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[iramuteq] / Rlib / textometrieR / R / cooccurrences.R

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-# #* Textometrie 
-# #* ANR project ANR-06-CORP-029
-# #* http://textometrie.ens-lsh.fr/
-# #* 
-# #* 2008 (C) Textometrie project
-# #* BSD New and Simplified BSD licenses
-# #* http://www.opensource.org/licenses/bsd-license.php
-# 
-# ## Sylvain Loiseau <sloiseau@u-paris10.fr>
-# 
-# ##
-# ## Ex. : type1 = 7 occ., type2 = 4 occ., separator = 11
-# ## Cf. Lafon, 1981 : 120 sqq.
-# ##
-# ## phyper(0:4, 4, 7 + 11, 7)
-# ##
-# 
-# ##
-# ## wrapper for the function "cooccurrences.directed.contexts" given a factor as
-# ## corpus and a separator.
-# ##
-
-cooccurrences <- function(corpus) {
- stop("not implemented yet");
-}
-
-# 
-# `cooccurrences.directed.corpus` <-
-# function (corpus, separator) {
-#   if (! is.factor(corpus)) stop("The corpus must be a factor.");
-#   if (! is.character(separator)) stop("Separator must be a character vector.");
-#   if (length(separator) != 1) stop("Separator must be a character of length 1");
-# 
-#   corpus <- corpus[drop=TRUE];
-#   frequencies = table(corpus);
-#   separatorfrequency = frequencies[separator];
-#   if (is.na(separatorfrequency)) {
-#     separatorfrequency = 0;
-#   }
-#   frequencies = frequencies[names(frequencies) != separator];
-#   types <- levels(corpus);
-#   types <- types[types != separator];
-# 
-#   contexts = get.contexts(corpus, separator);
-# 
-#   return(cooccurrences.directed.contexts(contexts, types, frequencies, separatorfrequency));
-# }
-# 
-# ##
-# ## Compute all the cooccurrence indices according to a context list (see the "get.contexts" function"),
-# ## the vector of types to be taken into account, their frequency, and the separator frequency.
-# ##
-# ## Compute the cooccurrency index (based on hypergeometric cumulative
-# ## probability) for each directed pair of token and return a matrix with tokens as column names
-# ## and row names where each cell give a cooccurrency index
-# ##
-# 
-# `cooccurrences.directed.contexts` <-
-# function(contexts, types, type.frequencies, separator.frequency) {
-# 
-#   all.cooccurrences.index <- matrix(0.0, nrow=length(types), ncol=length(type));
-#   rownames(all.cooccurrences.index) <- types;
-#   colnames(all.cooccurrences.index) <- types;
-# 
-#   for (i in 1:length(types)) {
-#     type = types[i];
-#     typefrequency = frequencies[i];
-#     for (j in c(1:(types-1), (types + 1):length(types))) {
-#       othertype = types[j];
-#       othertypefrequency = frequencies[j];
-#       cooccurrences = cooccurrences.frequency(contexts, type, othertype);
-#       all.cooccurrences.index[type, othertype] <-
-#         cooccurrences.directed.cooccurrents(cooccurrences, type, othertype, separatorfrequency);
-#     }
-#   }
-# 
-#   return(all.cooccurrences.index);
-# }
-# 
-# ##
-# ## In a list of contexts (see the "get.contexts" function), count the number of contexts
-# ## having both at least one occurrence of type1 and at least one occurrence of type2.
-# ##
-# 
-# `cooccurrences.frequency` <-
-# function(contexts, type1, type2) {
-# 
-#   if (! is.character(type1)) stop("Type1 must be a character vector.");
-#   if (length(type1) != 1) stop("Type1 must be a character of length 1");
-#   if (! is.character(type2)) stop("Type2 must be a character vector.");
-#   if (length(type2) != 1) stop("Type2 must be a character of length 1");
-#   
-#   is.cooccurring <- sapply(contexts,
-#       function(x) {
-#       if(type1 %in% x && type2 %in% x) return(1) else return(0)
-#       }
-#       );
-#   return(sum(is.cooccurring));
-# }
-# 
-# ##
-# ## 
-# ##
-# 
-# `cooccurrences.directed.cooccurrents` <-
-# function(cooccurrences, type1, type2, separator) {
-#   if (! is.integer(cooccurrences)) stop("cooccurrences must be integer.");
-#   if (! is.integer(type1)) stop("type1 must be integer.");
-#   if (! is.integer(type2)) stop("type2 must be integer.");
-#   if (! is.integer(separator)) stop("separator must be integer.");
-#   ph <- phyper(found, type2, type1 + separator, type1);
-#   return(ph);
-# }
-# 
-# ##
-# ## Create a list of character vectors according to a character vector and a separator string.
-# ##
-# 
-# `get.contexts` <-
-# function(corpus, separator) {
-#   if (! is.factor(corpus)) stop("The corpus must be a factor.");
-#   if (! is.character(separator)) stop("Separator must be a character vector.");
-#   if (length(separator) != 1) stop("Separator must be a vector of length 1");
-# 
-#   index.separator = which(corpus == separator);
-#   contexts = list();
-#   lastindex = 1;
-#   for(i in 1:(length(index.separator))) {
-#     contexts[[i]] = as.character(corpus[lastindex:(index.separator[i]-1)]);
-#     lastindex=index.separator[i] + 1;
-#   }
-#   contexts[[length(contexts) + 1]] <- as.character(corpus[lastindex:length(corpus)]);
-#   return(contexts);
-# }
-# 
-# ##
-# ## Create a lexical table according to a character vector and a separator string.
-# ##
-# 
-# `get.lexical.table` <-
-# function(corpus, separator) {
-#   if (! is.factor(corpus)) stop("The corpus must be a factor.");
-#   if (! is.character(separator)) stop("Separator must be a character vector.");
-#   if (length(separator) == 1) stop("Separator must be a character of length 1");
-# 
-#   contexts = get.contexts(corpus, separator);
-# 
-#   corpus <- corpus[drop=TRUE];
-#   types <- levels(corpus);
-#   types <- types[types != separator];
-# 
-#   lexical.table <- matrix(0, nrow=length(contexts), ncol=length(types));
-#   colnames(lexical.table) <- types;
-# 
-#   for (i in 1:length(contexts)) {
-#     freq.list <- table(contexts[[i]]);
-#     lexical.table[i, names(freq.list)] <- freq.list;
-#   }
-#   
-#   return(lexical.table);
-# }
-# 
-# `cooccurrences` <-
-# function(type1, type2, separator) {
-# #    max = ifelse(f > g, 2*g, i)
-# #    #choose(T, t);
-# }
-#