diff --git a/07_final_assignment/baboonSimulation.R b/07_final_assignment/baboonSimulation.R
index 05b480d..c9356df 100644
--- a/07_final_assignment/baboonSimulation.R
+++ b/07_final_assignment/baboonSimulation.R
@@ -6,7 +6,8 @@
                        NumNonwordsPresented=c(7832, 7832, 7832, 7832, 7832, 7832),
                        GeneralAccuracy=c(79.81, 73.41, 72.43, 73.15, 71.55, 71.14),
                        WordAccuracy=c(80.01, 74.83, 73.15, 79.26, 76.75, 75.38),
-                       NonwordAccuracy=c(79.61, 72.00, 71.72, 67.06, 66.33, 66.90))
+                       NonwordAccuracy=c(79.61, 72.00, 71.72, 67.06, 66.33, 66.90),
+                       stringsAsFactors=F)
 
 Resample <- function(x){
     x[sample.int(length(x))]
@@ -49,11 +50,23 @@
                          StimulusType=factor(character(BLOCK_SIZE),
                                              c("LearnedWord","NewWord","Nonword")),
                          stringsAsFactors=F)
+    data <- data[,c("Stimulus","StimulusType")]
     stopifnot(is.character(data$Stimulus))
 
-    result[1:25,] <- RandomPick(data[data$StimulusType == "NewWord",], 1)
-    result[26:50,] <- RandomPick(data[data$StimulusType == "LearnedWord",], 25)
-    result[51:100,] <- RandomPick(data[data$StimulusType == "Nonword",],50)
+    newSample <- RandomPick(data[data$StimulusType == "NewWord",], 1)
+    learnedSample <- RandomPick(data[data$StimulusType == "LearnedWord",], 25)
+    if( nrow(newSample) > 0 ) {
+        result[1:25,] <- newSample
+    } else {
+        result[1:25,] <- learnedSample
+    }
+    if( nrow(learnedSample) > 0 ){
+        result[26:50,] <- learnedSample
+    } else {
+        result[26:50,] <- newSample
+    }
+   
+    result[51:100,] <- RandomPick(data[data$StimulusType == "Nonword",], 50)
 
     result <- result[sample(nrow(result)),]
     rownames(result) <- 1:nrow(result)
@@ -92,12 +105,14 @@
             isNotFirstBlock <- curBlock > 1
             if ( isNotFirstBlock ){
                 newWordTrials <- block[block$StimulusType == "NewWord",]
-                stopifnot(length(unique(newWordTrials$Stimulus)) == 1)
-                wordResponses <- newWordTrials[newWordTrials$Response == "Word",]
-                wordResponseRate <- nrow(wordResponses) / nrow(newWordTrials)
-                if ( wordResponseRate >= 0.8 ){
-                     data$StimulusType[data$Stimulus == newWordTrials$Stimulus] <- "LearnedWord"
-                 }
+                if( length(unique(newWordTrials$Stimulus)) == 1 ){
+                    newWordTrials <- newWordTrials[1,]
+                    wordResponses <- newWordTrials[newWordTrials$Response == "Word",]
+                    wordResponseRate <- nrow(wordResponses) / nrow(newWordTrials)
+                    if ( wordResponseRate >= 0.8  ){
+                        data$StimulusType[data$Stimulus == newWordTrials$Stimulus] <- "LearnedWord"
+                    }
+                }
             }
             block <- CreateBlock(data)
             block$Block <- curBlock
@@ -216,5 +231,64 @@
     print(monkey$GetWeights())
     expect_equal(monkey$Rate("a"), "Word")
     expect_equal(monkey$Rate("c"), "Nonword")
- })
+})
+
+library(ndl)
+dat <- read.table("dataDan.txt", header=T, stringsAsFactors=F)
+dat$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)
+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))
+
+# 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
+
+monkeydat <- rbind(monkeydat,
+                   c(paste(monkeyName, "Sim1"),
+                     trialNum,
+                     learnedWordNum,
+                     presNonwordNum,
+                     genAcc,
+                     wordAcc,
+                     nonwordAcc))
+
+# TODO: Das in schöne Funktionen packen ;)
+
+# TODO: Durchlaufen lassen für ca 50k Trials für 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 für 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
+