelesLassoMLE(phiInit, rhoInit, piInit, gamInit, mini,
 				maxi, gamma, X, Y, thresh, eps, S$selected, ncores_inner, verbose)
     }
 		else
@@ -82,52 +79,41 @@ valse = function(X, Y, procedure='LassoMLE', selecMod='DDSE', gamma=1, mini=10,
 				print('run the procedure Lasso-Rank')
       #compute parameter estimations, with the Low Rank
       #Estimator, restricted on selected variables.
-      model = constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
+      models <- constructionModelesLassoRank(S$Pi, S$Rho, mini, maxi, X, Y, eps, A1,
 				rank.min, rank.max, ncores_inner, verbose)
-
-      ################################################
-      ### Regarder la SUITE  
-#      phi = runProcedure2()$phi
-#      Phi2 = Phi
-#      if (dim(Phi2)[1] == 0)
-#        Phi[, , 1:k,] <- phi
-#      else
-#      {
-#        Phi <- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
-#        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
-#        Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
-#      }
     }
-    model
+    models
   }
 
-	model_list <-
+	# List (index k) of lists (index lambda) of models
+	models_list <-
 		if (ncores_k > 1)
-			parLapply(cl, kmin:kmax, computeModel)
+			parLapply(cl, kmin:kmax, computeModels)
 		else
-			lapply(kmin:kmax, computeModel)
+			lapply(kmin:kmax, computeModels)
 	if (ncores_k > 1)
 		parallel::stopCluster(cl)
 
-	# Get summary "tableauRecap" from models
-	tableauRecap = t( sapply( seq_along(model_list), function(model) {
-		llh = matrix(ncol = 2)
-    for (l in seq_along(model))
-      llh = rbind(llh, model[[l]]$llh)
+	if (! requireNamespace("capushe", quietly=TRUE))
+	{
+		warning("'capushe' not available: returning all models")
+		return (models_list)
+	}
+
+	# Get summary "tableauRecap" from models ; TODO: jusqu'à ligne 114 à mon avis là c'est faux :/
+	tableauRecap = t( sapply( models_list, function(models) {
+		llh = do.call(rbind, lapply(models, function(model) model$llh)
     LLH = llh[-1,1]
     D = llh[-1,2]
 		c(LLH, D, rep(k, length(model)), 1:length(model))
-	} ) )
-
+	) } ) )
 	if (verbose)
 		print('Model selection')
-	tableauRecap = do.call( rbind, tableauRecap ) #stack list cells into a matrix
   tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
-  tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
+  tableauRecap = tableauRecap[!is.infinite(tableauRecap[,1]),]
   data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
 
-	require(capushe)
-  modSel = capushe(data, n)
+  modSel = capushe::capushe(data, n)
   indModSel <-
 		if (selecMod == 'DDSE')
 			as.numeric(modSel@DDSE@model)
diff --git a/pkg/R/selectVariables.R b/pkg/R/selectVariables.R
index 869e7bf..54eda38 100644
--- a/pkg/R/selectVariables.R
+++ b/pkg/R/selectVariables.R
@@ -34,7 +34,7 @@ selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambd
 	}
 
 	# Calcul pour un lambda
-	computeCoefs <-function(lambda)
+	computeCoefs <- function(lambda)
 	{
 		params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
 
diff --git a/pkg/tests/testthat.R b/pkg/tests/testthat.R
index d2761ea..88e5631 100644
--- a/pkg/tests/testthat.R
+++ b/pkg/tests/testthat.R
@@ -1,4 +1,4 @@
 library(testthat)
-library(valse #ou load_all()
+library(valse) #ou load_all()
 
 test_check("valse")
diff --git a/pkg/tests/testthat/helper-clustering.R b/pkg/tests/testthat/helper-clustering.R
deleted file mode 100644
index 785b7f0..0000000
--- a/pkg/tests/testthat/helper-clustering.R
+++ /dev/null
@@ -1,11 +0,0 @@
-# Compute the sum of (normalized) sum of squares of closest distances to a medoid.
-computeDistortion <- function(series, medoids)
-{
-	n <- ncol(series)
-	L <- nrow(series)
-	distortion <- 0.
-	for (i in seq_len(n))
-		distortion <- distortion + min( colSums( sweep(medoids,1,series[,i],'-')^2 ) / L )
-
-	sqrt( distortion / n )
-}
diff --git a/pkg/tests/testthat/helper-context1.R b/pkg/tests/testthat/helper-context1.R
new file mode 100644
index 0000000..b40f358
--- /dev/null
+++ b/pkg/tests/testthat/helper-context1.R
@@ -0,0 +1,5 @@
+# Potential helpers for context 1
+help <- function()
+{
+	#...
+}
diff --git a/pkg/tests/testthat/test-clustering.R b/pkg/tests/testthat/test-clustering.R
deleted file mode 100644
index 2e3a431..0000000
--- a/pkg/tests/testthat/test-clustering.R
+++ /dev/null
@@ -1,72 +0,0 @@
-context("clustering")
-
-test_that("clusteringTask1 behave as expected",
-{
-	# Generate 60 reference sinusoïdal series (medoids to be found),
-	# and sample 900 series around them (add a small noise)
-	n <- 900
-	x <- seq(0,9.5,0.1)
-	L <- length(x) #96 1/4h
-	K1 <- 60
-	s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
-	series <- matrix(nrow=L, ncol=n)
-	for (i in seq_len(n))
-		series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
-
-	getSeries <- function(indices) {
-		indices <- indices[indices <= n]
-		if (length(indices)>0) as.matrix(series[,indices]) else NULL
-	}
-
-	wf <- "haar"
-	ctype <- "absolute"
-	getContribs <- function(indices) curvesToContribs(as.matrix(series[,indices]),wf,ctype)
-
-	require("cluster", quietly=TRUE)
-	algoClust1 <- function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
-	indices1 <- clusteringTask1(1:n, getContribs, K1, algoClust1, 140, verbose=TRUE, parll=FALSE)
-	medoids_K1 <- getSeries(indices1)
-
-	expect_equal(dim(medoids_K1), c(L,K1))
-	# Not easy to evaluate result: at least we expect it to be better than random selection of
-	# medoids within initial series
-	distor_good <- computeDistortion(series, medoids_K1)
-	for (i in 1:3)
-		expect_lte( distor_good, computeDistortion(series,series[,sample(1:n, K1)]) )
-})
-
-test_that("clusteringTask2 behave as expected",
-{
-	# Same 60 reference sinusoïdal series than in clusteringTask1 test,
-	# but this time we consider them as medoids - skipping stage 1
-	# Here also we sample 900 series around the 60 "medoids"
-	n <- 900
-	x <- seq(0,9.5,0.1)
-	L <- length(x) #96 1/4h
-	K1 <- 60
-	K2 <- 3
-	#for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
-	s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
-	series <- matrix(nrow=L, ncol=n)
-	for (i in seq_len(n))
-		series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
-
-	getSeries <- function(indices) {
-		indices <- indices[indices <= n]
-		if (length(indices)>0) as.matrix(series[,indices]) else NULL
-	}
-
-	# Perfect situation: all medoids "after stage 1" are ~good
-	algoClust2 <- function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
-	indices2 <- clusteringTask2(1:K1, getSeries, K2, algoClust2, 210, 3, 4, 8, "little",
-		verbose=TRUE, parll=FALSE)
-	medoids_K2 <- getSeries(indices2)
-
-	expect_equal(dim(medoids_K2), c(L,K2))
-	# Not easy to evaluate result: at least we expect it to be better than random selection of
-	# synchrones within 1...K1 (from where distances computations + clustering was run)
-	distor_good <- computeDistortion(series, medoids_K2)
-#TODO: This fails; why?
-#	for (i in 1:3)
-#		expect_lte( distor_good, computeDistortion(series, series[,sample(1:K1,3)]) )
-})
diff --git a/pkg/tests/testthat/test-context1.R b/pkg/tests/testthat/test-context1.R
new file mode 100644
index 0000000..17c633f
--- /dev/null
+++ b/pkg/tests/testthat/test-context1.R
@@ -0,0 +1,11 @@
+context("Context1")
+
+test_that("function 1...",
+{
+	#expect_lte( ..., ...)
+})
+
+test_that("function 2...",
+{
+	#expect_equal(..., ...)
+})
diff --git a/pkg/vignettes/valse.Rmd b/pkg/vignettes/valse.Rmd
deleted file mode 100644
index e8164a1..0000000
--- a/pkg/vignettes/valse.Rmd
+++ /dev/null
@@ -1,23 +0,0 @@
----
-title: "valse package vignette"
-output: html_document
----
-
-```{r include = FALSE}
-library(valse)
-```
-
-The code below demonstrates blabla... in [valse](https://github.com/blabla/valse) package.
-Each plot displays blabla...
-
-## Des jolis plot 1
-
-```{r}
-#plotBla1(...)
-```
-
-## Des jolies couleurs 2
-
-```{r}
-#plotBla2(...)
-```