diff --git a/07_final_assignment/baboonSimulation.R b/07_final_assignment/baboonSimulation.R
index 8329e01..5cc73f4 100644
--- a/07_final_assignment/baboonSimulation.R
+++ b/07_final_assignment/baboonSimulation.R
@@ -1,6 +1,9 @@
+# install.packages("fields")
 library(testthat)
 library(ndl)
 library(compiler)
+library(fields)
+library(parallel)
 enableJIT(3)
 
 monkeydat <- data.frame(Monkey=c("DAN", "ART", "CAU", "DOR", "VIO", "ARI"),
@@ -268,6 +271,7 @@
     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,
@@ -297,7 +301,61 @@
 monkeydat
 
 
+resultdat <- data.frame(alpha=numeric(1), beta=numeric(1), NumTrials=numeric(1), NumWordsLearned=numeric(1),
+                       NumNonwordsPresented=numeric(1), GeneralAccuracy=numeric(1),
+                       WordAccuracy=numeric(1), NonwordAccuracy=numeric(1))
+write.table(resultdat, file="resultdat.txt", col.names=names(resultdat))
+
+trialCount <- 50000
+
+a.seq = seq(from=0, to=0.25, length.out=10)
+b.seq = seq(from=0, to=0.25, length.out=10)
+for( a in a.seq) {
+  for (b in b.seq) {
+    
+    resultdat = read.table("resultdat.txt")
+    
+    print(cat(a, b))
+    s <- simulateAndAnalyse(data, trialCount, alpha=a, beta=b)
+    s[1] <- as.character(trialCount)
+    resultdat <- rbind(resultdat,
+                       c(a, b, s[2:length(s)]))
+    
+    write.table(resultdat, file="resultdat.txt", col.names=names(resultdat))
+  }
+}
+
+resultdat = resultdat[-1, ] #remove 1st row which is just always 0 
+View(resultdat)
+
+res.matrix = matrix(nrow=length(a.seq), ncol=length(b.seq))
+counter = 1
+counter.a = 1
+for( a in a.seq) {
+  counter.b = 1
+  for (b in b.seq) {
+    res.matrix[counter.a, counter.b] = as.numeric(resultdat[counter, ]$GeneralAccuracy)
+    counter = counter + 1
+    counter.b = counter.b + 1
+  }
+  counter.a = counter.a + 1
+}
+
+View(res.matrix)
+
 # 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.
-# EOF
+
+#mat1 = matrix(rexp(200, rate=.1), ncol=50, nrow=50)
+#mat2 = matrix(rexp(200, rate=.1), ncol=50, nrow=50)
+
+image.plot(res.matrix, main="Word Accuracy", xlab="alpha", ylab="beta",
+           useRaster=TRUE, col = topo.colors(100))
+
+
+View(m1)
+
+
+
+# EOF
\ No newline at end of file