'update'

diff --git a/TODO b/TODO
index 7e6aa90..3c1fd78 100644 (file)
--- a/TODO
+++ b/TODO
@@ -48,3 +48,21 @@ utiliser du mixmod avec modèles allongés
 doit toutner sur machine plutôt standard, utilisateur "lambda"
 utiliser Rcpp ?
 
+=====
+
+trategies for upscaling
+From 25K to 25M : in 1000 chunks of 25K
+Reference values :
+ K 0 = 200 super consumers (SC)
+ K ∗ = 15 nal clusters
+1st strategy
+ Do 1000 times ONLY Energycon's 1st-step strategy on 25K clients
+ With the 1000 × K 0 SC perform a 2-step run leading to K ∗ clusters
+
+--> il faut s'arranger pour que 
+
+2nd strategy
+ Do 1000 times Energycon's 2-step strategy on 25K clients leading to
+ 1000 × K ∗ intermediate clusters
+ Treat the intermediate clusters as individual curves and perform a
+ single 2-step run to get K ∗ nal clusters

diff --git a/epclust/R/main.R b/epclust/R/main.R
index 6746d88..eded952 100644 (file)
--- a/epclust/R/main.R
+++ b/epclust/R/main.R
@@ -52,7 +52,7 @@ epclust = function(data, K, nb_series_per_chunk, min_series_per_chunk=10*K,
                stop("read/writeTmp should be functional (see defaults.R)")
        if (WER!="end" && WER!="mix")
                stop("WER takes values in {'end','mix'}")
-       #concerning ncores, any non-integer type will be treated as "use parallel:detectCores()"
+       #concerning ncores, any non-integer type will be treated as "use parallel:detectCores()/4"
 
        #1) acquire data (process curves, get as coeffs)
        #TODO: for data.frame and custom function, run in parallel (connections are sequential[?!])
@@ -98,7 +98,7 @@ epclust = function(data, K, nb_series_per_chunk, min_series_per_chunk=10*K,
 
        #2) process coeffs (by nb_series_per_chunk) and cluster them in parallel
        library(parallel)
-       ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores())
+       ncores = ifelse(is.integer(ncores), ncores, parallel::detectCores()%/%4)
        cl = parallel::makeCluster(ncores)
        parallel::clusterExport(cl=cl, varlist=c("TODO:", "what", "to", "export?"), envir=environment())
        #TODO: be careful of writing to a new temp file, then flush initial one, then re-use it...

diff --git a/epclust/R/stage2.R b/epclust/R/stage2.R
new file mode 100644 (file)
index 0000000..f952da2
--- /dev/null
+++ b/epclust/R/stage2.R
@@ -0,0 +1,139 @@
+#Entrée : courbes synchrones, soit après étape 1 itérée, soit après chaqure étape 1
+
+#(Benjamin)
+#à partir de là, "conso" == courbes synchrones
+n     <- nrow(conso)
+delta <- ncol(conso)
+
+
+#17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube]
+
+#TODO: une fonction qui fait lignes 59 à 91
+
+#cube:
+# Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
+#                 scalevector = scalevector4,
+#                 lt = delta, smooth = FALSE, 
+#                 nvoice = nvoice)      # observations node with CWT
+# 
+# #matrix:
+# ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
+# #Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
+# 
+# #NOTE: delta et lscvect pourraient etre gardés à part (communs)
+# for(i in 1:n) 
+#  Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) 
+# 
+# #rm(conso, Xcwt4); gc()
+# 
+# ## _.b WER^2 distances  ########
+# Xwer_dist    <- matrix(0.0, n, n)
+# for(i in 1:(n - 1)){
+#  mat1   <- vect2mat(Xcwt2[i,])
+#  for(j in (i + 1):n){
+#     mat2 <- vect2mat(Xcwt2[j,])
+#     num     <- Mod(mat1 * Conj(mat2))
+#     WX      <- Mod(mat1 * Conj(mat1))
+#     WY      <- Mod(mat2 * Conj(mat2))
+#     smsmnum <- smCWT(num, scalevector = scalevector4)
+#     smsmWX  <- smCWT(WX,  scalevector = scalevector4)
+#     smsmWY  <- smCWT(WY,  scalevector = scalevector4)
+#     wer2    <- sum(colSums(smsmnum)^2)  /
+#       sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
+#     Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2))
+#     Xwer_dist[j, i] <- Xwer_dist[i, j]
+#   }
+# }
+# diag(Xwer_dist) <- numeric(n)
+# 
+# save(Xwer_dist, file = "../res/2009_synchros200WER.Rdata")
+# save(Xwer_dist, file = "../res/2009_synchros200-randomWER.Rdata")
+
+
+
+#lignes 59 à 91 "dépliées" :
+Xcwt4   <- toCWT(conso, noctave = noctave4, dt = 1,
+                 scalevector = scalevector4,
+                 lt = delta, smooth = FALSE, 
+                 nvoice = nvoice)      # observations node with CWT
+ 
+ #matrix:
+ ############Xcwt2 <- matrix(0.0, nrow= n, ncol= 2 + delta * lscvect)
+ Xcwt2 <- matrix(NA_complex_, nrow= n, ncol= 2 + length((c(Xcwt4[,,1]))))
+ 
+ #NOTE: delta et lscvect pourraient etre gardés à part (communs)
+ for(i in 1:n) 
+    Xcwt2[i,] <- c(delta, lscvect, Xcwt4[,,i] / max(Mod(Xcwt4[,,i])) ) 
+ 
+ #rm(conso, Xcwt4); gc()
+ 
+ ## _.b WER^2 distances  ########
+ Xwer_dist    <- matrix(0.0, n, n)
+ for(i in 1:(n - 1)){
+  mat1   <- vect2mat(Xcwt2[i,])
+
+ #NOTE: vect2mat = as.matrix ?! (dans aux.R)
+  vect2mat <- function(vect){
+                 vect <- as.vector(vect)
+                 matrix(vect[-(1:2)], delta, lscvect)
+               }
+ 
+ for(j in (i + 1):n){
+     mat2 <- vect2mat(Xcwt2[j,])
+     num     <- Mod(mat1 * Conj(mat2))
+     WX      <- Mod(mat1 * Conj(mat1))
+     WY      <- Mod(mat2 * Conj(mat2))
+     smsmnum <- smCWT(num, scalevector = scalevector4)
+     smsmWX  <- smCWT(WX,  scalevector = scalevector4)
+     smsmWY  <- smCWT(WY,  scalevector = scalevector4)
+     wer2    <- sum(colSums(smsmnum)^2)  /
+       sum( sum(colSums(smsmWX) * colSums(smsmWY)) )
+     Xwer_dist[i, j] <- sqrt(delta * lscvect * (1 - wer2))
+     Xwer_dist[j, i] <- Xwer_dist[i, j]
+   }
+ }
+ diag(Xwer_dist) <- numeric(n)
+
+#fonction smCWT (dans aux.R)
+  smCWT <- function(CWT, sw=  0,  tw=  0, swabs= 0,
+                       nvoice= 12, noctave= 2, s0= 2, w0= 2*pi, 
+                                          lt= 24, dt= 0.5, scalevector )
+                {
+#         noctave  <- adjust.noctave(lt, dt, s0, tw, noctave)
+#         scalevector  <- 2^(0:(noctave * nvoice) / nvoice) * s0
+         wsp     <- Mod(CWT)  
+         smwsp   <- smooth.matrix(wsp, swabs)
+         smsmwsp <- smooth.time(smwsp, tw, dt, scalevector)
+         smsmwsp
+       }
+
+ #dans sowas.R
+smooth.matrix <- function(wt,swabs){
+  
+  if (swabs != 0)
+    smwt <- t(filter(t(wt),rep(1,2*swabs+1)/(2*swabs+1)))
+  else
+    smwt <- wt
+  
+  smwt
+  
+}
+smooth.time <- function(wt,tw,dt,scalevector){
+  
+  smwt <- wt
+  
+  if (tw != 0){
+    for (i in 1:length(scalevector)){
+      
+      twi <- as.integer(scalevector[i]*tw/dt)
+      smwt[,i] <- filter(wt[,i],rep(1,2*twi+1)/(2*twi+1))
+      
+    }
+  } 
+  smwt
+}
+
+#et filter() est dans stats::
+
+#cf. filters en C dans : https://svn.r-project.org/R/trunk/src/library/stats/src/filter.c
+

diff --git a/old_C_code/stage2_UNFINISHED/src/05_cluster2stepWER.r b/old_C_code/stage2_UNFINISHED/src/05_cluster2stepWER.r
index 69939b2..aa35d0c 100644 (file)
--- a/old_C_code/stage2_UNFINISHED/src/05_cluster2stepWER.r
+++ b/old_C_code/stage2_UNFINISHED/src/05_cluster2stepWER.r
@@ -45,13 +45,19 @@ rm(synchros09, nas)
 nvoice   <- 4
 # # noctave4 = 2^13 = 8192 half hours ~ 180 days
 noctave4 <- adjust.noctave(N = delta, dt = 1, s0 = 2,
-#                           tw = 0, noctave = 13)
+                           tw = 0, noctave = 13)
 # # 4 here represent 2^5 = 32 half-hours ~ 1 day
 scalevector4  <- 2^(4:(noctave4 * nvoice) / nvoice) * 2
 lscvect4      <- length(scalevector4)
 lscvect <- lscvect4  # i should clean my code: werFam demands a lscvect
 
 
+#(Benjamin)
+#à partir de là, "conso" == courbes synchrones
+n     <- nrow(conso)
+delta <- ncol(conso)
+
+
 #17000 colonnes coeff 1, puis 17000 coeff 2... [non : dans chaque tranche du cube]
 
 #TODO: une fonction qui fait lignes 59 à 91