library(testthat)
library(ndl)
library(compiler)
library(fields)
library(parallel)
library(doParallel)
library(foreach)
enableJIT(3)
monkeydat <- data.frame(Monkey=c("DAN", "ART", "CAU", "DOR", "VIO", "ARI"),
NumTrials=c(56689, 50985, 61142, 49608, 43041, 55407),
NumWordsLearned=c(308, 125, 112, 121, 81, 87),
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),
stringsAsFactors=F)
Resample <- function(x){
x[sample.int(length(x))]
}
test_that("Resample", {
x <- 1:10
expect_that(length(Resample(x[x > 8])), equals(2))
expect_that(length(Resample(x[x > 9])), equals(1))
expect_that(length(Resample(x[x > 10])), equals(0))
})
RandomPick <- function(data, n) {
if ( is.data.frame(data) ){
data[sample(nrow(data), n, replace=T),]
} else {
data[sample(length(data), n, replace=T)]
}
}
test_that("RandomPick", {
n <- 10
bigger <- 1:(3 * n)
equal <- 1:n
smaller <- c(1)
empty <- c()
df <- data.frame(a=c(1,2,3), b=c(4,5,6))
expect_equal(length(RandomPick(bigger, n)), n)
expect_equal(length(RandomPick(equal, n)), n)
expect_equal(length(RandomPick(smaller, n)), n)
expect_equal(length(RandomPick(empty, n)), 0)
expect_equal(nrow(RandomPick(df, n)), n)
})
CreateBlock <- function(data){
result <- data.frame(Stimulus=character(BLOCK_SIZE),
StimulusType=factor(character(BLOCK_SIZE),
c("LearnedWord","NewWord","Nonword")),
stringsAsFactors=F)
data <- data[,c("Stimulus","StimulusType")]
stopifnot(is.character(data$Stimulus))
newSample <- RandomPick(data[data$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)
result
}
BLOCK_SIZE <- 100
test_that("CreateBlock", {
stim <- as.character(1:300)
data <- data.frame(Stimulus=stim, stringsAsFactors=F)
data$StimulusType[1:100] <- "NewWord"
data$StimulusType[101:200] <- "LearnedWord"
data$StimulusType[201:300] <- "Nonword"
block <- CreateBlock(data)
trialNewWords <- block$Stimulus[ block$StimulusType == "NewWord" ]
trialLearnedWords <- block$Stimulus[ block$StimulusType == "LearnedWord" ]
trialNonwords <- block$Stimulus[ block$StimulusType == "Nonword" ]
expect_that(nrow(block), equals(BLOCK_SIZE))
expect_that(length(trialNewWords), equals(25))
expect_that(length(unique(trialNewWords)), equals(1))
expect_that(length(trialLearnedWords), equals(25))
expect_that(length(trialNonwords), equals(50))
# TODO: Decide what should be expected for empty vectors?
})
########## DELETE
st.stimulus = character(trialCount)
st.stimulustype = character(trialCount)
st.block = numeric(trialCount)
st.trialinblock = numeric(trialCount)
st.trial = numeric(trialCount)
st.response = character(trialCount)
#View(block[curTrialNumInBlock, ])
#asdfasdf = block[curTrialNumInBlock, ]
#stop("try")
st.stimulus[curTrialNum] = block[curTrialNumInBlock, ][1]
lev = unclass(block[curTrialNumInBlock, ][2])
st.stimulustype[curTrialNum] = ifelse(lev==1, "LearnedWord", ifelse(lev==2, "NewWord", "Nonword"))
st.block[curTrialNum] = block[curTrialNumInBlock, ][3]
st.trialinblock[curTrialNum] = block[curTrialNumInBlock, ][4]
st.trial[curTrialNum] = block[curTrialNumInBlock, ][5]
st.response[curTrialNum] = block[curTrialNumInBlock, ][6]
#trials[curTrialNum, ] <- block[curTrialNumInBlock, ]
trialsResult = data.frame(cbind(st.stimulus, st.stimulustype, st.block,
st.trialinblock, st.trial,
st.response))
colnames(trialsResult) <- c("Stimulus", "StimulusType", "Block",
"TrialInBlock", "Trial", "Response")
#trials <- data.frame(Stimulus=character(trialCount),
# StimulusType=character(trialCount),
# Block=numeric(trialCount),
# TrialInBlock=numeric(trialCount),
# Trial=numeric(trialCount),
# Response=character(trialCount))
######
PresentTrials <- function(trialCount, present, data){
trials = data.frame()
for ( curTrialNum in 1:trialCount ) {
curBlock <- ( (curTrialNum - 1) %/% BLOCK_SIZE) + 1
curTrialNumInBlock <- ( (curTrialNum - 1) %% BLOCK_SIZE ) + 1
isNewBlock <- curTrialNumInBlock == 1
if ( isNewBlock ) {
if ( curBlock > 1 ){
newWordTrials <- block[block$StimulusType == "NewWord", ]
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
block$TrialInBlock <- 1:nrow(block)
}
curTrial <- block[curTrialNumInBlock,]
curStim <- curTrial$Stimulus
curStimIsWord <- present(curStim, ifelse(curTrial$StimulusType == "Nonword", "Nonword", "Word"))
block$Trial[curTrialNumInBlock] <- curTrialNum
block$Response[curTrialNumInBlock] <- curStimIsWord
trials <- rbind(trials, block[curTrialNumInBlock, ])
}
rownames(trials) <- 1:trialCount
result <- list(Trials=trials, Stimuli=data)
return(result)
}
test_that("PresentTrials", {
stim <- as.character(1:300)
data <- data.frame(Stimulus=stim, stringsAsFactors=F)
data$StimulusType[1:100] <- "NewWord"
data$StimulusType[101:200] <- "LearnedWord"
data$StimulusType[201:300] <- "Nonword"
count <- BLOCK_SIZE * 2.5
result <- PresentTrials(count,
function(cue, outcome){
return(outcome)
},
data)
trials <- result$Trials
stimuli <- result$Stimuli
expect_equal(nrow(trials), count)
expect_equal(length(stimuli$StimulusType[stimuli$StimulusType == "LearnedWord"]), 102)
})
# The original colSums fails if matrix filters to 1 row and gets implicit castet to a vector
ColSums <- function(x) {
if(is.matrix(x)){
return(colSums(x))
} else {
return(x)
}
}
test_that("ColSums",{
m2x2 <- matrix(1,2,2)
colnames(m2x2) <- c("a","b")
expect_equal(ColSums(m2x2), c(a=2,b=2))
expect_equal(ColSums(m2x2[1,]), c(a=1,b=1))
})
MakeMonkey <- function(cueset, outcomeset=c("Word", "Nonword"),
alpha=sqrt(0.001), beta=sqrt(0.001), lambda=1.0){
weights <- matrix(0, length(cueset), length(outcomeset))
rownames(weights) <- cueset
colnames(weights) <- outcomeset
rate <- function(cue){
cues <- unlist(strsplit(cue, "_"))
cueWeights <- weights[cues, outcomeset]
totalActivation <- ColSums(cueWeights)
maxActivated <- names(totalActivation[totalActivation == max(totalActivation)])
return(RandomPick(maxActivated,1))
}
learner <- function(stim, resp){
cues <- unlist(strsplit(stim, "_"))
cueWeights <- weights[cues, outcomeset]
totalActivation <- ColSums(cueWeights)
for (j in 1:length(cues)) {
if (resp == "Nonword") {
yesType <- "Nonword"
noType <- "Word"
} else if (resp == "Word") {
yesType <- "Word"
noType <- "Nonword"
} else {
stop("Unknown outcome", resp)
}
weights[cues[j],yesType] <<- weights[cues[j],yesType] +
alpha * beta * (lambda - totalActivation[yesType])
weights[cues[j],noType] <<- weights[cues[j],noType] +
alpha * beta * (0 - totalActivation[noType])
}
return(rate(stim))
}
give_weights <- function(){
return(weights)
}
list(
GetWeights=give_weights,
Learner=learner,
Rate=rate
)
}
test_that("MakeMonkey",{
cueset <- c("a","b","c")
monkey <- MakeMonkey(cueset)
for (i in 1:1000){
monkey$Learner("a_b","Word")
monkey$Learner("b_c","Nonword")
}
print(monkey$GetWeights())
expect_equal(monkey$Rate("a"), "Word")
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)
}
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)
return(pres)
}
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)
}
simulateAndAnalyse <- function(data, trialCount, alpha=sqrt(0.001), beta=sqrt(0.001)){
p <- runSim(data, trialCount, alpha, beta, lambda=1)
analyseSim(p)
}
data <- prepareDat()
testfunc = function() {
n <- 10
trialCount <- 50000
for( a in 0:n) {
k <- 1.0 / n * a
print(k)
#s <- simulateAndAnalyse(data, trialCount, alpha=1, beta=k)
s <- simulateAndAnalyse(data, trialCount, alpha=0.000001, beta=0.00001)
s[1] <- as.character(k)
monkeydat <- rbind(monkeydat,
s)
}
return(monkeydat)
}
testfunc()
first.result = c()
system.time(first.result <- testfunc())
first.result
# user system elapsed
# 10.05 0.00 10.06
# user system elapsed
# 17.39 0.03 17.87
###############################################################################################
x = matrix(nrow=2, ncol=0)
for(a in seq(from=0.01, to=0.20, by=0.025)) {
for(b in seq(from=0.01, to=a, by=0.025)) {
x = cbind(x, c(a, b))
}
}
l = 12
st.1 = x[, 1:l]
st.2 = x[, (l+1):(2*l)]
st.3 = x[, (2*l+1):(3*l)]
get.a = function(i, index) {
if(i==1) return(st.1[1, index])
if(i==2) return(st.2[1, index])
if(i==3) return(st.3[1, index])
}
get.b = function(i, index) {
if(i==1) return(st.1[2, index])
if(i==2) return(st.2[2, index])
if(i==3) return(st.3[2, index])
}
do.experiment = function(i) {
filename = paste("resultdat", i, ".txt", sep="")
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=filename, col.names=names(resultdat))
trialCount <- 50000
for(index in 1:l) {
a = get.a(i, index)
b = get.b(i, index)
resultdat = read.table(filename)
print(paste("i: ", i, " a: ", a, " b: ", b, sep="" ))
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=filename, col.names=names(resultdat))
}
resultdat = resultdat[-1, ] #remove 1st row which is just always 0
write.table(resultdat, file=filename, col.names=names(resultdat))
return(resultdat)
}
num.cores = detectCores() - 1 #don't use all if you want to do anything else
cl <- makeCluster(num.cores)
registerDoParallel(cl)
foreach(i=1:num.cores) %dopar% do.experiment(i)
###########################
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.
#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)
dat03 = data.frame()
for(i in 0:3) {
d = read.table(paste("resultdat", i, ".txt", sep=""))
d = d[-1, ]
dat03 = rbind(dat03, d)
}
View(dat03)
write.table(dat03, file="preliminary_results.txt", col.names=names(dat03))
# EOF
