diff --git a/README.md b/README.md
index 4bc0c94..c947e4b 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,4 @@
 # KPMS-IT4I-EX
 Companion to KPMS-IT4I with exercises. It is separate, to avoid lecture pdf replication on IT4I systems.
 
+# ASwR
diff --git a/code/UKOL5.sh b/code/UKOL5.sh
new file mode 100644
index 0000000..cc43b8a
--- /dev/null
+++ b/code/UKOL5.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+#PBS -N rf
+#PBS -l select=1:ncpus=128,walltime=00:05:00
+#PBS -q qexp
+#PBS -e UKOL5.e
+#PBS -o UKOL5.o
+
+cd ~/KPMS-IT4I-EX-1/code
+pwd
+
+module load R
+echo "loaded R"
+
+
+time Rscript UKOL5_simulace.r 128
\ No newline at end of file
diff --git a/code/rf_cv_karolina_pbs.sh b/code/rf_cv_karolina_pbs.sh
index cc069a6..32791a9 100644
--- a/code/rf_cv_karolina_pbs.sh
+++ b/code/rf_cv_karolina_pbs.sh
@@ -5,15 +5,15 @@
 #PBS -e rf_cv.e
 #PBS -o rf_cv.o
 
-cd ~/KPMS-IT4I-EX/code
+cd ~/KPMS-IT4I-EX-1/code
 pwd
 
 module load R
 echo "loaded R"
 
-time Rscript rf_cv_serial.r
-time Rscript rf_cv_mc.r 8
-time Rscript rf_cv_mc.r 16
+#time Rscript rf_cv_serial.r
+#time Rscript rf_cv_mc.r 8
+#time Rscript rf_cv_mc.r 16
 time Rscript rf_cv_mc.r 32
 time Rscript rf_cv_mc.r 64
 time Rscript rf_cv_mc.r 128
diff --git a/code/rf_cv_mc.r b/code/rf_cv_mc.r
index 4e6ca52..9037582 100644
--- a/code/rf_cv_mc.r
+++ b/code/rf_cv_mc.r
@@ -26,15 +26,16 @@ fold_err = function(i, cv_pars, folds, train) {
 nc = as.numeric(commandArgs(TRUE)[1])
 cat("Running with", nc, "cores\n")
 system.time({
-cv_err = parallel::mclapply(1:nrow(cv_pars), fold_err, cv_pars, folds = folds,
+  cv_err = parallel::mclapply(1:nrow(cv_pars), fold_err, cv_pars, folds = folds,
                               train = train, mc.cores = nc) 
-err = tapply(unlist(cv_err), cv_pars[, "mtry"], sum)
+  err = tapply(unlist(cv_err), cv_pars[, "mtry"], sum)
 })
 pdf(paste0("rf_cv_mc", nc, ".pdf")); plot(mtry_val, err/(n - n_test)); dev.off()
 
-rf.all = randomForest(lettr ~ ., train, ntree = ntree)
-pred = predict(rf.all, test)
-correct = sum(pred == test$lettr)
+ntree_new = lapply(splitIndices(200, nc), length)
+rf = function(x) randomForest(lettr ~ ., train, ntree=x, norm.votes = FALSE)
+rf.out = mclapply(ntree_new, rf, mc.cores = nc)
+rf.all = do.call(combine, rf.out)
 
 mtry = mtry_val[which.min(err)]
 rf.all = randomForest(lettr ~ ., train, ntree = ntree, mtry = mtry)
@@ -42,4 +43,3 @@ pred_cv = predict(rf.all, test)
 correct_cv = sum(pred_cv == test$lettr)
 cat("Proportion Correct: ", correct/n_test, "(mtry = ", floor((ncol(test) - 1)/3),
     ") with cv:", correct_cv/n_test, "(mtry = ", mtry, ")\n", sep = "")
-