diff --git a/07_final_assignment/baboonSimulation.R b/07_final_assignment/baboonSimulation.R
index 0887361..8329e01 100644
--- a/07_final_assignment/baboonSimulation.R
+++ b/07_final_assignment/baboonSimulation.R
@@ -1,5 +1,7 @@
 library(testthat)
 library(ndl)
+library(compiler)
+enableJIT(3)
 
 monkeydat <- data.frame(Monkey=c("DAN", "ART", "CAU", "DOR", "VIO", "ARI"),
                        NumTrials=c(56689, 50985, 61142, 49608, 43041, 55407),
@@ -234,62 +236,68 @@
     expect_equal(monkey$Rate("c"), "Nonword")
 })
 
+prepareDat <- function(){
+    dat <- read.table("dataDan.txt", header=T, stringsAsFactors=F)
+    dat$Cues <- orthoCoding(dat$String, grams=2)
+    dat$Frequency <- 1
+    dat$Stimulus <- dat$Cues
+    dat$StimulusType <- factor(ifelse(dat$Type == "word", "NewWord", "Nonword"), c("Nonword","NewWord","LearnedWord"))
+    dat$Outcomes <- factor(ifelse(dat$Type == "word", "Word", "Nonword"), c("Word","Nonword"))
+    dat <- dat[sample(1:nrow(dat)),]
+    cues <- unique(unlist(strsplit(dat$Cues, "_")))
+    outcomes <- levels(dat$Outcomes)
+    list(cues=cues,outcomes=outcomes,dat=dat)
+}
 
-dat <- read.table("dataDan.txt", header=T, stringsAsFactors=F)
-dat$Cues <- orthoCoding(dat$String, grams=2)
-dat$Frequency <- 1
-dat$Stimulus <- dat$Cues
-dat$StimulusType <- factor(ifelse(dat$Type == "word", "NewWord", "Nonword"), c("Nonword","NewWord","LearnedWord"))
-dat$Outcomes <- factor(ifelse(dat$Type == "word", "Word", "Nonword"), c("Word","Nonword"))
+runSim <- function(data, trialCount, alpha, beta, lambda) {
+    monkey <- MakeMonkey(data$cues, data$outcomes, alpha, beta, lambda=1.0)
+    trialNum <- round(trialCount)
+    pres <- PresentTrials(
+                    trialNum,
+                    monkey$Learner,data$dat)
+    pres
+}
 
-dat <- dat[sample(1:nrow(dat)),]
-cues <- unique(unlist(strsplit(dat$Cues, "_")))
-outcomes <- levels(dat$Outcomes)
-monkey <- MakeMonkey(cues, outcomes, alpha=sqrt(0.001), beta=sqrt(0.001), lambda=1.0)
-monkeyName <- "DAN"
-trialNum <- round(0.5 * as.numeric(monkeydat[monkeydat$Monkey == monkeyName,"NumTrials"]))
-system.time(pres <- PresentTrials(
-    trialNum,
-    monkey$Learner,dat))
+analyseSim <- function(pres){
+    learnedWordNum <- nrow(pres$Stimuli[pres$Stimuli$StimulusType == "LearnedWord",])
+    trialNum <- nrow(pres$Trials)
+    nonwordTrials <- pres$Trials[pres$Trials$StimulusType == "Nonword",]
+    wordTrials <- pres$Trials[pres$Trials$StimulusType != "Nonword",]
+    presNonwordNum <- length(unique(nonwordTrials$Stimulus))
+    presWordNum <- length(unique(wordTrials$Stimulus))
+    nonwordAcc <- nrow(nonwordTrials[nonwordTrials$Response == "Nonword",]) / nrow(nonwordTrials)
+    wordAcc <- nrow(nonwordTrials[wordTrials$Response == "Word",]) / nrow(wordTrials)
+    genAcc <- (nrow(nonwordTrials) * nonwordAcc + nrow(wordTrials) * wordAcc) / trialNum
+    c("Sim",
+      trialNum,
+      learnedWordNum,
+      presNonwordNum,
+      genAcc,
+      wordAcc,
+      nonwordAcc)
+}
 
-# Analyse data:
-learnedWordNum <- nrow(pres$Stimuli[pres$Stimuli$StimulusType == "LearnedWord",])
-trialNum <- nrow(pres$Trials)
-nonwordTrials <- pres$Trials[pres$Trials$StimulusType == "Nonword",]
-wordTrials <- pres$Trials[pres$Trials$StimulusType != "Nonword",]
-presNonwordNum <- length(unique(nonwordTrials$Stimulus))
-presWordNum <- length(unique(wordTrials$Stimulus))
-nonwordAcc <- nrow(nonwordTrials[nonwordTrials$Response == "Nonword",]) / nrow(nonwordTrials)
-wordAcc <- nrow(nonwordTrials[wordTrials$Response == "Word",]) / nrow(wordTrials)
-genAcc <- (nrow(nonwordTrials) * nonwordAcc + nrow(wordTrials) * wordAcc) / trialNum
+simulateAndAnalyse <- function(data, trialCount, alpha=sqrt(0.001), beta=sqrt(0.001)){
+    p <- runSim(data, trialCount, alpha, beta, lambda=1)
+    analyseSim(p)
+}
 
-monkeydat <- rbind(monkeydat,
-                   c(paste(monkeyName, "Sim1"),
-                     trialNum,
-                     learnedWordNum,
-                     presNonwordNum,
-                     genAcc,
-                     wordAcc,
-                     nonwordAcc))
+data <- prepareDat()
 
-# TODO: Das in schoene Funktionen packen ;)
+n <- 10
+trialCount <- 1000
+for( a in 0:n) {
+    k <- 1.0 / n * a
+    print(k)
+    s <- simulateAndAnalyse(data, trialCount, alpha=1, beta=k)
+    s[1] <- as.character(k)
+    monkeydat <- rbind(monkeydat,
+                       s)
+}
+monkeydat
+
 
 # TODO: Durchlaufen lassen fuer ca 50k Trials fuer verschiedene alpha, beta zwischen 0 und 1
 # TODO: Plotten der verschiedenen Ergebnisse (num,acc) als heatmap 2d plot.
 # TODO: Plotten der Unterschiede zu den Vorgabeaffen und optimale alpha beta fuer diese finden.
-
-# what would be the theoretic success rates?
-wRW <- monkey$GetWeights()
-aRW <- estimateActivations(dat, wRW)$activationMatrix
-aRW <- aRW[,order(colnames(aRW))]
-dat$ChoiceRW <- apply(aRW, 1, FUN=function(v){
-                   if(v["Nonword"] >= v["Word"]) {
-                     return("Nonword")
-                   } else {
-                     return("Word")
-                   }})
-table(dat$Type, dat$ChoiceRW)
-
-
 # EOF
-