R/gui.classify.R

In stylo: Stylometric Multivariate Analyses

# #################################################
# Function for displaying simple yet effective graphical interface (GUI).
# If you execute this option, the values stored in the function 
# stylo.default.settings() will serve as default for the GUI for the first run.
# In the subsequent runs, recent values will appear as default in the GUI.
# No optional arguments.
# Usage: gui.classify()
# #################################################    


gui.classify = function(...) {
  





  # using recently used settings to overwrite the default options
  restored.variables = list()
  if(file.exists("classify_config.txt") == TRUE) {
    source("classify_config.txt", local = TRUE) 
    # adding them on a list
    filtered.variables = ls()[!ls() %in% c("restored.variables","filtered.variables")]
    for(i in filtered.variables) {
      restored.variables[[i]] = get(i)
      }
    }



  # loading default settings
  default.variables = stylo.default.settings(...)

  # if any command-line arguments have been passed by a user, they will
  # be stored on the following list and used to overwrite the defaults
  passed.arguments = list(...)
  


    
# Code that enables overwriting the variables with custom settings.
# A magnificent snipped for combining two lists 
# http://stackoverflow.com/questions/13811501/r-merge-lists-with-overwrite-and-recursion
merge.lists <- function(a, b) {
    a.names <- names(a)
    b.names <- names(b)
    m.names <- sort(unique(c(a.names, b.names)))
    sapply(m.names, function(i) {
        if (is.list(a[[i]]) & is.list(b[[i]])) merge.lists(a[[i]], b[[i]])
        else if (i %in% b.names) b[[i]]
        else a[[i]]
    }, simplify = FALSE)
}

# if any variables have been passed as arguments, they will overwrite
# the default settings
variables.tmp = merge.lists(default.variables,restored.variables)
variables = merge.lists(variables.tmp, passed.arguments)





# #############################################################################
# Explicit assignment of all the variables, in order to avoid attach()
# #############################################################################

add.to.margins = variables$add.to.margins
analysis.type = variables$analysis.type
analyzed.features = variables$analyzed.features
classification.method = variables$classification.method
colors.on.graphs = variables$colors.on.graphs
consensus.strength = variables$consensus.strength
corpus.format = variables$corpus.format
corpus.lang = variables$corpus.lang
culling.incr = variables$culling.incr
culling.max = variables$culling.max
culling.min = variables$culling.min
culling.of.all.samples = variables$culling.of.all.samples
custom.graph.title = variables$custom.graph.title
delete.pronouns = variables$delete.pronouns
dendrogram.layout.horizontal = variables$dendrogram.layout.horizontal
display.on.screen = variables$display.on.screen
distance.measure = variables$distance.measure
dump.samples = variables$dump.samples
encoding = variables$encoding
final.ranking.of.candidates = variables$final.ranking.of.candidates
how.many.correct.attributions = variables$how.many.correct.attributions
interactive.files = variables$interactive.files
k.value = variables$k.value
l.value = variables$l.value
label.offset = variables$label.offset
linkage = variables$linkage
mfw.incr = variables$mfw.incr
mfw.list.cutoff = variables$mfw.list.cutoff
mfw.max = variables$mfw.max
mfw.min = variables$mfw.min
ngram.size = variables$ngram.size
preserve.case = variables$preserve.case
number.of.candidates = variables$number.of.candidates
number.of.samples = variables$number.of.samples
outputfile = variables$outputfile
passed.arguments = variables$passed.arguments
pca.visual.flavour = variables$pca.visual.flavour
plot.custom.height = variables$plot.custom.height
plot.custom.width = variables$plot.custom.width
plot.font.size = variables$plot.font.size
plot.line.thickness = variables$plot.line.thickness
plot.options.reset = variables$plot.options.reset
reference.wordlist.of.all.samples = variables$reference.wordlist.of.all.samples
sample.size = variables$sample.size
sampling = variables$sampling
sampling.with.replacement = variables$sampling.with.replacement
save.analyzed.features = variables$save.analyzed.features
save.analyzed.freqs = variables$save.analyzed.freqs
save.distance.tables = variables$save.distance.tables
start.at = variables$start.at
svm.coef0 = variables$svm.coef0
svm.cost = variables$svm.cost
svm.degree = variables$svm.degree
svm.kernel = variables$svm.kernel
text.id.on.graphs = variables$text.id.on.graphs
titles.on.graphs = variables$titles.on.graphs
txm.compatibility.mode = variables$txm.compatibility.mode
use.custom.list.of.files = variables$use.custom.list.of.files
use.existing.freq.tables = variables$use.existing.freq.tables
use.existing.wordlist = variables$use.existing.wordlist
write.jpg.file = variables$write.jpg.file
write.pdf.file = variables$write.pdf.file
write.png.file = variables$write.png.file
write.svg.file = variables$write.svg.file
z.scores.of.all.samples = variables$z.scores.of.all.samples

show.features = variables$show.features
# #############################################################################


# a not very elegant way of switching from strings into logical values
encoding.orig = variables$encoding
  if(variables$encoding == "UTF-8") {
    encoding = FALSE
  } else {
    encoding = TRUE
  }



  
  .Tcl("font create myDefaultFont -family tahoma -size 8")
  .Tcl("option add *font myDefaultFont")  
  
    tt <- tktoplevel()
    tktitle(tt) <- "Stylometry with R: enter analysis parameters"
    
    push_OK <- function(){
        tkdestroy(tt)
        }
  
  corpus.format <- tclVar(corpus.format)
  mfw.min <- tclVar(mfw.min)
  mfw.max <- tclVar(mfw.max)
  mfw.incr <- tclVar(mfw.incr)
  start.at <- tclVar(start.at)
  culling.min <- tclVar(culling.min)
  culling.max <- tclVar(culling.max)
  culling.incr <- tclVar(culling.incr)
  ngram.size <- tclVar(ngram.size)
  preserve.case <- tclVar(preserve.case)
  analyzed.features <- tclVar(analyzed.features)
  use.existing.freq.tables <- tclVar(use.existing.freq.tables)
  use.existing.wordlist <- tclVar(use.existing.wordlist)
  interactive.files <- tclVar(interactive.files)
  use.custom.list.of.files <- tclVar(use.custom.list.of.files)
  mfw.list.cutoff <- tclVar(mfw.list.cutoff)
  #
  classification.method <- tclVar(classification.method)
  culling.of.all.samples <- tclVar(culling.of.all.samples)
  reference.wordlist.of.all.samples <- tclVar(reference.wordlist.of.all.samples)
  z.scores.of.all.samples <- tclVar(z.scores.of.all.samples)
  number.of.candidates <- tclVar(number.of.candidates)
  final.ranking.of.candidates <- tclVar(final.ranking.of.candidates)
  how.many.correct.attributions <- tclVar(how.many.correct.attributions)
  k.value <- tclVar(k.value)
  l.value <- tclVar(l.value)
  svm.kernel <- tclVar(svm.kernel)
  svm.degree <- tclVar(svm.degree)
  svm.coef0 <- tclVar(svm.coef0)
  svm.cost <- tclVar(svm.cost)
  #
  delete.pronouns <- tclVar(delete.pronouns)
  corpus.lang <- tclVar(corpus.lang)
  distance.measure <- tclVar(distance.measure)
  save.distance.tables <- tclVar(save.distance.tables)
  save.analyzed.features <- tclVar(save.analyzed.features)
  save.analyzed.freqs <- tclVar(save.analyzed.freqs)
  sampling <- tclVar(sampling)
  sample.size <- tclVar(sample.size)
  number.of.samples <- tclVar(number.of.samples)
  consensus.strength <- tclVar(consensus.strength)
  dump.samples <- tclVar(dump.samples)
  encoding <- tclVar(encoding)

  
  
  
  
  
  f1 <- tkframe(tt)
  f2 <- tkframe(tt)
  f3 <- tkframe(tt)
  f4 <- tkframe(tt)
  f5 <- tkframe(tt)
  
  # layout of the GUI begins here:
  tab1 <- function() {
  tkgrid(f1,row=1,column=0,columnspan=5)
  tkgrid.forget(f2)
  tkgrid.forget(f3)
  tkgrid.forget(f4)
  tkgrid.forget(f5)
  tkconfigure(t1.but,state="disabled", background="white")
  tkconfigure(t2.but,state="normal", background="aliceblue")
  tkconfigure(t3.but,state="normal", background="aliceblue")
  tkconfigure(t4.but,state="normal", background="aliceblue")
  tkconfigure(t5.but,state="normal", background="aliceblue")
  }
  tab2 <- function() {
  tkgrid(f2,row=1,column=0,columnspan=5)
  tkgrid.forget(f1)
  tkgrid.forget(f3)
  tkgrid.forget(f4)
  tkgrid.forget(f5)
  tkconfigure(t2.but,state="disabled", background="white")
  tkconfigure(t1.but,state="normal", background="aliceblue")
  tkconfigure(t3.but,state="normal", background="aliceblue")
  tkconfigure(t4.but,state="normal", background="aliceblue")
  tkconfigure(t5.but,state="normal", background="aliceblue")
  }
  tab3 <- function() {
  tkgrid(f3,row=1,column=0,columnspan=5)
  tkgrid.forget(f1)
  tkgrid.forget(f2)
  tkgrid.forget(f4)
  tkgrid.forget(f5)
  tkconfigure(t3.but,state="disabled", background="white")
  tkconfigure(t1.but,state="normal", background="aliceblue")
  tkconfigure(t2.but,state="normal", background="aliceblue")
  tkconfigure(t4.but,state="normal", background="aliceblue")
  tkconfigure(t5.but,state="normal", background="aliceblue")
  }
  tab4 <- function() {
  tkgrid(f4,row=1,column=0,columnspan=5)
  tkgrid.forget(f1)
  tkgrid.forget(f2)
  tkgrid.forget(f3)
  tkgrid.forget(f5)
  tkconfigure(t4.but,state="disabled", background="white")
  tkconfigure(t1.but,state="normal", background="aliceblue")
  tkconfigure(t2.but,state="normal", background="aliceblue")
  tkconfigure(t3.but,state="normal", background="aliceblue")
  tkconfigure(t5.but,state="normal", background="aliceblue")
  }
  tab5 <- function() {
  tkgrid(f5,row=1,column=0,columnspan=5)
  tkgrid.forget(f1)
  tkgrid.forget(f2)
  tkgrid.forget(f3)
  tkgrid.forget(f4)
  tkconfigure(t5.but,state="disabled", background="white")
  tkconfigure(t1.but,state="normal", background="aliceblue")
  tkconfigure(t2.but,state="normal", background="aliceblue")
  tkconfigure(t3.but,state="normal", background="aliceblue")
  tkconfigure(t4.but,state="normal", background="aliceblue")
  }
  t1.but <- tkbutton(tt,text="     INPUT & LANGUAGE     ",command=tab1)
  t2.but <- tkbutton(tt,text="         FEATURES         ",command=tab2)
  t3.but <- tkbutton(tt,text="        STATISTICS        ",command=tab3)
  t4.but <- tkbutton(tt,text="         SAMPLING         ",command=tab4)
  t5.but <- tkbutton(tt,text="          OUTPUT          ",command=tab5)
  tkgrid(t1.but)
  tkgrid(t2.but, column=1, row=0)
  tkgrid(t3.but, column=2, row=0)
  tkgrid(t4.but, column=3, row=0)
  tkgrid(t5.but, column=4, row=0)
  # Grid for individual tabs
   
  # initial state!
  tkgrid(f1,row=1,column=0,columnspan=5)
  tkconfigure(t1.but,state="disabled", background="white")
  tkconfigure(t2.but,state="normal", background="aliceblue")
  tkconfigure(t3.but,state="normal", background="aliceblue")
  tkconfigure(t4.but,state="normal", background="aliceblue")
  tkconfigure(t5.but,state="normal", background="aliceblue")
  
  # the OK button: active on each tab
  #
  button_1 <- tkbutton(tt,text="       OK       ",command=push_OK,relief="raised",background="aliceblue")
  tkbind(button_1,"<Return>",push_OK) 
  tkgrid(button_1,columnspan=10)
  tk2tip(button_1, "Press this only if you've visited all the tabs, or if you know\nyou want to leave values in some as they are.")
  
  
  ########################################################################################################################
  # layout of the GUI begins here:
  #
  tkgrid(tklabel(f1,text="    "),padx=0,pady=0) # blank line (serving as the top margin)
  tkgrid(tklabel(f2,text="    ")) # blank line (serving as the top margin)
  tkgrid(tklabel(f3,text="    ")) # blank line (serving as the top margin)
  tkgrid(tklabel(f4,text="    ")) # blank line (serving as the top margin)
  tkgrid(tklabel(f5,text="    ")) # blank line (serving as the top margin)
  
  # first row: INPUT
  #
  entry_TXT <- tkradiobutton(f1)
  entry_XML <- tkradiobutton(f1)
  entry_XMLDrama <- tkradiobutton(f1)
  entry_XMLNoTitles <- tkradiobutton(f1)
  entry_HTML <- tkradiobutton(f1)
  #
  tkconfigure(entry_TXT,variable=corpus.format,value="plain")
  tkconfigure(entry_XML,variable=corpus.format,value="xml")
  tkconfigure(entry_XMLDrama,variable=corpus.format,value="xml.drama")
  tkconfigure(entry_XMLNoTitles,variable=corpus.format,value="xml.notitles")
  tkconfigure(entry_HTML,variable=corpus.format,value="html")
  #
  entrylabel_TXT <- tklabel(f1,text="plain text")
  entrylabel_XML <- tklabel(f1,text="xml")
  entrylabel_XMLDrama <- tklabel(f1,text="xml (plays)")
  entrylabel_XMLNoTitles <- tklabel(f1,text="xml (no titles)")
  entrylabel_HTML <- tklabel(f1,text="html")
  #
  tkgrid(tklabel(f1,text="       INPUT:"),entrylabel_TXT,entrylabel_XML,entrylabel_XMLDrama,entrylabel_XMLNoTitles,entrylabel_HTML,columnspan=1)
  tkgrid(tklabel(f1,text="            "),entry_TXT,entry_XML,entry_XMLDrama,entry_XMLNoTitles,entry_HTML,columnspan=1)
  # Tooltips for the above
  tk2tip(entrylabel_TXT, "Plain text files. \nIf your corpus does not contain diacritics, no encoding is needed. \nOtherwise, use ANSI for Windows, UTF-8 for Mac/Linux.")
  tk2tip(entrylabel_XML, "XML: all tags and TEI headers are removed.")
  tk2tip(entrylabel_XMLDrama, "XML for plays: all tags, TEI headers, \nand speakers' names between <speaker>...</speaker> tags are removed.")
  tk2tip(entrylabel_XMLNoTitles, "XML contents only: all tags, TEI headers, \nand chapter/section (sub)titles between <head>...</head> tags are removed.")
  tk2tip(entrylabel_HTML, "HTML headers, menus, links and other tags are removed.")
  tkgrid(tklabel(f1,text="    ")) # blank line for aesthetic purposes
  
  # next row: LANGUAGE
  #
  entry_ENG <- tkradiobutton(f1)
  entry_EN2 <- tkradiobutton(f1)
  entry_EN3 <- tkradiobutton(f1)
  entry_POL <- tkradiobutton(f1)
  entry_LAT <- tkradiobutton(f1)
  entry_LA2 <- tkradiobutton(f1)
  entry_FRA <- tkradiobutton(f1)
  entry_GER <- tkradiobutton(f1)
  entry_HUN <- tkradiobutton(f1)
  entry_ITA <- tkradiobutton(f1)
  entry_DUT <- tkradiobutton(f1)
  entry_SPA <- tkradiobutton(f1)
  entry_CJK <- tkradiobutton(f1)
  entry_OTH <- tkradiobutton(f1)
  cb_UTF <- tkcheckbutton(f1)

  #
  tkconfigure(entry_ENG,variable=corpus.lang,value="English")
  tkconfigure(entry_EN2,variable=corpus.lang,value="English.contr")
  tkconfigure(entry_EN3,variable=corpus.lang,value="English.all")
  tkconfigure(entry_LAT,variable=corpus.lang,value="Latin")
  tkconfigure(entry_LA2,variable=corpus.lang,value="Latin.corr")
  tkconfigure(entry_POL,variable=corpus.lang,value="Polish")
  tkconfigure(entry_FRA,variable=corpus.lang,value="French")
  tkconfigure(entry_GER,variable=corpus.lang,value="German")
  tkconfigure(entry_HUN,variable=corpus.lang,value="Hungarian")
  tkconfigure(entry_ITA,variable=corpus.lang,value="Italian")
  tkconfigure(entry_DUT,variable=corpus.lang,value="Dutch")
  tkconfigure(entry_SPA,variable=corpus.lang,value="Spanish")
  tkconfigure(entry_CJK,variable=corpus.lang,value="CJK")
  tkconfigure(entry_OTH,variable=corpus.lang,value="Other")
  tkconfigure(cb_UTF,variable=encoding)

  #
  entrylabel_ENG <- tklabel(f1,text="    English     ")
  entrylabel_POL <- tklabel(f1,text="    Polish      ")
  entrylabel_LAT <- tklabel(f1,text="    Latin       ")
  entrylabel_FRA <- tklabel(f1,text="    French      ")
  entrylabel_GER <- tklabel(f1,text="    German      ")
  entrylabel_HUN <- tklabel(f1,text="   Hungarian    ")
  entrylabel_ITA <- tklabel(f1,text="    Italian     ")
  entrylabel_EN2 <- tklabel(f1,text="English (contr.)")
  entrylabel_EN3 <- tklabel(f1,text="  English (ALL) ")
  entrylabel_LA2 <- tklabel(f1,text="Latin (u/v > u) ")
  entrylabel_DUT <- tklabel(f1,text="     Dutch      ")
  entrylabel_SPA <- tklabel(f1,text="    Spanish     ")
  entrylabel_CJK <- tklabel(f1,text="      CJK       ")
  entrylabel_OTH <- tklabel(f1,text="     Other      ")
  entrylabel_UTF <- tklabel(f1,text="     Native encoding      ")

  #
  tkgrid(tklabel(f1,text="LANGUAGE: "),entrylabel_ENG,entrylabel_EN2,entrylabel_EN3,entrylabel_LAT,entrylabel_LA2)
  tkgrid(tklabel(f1,text="          "),entry_ENG,entry_EN2,entry_EN3,entry_LAT,entry_LA2)
  tkgrid(tklabel(f1,text="          "),entrylabel_POL,entrylabel_HUN,entrylabel_FRA,entrylabel_ITA,entrylabel_SPA)
  tkgrid(tklabel(f1,text="          "),entry_POL,entry_HUN,entry_FRA,entry_ITA,entry_SPA)
  tkgrid(tklabel(f1,text="          "),entrylabel_DUT,entrylabel_GER, entrylabel_CJK, entrylabel_OTH,tklabel(f1,text=""),entrylabel_UTF)
  tkgrid(tklabel(f1,text="          "),entry_DUT,entry_GER, entry_CJK, entry_OTH,tklabel(f1,text=""),cb_UTF)
  tkgrid(tklabel(f1,text="    ")) # blank line for aesthetic purposes
  
  # Tooltips for the above
  tk2tip(entrylabel_ENG, "Plain English: contractions and \ncompound words are split")
  tk2tip(entrylabel_POL, "Plain Polish: contractions and \ncompound words are split")
  tk2tip(entrylabel_EN2, "Modified English: \ncontractions are not split")
  tk2tip(entrylabel_EN3, "Further Modified English: contractions \nand compound words are not split")
  tk2tip(entrylabel_LAT, "Plain Latin: U and V \ntreated as distinct letters")
  tk2tip(entrylabel_FRA, "Plain French: contractions and \ncompound words are split")
  tk2tip(entrylabel_GER, "Plain German: contractions and \ncompound words are split")
  tk2tip(entrylabel_HUN, "Plain Hungarian: contractions and \ncompound words are split")
  tk2tip(entrylabel_ITA, "Plain Italian: contractions and \ncompound words are split")
  tk2tip(entrylabel_LA2, "Modified Latin: U and V \nboth treated as U")
  tk2tip(entrylabel_DUT, "Plain Dutch: contractions and \ncompound words are split")
  tk2tip(entrylabel_SPA, "Plain Castilian: contractions and \ncompound words are split")
  tk2tip(entrylabel_CJK, "Chinese, Japanese and Korean \n(experimental)")
  tk2tip(entrylabel_OTH, "Other language than the ones listed above.")
  tk2tip(entrylabel_UTF, "Check this box if your texts are in a \nnative encoding other than UTF-8. \n(May apply on Windows machines.) \nUse the function check_encoding() \nif you are uncertain.")

  
  # next row: TEXT FEATURES
  entry_W <- tkradiobutton(f2)
  entry_L <- tkradiobutton(f2)
  cb_NGRAMS <- tkcheckbutton(f2)
  entry_NGRAMSIZE <- tkentry(f2,textvariable=ngram.size,width="8")
  cb_PRESERVECASE <- tkcheckbutton(f2)

  #
  tkconfigure(entry_W,variable=analyzed.features,value="w")
  tkconfigure(entry_L,variable=analyzed.features,value="c")
  tkconfigure(cb_PRESERVECASE,variable=preserve.case)
  #
  entrylabel_W <- tklabel(f2,text="words")
  entrylabel_L <- tklabel(f2,text="chars")
  entrylabel_NGRAMSIZE <- tklabel(f2,text="ngram size")
  entrylabel_PRESERVECASE <- tklabel(f2,text="preserve case")
  #
  tkgrid(tklabel(f2,text="        FEATURES:"),entrylabel_W,entrylabel_L,entrylabel_NGRAMSIZE, entrylabel_PRESERVECASE)
  tkgrid(tklabel(f2,text="                 "),entry_W,entry_L,entry_NGRAMSIZE, cb_PRESERVECASE)
  
  # Tooltips for the above
  tk2tip(entrylabel_W, "Select this to work on words")
  tk2tip(entrylabel_L, "Select this to work on characters \n(does not make much sense unless you use ngrams)")
  tk2tip(entrylabel_NGRAMSIZE, "State your n for n-grams \nto work on word/char clusters of n")
  tk2tip(entrylabel_PRESERVECASE, "Whether or not to lowercase all characters")
  tkgrid(tklabel(f2,text="    ")) # blank line for aesthetic purposes
  
  # next row: MFW SETTINGS
  #
  entry_MFW_MIN <- tkentry(f2,textvariable=mfw.min,width="8")
  entry_MFW_MAX <- tkentry(f2,textvariable=mfw.max,width="8")
  entry_MFW_INCR <- tkentry(f2,textvariable=mfw.incr,width="8")
  entry_START_AT <- tkentry(f2,textvariable=start.at,width="8")
  #
  entrylabel_MFW_MIN <- tklabel(f2,text="Minimum")
  entrylabel_MFW_MAX <- tklabel(f2,text="Maximum")
  entrylabel_MFW_INCR <- tklabel(f2,text="Increment")
  entrylabel_START_AT <- tklabel(f2,text="Start at freq. rank")
  #
  tkgrid(tklabel(f2,text="MFW SETTINGS:"),entrylabel_MFW_MIN,entrylabel_MFW_MAX,entrylabel_MFW_INCR,entrylabel_START_AT)
  tkgrid(tklabel(f2,text="             "),entry_MFW_MIN,entry_MFW_MAX,entry_MFW_INCR,entry_START_AT)
  tkgrid(tklabel(f2,text="    ")) # blank line for aesthetic purposes
  
  # Tooltips for the above
  tk2tip(entrylabel_MFW_MIN, "Set the minimum number of most frequent words. \nThe script will conduct its first analysis for \nthe number of words specified here")
  tk2tip(entrylabel_MFW_MAX, "Set the maximum number of most frequent words. \nThe script will conduct its final analysis for \nthe number of words specified here")
  tk2tip(entrylabel_MFW_INCR, "Set the increment added to \nthe minimum number of most frequent \nwords for each subsequent analysis.")
  tk2tip(entrylabel_START_AT, "Set the number of words from the top of \nthe frequency list to skip in the analysis.")
  
  # next row: CULLING
  #
  cb_DEL_PRON <- tkcheckbutton(f2)
  #
  entry_CUL_MIN <- tkentry(f2,textvariable=culling.min,width="8")
  entry_CUL_MAX <- tkentry(f2,textvariable=culling.max,width="8")
  entry_CUL_INCR <- tkentry(f2,textvariable=culling.incr,width="8")
  entry_CUT_OFF <- tkentry(f2,textvariable=mfw.list.cutoff,width="8")
  tkconfigure(cb_DEL_PRON,variable=delete.pronouns)
  #
  entrylabel_CUL_MIN <- tklabel(f2,text="Minimum")
  entrylabel_CUL_MAX <- tklabel(f2,text="Maximum")
  entrylabel_CUL_INCR <- tklabel(f2,text="Increment")
  entrylabel_CUT_OFF <- tklabel(f2,text="List Cutoff")
  cblabel_DEL_PRON <- tklabel(f2,text="Delete pronouns")
  #
  tkgrid(tklabel(f2,text="         CULLING:"),entrylabel_CUL_MIN,entrylabel_CUL_MAX, entrylabel_CUL_INCR,entrylabel_CUT_OFF,cblabel_DEL_PRON)
  tkgrid(tklabel(f2,text="                 "),entry_CUL_MIN,entry_CUL_MAX,entry_CUL_INCR,entry_CUT_OFF,cb_DEL_PRON)
  tkgrid(tklabel(f2,text="    ")) # blank line for aesthetic purposes
    
  # next row: LISTS & FILES
  #
  cb_FREQS <- tkcheckbutton(f2)
  cb_LISTS <- tkcheckbutton(f2)
  cb_INTFILES <- tkcheckbutton(f2)
  cb_MYFILES <- tkcheckbutton(f2)
  #
  tkconfigure(cb_FREQS,variable=use.existing.freq.tables)
  tkconfigure(cb_LISTS,variable=use.existing.wordlist)
  tkconfigure(cb_INTFILES,variable=interactive.files)
  tkconfigure(cb_MYFILES,variable=use.custom.list.of.files)
  #
  cblabel_FREQS <- tklabel(f2,text="Existing frequencies")
  cblabel_LISTS <- tklabel(f2,text="Existing wordlist")
  cblabel_INTFILES <- tklabel(f2,text="Select files manually")
  cblabel_MYFILES <- tklabel(f2,text="List of files")
  #
  tkgrid(tklabel(f2,text="    VARIOUS:"),cblabel_FREQS,cblabel_LISTS,cblabel_INTFILES,cblabel_MYFILES)
  tkgrid(tklabel(f2,text="            "),cb_FREQS,cb_LISTS,cb_INTFILES,cb_MYFILES)
    
  # Tooltips for the above  
  tk2tip(entrylabel_CUL_MIN, "State the minimum culling setting. \n0 means no words are omitted from the analysis. \n50 means a word needs to appear in \nat least 50% of the texts to be included in the analysis. \n100 means that only words appearing in all the texts \nwill be included in the analysis")
  tk2tip(entrylabel_CUL_MAX, "State the maximum culling setting. \n0 means no words are omitted from the analysis. \n50 means a word needs to appear in \nat least 50% of the texts to be included in the analysis. \n100 means that only words appearing in all the texts \nwill be included in the analysis")
  tk2tip(entrylabel_CUL_INCR, "State the increment added to the minimum culling \nsetting for each subsequent analysis.")
  tk2tip(entrylabel_CUT_OFF, "Set the maximum size of the word frequency table. \nAnything above 5000 requires patience and a fast computer")
  tk2tip(cblabel_DEL_PRON, "Select if you want to omit pronouns in the analysis. \nThis improves attribution in some languages")
  tk2tip(cblabel_FREQS, "Select to use the frequency lists generated by the previous analysis. \nThis speeds up the process dramatically. \nA very bad idea if you've just changed your selection of texts!")
  tk2tip(cblabel_LISTS, "Select to use the wordlist generated by \nthe previous analysis or a custom wordlist.")
  tk2tip(cblabel_INTFILES, "Select this to manually select files \nrather than use the entire corpus. \nMake sure that \"Existing frequencies\" is unchecked!")
  tk2tip(cblabel_MYFILES, "Select this if you want to use custom list \nof files to be loaded; the list should be stored \nin \"files_to_analyze.txt\", and the entries delimited \nwith spaces, tabs, or newlines.")
  
  
  tkgrid(tklabel(f2,text="    ")) # blank line for aesthetic purposes 
  
  # next row: STATISTICS
  #
  #
  entry_DELTA <- tkradiobutton(f3)
  entry_KNN <- tkradiobutton(f3)
  entry_SVM <- tkradiobutton(f3)
  entry_NBAYES <- tkradiobutton(f3)
  entry_NSC <- tkradiobutton(f3)
  #
  tkconfigure(entry_DELTA,variable=classification.method,value="delta")
  tkconfigure(entry_KNN,variable=classification.method,value="knn")
  tkconfigure(entry_SVM,variable=classification.method,value="svm")
  tkconfigure(entry_NBAYES,variable=classification.method,value="naivebayes")
  tkconfigure(entry_NSC,variable=classification.method,value="nsc")
  #
  entrylabel_DELTA <- tklabel(f3,text="Delta")
  entrylabel_KNN <- tklabel(f3,text="k-NN")
  entrylabel_SVM <- tklabel(f3,text="SVM")
  entrylabel_NBAYES <- tklabel(f3,text="NaiveBayes")
  entrylabel_NSC <- tklabel(f3,text="NSC")
  #
  tkgrid(tklabel(f3,text=" STATISTICS:"),entrylabel_DELTA,entrylabel_KNN,entrylabel_SVM,entrylabel_NBAYES,entrylabel_NSC)
  tkgrid(tklabel(f3,text="            "),entry_DELTA,entry_KNN,entry_SVM,entry_NBAYES,entry_NSC)
  tkgrid(tklabel(f3,text="    ")) # blank line for aesthetic purposes
  # Tooltips for the above
  tk2tip(entrylabel_DELTA, "Burrows's Delta")
  tk2tip(entrylabel_KNN, "k-Nearest Neighbor Classification")
  tk2tip(entrylabel_SVM, "Support Vector Machines")
  tk2tip(entrylabel_NBAYES, "Naive Bayes Classification. To use this method, you should have \nat least two texts of each class (author, genre, etc.) in the primary set")
  tk2tip(entrylabel_NSC, "Nearest Shrunken Centroids Classification. To use this method, you should have \nat least two texts of each class (author, genre, etc.) in the primary set")

  # next row: GENERAL OPTIONS
  cb_ALL_C <- tkcheckbutton(f3)
  cb_ALL_L <- tkcheckbutton(f3)
  cb_ALL_Z <- tkcheckbutton(f3)
  #
  tkconfigure(cb_ALL_C,variable=culling.of.all.samples)
  tkconfigure(cb_ALL_L,variable=reference.wordlist.of.all.samples)
  tkconfigure(cb_ALL_Z,variable=z.scores.of.all.samples)
  #
  cblabel_ALL_C <- tklabel(f3,text="ALL culling")
  cblabel_ALL_L <- tklabel(f3,text="ALL wordlists")
  cblabel_ALL_Z <- tklabel(f3,text="ALL z-scores")
  #
  tkgrid(tklabel(f3,text=" GENERAL:"),cblabel_ALL_C,cblabel_ALL_L,cblabel_ALL_Z)
  tkgrid(tklabel(f3,text="            "),cb_ALL_C,cb_ALL_L,cb_ALL_Z)
  tkgrid(tklabel(f3,text="    ")) # blank line for aesthetic purposes
  # Tooltips for the above
  tk2tip(cblabel_ALL_C, "If this is left unchecked, the culling procedure \nis based on data from the primary set alone. \nChecking this box ensures words present in ALL texts \nof BOTH sets ar affected by the culling.")
  tk2tip(cblabel_ALL_L, "If this is left unchecked, both frequency tables \nare built using a pre-prepared wordlist of the primary set. \nChecking this box compiles the list \nbasing on both primary and secondary sets.")
  tk2tip(cblabel_ALL_Z, "If this is left unchecked, then the z-scores \nare based on the primary set only (applicable to Delta).")

   # next row: DISTANCES
  #
  entry_CD <- tkradiobutton(f3)
  entry_WD <- tkradiobutton(f3)
  entry_ED <- tkradiobutton(f3)
  entry_ES <- tkradiobutton(f3)
  entry_EN <- tkradiobutton(f3)
  entry_MH <- tkradiobutton(f3)
  entry_CB <- tkradiobutton(f3)
  entry_EU <- tkradiobutton(f3)
  entry_CS <- tkradiobutton(f3)
  entry_MM <- tkradiobutton(f3)

  #
  tkconfigure(entry_CD,variable=distance.measure,value="delta")
  tkconfigure(entry_WD,variable=distance.measure,value="wurzburg")
  tkconfigure(entry_ED,variable=distance.measure,value="eder")
  tkconfigure(entry_ES,variable=distance.measure,value="simple")
  tkconfigure(entry_EN,variable=distance.measure,value="entropy")
  tkconfigure(entry_MH,variable=distance.measure,value="manhattan")
  tkconfigure(entry_CB,variable=distance.measure,value="canberra")
  tkconfigure(entry_EU,variable=distance.measure,value="euclidean")
  tkconfigure(entry_CS,variable=distance.measure,value="cosine")
  tkconfigure(entry_MM,variable=distance.measure,value="minmax")
  #
  entrylabel_CD <- tklabel(f3,text="Classic Delta")
  entrylabel_WD <- tklabel(f3,text="Cosine Delta")
  entrylabel_ED <- tklabel(f3,text="Eder's Delta")
  entrylabel_ES <- tklabel(f3,text="Eder's Simple")
  entrylabel_EN <- tklabel(f3,text="Entropy")
  entrylabel_MH <- tklabel(f3,text="Manhattan")
  entrylabel_CB <- tklabel(f3,text="Canberra")
  entrylabel_EU <- tklabel(f3,text="Euclidean")
  entrylabel_CS <- tklabel(f3,text="Cosine")
  entrylabel_MM <- tklabel(f3,text="Min-Max")
  #
  tkgrid(tklabel(f3,text="  DELTA DISTANCE:  "),entrylabel_CD,entrylabel_WD,entrylabel_ED,entrylabel_ES,entrylabel_EN)
  tkgrid(tklabel(f3,text="            "),entry_CD,entry_WD,entry_ED,entry_ES,entry_EN)
  tkgrid(tklabel(f3,text="            "),entrylabel_MH,entrylabel_CB,entrylabel_EU,entrylabel_CS,entrylabel_MM)
  tkgrid(tklabel(f3,text="            "),entry_MH,entry_CB,entry_EU,entry_CS,entry_MM)
  tkgrid(tklabel(f3,text="    ")) # blank line for aesthetic purposes

  # Tooltips for the above
  tk2tip(entrylabel_CD, "Select the Classic Delta measure as introduced by Burrows.")
  tk2tip(entrylabel_WD, "Select Cosine Delta (aka Wurzburg Delta), which seems to outperform other measures.")
  tk2tip(entrylabel_ED, "Select Eder's Delta (weights the frequencies, in order to decrease the influence of the less frequent words).")
  tk2tip(entrylabel_ES, "Select Eder's Simple measure (the frequencies are transformed via square root).")
  tk2tip(entrylabel_EN, "Select Entropy Delta, or Manhattan appied to entropy-transformed dataset.")
  tk2tip(entrylabel_MH, "Select Manhattan Distance (obvious and well documented).")
  tk2tip(entrylabel_CB, "Select Canberra Distance (risky, but sometimes amazingly good).")
  tk2tip(entrylabel_EU, "Select Euclidean Distance (basic and the most *natural*).")
  tk2tip(entrylabel_CS, "Select Cosine Distance (a classic distance in multidimensional methods).")
  tk2tip(entrylabel_MM, "Select Min-Max distance (aka Ruzicka distance).")

  
  # next row: OTHER METHODS
  
  entry_LINEAR <- tkradiobutton(f3)
  entry_POLYNOMIAL <- tkradiobutton(f3)
  entry_RADIAL <- tkradiobutton(f3)
  #
  tkconfigure(entry_LINEAR,variable=svm.kernel,value="linear")
  tkconfigure(entry_POLYNOMIAL,variable=svm.kernel,value="polynomial")
  tkconfigure(entry_RADIAL,variable=svm.kernel,value="radial")
  #
  entrylabel_LINEAR <- tklabel(f3,text="Linear")
  entrylabel_POLYNOMIAL <- tklabel(f3,text="Polynomial")
  entrylabel_RADIAL <- tklabel(f3,text="Radial")
  #
  entry_DEGREE <- tkentry(f3,textvariable=svm.degree,width="5")
  entry_COEF0 <- tkentry(f3,textvariable=svm.coef0,width="5")
  entry_COST <- tkentry(f3,textvariable=svm.cost,width="5")
  #
  entrylabel_DEGREE <- tklabel(f3,text="degree")
  entrylabel_COEF0 <- tklabel(f3,text="coef0")
  entrylabel_COST <- tklabel(f3,text="cost")
  #
  tk2tip(entrylabel_LINEAR, "Linear kernel used in training and predicting.\n = u'*v")
  tk2tip(entrylabel_POLYNOMIAL, "Polynomial kernel used in training and predicting.\n = (gamma*u'*v + coef0)^degree")
  tk2tip(entrylabel_RADIAL, "Radial kernel used in training and predicting.\n  = exp(-gamma*|u-v|^2)")
  tk2tip(entrylabel_DEGREE, "Parameter needed for kernel of type \"polynomial\" (default: 3).")
  tk2tip(entrylabel_COEF0, "Parameter needed for kernel of type \"polynomial\" (default: 0).")
  tk2tip(entrylabel_COST, "Cost of constraints violation (default: 1);\n it is the \"C\"-constant of the regularization\n term in the Lagrange formulation.")  
  #
  entry_K <- tkentry(f3,textvariable=k.value,width="5")
  entry_L <- tkentry(f3,textvariable=l.value,width="5")
  #  
  entrylabel_K <- tklabel(f3,text="k value")
  entrylabel_L <- tklabel(f3,text="l value")
  # Tooltips for the above
  tk2tip(entrylabel_K, "Specify the \"k\" value, or number of neighbors considered.")
  tk2tip(entrylabel_L, "Minimum vote for definite decision, otherwise \"doubt\".\n(More precisely, less than \"k-l\" dissenting votes are allowed,\neven if k is increased by ties.)")
  #
  tkgrid(tklabel(f3,text="  SVM OPTIONS:     "),entrylabel_LINEAR,entrylabel_POLYNOMIAL,entrylabel_RADIAL,entrylabel_DEGREE,entrylabel_COEF0)
  tkgrid(tklabel(f3,text="    "),entry_LINEAR,entry_POLYNOMIAL,entry_RADIAL,entry_DEGREE,entry_COEF0)
  tkgrid(tklabel(f3,text="    "),tklabel(f3,text="    "),tklabel(f3,text="    "),tklabel(f3,text="    "),tklabel(f3,text="    "),entrylabel_COST) # blank line for aesthetic purposes
  tkgrid(tklabel(f3,text="  k-NN OPTIONS:    "),entrylabel_K,entrylabel_L,tklabel(f3,text="    "),tklabel(f3,text="    "),entry_COST)
  tkgrid(tklabel(f3,text="    "),entry_K,entry_L)
  tkgrid(tklabel(f3,text="    ")) # blank line for aesthetic purposes

  
  
  # next tab: SAMPLING
  entry_SAMP <- tkradiobutton(f4)
  entry_RAND <- tkradiobutton(f4)
  entry_NOSAMP <- tkradiobutton(f4)
    
  tkconfigure(entry_SAMP, variable=sampling, value="normal.sampling")
  tkconfigure(entry_RAND, variable=sampling, value="random.sampling")
  tkconfigure(entry_NOSAMP, variable=sampling, value="no.sampling")
    
  entry_SAMPLESIZE <- tkentry(f4,textvariable=sample.size,width="10")
  entry_NO.OF.SAMPLES <- tkentry(f4,textvariable=number.of.samples,width="10")
  
  entrylabel_SAMP <- tklabel(f4,text="Normal sampling")
  entrylabel_RAND <- tklabel(f4,text="Random sampling")
  entrylabel_NOSAMP <- tklabel(f4,text="No sampling")
  
  entrylabel_SAMPLESIZE <- tklabel(f4,text="Sample size")
  entrylabel_NO.OF.SAMPLES <- tklabel(f4,text="Random samples")
  
  
  # Position and display sampling parameters on the grid:
  tkgrid(entrylabel_NOSAMP,entrylabel_SAMP, entrylabel_RAND)
  tkgrid(entry_NOSAMP, entry_SAMP, entry_RAND)
  tkgrid(tklabel(f4,text="    "),entrylabel_SAMPLESIZE, entrylabel_NO.OF.SAMPLES)
  tkgrid(tklabel(f4,text="    "),entry_SAMPLESIZE, entry_NO.OF.SAMPLES)
  tkgrid(tklabel(f4,text="    ")) # blank line for aesthetic purposes
  
  # Tooltips for the above
  tk2tip(entrylabel_SAMP, "Specify whether the texts in the corpus should be divided in equal-sized samples.")
  tk2tip(entrylabel_SAMPLESIZE, "Specify the size for the samples (expressed in words). \nOnly relevant when normal sampling is switched on.")
  tk2tip(entrylabel_RAND, "When the analyzed texts are significantly unequal in length, \nit is not a bad idea to prepare samples as randomly chosen *bags of words*. \nIf this option is switched on, the desired size of a sample should be indicated.")
  tk2tip(entrylabel_NO.OF.SAMPLES, "Specify the number of random samples per input text. \nOnly relevant when random sampling is switched on.")
  tk2tip(entrylabel_NOSAMP, "No internal sampling will be performed: entire texts are considered as samples.")
  
  # next row: OUTPUT
  #
  cb_QUEER <- tkcheckbutton(f5)
  cb_GUESS <- tkcheckbutton(f5)
  #
  tkconfigure(cb_QUEER,variable=final.ranking.of.candidates)
  tkconfigure(cb_GUESS,variable=how.many.correct.attributions)
  entry_CAND <- tkentry(f5,textvariable=number.of.candidates,width="8")
  #
  cblabel_QUEER <- tklabel(f5,text="Misclassifications")
  cblabel_GUESS <- tklabel(f5,text="Count good guesses")
  entrylabel_CAND <- tklabel(f5,text="No. of candidates")
  #
  tkgrid(tklabel(f5,text="  GENERAL:  "),cblabel_QUEER,cblabel_GUESS,entrylabel_CAND)
  tkgrid(tklabel(f5,text="                   "),cb_QUEER,cb_GUESS,entry_CAND)
  tkgrid(tklabel(f5,text="    ")) # blank line for aesthetic purposes
  # Tooltips for the above
  tk2tip(cblabel_QUEER, "Select to list misattributions in the final results file.")
  tk2tip(cblabel_GUESS, "Select to count the proportion of correct attributions.")
  tk2tip(entrylabel_CAND, "Set the number of candidates in the final results file.")

  # next row: VARIOUS
  cb_TABLESAVE <- tkcheckbutton(f5)
  cb_FEATURESAVE <- tkcheckbutton(f5)
  cb_FREQSAVE <-tkcheckbutton(f5)
  cb_DUMPSAMPLES<-tkcheckbutton(f5)
  #
  tkconfigure(cb_TABLESAVE,variable=save.distance.tables)
  tkconfigure(cb_FEATURESAVE,variable=save.analyzed.features)
  tkconfigure(cb_FREQSAVE,variable=save.analyzed.freqs)
  tkconfigure(cb_DUMPSAMPLES,variable=dump.samples)
  #
  cblabel_TABLESAVE <- tklabel(f5,text="Save distance table")
  cblabel_FEATURESAVE <- tklabel(f5,text="Save features")
  cblabel_FREQSAVE <- tklabel(f5,text="Save frequencies")
  cblabel_DUMPSAMPLES <- tklabel(f5,text="Dump samples")
  #
  tkgrid(tklabel(f5,text="    VARIOUS:   "), cblabel_TABLESAVE,cblabel_FEATURESAVE,cblabel_FREQSAVE,cblabel_DUMPSAMPLES)
  tkgrid(tklabel(f5,text="            "), cb_TABLESAVE,cb_FEATURESAVE,cb_FREQSAVE,cb_DUMPSAMPLES)
  tkgrid(tklabel(f5,text="    ")) # blank line for aesthetic purposes
  # Tooltips for the above
  tk2tip(cblabel_TABLESAVE, "Save final distance table(s) in separate text file(s).")
  tk2tip(cblabel_FEATURESAVE, "Save final feature (word, n-gram) list(s), e.g. the words actually used in the analysis.")
  tk2tip(cblabel_FREQSAVE, "Save frequency table(s) in separate text file(s).")
  tk2tip(cblabel_DUMPSAMPLES, "Save a dump of all samples in the directory 'Dumps' for post-analysis inspection.")

  
  # next row: the OK button
  #
  # button_1 <- tkbutton(tt,text="     OK     ",command=push_OK,relief="groove")
  # tkbind(button_1,"<Return>",push_OK) 
  # tkgrid(button_1,columnspan="10")
  tkgrid(tklabel(tt,text="    ")) # blank line (i.e., bottom margin)
  
  
  # wait until we have input
  tkwait.window(tt)
  
  variables$analyzed.features = as.character(tclvalue(analyzed.features))
  variables$ngram.size = as.numeric(tclvalue(ngram.size))
  variables$corpus.format = as.character(tclvalue(corpus.format))
  variables$preserve.case = as.logical(as.numeric(tclvalue(preserve.case)))
  variables$mfw.min = as.numeric(tclvalue(mfw.min))
  variables$mfw.max = as.numeric(tclvalue(mfw.max))
  variables$mfw.incr = as.numeric(tclvalue(mfw.incr))
  variables$start.at = as.numeric(tclvalue(start.at))
  variables$culling.min = as.numeric(tclvalue(culling.min))
  variables$culling.max = as.numeric(tclvalue(culling.max))
  variables$culling.incr = as.numeric(tclvalue(culling.incr))
  variables$use.existing.freq.tables = as.logical(as.numeric(tclvalue(use.existing.freq.tables)))
  variables$use.existing.wordlist = as.logical(as.numeric(tclvalue(use.existing.wordlist)))
  variables$interactive.files = as.logical(as.numeric(tclvalue(interactive.files)))
  variables$use.custom.list.of.files = as.logical(as.numeric(tclvalue(use.custom.list.of.files)))
  variables$classification.method = as.character(tclvalue(classification.method))
  variables$culling.of.all.samples = as.logical(as.numeric(tclvalue(culling.of.all.samples)))
  variables$reference.wordlist.of.all.samples = as.logical(as.numeric(tclvalue(reference.wordlist.of.all.samples)))
  variables$z.scores.of.all.samples = as.logical(as.numeric(tclvalue(z.scores.of.all.samples)))
  variables$number.of.candidates = as.numeric(tclvalue(number.of.candidates))
  variables$final.ranking.of.candidates = as.logical(as.numeric(tclvalue(final.ranking.of.candidates)))
  variables$how.many.correct.attributions = as.logical(as.numeric(tclvalue(how.many.correct.attributions)))
  variables$delete.pronouns = as.logical(as.numeric(tclvalue(delete.pronouns)))
  variables$dump.samples = as.logical(as.numeric(tclvalue(dump.samples)))
  variables$save.distance.tables = as.logical(as.numeric(tclvalue(save.distance.tables)))
  variables$save.analyzed.features = as.logical(as.numeric(tclvalue(save.analyzed.features)))
  variables$save.analyzed.freqs = as.logical(as.numeric(tclvalue(save.analyzed.freqs)))
  variables$sampling = as.character(tclvalue(sampling))
  variables$sample.size = as.numeric(tclvalue(sample.size))
  variables$number.of.samples = as.numeric(tclvalue(number.of.samples))
  variables$mfw.list.cutoff = as.numeric(tclvalue(mfw.list.cutoff))
  variables$distance.measure = as.character(tclvalue(distance.measure))
  variables$corpus.lang = as.character(tclvalue(corpus.lang))
  variables$consensus.strength = as.numeric(tclvalue(consensus.strength))
  variables$k.value = as.numeric(tclvalue(k.value))
  variables$l.value = as.numeric(tclvalue(l.value))
  variables$svm.kernel = as.character(tclvalue(svm.kernel))
  variables$svm.degree = as.numeric(tclvalue(svm.degree))  
  variables$svm.coef0 = as.numeric(tclvalue(svm.coef0))
  variables$svm.cost = as.numeric(tclvalue(svm.cost))
  variables$encoding = as.logical(as.numeric(tclvalue(encoding)))

  .Tcl("font delete myDefaultFont")


  # switching back from logical values into strings
  if(variables$encoding == FALSE) {
    variables$encoding = "UTF-8"
  } else {
    variables$encoding = "native.enc"
  }



return(variables)
}