GENENET8 update

1 files changed, 0 insertions, 294 deletions

diff --git a/sourcecodes/parameter_learning/code_backup/prepareInput.m b/sourcecodes/parameter_learning/code_backup/prepareInput.m
deleted file mode 100644
index 838dcd2c..00000000
--- a/sourcecodes/parameter_learning/code_backup/prepareInput.m
+++ /dev/null
@@ -1,294 +0,0 @@
-function  [ ] = prepareInput( pre )
-   %   
-   %  This function takes files that are uploaded to BNW and creates output
-   %    files that can be used for structure and parameter learning.
-   %  It replaces php code that was previously in bn_file_load_gom.php.
-   %    There are several improvements in performance and ease of use:
-   %     1) Loading files is significantly (~5x) faster for large input files.
-   %     2) The allowed values for discrete variables are more flexible. 
-   %           (e.g., A genotype variable be 'B' and 'D' instead of having
-   %               to replace to make them '1' and '2'.)
-   %     3) Continuous variables may be identified as continuous in some cases
-   %            even if there is not a period.
-   %     4) The states of discrete variables should be correctly ordered in
-   %            almost all cases.
-   %     5) An additional output file is written that will let users check if
-   %            the input file has been uploaded and parsed correctly.
-   %     6) Future updates to this code should be easier than updating the php.
-   %      
-   %
-   %  Input: ???continuous_input_orig.txt
-   %    This is the input file that is uploaded to BNW.
-   %    It is directly written out by the BNW php code with no modification.
-   %    The file format is a header line containing the variable names
-   %     followed by the data, with each case in a row.
-   %
-   %  Output: There are many output files.
-   %    1) The main output file is ???continuous_input.txt that can be
-   %       used by the structure learning code and parameter learning codes.
-   %       The first line is variable names, the second line is the node type
-   %          (continuous nodes should have 1, discrete nodes have the number
-   %           of states), and the rest is the data.
-   %    2) A new output file is ???input_desc.txt, a file that describes the
-   %        data so users can check that it has been parsed correctly.
-   %    3) ???nlevels.txt: The states of discrete variables.
-   %    4) ???name.txt: The names of the variables as uploaded.
-   %    5) ???type.txt: The number of states for each variables
-   %            (1 indicates a continuous variable.)
-   %    6/7) ???nnode.txt and ???nrows.txt: number of nodes and cases
-   %    8-12) ???ban.txt, ???white.txt, ???k.txt, ???thr.txt, and
-   %          ???parent.txt: Files with default values for structure learning. 
-   %
-
-%  open file for input, include error handling
-dfile=strcat(pre,'continuous_input_orig.txt');
-
-fin = fopen(dfile,'r');
-if fin < 0
-   error(['Could not open ',dfile,' for input']);
-end
-
-% Get the number of cases (the number of rows in the file excluding the header)
-ncases = fskipl(fin,Inf) - 1;
-
-frewind(fin);
-
-% Read in first line to get the number of nodes and the node labels.
-buffer = fgetl(fin);    %get header line as a string
-nnodes = numel(strfind(buffer,"\t")) + 1;
-labels = cell(1,nnodes);
-for j=1:nnodes
-    [next,buffer] = strtok(buffer);
-    labels{j} = next;
-end
-
-% Read in the data
-data = cell(ncases,nnodes);
-for i = 1:ncases
-    buffer = fgetl(fin);
-    for j = 1:nnodes
-         [next,buffer] = strtok(buffer);
-         data{i,j} = next;
-    end
-end
-
-% Determine whether or not the nodes are continuous or discrete.
-% First, treat them as all discrete and get the states and number of stats(levels).
-levels = cell(1,nnodes);
-states = [];
-for j = 1:nnodes
-   states{end+1} = unique(data(:,j));
-   levels{j} = size(states{j},1);
-end
-
-reason = cell(1,nnodes);
-%Now do some checks to see if nodes are discrete or continuous
-for j = 1:nnodes
-    % If there are 3 or less unique values, I will assume that the node is discrete.
-    if levels{j} < 4;
-        reason{j} = "It was determined to be discrete because there are a small number (<4) of possible values.";
-        continue
-    % If there are as many unique values as a third of the number of cases,
-    %      I will assume that the node is continuous.
-    elseif levels{j} > ncases/3;
-       levels{j} = 1;
-       reason{j} = "It was determined to be continuous because there are a large number of possible values compared to the number of cases.";
-       continue
-    % If there are more than twenty unique values,
-    %      I will assume that the node is continuous.
-    elseif levels{j} > 20;
-       levels{j} = 1;
-       reason{j} = "It was determined to be continuous because there are many (>20) possible values.";
-       continue
-    % Otherwise, I will scan through the individual values.
-    % If any of the values contain a '.', I will assume it is continuous.
-    else
-       reason{j} = "It was determined to be discrete by default.";
-       period_test = 0;
-       column = data(:,j);
-       k = 1;
-       while period_test == 0 
-           period_test = sum(cell2mat(strfind(column(k),".")));
-           if period_test != 0;
-              reason{j} = "This variable was determined to be continuous because there were several possible values and at least one value contained a period(.).";
-              levels{j} = 1;
-           end
-           k++;
-           if k > ncases
-              break
-           end
-        end
-    end
-end
-
-%I need to check if any discrete nodes are listed after continuous nodes.
-%If so, I need to rearrange the columns.
-max_disc = 0;
-min_cont = nnodes + 1;
-for i = 1:nnodes
-    if levels{i} > 1
-       max_disc = i;
-    elseif min_cont == nnodes+1
-       min_cont = i;
-    end
-end
-%If max_disc > min_cont, you need to rearrange the nodes
-%  to put the discrete nodes first.
-if max_disc > min_cont
-  levels_old = levels;
-  labels_old = labels;
-  data_old = data;
-  states_old = states;
-  reason_old = reason;
-  new_order = {};
-  for i=1:nnodes
-    if levels_old{i} > 1
-      new_order{end+1} = i;
-    end
-  end
-  for i=1:nnodes
-    if levels_old{i} == 1
-      new_order{end+1} = i;
-    end
-  end
-  labels = {};
-  levels = {};
-  states = {};
-  reason = {};
-  for i =1:nnodes
-    labels{i} = labels_old{new_order{i}};
-    levels{i} = levels_old{new_order{i}};
-    states{i} = states_old{new_order{i}};
-    reason{i} = reason_old{new_order{i}};
-    for j=1:ncases
-      data{j,i} = data_old{j,new_order{i}};
-    end
-  end
-  
-endif
-
-
-%Write other files that are used by BNW for this key.
-%The first group of files establish default settings for structure learning.
-outfile = strcat(pre,'white.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'From\tTo\n');
-fclose(fout);
-
-outfile = strcat(pre,'ban.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'From\tTo\n');
-fclose(fout);
-
-outfile = strcat(pre,'k.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'1\n');
-fclose(fout);
-
-outfile = strcat(pre,'parent.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'4\n');
-fclose(fout);
-
-outfile = strcat(pre,'thr.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'0.5\n');
-fclose(fout);
-
-
-%The next group of files have information about the uploaded file.
-outfile = strcat(pre,'name.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'%s\t',labels{1:end-1});
-fprintf(fout,'%s\n',labels{end});
-fclose(fout);
-
-outfile = strcat(pre,'nnode.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'%i\n',nnodes);
-fclose(fout);
-
-outfile = strcat(pre,'nrows.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'%i\n',ncases);
-fclose(fout);
-
-outfile = strcat(pre,'type.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'%s\t',labels{1:end-1});
-fprintf(fout,'%s\n',labels{end});
-fprintf(fout,'%i\t',levels{1:end-1});
-fprintf(fout,'%i\n',levels{end});
-fclose(fout);
-
-%This output file contains the states for discrete nodes.
-% The unique matlab function already sorts the states.
-outfile = strcat(pre,'nlevels.txt');
-fout = fopen(outfile,'w');
-for i = 1:nnodes
-    if levels{i} > 1
-        fprintf(fout,'%s\t',labels{i},states{i}{1:end-1});
-        fprintf(fout,'%s\n',states{i}{end});
-    end
-end
-fclose(fout);
-
-
-%Print a file with a short description of the input.
-descfile = strcat(pre,'input_desc.txt');
-dout = fopen(descfile,'w');
-fprintf(dout,['As loaded, the input file had the following properties:\n\n']);
-dout = fopen(descfile,'a');
-fprintf(dout,'There are %i variables and %i cases(rows).\n',size(labels,2),ncases);
-fprintf(dout,'The variable names are:\n');
-fprintf(dout,'%s\t',labels{1:end-1});
-fprintf(dout,'%s\n\n',labels{end});
-for i=1:nnodes
-    if levels{i} == 1
-       fprintf(dout,'%s is a continuous variable.\n',labels{i});
-       fprintf(dout,'%s\n',reason{i});
-       column = str2double(data(:,i));
-       colmean = mean(column);
-       colstd = std(column);
-       fprintf(dout,'It has a mean of %6.3f and a standard deviation of %6.3f\n\n',mean(column),std(column))
-    else 
-       fprintf(dout,'%s is a discrete variable with %i states.\n',labels{i},levels{i});
-       fprintf(dout,'%s\n',reason{i});
-       fprintf(dout,'The states are: ');
-       fprintf(dout,'%s ',states{i}{1:end-1});
-       fprintf(dout,'%s\n\n',states{i}{end});
-    end
-end
-fclose(fout);
-
-outfile = strcat(pre,'continuous_input.txt');
-fout = fopen(outfile,'w');
-fprintf(fout,'%s\t',labels{1:end-1});
-fprintf(fout,'%s\n',labels{end});
-fprintf(fout,'%i\t',levels{1:end-1});
-fprintf(fout,'%i\n',levels{end});
-%Need to replace states in discrete variables with integers for BNT
-for i = 1:nnodes
-    if levels{i} > 1
-	for j = 1:ncases
-            for k=1:size(states{i},1)
-	      if data{j,i} == states{i}{k}
-                 data{j,i} = sprintf('%i',num2cell(k){1});;
-                 break
-              end
-            end
-        end
-     end
-end
-for i = 1:ncases
-      fprintf(fout,'%s\t',data{i,1:end-1});
-      fprintf(fout,'%s\n',data{i,end});
-end
-fclose(fout);
-
-
-
-
-
-end
-%  end of prepareInput.m
\ No newline at end of file