03_sRSA_X9_ModelPredictions.R
In CognitiveModeling/priorinference_iterative: Prior Inference Iterative
#source("new iterative functions.R")
library(priorinferenceiterative)
############################################################################################
iterative12 <- 1 ###########################################################################
############################################################################################
# 1 iterative
# 2 non-iterative
indorglobal <- 1
# 1 individual optimization used
# 2 global optimization used
parSetting <- 2
# 1 both fixed + 1 free parameter
# 2 second free parameter + both free
# --------- Specify which functions are used for the new iterative optimization ---------
##############################################################
funcType <- 3 ###############################################
##############################################################
# 1 iterative independent of trial order (half evidence, half prior rate)
# 2 iterative independent of trial order (1-prior rate)
# 3 iterative dependent on trial order
library("priorinference")
# Data file from Ella
x9data = read.csv(
"ella_total_allDataCleaned.csv",
header = TRUE,
na.strings = c("", " ", "NA")
)
x9data <- x9data[, colnames(x9data)!="X"]
#arguments for the new function simplePragmaticSpeakerWithPrefPriorAll_dependentOnOrder
weights <- c(0.3, 0.4, 0.5, 0.6)
trial <- c(0, 1, 2, 3)
# adding feature property codes (which feature was uttereed, which features were questioned)
uttFeat <- ifelse(x9data$utterance=="green" | x9data$utterance=="red" | x9data$utterance=="blue", 3,
ifelse(x9data$utterance=="solid" | x9data$utterance=="striped" | x9data$utterance=="polka-dotted", 2, 1))
x9data$uttFeat <- uttFeat
targetFeat <- x9data$targetFeatureNum
## adding the 1-27 target and object1, object2 & object3 code.
temp <- x9data$simulatedAnswer
temp2 <- (temp - temp %% 10) / 10
temp3 <- (temp2 - temp2 %% 10) / 10
targetOC27 <- temp3 + 3 * ((temp2 %% 10) - 1) + 9 * ((temp %% 10) - 1)
x9data$targetOC27 <- targetOC27
temp <- x9data$obj1
temp2 <- (temp - temp %% 10) / 10
temp3 <- (temp2 - temp2 %% 10) / 10
obj1OC27 <- temp3 + 3 * ((temp2 %% 10) - 1) + 9 * ((temp %% 10) - 1)
x9data$obj1OC27 <- obj1OC27
temp <- x9data$obj2
temp2 <- (temp - temp %% 10) / 10
temp3 <- (temp2 - temp2 %% 10) / 10
obj2OC27 <- temp3 + 3 * ((temp2 %% 10) - 1) + 9 * ((temp %% 10) - 1)
x9data$obj2OC27 <- obj2OC27
temp <- x9data$obj3
temp2 <- (temp - temp %% 10) / 10
temp3 <- (temp2 - temp2 %% 10) / 10
obj3OC27 <- temp3 + 3 * ((temp2 %% 10) - 1) + 9 * ((temp %% 10) - 1)
x9data$obj3OC27 <- obj3OC27
## now determining the recorded subject responses
subjectResponses <- matrix(0,length(x9data$workerid),3)
for(i in c(1:length(x9data$workerid))) {
subjectResponses[i,1] <- x9data$normResponse0[i] + 1e-100
subjectResponses[i,2] <- x9data$normResponse1[i] + 1e-100
subjectResponses[i,3] <- x9data$normResponse2[i] + 1e-100
# subjectResponses[i,1:3] <- subjectResponses[i,1:3] / sum(subjectResponses[i,1:3]) # Ella already normalized the data
}
## ordering the recorded subject responses such that they can be compared directly
# to the model predictions
## (particularly for visual comparison in the table)
subjectResponsesOrdered <- matrix(NA ,length(x9data$workerid),9)
for(i in c(1:length(x9data$workerid))) {
for(j in 1:3) {
subjectResponsesOrdered[i, (j+(targetFeat[i]-1)*3)] <- subjectResponses[i,j]
}
}
subjectResponsesOrdered <- round(subjectResponsesOrdered, digits=5)
## Reordering objects in input data
targetObject <- rep(NA, length(x9data$workerid))
object2 <- rep(NA, length(x9data$workerid))
object3 <- rep(NA, length(x9data$workerid))
for (i in 1:length(x9data$workerid)){
if(targetOC27[i] == obj1OC27[i]){
targetObject[i] <- targetOC27[i]
object2[i] <- obj2OC27[i]
object3[i] <- obj3OC27[i]
} else if (targetOC27[i] == obj2OC27[i])
{targetObject[i] <- obj2OC27[i]
object2[i] <- obj1OC27[i]
object3[i] <- obj3OC27[i]
} else {
targetObject[i] <- obj3OC27[i]
object2[i] <- obj1OC27[i]
object3[i] <- obj2OC27[i]
}
}
## recording KL divergence and parameters (base model, 1 param, 2 params)
workerIDs <- x9data$workerid
idMax <- max(workerIDs)
llWorkers12 <- matrix(0,length(unique(workerIDs)), 8)
paramsWorkers12 <- matrix(0,length(unique(workerIDs)), 10)
##########
## recording KL divergence and parameters (base model, 1 param, 2 params)
workerIDs <- x9data$workerid
idMax <- max(workerIDs)
#################################################
if(iterative12 == 1) { #iterative
if(indorglobal == 1){ #individual
if(funcType == 1){
paramsWorkers12 <- as.matrix(read.csv("optimized/x9params_simpleRSA_indOpt_iterative_indep_half.csv"))
llWorkers12 <- as.matrix(read.csv("optimized/x9KLDivs_simpleRSA_indOpt_iterative_indep_half.csv"))
print("used funcType == 1")
} else if (funcType == 2) {
paramsWorkers12 <- as.matrix(read.csv("optimized/x9params_simpleRSA_indOpt_iterative_indep_pr.csv"))
llWorkers12 <- as.matrix(read.csv("optimized/x9KLDivs_simpleRSA_indOpt_iterative_indep_pr.csv"))
print("used funcType == 2")
} else {
paramsWorkers12 <- as.matrix(read.csv("optimized/x9params_simpleRSA_indOpt_iterative_dep.csv"))
llWorkers12 <- as.matrix(read.csv("optimized/x9KLDivs_simpleRSA_indOpt_iterative_dep.csv"))
print("used funcType == 3")
}
} else { #iterative global
paramsWorkers12 <- as.matrix(read.csv("x9Params_simpleRSA_globalOpt_iterative.csv"))
llWorkers12 <- as.matrix(read.csv("x9KLDivs_simpleRSA_globalOpt_iterative.csv"))
}
# not relevant for iterative scenario
} else { # non iterative
if(indorglobal == 1){
paramsWorkers12 <- as.matrix(read.csv("x9Params_simpleRSA_indOpt_nonIterative.csv"))
llWorkers12 <- as.matrix(read.csv("x9KLDivs_simpleRSA_indOpt_nonIterative.csv"))
}
else {
paramsWorkers12 <- as.matrix(read.csv("x9Params_simpleRSA_globalOpt_noniterative.csv"))
llWorkers12 <- as.matrix(read.csv("x9KLDivs_simpleRSA_globalOpt_noniterative.csv"))
}
}
llWorkers12 <- llWorkers12[,c(2:ncol(llWorkers12))]
paramsWorkers12 <- paramsWorkers12[,c(2:ncol(paramsWorkers12))]
###########################################
constellationCode <- matrix(0,length(x9data$workerid),6)
uniqueCCode <- rep(0, length(x9data$workerid))
postListMat1Opt <- matrix(0,length(x9data$workerid),9)
postListMat2Opt <- matrix(0,length(x9data$workerid),9)
# added:
postListMat3Opt <- matrix(0,length(x9data$workerid),9)
postListMat45Opt <- matrix(0,length(x9data$workerid),9)
postListMat4Opt <- matrix(0,length(x9data$workerid),9)
postListMat56Opt <- matrix(0,length(x9data$workerid),9)
postListMat78Opt <- matrix(0,length(x9data$workerid),9)
postListMat910Opt <- matrix(0,length(x9data$workerid),9)
###########################################
# when global optimization was used
if(indorglobal == 2){
params1 <- paramsWorkers12[1,2]
params2 <- paramsWorkers12[1,3]
params12 <- paramsWorkers12[1,c(4,5)]
}
logLik <- rep(0,length(x9data$workerid))
workerID <- -1
#weights <- c(0.3, 0.4, 0.5, 0.6)
#trial <- c(0, 1, 2, 3)
#switched x9data$X with x9data$workerid, because we removed X
for(i in c(1:length(x9data$workerid))) {
objectConstellation <- c(targetObject[i],object2[i],object3[i])
print("objectConstellation")
print(objectConstellation)
featChoice <- uttFeat[i]
constellationCode[i,] <- getConstellationCode(objectConstellation, featChoice)[[1]]
print("constellationCode[i,]")
print(constellationCode[i,])
uc <- 0
for(j in c(1:6)) {
uc <- (uc * 10) + constellationCode[i,j]
}
uniqueCCode[i] <- uc
if(indorglobal == 1){
if(workerID != x9data$workerid[i]) {
workerID <- x9data$workerid[i]
# all three optimized params <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(11:13)]
if(funcType == 1){
# ---- one parameter optimized ----
#params1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(2)] # softness
#params2 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(3)] # obedience
#params3 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(4)] # prior rate
# ---- two parameter optimized ----
#params12 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(4:5)] # softness and obedience
} else if (funcType == 2) {
#funcType == 3
# ---- one parameter optimized ----
params1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(2)] # softness
params2 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(3)] # obedience
params3 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(4)] # prior rate
# ---- two parameter optimized ----
params13 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(5:6)] # softness and prior rate
params13_1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(7:8)] # softness and prior rate
params23 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(9:10)] # obedience and prior rate
# print(params)
} else {
#funcType == 3
# ---- one parameter optimized ----
params1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(2)] # softness, obed = 0
params1_1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(3)] # softness, obed = 0.1
params2 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(4)] # obedience
# ---- two parameter optimized ----
params12 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(5:6)] # softness and obedience
}
}
}
validUtterances <- determineValidUtterances(objectConstellation)
if( (i-1)%%4 == 0) {
priorPrefAll_1 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
priorPrefAll_2 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
priorPrefAll_56 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
priorPrefAll_78 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
priorPrefAll_910 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
} # uniform focussing on the feature type in question.
if(iterative12 == 1) { #iterative
if(parSetting == 1) { # one parameter optimized, two other fixed
if (funcType == 1){
# iterative function independent of trial order half, half
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
0, 0, priorPrefAll_1)
# 1st parameter optimized: softness 0, 0, priorPrefAll_1, 0.5)
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
abs(params1[1]), 0, priorPrefAll_2)
# 2nd parameter optimized: obedience abs(params1[1]), 0, priorPrefAll_2, 0.5)
postListMat3Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
0 , abs(params2[1]), priorPrefAll_2)
} else if(funcType == 2){
# iterative function independent of trial order (1 - prior rate)
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0, 0, priorPrefAll_1, 0.5)
# 1st parameter optimized: softness
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
abs(params1[1]), 0, priorPrefAll_2, 0.5)
# 2nd parameter optimized: obedience
postListMat3Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0 , abs(params2[1]), priorPrefAll_2, 0.5)
# 3rd parameter optimized: prior rate
postListMat4Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0 , 0, priorPrefAll_2, abs(params3[1]))
} else {
# funcType == 3
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
0, 0, priorPrefAll_1)
# 1st parameter optimized: softness , obed = 0
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
abs(params1[1]), 0, priorPrefAll_2)
# 1st parameter optimized: softness, obed = 0.1
postListMat3Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
abs(params1_1[1]), 0.1, priorPrefAll_2)
# 3rd parameter optimized: obedience
postListMat4Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
0 , abs(params2[1]), priorPrefAll_2)
}
} else if(parSetting == 2) {
# 2 parameter optimization
if (funcType == 1){
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
0, 0, priorPrefAll_1)
postListMat45Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
abs(params12[1]), abs(params12[2]), priorPrefAll_2)
} else if (funcType == 2){
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0, 0, priorPrefAll_1, 0.5)
postListMat56Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
abs(params13[1]), 0, priorPrefAll_56, abs(params13[2]))
postListMat78Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
abs(params13_1[1]), 0.1, priorPrefAll_78, abs(params13_1[2]))
postListMat910Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0, abs(params23[1]), priorPrefAll_910, abs(params23[2]))
} else {
#funcType == 3
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
0, 0, priorPrefAll_1)
postListMat56Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
abs(params12[1]), abs(params12[2]), priorPrefAll_1)
}
}
priorPrefAll_1 <- postListMat1Opt[i,]
priorPrefAll_2 <- postListMat2Opt[i,]
priorPrefAll_3 <- postListMat3Opt[i,]
#priorPrefAll_4 <- postListMat4Opt[i,]
#priorPrefAll_5_6 <- postListMat56Opt[i,]
#priorPrefAll_7_8 <- postListMat78Opt[i,]
#priorPrefAll_9_10 <- postListMat910Opt[i,]
# not relevant for iterative scenario
}else if(iterative12 == 2) {
if(parSetting == 1) {
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions(objectConstellation, featChoice,
0, 0, priorPrefAll_1, weights, trial)
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions(objectConstellation, featChoice,
abs(params1[1]), 0, priorPrefAll_2, weights, trial)
}else if(parSetting == 2) {
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions(objectConstellation, featChoice,
0, abs(params2[1]), priorPrefAll_1, weights, trial)
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions(objectConstellation, featChoice,
abs(params12[1]), abs(params12[2]), priorPrefAll_2, weights, trial)
}
}
}
###########
## adding all those values to the x9data table.
subjectResponsesOrdered <- round(subjectResponsesOrdered, digits=5)
colnames(subjectResponsesOrdered) <- colnames(subjectResponsesOrdered, do.NULL = FALSE, prefix = "DataPost_")
x9data <- data.frame(x9data, as.data.frame(subjectResponsesOrdered))
# default parameters
postListMat1Opt <- round(postListMat1Opt, digits=5)
colnames(postListMat1Opt) <- colnames(postListMat1Opt, do.NULL = FALSE, prefix = "Post1_")
consCodeAndPosteriors <- data.frame(as.data.frame(postListMat1Opt))
x9data <- data.frame(x9data, consCodeAndPosteriors)
# ------ one parameter optimized ------
# softness
postListMat2Opt <- round(postListMat2Opt, digits=5)
colnames(postListMat2Opt) <- colnames(postListMat2Opt, do.NULL = FALSE, prefix = "Post2_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat2Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
#obedience
postListMat3Opt <- round(postListMat3Opt, digits=5)
colnames(postListMat3Opt) <- colnames(postListMat3Opt, do.NULL = FALSE, prefix = "Post3_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat3Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
# if func 2 used: prior rate
# if func 3 used: obedience
postListMat4Opt <- round(postListMat4Opt, digits=5)
colnames(postListMat4Opt) <- colnames(postListMat4Opt, do.NULL = FALSE, prefix = "Post4_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat4Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
# ------- two parameters optimized ------
# ---- if funcType == 1: -----
# softness and obedience (pr = 0.5)
#postListMat45Opt <- round(postListMat45Opt, digits=5)
#colnames(postListMat45Opt) <- colnames(postListMat45Opt, do.NULL = FALSE, prefix = "Post4_")
#consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat45Opt))
#x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
#x9data$CCode <- uniqueCCode
#x9data$logLik <- logLik
# ----- if funcType == 2: -----
# softness and prior rate (obed = 0)
# or
# ----- if funcType == 3: -----
# softness and obedience
postListMat56Opt <- round(postListMat56Opt, digits=5)
colnames(postListMat56Opt) <- colnames(postListMat56Opt, do.NULL = FALSE, prefix = "Post5_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat56Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
# --- funcType == 2: -----
# softness and prior rate (obed = 0.1)
postListMat78Opt <- round(postListMat78Opt, digits=5)
colnames(postListMat78Opt) <- colnames(postListMat78Opt, do.NULL = FALSE, prefix = "Post6_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat78Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
# obedience and prior rate (soft = 0)
postListMat910Opt <- round(postListMat910Opt, digits=5)
colnames(postListMat910Opt) <- colnames(postListMat910Opt, do.NULL = FALSE, prefix = "Post7_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat910Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
if(iterative12 == 1) { #iterative
if(indorglobal == 1){ #individual
if(parSetting == 1) { #one parameter optimized
if(funcType == 1){
# optimized softness
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt1_fixed0_0.5_iterative_indep_half.csv")
# optimized obedience
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt2_fixed0_0.5_iterative_indep_half.csv")
} else if(funcType == 2){
# optimized softness
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt1_fixed0_0.5_iterative_indep_pr.csv")
# optimized obedience
write.csv(x9data, "data/x9dataAugm_SRSAindOpt2_fixed0_0.5_iterative_indep_pr.csv")
# optimized prior rate
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt3_fixed0_0.5_iterative_indep_pr.csv")
} else {
# funcType == 3
# optimized softness, obed = 0
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt1_fixed0_iterative_dep.csv")
# optimized softness, obed = 0.1
# write.csv(x9data, "data/x9dataAugm_SRSAindOpt1_fixed0.1_iterative_dep.csv")
# optimized obedience, soft = 0
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt2_fixed0_iterative_dep.csv")
}
}else if(parSetting == 2) {
if (funcType == 1){
# two parameter optim
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt13_fixed_notObey0_iterative_indep_half.csv")
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt13_fixed_notObey0.1_iterative_indep_half.csv")
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt12_fixed_pr0.5_iterative_indep_half.csv")
} else if (funcType == 2){
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt13_fixed_notObey0_iterative_indep_pr.csv")
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt13_fixed_notObey0.1_iterative_indep_pr.csv")
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt23_fixed_pref0_iterative_indep_pr.csv")
}else {
# funcType == 3
write.csv(x9data, "data/x9dataAugm_SRSAindOpt12_iterative_dep.csv")
}
}
# global
} else {
if(parSetting == 1) {
write.csv(x9data, "data/x9dataAugm_SRSAglobalOpt_fixed00_andOpt1fixed0_iterative_withNewFunctions.csv")
}else if(parSetting == 2) {
write.csv(x9data, "data/x9dataAugm_SRSAglobalOpt_fixed0Opt2_andOpt12_iterative_withNewFunctions.csv")
}
}
# non-iterative
} else if(iterative12 == 2) {
if(indorglobal == 1){
if(parSetting == 1) {
write.csv(x9data, "data/x9dataAugm_SRSAindOpt_fixed00_andOpt1fixed0_nonIterative_withNewFunctions.csv")
}else if(parSetting == 2) {
write.csv(x9data, "data/x9dataAugm_SRSAindOpt_fixed0Opt2_andOpt1_nonIterative_withNewFunctions.csv")
}
} else {
if(parSetting == 1) {
write.csv(x9data, "data/x9dataAugm_SRSAglobalOpt_fixed00_andOpt1fixed0_noniterative_withNewFunctions.csv")
}else if(parSetting == 2) {
write.csv(x9data, "data/x9dataAugm_SRSAglobalOpt_fixed0Opt2_andOpt12_noniterative_withNewFunctions.csv")
}
}
}
CognitiveModeling/priorinference_iterative documentation built on Dec. 17, 2021, 3:01 p.m.
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#source("new iterative functions.R")
library(priorinferenceiterative)
############################################################################################
iterative12 <- 1 ###########################################################################
############################################################################################
# 1 iterative
# 2 non-iterative
indorglobal <- 1
# 1 individual optimization used
# 2 global optimization used
parSetting <- 2
# 1 both fixed + 1 free parameter
# 2 second free parameter + both free
# --------- Specify which functions are used for the new iterative optimization ---------
##############################################################
funcType <- 3 ###############################################
##############################################################
# 1 iterative independent of trial order (half evidence, half prior rate)
# 2 iterative independent of trial order (1-prior rate)
# 3 iterative dependent on trial order
library("priorinference")
# Data file from Ella
x9data = read.csv(
"ella_total_allDataCleaned.csv",
header = TRUE,
na.strings = c("", " ", "NA")
)
x9data <- x9data[, colnames(x9data)!="X"]
#arguments for the new function simplePragmaticSpeakerWithPrefPriorAll_dependentOnOrder
weights <- c(0.3, 0.4, 0.5, 0.6)
trial <- c(0, 1, 2, 3)
# adding feature property codes (which feature was uttereed, which features were questioned)
uttFeat <- ifelse(x9data$utterance=="green" | x9data$utterance=="red" | x9data$utterance=="blue", 3,
ifelse(x9data$utterance=="solid" | x9data$utterance=="striped" | x9data$utterance=="polka-dotted", 2, 1))
x9data$uttFeat <- uttFeat
targetFeat <- x9data$targetFeatureNum
## adding the 1-27 target and object1, object2 & object3 code.
temp <- x9data$simulatedAnswer
temp2 <- (temp - temp %% 10) / 10
temp3 <- (temp2 - temp2 %% 10) / 10
targetOC27 <- temp3 + 3 * ((temp2 %% 10) - 1) + 9 * ((temp %% 10) - 1)
x9data$targetOC27 <- targetOC27
temp <- x9data$obj1
temp2 <- (temp - temp %% 10) / 10
temp3 <- (temp2 - temp2 %% 10) / 10
obj1OC27 <- temp3 + 3 * ((temp2 %% 10) - 1) + 9 * ((temp %% 10) - 1)
x9data$obj1OC27 <- obj1OC27
temp <- x9data$obj2
temp2 <- (temp - temp %% 10) / 10
temp3 <- (temp2 - temp2 %% 10) / 10
obj2OC27 <- temp3 + 3 * ((temp2 %% 10) - 1) + 9 * ((temp %% 10) - 1)
x9data$obj2OC27 <- obj2OC27
temp <- x9data$obj3
temp2 <- (temp - temp %% 10) / 10
temp3 <- (temp2 - temp2 %% 10) / 10
obj3OC27 <- temp3 + 3 * ((temp2 %% 10) - 1) + 9 * ((temp %% 10) - 1)
x9data$obj3OC27 <- obj3OC27
## now determining the recorded subject responses
subjectResponses <- matrix(0,length(x9data$workerid),3)
for(i in c(1:length(x9data$workerid))) {
subjectResponses[i,1] <- x9data$normResponse0[i] + 1e-100
subjectResponses[i,2] <- x9data$normResponse1[i] + 1e-100
subjectResponses[i,3] <- x9data$normResponse2[i] + 1e-100
# subjectResponses[i,1:3] <- subjectResponses[i,1:3] / sum(subjectResponses[i,1:3]) # Ella already normalized the data
}
## ordering the recorded subject responses such that they can be compared directly
# to the model predictions
## (particularly for visual comparison in the table)
subjectResponsesOrdered <- matrix(NA ,length(x9data$workerid),9)
for(i in c(1:length(x9data$workerid))) {
for(j in 1:3) {
subjectResponsesOrdered[i, (j+(targetFeat[i]-1)*3)] <- subjectResponses[i,j]
}
}
subjectResponsesOrdered <- round(subjectResponsesOrdered, digits=5)
## Reordering objects in input data
targetObject <- rep(NA, length(x9data$workerid))
object2 <- rep(NA, length(x9data$workerid))
object3 <- rep(NA, length(x9data$workerid))
for (i in 1:length(x9data$workerid)){
if(targetOC27[i] == obj1OC27[i]){
targetObject[i] <- targetOC27[i]
object2[i] <- obj2OC27[i]
object3[i] <- obj3OC27[i]
} else if (targetOC27[i] == obj2OC27[i])
{targetObject[i] <- obj2OC27[i]
object2[i] <- obj1OC27[i]
object3[i] <- obj3OC27[i]
} else {
targetObject[i] <- obj3OC27[i]
object2[i] <- obj1OC27[i]
object3[i] <- obj2OC27[i]
}
}
## recording KL divergence and parameters (base model, 1 param, 2 params)
workerIDs <- x9data$workerid
idMax <- max(workerIDs)
llWorkers12 <- matrix(0,length(unique(workerIDs)), 8)
paramsWorkers12 <- matrix(0,length(unique(workerIDs)), 10)
##########
## recording KL divergence and parameters (base model, 1 param, 2 params)
workerIDs <- x9data$workerid
idMax <- max(workerIDs)
#################################################
if(iterative12 == 1) { #iterative
if(indorglobal == 1){ #individual
if(funcType == 1){
paramsWorkers12 <- as.matrix(read.csv("optimized/x9params_simpleRSA_indOpt_iterative_indep_half.csv"))
llWorkers12 <- as.matrix(read.csv("optimized/x9KLDivs_simpleRSA_indOpt_iterative_indep_half.csv"))
print("used funcType == 1")
} else if (funcType == 2) {
paramsWorkers12 <- as.matrix(read.csv("optimized/x9params_simpleRSA_indOpt_iterative_indep_pr.csv"))
llWorkers12 <- as.matrix(read.csv("optimized/x9KLDivs_simpleRSA_indOpt_iterative_indep_pr.csv"))
print("used funcType == 2")
} else {
paramsWorkers12 <- as.matrix(read.csv("optimized/x9params_simpleRSA_indOpt_iterative_dep.csv"))
llWorkers12 <- as.matrix(read.csv("optimized/x9KLDivs_simpleRSA_indOpt_iterative_dep.csv"))
print("used funcType == 3")
}
} else { #iterative global
paramsWorkers12 <- as.matrix(read.csv("x9Params_simpleRSA_globalOpt_iterative.csv"))
llWorkers12 <- as.matrix(read.csv("x9KLDivs_simpleRSA_globalOpt_iterative.csv"))
}
# not relevant for iterative scenario
} else { # non iterative
if(indorglobal == 1){
paramsWorkers12 <- as.matrix(read.csv("x9Params_simpleRSA_indOpt_nonIterative.csv"))
llWorkers12 <- as.matrix(read.csv("x9KLDivs_simpleRSA_indOpt_nonIterative.csv"))
}
else {
paramsWorkers12 <- as.matrix(read.csv("x9Params_simpleRSA_globalOpt_noniterative.csv"))
llWorkers12 <- as.matrix(read.csv("x9KLDivs_simpleRSA_globalOpt_noniterative.csv"))
}
}
llWorkers12 <- llWorkers12[,c(2:ncol(llWorkers12))]
paramsWorkers12 <- paramsWorkers12[,c(2:ncol(paramsWorkers12))]
###########################################
constellationCode <- matrix(0,length(x9data$workerid),6)
uniqueCCode <- rep(0, length(x9data$workerid))
postListMat1Opt <- matrix(0,length(x9data$workerid),9)
postListMat2Opt <- matrix(0,length(x9data$workerid),9)
# added:
postListMat3Opt <- matrix(0,length(x9data$workerid),9)
postListMat45Opt <- matrix(0,length(x9data$workerid),9)
postListMat4Opt <- matrix(0,length(x9data$workerid),9)
postListMat56Opt <- matrix(0,length(x9data$workerid),9)
postListMat78Opt <- matrix(0,length(x9data$workerid),9)
postListMat910Opt <- matrix(0,length(x9data$workerid),9)
###########################################
# when global optimization was used
if(indorglobal == 2){
params1 <- paramsWorkers12[1,2]
params2 <- paramsWorkers12[1,3]
params12 <- paramsWorkers12[1,c(4,5)]
}
logLik <- rep(0,length(x9data$workerid))
workerID <- -1
#weights <- c(0.3, 0.4, 0.5, 0.6)
#trial <- c(0, 1, 2, 3)
#switched x9data$X with x9data$workerid, because we removed X
for(i in c(1:length(x9data$workerid))) {
objectConstellation <- c(targetObject[i],object2[i],object3[i])
print("objectConstellation")
print(objectConstellation)
featChoice <- uttFeat[i]
constellationCode[i,] <- getConstellationCode(objectConstellation, featChoice)[[1]]
print("constellationCode[i,]")
print(constellationCode[i,])
uc <- 0
for(j in c(1:6)) {
uc <- (uc * 10) + constellationCode[i,j]
}
uniqueCCode[i] <- uc
if(indorglobal == 1){
if(workerID != x9data$workerid[i]) {
workerID <- x9data$workerid[i]
# all three optimized params <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(11:13)]
if(funcType == 1){
# ---- one parameter optimized ----
#params1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(2)] # softness
#params2 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(3)] # obedience
#params3 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(4)] # prior rate
# ---- two parameter optimized ----
#params12 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(4:5)] # softness and obedience
} else if (funcType == 2) {
#funcType == 3
# ---- one parameter optimized ----
params1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(2)] # softness
params2 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(3)] # obedience
params3 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(4)] # prior rate
# ---- two parameter optimized ----
params13 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(5:6)] # softness and prior rate
params13_1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(7:8)] # softness and prior rate
params23 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(9:10)] # obedience and prior rate
# print(params)
} else {
#funcType == 3
# ---- one parameter optimized ----
params1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(2)] # softness, obed = 0
params1_1 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(3)] # softness, obed = 0.1
params2 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(4)] # obedience
# ---- two parameter optimized ----
params12 <- paramsWorkers12[which(paramsWorkers12[,1]==workerID)[1],c(5:6)] # softness and obedience
}
}
}
validUtterances <- determineValidUtterances(objectConstellation)
if( (i-1)%%4 == 0) {
priorPrefAll_1 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
priorPrefAll_2 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
priorPrefAll_56 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
priorPrefAll_78 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
priorPrefAll_910 <- getPreferencesPrior(x9data[i,"targetFeatureNum"])
} # uniform focussing on the feature type in question.
if(iterative12 == 1) { #iterative
if(parSetting == 1) { # one parameter optimized, two other fixed
if (funcType == 1){
# iterative function independent of trial order half, half
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
0, 0, priorPrefAll_1)
# 1st parameter optimized: softness 0, 0, priorPrefAll_1, 0.5)
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
abs(params1[1]), 0, priorPrefAll_2)
# 2nd parameter optimized: obedience abs(params1[1]), 0, priorPrefAll_2, 0.5)
postListMat3Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
0 , abs(params2[1]), priorPrefAll_2)
} else if(funcType == 2){
# iterative function independent of trial order (1 - prior rate)
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0, 0, priorPrefAll_1, 0.5)
# 1st parameter optimized: softness
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
abs(params1[1]), 0, priorPrefAll_2, 0.5)
# 2nd parameter optimized: obedience
postListMat3Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0 , abs(params2[1]), priorPrefAll_2, 0.5)
# 3rd parameter optimized: prior rate
postListMat4Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0 , 0, priorPrefAll_2, abs(params3[1]))
} else {
# funcType == 3
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
0, 0, priorPrefAll_1)
# 1st parameter optimized: softness , obed = 0
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
abs(params1[1]), 0, priorPrefAll_2)
# 1st parameter optimized: softness, obed = 0.1
postListMat3Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
abs(params1_1[1]), 0.1, priorPrefAll_2)
# 3rd parameter optimized: obedience
postListMat4Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
0 , abs(params2[1]), priorPrefAll_2)
}
} else if(parSetting == 2) {
# 2 parameter optimization
if (funcType == 1){
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
0, 0, priorPrefAll_1)
postListMat45Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep(objectConstellation, featChoice,
abs(params12[1]), abs(params12[2]), priorPrefAll_2)
} else if (funcType == 2){
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0, 0, priorPrefAll_1, 0.5)
postListMat56Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
abs(params13[1]), 0, priorPrefAll_56, abs(params13[2]))
postListMat78Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
abs(params13_1[1]), 0.1, priorPrefAll_78, abs(params13_1[2]))
postListMat910Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_indep_pr(objectConstellation, featChoice,
0, abs(params23[1]), priorPrefAll_910, abs(params23[2]))
} else {
#funcType == 3
# default parameters
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
0, 0, priorPrefAll_1)
postListMat56Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions_dep(objectConstellation, featChoice,
abs(params12[1]), abs(params12[2]), priorPrefAll_1)
}
}
priorPrefAll_1 <- postListMat1Opt[i,]
priorPrefAll_2 <- postListMat2Opt[i,]
priorPrefAll_3 <- postListMat3Opt[i,]
#priorPrefAll_4 <- postListMat4Opt[i,]
#priorPrefAll_5_6 <- postListMat56Opt[i,]
#priorPrefAll_7_8 <- postListMat78Opt[i,]
#priorPrefAll_9_10 <- postListMat910Opt[i,]
# not relevant for iterative scenario
}else if(iterative12 == 2) {
if(parSetting == 1) {
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions(objectConstellation, featChoice,
0, 0, priorPrefAll_1, weights, trial)
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions(objectConstellation, featChoice,
abs(params1[1]), 0, priorPrefAll_2, weights, trial)
}else if(parSetting == 2) {
postListMat1Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions(objectConstellation, featChoice,
0, abs(params2[1]), priorPrefAll_1, weights, trial)
postListMat2Opt[i,] <- determineSpeakerPostListPrefsSimpleRSAWithPriorPref_NewFunctions(objectConstellation, featChoice,
abs(params12[1]), abs(params12[2]), priorPrefAll_2, weights, trial)
}
}
}
###########
## adding all those values to the x9data table.
subjectResponsesOrdered <- round(subjectResponsesOrdered, digits=5)
colnames(subjectResponsesOrdered) <- colnames(subjectResponsesOrdered, do.NULL = FALSE, prefix = "DataPost_")
x9data <- data.frame(x9data, as.data.frame(subjectResponsesOrdered))
# default parameters
postListMat1Opt <- round(postListMat1Opt, digits=5)
colnames(postListMat1Opt) <- colnames(postListMat1Opt, do.NULL = FALSE, prefix = "Post1_")
consCodeAndPosteriors <- data.frame(as.data.frame(postListMat1Opt))
x9data <- data.frame(x9data, consCodeAndPosteriors)
# ------ one parameter optimized ------
# softness
postListMat2Opt <- round(postListMat2Opt, digits=5)
colnames(postListMat2Opt) <- colnames(postListMat2Opt, do.NULL = FALSE, prefix = "Post2_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat2Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
#obedience
postListMat3Opt <- round(postListMat3Opt, digits=5)
colnames(postListMat3Opt) <- colnames(postListMat3Opt, do.NULL = FALSE, prefix = "Post3_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat3Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
# if func 2 used: prior rate
# if func 3 used: obedience
postListMat4Opt <- round(postListMat4Opt, digits=5)
colnames(postListMat4Opt) <- colnames(postListMat4Opt, do.NULL = FALSE, prefix = "Post4_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat4Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
# ------- two parameters optimized ------
# ---- if funcType == 1: -----
# softness and obedience (pr = 0.5)
#postListMat45Opt <- round(postListMat45Opt, digits=5)
#colnames(postListMat45Opt) <- colnames(postListMat45Opt, do.NULL = FALSE, prefix = "Post4_")
#consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat45Opt))
#x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
#x9data$CCode <- uniqueCCode
#x9data$logLik <- logLik
# ----- if funcType == 2: -----
# softness and prior rate (obed = 0)
# or
# ----- if funcType == 3: -----
# softness and obedience
postListMat56Opt <- round(postListMat56Opt, digits=5)
colnames(postListMat56Opt) <- colnames(postListMat56Opt, do.NULL = FALSE, prefix = "Post5_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat56Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
# --- funcType == 2: -----
# softness and prior rate (obed = 0.1)
postListMat78Opt <- round(postListMat78Opt, digits=5)
colnames(postListMat78Opt) <- colnames(postListMat78Opt, do.NULL = FALSE, prefix = "Post6_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat78Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
# obedience and prior rate (soft = 0)
postListMat910Opt <- round(postListMat910Opt, digits=5)
colnames(postListMat910Opt) <- colnames(postListMat910Opt, do.NULL = FALSE, prefix = "Post7_")
consCodeAndPosteriorsNO <- data.frame(as.data.frame(postListMat910Opt))
x9data <- data.frame(x9data, consCodeAndPosteriorsNO)
x9data$CCode <- uniqueCCode
x9data$logLik <- logLik
if(iterative12 == 1) { #iterative
if(indorglobal == 1){ #individual
if(parSetting == 1) { #one parameter optimized
if(funcType == 1){
# optimized softness
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt1_fixed0_0.5_iterative_indep_half.csv")
# optimized obedience
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt2_fixed0_0.5_iterative_indep_half.csv")
} else if(funcType == 2){
# optimized softness
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt1_fixed0_0.5_iterative_indep_pr.csv")
# optimized obedience
write.csv(x9data, "data/x9dataAugm_SRSAindOpt2_fixed0_0.5_iterative_indep_pr.csv")
# optimized prior rate
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt3_fixed0_0.5_iterative_indep_pr.csv")
} else {
# funcType == 3
# optimized softness, obed = 0
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt1_fixed0_iterative_dep.csv")
# optimized softness, obed = 0.1
# write.csv(x9data, "data/x9dataAugm_SRSAindOpt1_fixed0.1_iterative_dep.csv")
# optimized obedience, soft = 0
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt2_fixed0_iterative_dep.csv")
}
}else if(parSetting == 2) {
if (funcType == 1){
# two parameter optim
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt13_fixed_notObey0_iterative_indep_half.csv")
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt13_fixed_notObey0.1_iterative_indep_half.csv")
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt12_fixed_pr0.5_iterative_indep_half.csv")
} else if (funcType == 2){
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt13_fixed_notObey0_iterative_indep_pr.csv")
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt13_fixed_notObey0.1_iterative_indep_pr.csv")
#write.csv(x9data, "data/x9dataAugm_SRSAindOpt23_fixed_pref0_iterative_indep_pr.csv")
}else {
# funcType == 3
write.csv(x9data, "data/x9dataAugm_SRSAindOpt12_iterative_dep.csv")
}
}
# global
} else {
if(parSetting == 1) {
write.csv(x9data, "data/x9dataAugm_SRSAglobalOpt_fixed00_andOpt1fixed0_iterative_withNewFunctions.csv")
}else if(parSetting == 2) {
write.csv(x9data, "data/x9dataAugm_SRSAglobalOpt_fixed0Opt2_andOpt12_iterative_withNewFunctions.csv")
}
}
# non-iterative
} else if(iterative12 == 2) {
if(indorglobal == 1){
if(parSetting == 1) {
write.csv(x9data, "data/x9dataAugm_SRSAindOpt_fixed00_andOpt1fixed0_nonIterative_withNewFunctions.csv")
}else if(parSetting == 2) {
write.csv(x9data, "data/x9dataAugm_SRSAindOpt_fixed0Opt2_andOpt1_nonIterative_withNewFunctions.csv")
}
} else {
if(parSetting == 1) {
write.csv(x9data, "data/x9dataAugm_SRSAglobalOpt_fixed00_andOpt1fixed0_noniterative_withNewFunctions.csv")
}else if(parSetting == 2) {
write.csv(x9data, "data/x9dataAugm_SRSAglobalOpt_fixed0Opt2_andOpt12_noniterative_withNewFunctions.csv")
}
}
}
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