Deleting old versions of files

27 files changed, 251 insertions, 2770 deletions

diff --git a/sourcecodes/parameter_learning/Predictmultipleintrvention.m b/sourcecodes/parameter_learning/Predictmultipleintrvention.m
deleted file mode 100644
index 1b9fa2f4..00000000
--- a/sourcecodes/parameter_learning/Predictmultipleintrvention.m
+++ /dev/null
@@ -1,95 +0,0 @@
-function Predictmultipleintrvention(pre)
-dfile=strcat(pre,'structure_input.txt');
-sfile=dfile;
-dfile=strcat(pre,'continuous_input.txt');
-nnodefile=strcat(pre,'nnode.txt');
-
-fnnode = fopen(nnodefile,'r');
-nnodes = fscanf(fnnode,'%d');
-
-fvarnamefile=strcat(pre,'varname.txt');
-
-varfile = fopen(fvarnamefile,'r');
-
-Std_flag=true;
-[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag);
-
-[bnet]=parameterLearning(bnet,cases);
-
-fvarfile=strcat(pre,'var.txt');
-fvar = fopen(fvarfile,'r');                           
-select_var_new = fscanf(fvar,'%d');
-
-nm = numel(select_var_new);
-
-varlabels = cell(1,nm);
-varbuffer = fgetl(varfile);    %get header line as a string
-for j=1:nm
-    [varnext,varbuffer] = strtok(varbuffer);
-    varlabels{j} = varnext;
-    for i=1:nnodes    
-        if strcmp(varlabels{j},labels{i})
-            select_var_new(j)=i;
-        end
-     end    
-    
-end
-
-
-
-
-fvardfile=strcat(pre,'vardata.txt');
-
-fvard = fopen(fvardfile,'r');
-
-select_var_data_new = fscanf(fvard,'%f');
-
-means_orig = cell(1,nnodes);
-stdevs_orig = cell(1,nnodes);
-labels_orig = cell(1,nnodes);
-%Read in original means and standard deviations
-mapfile = strcat(pre,'map.txt');
-fmap = fopen(mapfile,'r');
-for i=1:nnodes
-    buffer = fgetl(mapfile);
-    temp = cell(1,4);
-    for j=1:4
-        [next,buffer] = strtok(buffer);
-        temp{j} = next;
-    end
-    labels_orig{i} = temp{1};
-    means_orig{i} = str2num(temp{4});
-    stdevs_orig{i} = str2num(temp{3});
-end
-fclose(fmap);
-
-%Need to map the means and stdevs to the correct labels
-means = cell(1,nnodes);
-stdevs = cell(1,nnodes);
-%Read in labels in new order.
-labelsnew = cell(1,nnodes);
-mapdatafile = strcat(pre,'mapdata.txt');
-fmapdata = fopen(mapdatafile,'r');
-buffer = fgetl(fmapdata);
-for i = 1:nnodes
-    [next,buffer ] = strtok(buffer);
-    labelsnew{i} = next;
-end
-fclose(fmapdata);
-for i = 1:nnodes
-    for j = 1:nnodes
-       if strcmp(labelsnew{i},labels_orig{j})
-          means{i} = means_orig{j};
-          stdevs{i} = stdevs_orig{j};
-          break
-       end
-    end
-end
-
-filename=strcat(pre,'net_figure_new.txt');
-
-drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new);
-
-writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,select_var_new,select_var_data_new);
-
-end
diff --git a/sourcecodes/parameter_learning/code_backup/Predictmultiple.m b/sourcecodes/parameter_learning/code_backup/Predictmultiple.m
deleted file mode 100644
index 9d107628..00000000
--- a/sourcecodes/parameter_learning/code_backup/Predictmultiple.m
+++ /dev/null
@@ -1,72 +0,0 @@
-function Predictmultiple(pre)
-dfile=strcat(pre,'structure_input.txt');
-sfile=dfile;
-dfile=strcat(pre,'continuous_input.txt');
-nnodefile=strcat(pre,'nnode.txt');
-
-fnnode = fopen(nnodefile,'r');
-nnodes = fscanf(fnnode,'%d');
-
-Std_flag=true;
-[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag);
-
-[bnet]=parameterLearning(bnet,cases);
-
-fvarfile=strcat(pre,'var.txt');
-fvar = fopen(fvarfile,'r');                         
-select_var_new = fscanf(fvar,'%d');
-
-fvardfile=strcat(pre,'vardata.txt');
-fvard = fopen(fvardfile,'r');
-select_var_data_new = fscanf(fvard,'%f');
-
-means_orig = cell(1,nnodes);
-stdevs_orig = cell(1,nnodes);
-labels_orig = cell(1,nnodes);
-%Read in original means and standard deviations
-mapfile = strcat(pre,'map.txt');
-fmap = fopen(mapfile,'r');
-for i=1:nnodes
-    buffer = fgetl(mapfile);
-    temp = cell(1,3);
-    for j=1:3
-        [next,buffer] = strtok(buffer);
-        temp{j} = next;
-    end
-    labels_orig{i} = temp{1};
-    means_orig{i} = str2num(temp{3});
-    stdevs_orig{i} = str2num(temp{2});
-end
-fclose(fmap);
-
-%Need to map the means and stdevs to the correct labels
-means = cell(1,nnodes);
-stdevs = cell(1,nnodes);
-%Read in labels in new order.
-labelsnew = cell(1,nnodes);
-mapdatafile = strcat(pre,'mapdata.txt');
-fmapdata = fopen(mapdatafile,'r');
-buffer = fgetl(fmapdata);
-for i = 1:nnodes
-    [next,buffer ] = strtok(buffer);
-    labelsnew{i} = next;
-end
-fclose(fmapdata);
-for i = 1:nnodes
-    for j = 1:nnodes
-       if strcmp(labelsnew{i},labels_orig{j})
-          means{i} = means_orig{j};
-          stdevs{i} = stdevs_orig{j};
-          break
-       end
-    end
-end
-
-
-filename=strcat(pre,'net_figure_new.txt');
-
-drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new);
-
-writeParameters_ev(pre,bnet,nnodes,labels,cases,stdevs,means,select_var_new,select_var_data_new);
-
-end
diff --git a/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m b/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m
deleted file mode 100644
index e9f741f2..00000000
--- a/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m
+++ /dev/null
@@ -1,95 +0,0 @@
-function Predictmultipleintrvention(pre)
-dfile=strcat(pre,'structure_input.txt');
-sfile=dfile;
-dfile=strcat(pre,'continuous_input.txt');
-nnodefile=strcat(pre,'nnode.txt');
-
-fnnode = fopen(nnodefile,'r');
-nnodes = fscanf(fnnode,'%d');
-
-fvarnamefile=strcat(pre,'varname.txt');
-
-varfile = fopen(fvarnamefile,'r');
-
-Std_flag=true;
-[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag);
-
-[bnet]=parameterLearning(bnet,cases);
-
-fvarfile=strcat(pre,'var.txt');
-fvar = fopen(fvarfile,'r');                           
-select_var_new = fscanf(fvar,'%d');
-
-nm = numel(select_var_new);
-
-varlabels = cell(1,nm);
-varbuffer = fgetl(varfile);    %get header line as a string
-for j=1:nm
-    [varnext,varbuffer] = strtok(varbuffer);
-    varlabels{j} = varnext;
-    for i=1:nnodes    
-        if strcmp(varlabels{j},labels{i})
-            select_var_new(j)=i;
-        end
-     end    
-    
-end
-
-
-
-
-fvardfile=strcat(pre,'vardata.txt');
-
-fvard = fopen(fvardfile,'r');
-
-select_var_data_new = fscanf(fvard,'%f');
-
-means_orig = cell(1,nnodes);
-stdevs_orig = cell(1,nnodes);
-labels_orig = cell(1,nnodes);
-%Read in original means and standard deviations
-mapfile = strcat(pre,'map.txt');
-fmap = fopen(mapfile,'r');
-for i=1:nnodes
-    buffer = fgetl(mapfile);
-    temp = cell(1,3);
-    for j=1:3
-        [next,buffer] = strtok(buffer);
-        temp{j} = next;
-    end
-    labels_orig{i} = temp{1};
-    means_orig{i} = str2num(temp{3});
-    stdevs_orig{i} = str2num(temp{2});
-end
-fclose(fmap);
-
-%Need to map the means and stdevs to the correct labels
-means = cell(1,nnodes);
-stdevs = cell(1,nnodes);
-%Read in labels in new order.
-labelsnew = cell(1,nnodes);
-mapdatafile = strcat(pre,'mapdata.txt');
-fmapdata = fopen(mapdatafile,'r');
-buffer = fgetl(fmapdata);
-for i = 1:nnodes
-    [next,buffer ] = strtok(buffer);
-    labelsnew{i} = next;
-end
-fclose(fmapdata);
-for i = 1:nnodes
-    for j = 1:nnodes
-       if strcmp(labelsnew{i},labels_orig{j})
-          means{i} = means_orig{j};
-          stdevs{i} = stdevs_orig{j};
-          break
-       end
-    end
-end
-
-filename=strcat(pre,'net_figure_new.txt');
-
-drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new);
-
-writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,select_var_new,select_var_data_new);
-
-end
diff --git a/sourcecodes/parameter_learning/code_backup/checkDiscreteNodes.m b/sourcecodes/parameter_learning/code_backup/checkDiscreteNodes.m
deleted file mode 100644
index c9d0692c..00000000
--- a/sourcecodes/parameter_learning/code_backup/checkDiscreteNodes.m
+++ /dev/null
@@ -1,37 +0,0 @@
-function [ ] = checkDiscreteNodes( bnet, cases)
-    %checkDiscreteNodes Checks if states of discrete nodes are be integers from 1 to M
-    %   where M is the number of states of the node.  (M should be the same as
-    %   node_sizes in the bnet).
-    % 
-    %Input:
-    %   bnet: BNT bnet
-    %   cases: cell array of data
-    %
-%
-node_sizes = bnet.node_sizes;
-dnodes = bnet.dnodes;
-ndisc = size(dnodes,2);
-ncases = size(cases,2);
-
-%check to see that all data for discrete nodes are integers
-for i = 1:ndisc
-    inode = dnodes(i);
-    data = cases(inode,:);
-    isize = node_sizes(inode);
-    states = zeros(1,isize);
-    for j = 1:isize
-        states(j) = j;
-    end
-    for j = 1:ncases
-        k = int64(data{j});
-        if ~any(k==states)
-            error(['Discrete nodes must be integers from 1 to the number of states']);
-        end
-    end
-end
-
-    
-end
-
-
-
diff --git a/sourcecodes/parameter_learning/code_backup/checkStructure.m b/sourcecodes/parameter_learning/code_backup/checkStructure.m
deleted file mode 100644
index b4de9403..00000000
--- a/sourcecodes/parameter_learning/code_backup/checkStructure.m
+++ /dev/null
@@ -1,78 +0,0 @@
-function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, cases, dag, node_sizes)
-    %checkStructure Check to see if nodes are sorted correctly.  Nodes must be
-    %   in topological order (i.e., parents before children) before parameter
-    %   learning can take place. This function performs this sorting.
-    %
-    %Input and output have the same meaning.  The output has just been
-    %topologically ordered.
-    %   labels = cell array with the names of the nodes.
-    %   cases = cell array with the data.
-    %   dag = matrix with the strucutre of the network.
-    %   node_sizes = vector with the size of each node.
-
-%make connections array
-%count how big you need the connections array to be
-nnodes = size(dag,1);
-narcs = 0;
-for i = 1:nnodes
-    for j = 1:nnodes
-        if dag(i,j) == 1
-            narcs = narcs + 1;
-        end
-    end
-end
-%fill connections array with label names
-connections = cell(narcs,2);
-ncount = 0;
-for i = 1:nnodes
-    for j = 1:nnodes
-        if dag(i,j) == 1
-            ncount = ncount + 1;
-            connections{ncount,1} = labels{i};
-            connections{ncount,2} = labels{j};
-        end
-    end
-end
-
-%get topologically sorted dag and labels
-[new_dag, new_labels] = mk_adj_mat(connections, labels, 1);
-
-%check to see if order changed
-ord_flag = 0;
-for i = 1:nnodes
-    if ~strcmp(new_labels{i},labels{i})
-        ord_flag = 1;
-    end
-end
-
-if ord_flag
-    %get new ordering of nodes
-    order = cell(1,nnodes);
-    for i = 1:nnodes
-        for j = 1:nnodes
-            if strcmp(new_labels{j},labels{i})
-                order{i} = j;
-            end
-        end
-    end
-
-    %reorder cases and node_sizes
-    new_cases = cell(size(cases));
-    for i = 1:nnodes
-        new_cases(order{i},:) = cases(i,:);
-    end
-    new_node_sizes = zeros(1,nnodes);
-    for i = 1:nnodes
-        new_node_sizes(order{i}) = node_sizes(i);
-    end
-
-
-    dag = new_dag;
-    cases = new_cases;
-    node_sizes = new_node_sizes;
-    labels = new_labels;    
-end
-
-end
-%end checkStructure.m
-
diff --git a/sourcecodes/parameter_learning/code_backup/drawFigure.m b/sourcecodes/parameter_learning/code_backup/drawFigure.m
deleted file mode 100644
index f84bffa3..00000000
--- a/sourcecodes/parameter_learning/code_backup/drawFigure.m
+++ /dev/null
@@ -1,390 +0,0 @@
-function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata)
-%drawFigure writes the parameters and data that are needed to draw the
-%structure of a Bayesian network for BNW.
-% This is the first function that
-
-
-
-if nargin < 8,
-    drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means);
-else
-    drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata);
-end;
-
-end
-
-
-
-function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata)
-%Function to use if there is no entered evidence. 
-%         
-%
-%Before each printed line, I will have a line that starts with %%%
-% that describes what will be on that line
-
-%Create an empty evidence cell array.
-
-%val=cases;
-%for i = 1:nnodes
-% val(i,1)=val(i,2);
-
-%end
-
-A=cell2mat(cases');
-Amax=max(A);
-Amin=min(A);
-
-
-evidence = cell(1,nnodes);
-engine = jtree_inf_engine(bnet);
-
-evidence{selectvar}=selectdata;
-
-[engine,loglik]=enter_evidence(engine,evidence);
-
-%Open the file, and write the nodes to a file.
-fileID = fopen(filename,'w');
-
-%%%%Evidence node
-fprintf(fileID,'%i\n',selectvar);
-%%% The number of nodes
-fprintf(fileID,'%i\n',nnodes);
-%Get canvas size
-labels_temp = cellstr(labels);
-[x,y] = make_layout(bnet.dag);
-
-x = x - min(x);
-y = 1 - y;
-y = y - min(y);
-
-[x_dim,y_dim] = canvasSize(nnodes,x,y);
-
-%%% The dimensions of the canvas for the javascript code
-fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim)
-
-x = x*x_dim;
-y = y*y_dim;
-for i = 1:nnodes,
-%%% The name and X- and Y-positions of each node
-    fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i)));
-end
-
-%Get the number of parents and children for each node.
-num_par = zeros(1,nnodes);
-%For parents, sum down columns
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(j,i) == 1,
-            num_par(i) = num_par(i) + 1;
-        end
-    end
-end
-num_child = zeros(1,nnodes);
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(i,j) == 1,
-            num_child(i) = num_child(i) + 1;
-        end
-    end
-end
-
-
-for i = 1:nnodes,
-    %%% The name and type of each node (1=continuous, the number of states
-    %%% if it is discrete
-    fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i));
-    %%% The size of the node, I am going to keep them 
-    %%% 250(width) by 150(height) for now
-    %Could modify this to change the width based on the length of the node
-    %name
-    fprintf(fileID,'%i\t%i\n',250,150);
-    %%% The number of parents of the node, and the parents
-    if num_par(i) == 0;
-        %%% If no parents:
-        fprintf(fileID,'%i\n',num_par(i));
-    else
-        parents = zeros(1,num_par(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(j,i) == 1,
-             parents(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_par(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_par(i),parents(1,:));
-    end
-    
-    
-    %%% The number of children of the node, and the children
-    if num_child(i) == 0;
-        %%% If no children:
-        fprintf(fileID,'%i\n',num_child(i));
-    else
-        children = zeros(1,num_child(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(i,j) == 1,
-             children(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_child(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_child(i),children(1,:));
-    end
-    
-    predict = marginal_nodes(engine,i);
-    if isempty(evidence{i})
-      if bnet.node_sizes(i) ~= 1,
-        for j = 1:bnet.node_sizes(i),
-            %%%For discrete nodes, the state and the percent of that state
-            fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j));
-        end;
-      else
-
-        [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i));
-        %%%For continuous nodes, print x and the pdf of a normal curve.
-        for j = 1:101,
-            %%Undo standardization
-            xvals(j,1) = xvals(j,1)*stdevs{i}+means{i}
-            fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1));
-        end;
-      end;
-    else
-      fprintf(fileID,'%6.4f\t%6.4f\n',selectdata,1);
-    end
-    
-end
-%fprintf(fileID,'%s\t %\n',labels_temp{:});
-
-
-fclose(fileID);
-
-end
-
-
-
-
-
-
-function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means)
-%Function to use if there is no entered evidence. 
-%         
-%
-%Before each printed line, I will have a line that starts with %%%
-% that describes what will be on that line
-A=cell2mat(cases');
-Amax=max(A);
-Amin=min(A);
-
-%Create an empty evidence cell array.
-evidence = cell(1,nnodes);
-engine = jtree_inf_engine(bnet);
-[engine,loglik] = enter_evidence(engine,evidence);
-
-%Open the file, and write the nodes to a file.
-fileID = fopen(filename,'w');
-%%% The number of nodes
-fprintf(fileID,'%i\n',nnodes);
-
-%Get canvas size
-
-labels_temp = cellstr(labels);
-[x,y] = make_layout(bnet.dag);
-%[x,y] = layout_dag(bnet.dag);
-
-
-x = x - min(x);
-y = 1 - y;
-y = y - min(y);
-
-[x_dim,y_dim] = canvasSize(nnodes,x,y);
-
-%%% The dimensions of the canvas for the javascript code
-fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim)
-
-x = x*x_dim;
-y = y*y_dim;
-for i = 1:nnodes,
-%%% The name and X- and Y-positions of each node
-    fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i)));
-end
-
-%Get the number of parents and children for each node.
-num_par = zeros(1,nnodes);
-%For parents, sum down columns
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(j,i) == 1,
-            num_par(i) = num_par(i) + 1;
-        end
-    end
-end
-num_child = zeros(1,nnodes);
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(i,j) == 1,
-            num_child(i) = num_child(i) + 1;
-        end
-    end
-end
-
-
-for i = 1:nnodes,
-    %%% The name and type of each node (1=continuous, the number of states
-    %%% if it is discrete
-    fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i));
-    %%% The size of the node, I am going to keep them 
-    %%% 250(width) by 150(height) for now
-    %Could modify this to change the width based on the length of the node
-    %name
-    fprintf(fileID,'%i\t%i\n',250,150);
-    %%% The number of parents of the node, and the parents
-    if num_par(i) == 0;
-        %%% If no parents:
-        fprintf(fileID,'%i\n',num_par(i));
-    else
-        parents = zeros(1,num_par(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(j,i) == 1,
-             parents(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_par(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_par(i),parents(1,:));
-    end
-    
-    
-    %%% The number of children of the node, and the children
-    if num_child(i) == 0;
-        %%% If no children:
-        fprintf(fileID,'%i\n',num_child(i));
-    else
-        children = zeros(1,num_child(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(i,j) == 1,
-             children(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_child(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_child(i),children(1,:));
-    end
-    
-    predict = marginal_nodes(engine,i);
-    if bnet.node_sizes(i) ~= 1,
-        for j = 1:bnet.node_sizes(i),
-            %%%For discrete nodes, the state and the percent of that state
-            fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j));
-        end;
-    else
-        %cases(i)
-       % MAX(cases(i))
-       % MIN(cases(i))
-        [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i));
-        %%%For continuous nodes, print x and the pdf of a normal curve.
-        for j = 1:101,
-            %%Undo standardization
-            x_vals(j,1) = x_vals(j,1)*stdevs{i}+means{i};
-            fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1));
-        end;
-    end;
-end
-%fprintf(fileID,'%s\t %\n',labels_temp{:});
-
-
-fclose(fileID);
-
-end
-
-
-function [x_dim, y_dim] = canvasSize(nnodes,x,y)
-%canvasSize Function to calculate the size of the canvas to
-%           build the network structure
-
-
-%I am going to assume that the node size will be
-% height = 150, width = 250
-% so there will be a node spacing of 
-% 200 (in y-dim) and 300 (in x-dim).
-y_space = 200;
-x_space = 300;
-
-%Set default minimum x and y dimensions
-x_dim = 1200;
-y_dim = 1200;
-
-%get unique y values
-y_unique = unique(y);
-size_y = size(y_unique,2);
-y_dim_temp = size_y*y_space;
-
-%get the maximum nodes in any layer
-size_x = zeros(1,size_y);
-for i = 1:size_y,
-    for j = 1:nnodes,
-        if y_unique(i) == y(j),
-            size_x(1,i) = size_x(1,i) + 1;
-        end;
-    end;
-end;
-size_x = max(size_x);
-x_dim_temp = size_x*x_space;
-
-if x_dim_temp > x_dim,
-    x_dim = x_dim_temp;
-end;
-
-if y_dim_temp > y_dim,
-    y_dim = y_dim_temp;
-end;
-end
-
-function [x_vals,y_vals] = calcGaussian(mu,Sigma,maxval,minval)
-%Function to calculate 101 points of Gaussian function to use in plotting
-% Gets the probability density of the mean value and 50 evenly spaced
-% points up to 3Sigma below the mean and 50 evenly space points up to
-% 3Sigma above the mean.
-%maxval
-%minval
-x_vals = zeros(101,1);
-y_vals = zeros(101,1);
-
-%x_vals(1,1) = mu - 3*Sigma;
-x_vals(1,1) = minval - 1;
-gap=((maxval+1)-(minval - 1))/100;
-%x_vals(1,1) = 0;%mu - 3*Sigma;
-for i = 1:100,
-   % x_vals(i+1,1) = x_vals(1,1) + i*6*Sigma/100;
-    x_vals(i+1,1) = x_vals(i,1) + gap;
- %x_vals(i+1,1) = x_vals(i,1) + 1/100;
-end
-
-for i = 1:101,
-    y_vals(i,1) = normpdf(x_vals(i,1),mu,Sigma);
-end
-
-end
diff --git a/sourcecodes/parameter_learning/code_backup/drawFigure.m~ b/sourcecodes/parameter_learning/code_backup/drawFigure.m~
deleted file mode 100644
index 404a65f7..00000000
--- a/sourcecodes/parameter_learning/code_backup/drawFigure.m~
+++ /dev/null
@@ -1,388 +0,0 @@
-function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata)
-%drawFigure writes the parameters and data that are needed to draw the
-%structure of a Bayesian network.
-
-
-if nargin < 8,
-    drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means);
-else
-    drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata);
-end;
-
-end
-
-
-
-function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata)
-%Function to use if there is no entered evidence. 
-%         
-%
-%Before each printed line, I will have a line that starts with %%%
-% that describes what will be on that line
-
-%Create an empty evidence cell array.
-
-%val=cases;
-%for i = 1:nnodes
-% val(i,1)=val(i,2);
-
-%end
-
-A=cell2mat(cases');
-Amax=max(A);
-Amin=min(A);
-
-
-evidence = cell(1,nnodes);
-engine = jtree_inf_engine(bnet);
-
-evidence{selectvar}=selectdata;
-
-[engine,loglik]=enter_evidence(engine,evidence);
-
-%Open the file, and write the nodes to a file.
-fileID = fopen(filename,'w');
-
-%%%%Evidence node
-fprintf(fileID,'%i\n',selectvar);
-%%% The number of nodes
-fprintf(fileID,'%i\n',nnodes);
-%Get canvas size
-labels_temp = cellstr(labels);
-[x,y] = make_layout(bnet.dag);
-
-x = x - min(x);
-y = 1 - y;
-y = y - min(y);
-
-[x_dim,y_dim] = canvasSize(nnodes,x,y);
-
-%%% The dimensions of the canvas for the javascript code
-fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim)
-
-x = x*x_dim;
-y = y*y_dim;
-for i = 1:nnodes,
-%%% The name and X- and Y-positions of each node
-    fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i)));
-end
-
-%Get the number of parents and children for each node.
-num_par = zeros(1,nnodes);
-%For parents, sum down columns
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(j,i) == 1,
-            num_par(i) = num_par(i) + 1;
-        end
-    end
-end
-num_child = zeros(1,nnodes);
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(i,j) == 1,
-            num_child(i) = num_child(i) + 1;
-        end
-    end
-end
-
-
-for i = 1:nnodes,
-    %%% The name and type of each node (1=continuous, the number of states
-    %%% if it is discrete
-    fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i));
-    %%% The size of the node, I am going to keep them 
-    %%% 250(width) by 150(height) for now
-    %Could modify this to change the width based on the length of the node
-    %name
-    fprintf(fileID,'%i\t%i\n',250,150);
-    %%% The number of parents of the node, and the parents
-    if num_par(i) == 0;
-        %%% If no parents:
-        fprintf(fileID,'%i\n',num_par(i));
-    else
-        parents = zeros(1,num_par(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(j,i) == 1,
-             parents(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_par(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_par(i),parents(1,:));
-    end
-    
-    
-    %%% The number of children of the node, and the children
-    if num_child(i) == 0;
-        %%% If no children:
-        fprintf(fileID,'%i\n',num_child(i));
-    else
-        children = zeros(1,num_child(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(i,j) == 1,
-             children(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_child(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_child(i),children(1,:));
-    end
-    
-    predict = marginal_nodes(engine,i);
-    if isempty(evidence{i})
-      if bnet.node_sizes(i) ~= 1,
-        for j = 1:bnet.node_sizes(i),
-            %%%For discrete nodes, the state and the percent of that state
-            fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j));
-        end;
-      else
-
-        [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i));
-        %%%For continuous nodes, print x and the pdf of a normal curve.
-        for j = 1:101,
-            %%Undo standardization
-            xvals(j,1) = xvals(j,1)*stdevs{i}+means{i}
-            fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1));
-        end;
-      end;
-    else
-      fprintf(fileID,'%6.4f\t%6.4f\n',selectdata,1);
-    end
-    
-end
-%fprintf(fileID,'%s\t %\n',labels_temp{:});
-
-
-fclose(fileID);
-
-end
-
-
-
-
-
-
-function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means)
-%Function to use if there is no entered evidence. 
-%         
-%
-%Before each printed line, I will have a line that starts with %%%
-% that describes what will be on that line
-A=cell2mat(cases');
-Amax=max(A);
-Amin=min(A);
-
-%Create an empty evidence cell array.
-evidence = cell(1,nnodes);
-engine = jtree_inf_engine(bnet);
-[engine,loglik] = enter_evidence(engine,evidence);
-
-%Open the file, and write the nodes to a file.
-fileID = fopen(filename,'w');
-%%% The number of nodes
-fprintf(fileID,'%i\n',nnodes);
-
-%Get canvas size
-
-labels_temp = cellstr(labels);
-[x,y] = make_layout(bnet.dag);
-%[x,y] = layout_dag(bnet.dag);
-
-
-x = x - min(x);
-y = 1 - y;
-y = y - min(y);
-
-[x_dim,y_dim] = canvasSize(nnodes,x,y);
-
-%%% The dimensions of the canvas for the javascript code
-fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim)
-
-x = x*x_dim;
-y = y*y_dim;
-for i = 1:nnodes,
-%%% The name and X- and Y-positions of each node
-    fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i)));
-end
-
-%Get the number of parents and children for each node.
-num_par = zeros(1,nnodes);
-%For parents, sum down columns
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(j,i) == 1,
-            num_par(i) = num_par(i) + 1;
-        end
-    end
-end
-num_child = zeros(1,nnodes);
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(i,j) == 1,
-            num_child(i) = num_child(i) + 1;
-        end
-    end
-end
-
-
-for i = 1:nnodes,
-    %%% The name and type of each node (1=continuous, the number of states
-    %%% if it is discrete
-    fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i));
-    %%% The size of the node, I am going to keep them 
-    %%% 250(width) by 150(height) for now
-    %Could modify this to change the width based on the length of the node
-    %name
-    fprintf(fileID,'%i\t%i\n',250,150);
-    %%% The number of parents of the node, and the parents
-    if num_par(i) == 0;
-        %%% If no parents:
-        fprintf(fileID,'%i\n',num_par(i));
-    else
-        parents = zeros(1,num_par(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(j,i) == 1,
-             parents(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_par(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_par(i),parents(1,:));
-    end
-    
-    
-    %%% The number of children of the node, and the children
-    if num_child(i) == 0;
-        %%% If no children:
-        fprintf(fileID,'%i\n',num_child(i));
-    else
-        children = zeros(1,num_child(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(i,j) == 1,
-             children(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_child(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_child(i),children(1,:));
-    end
-    
-    predict = marginal_nodes(engine,i);
-    if bnet.node_sizes(i) ~= 1,
-        for j = 1:bnet.node_sizes(i),
-            %%%For discrete nodes, the state and the percent of that state
-            fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j));
-        end;
-    else
-        %cases(i)
-       % MAX(cases(i))
-       % MIN(cases(i))
-        [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i));
-        %%%For continuous nodes, print x and the pdf of a normal curve.
-        for j = 1:101,
-            %%Undo standardization
-            x_vals(j,1) = x_vals(j,1)*stdevs{i}+means{i};
-            fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1));
-        end;
-    end;
-end
-%fprintf(fileID,'%s\t %\n',labels_temp{:});
-
-
-fclose(fileID);
-
-end
-
-
-function [x_dim, y_dim] = canvasSize(nnodes,x,y)
-%canvasSize Function to calculate the size of the canvas to
-%           build the network structure
-
-
-%I am going to assume that the node size will be
-% height = 150, width = 250
-% so there will be a node spacing of 
-% 200 (in y-dim) and 300 (in x-dim).
-y_space = 200;
-x_space = 300;
-
-%Set default minimum x and y dimensions
-x_dim = 1200;
-y_dim = 1200;
-
-%get unique y values
-y_unique = unique(y);
-size_y = size(y_unique,2);
-y_dim_temp = size_y*y_space;
-
-%get the maximum nodes in any layer
-size_x = zeros(1,size_y);
-for i = 1:size_y,
-    for j = 1:nnodes,
-        if y_unique(i) == y(j),
-            size_x(1,i) = size_x(1,i) + 1;
-        end;
-    end;
-end;
-size_x = max(size_x);
-x_dim_temp = size_x*x_space;
-
-if x_dim_temp > x_dim,
-    x_dim = x_dim_temp;
-end;
-
-if y_dim_temp > y_dim,
-    y_dim = y_dim_temp;
-end;
-end
-
-function [x_vals,y_vals] = calcGaussian(mu,Sigma,maxval,minval)
-%Function to calculate 101 points of Gaussian function to use in plotting
-% Gets the probability density of the mean value and 50 evenly spaced
-% points up to 3Sigma below the mean and 50 evenly space points up to
-% 3Sigma above the mean.
-%maxval
-%minval
-x_vals = zeros(101,1);
-y_vals = zeros(101,1);
-
-%x_vals(1,1) = mu - 3*Sigma;
-x_vals(1,1) = minval - 1;
-gap=((maxval+1)-(minval - 1))/100;
-%x_vals(1,1) = 0;%mu - 3*Sigma;
-for i = 1:100,
-   % x_vals(i+1,1) = x_vals(1,1) + i*6*Sigma/100;
-    x_vals(i+1,1) = x_vals(i,1) + gap;
- %x_vals(i+1,1) = x_vals(i,1) + 1/100;
-end
-
-for i = 1:101,
-    y_vals(i,1) = normpdf(x_vals(i,1),mu,Sigma);
-end
-
-end
diff --git a/sourcecodes/parameter_learning/code_backup/drawFigureM.m b/sourcecodes/parameter_learning/code_backup/drawFigureM.m
deleted file mode 100644
index 91b8698f..00000000
--- a/sourcecodes/parameter_learning/code_backup/drawFigureM.m
+++ /dev/null
@@ -1,230 +0,0 @@
-function [] = drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata)
-%drawFigureM writes the parameters and data that are needed to draw the
-%structure of a Bayesian network after added evidence or intervention
-
-fileID = fopen(filename,'w');
-
-
-A=cell2mat(cases');
-Amax=max(A);
-Amin=min(A);
-
-
-evidence = cell(1,nnodes);
-engine = jtree_inf_engine(bnet);
-
-m = size(selectvar,1);
-
-ev_dat = zeros(1,nnodes);
-for i = 1:m,
-    di=selectvar(i,1);
-    ev_dat(di)=selectdata(i,1);   
-%Need to standardized evidence for continuous nodes.
-    if bnet.node_sizes(di) == 1
-        ev_dat(di) = (ev_dat(di) - means{di}) / stdevs{di};
-    end
-    evidence{di}=ev_dat(di);
-    fprintf(fileID,'%i\t',di);
-end
-
-fprintf(fileID,'\n');
-
-[engine,loglik]=enter_evidence(engine,evidence);
-
-%Open the file, and write the nodes to a file.
-%%% The number of nodes
-fprintf(fileID,'%i\n',nnodes);
-%Get canvas size
-labels_temp = cellstr(labels);
-[x,y] = make_layout(bnet.dag);
-x = x - min(x);
-y = 1 - y;
-y = y - min(y);
-[x_dim,y_dim] = canvasSize(nnodes,x,y);
-
-%%% The dimensions of the canvas for the javascript code
-fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim);
-x = x*x_dim;
-y = y*y_dim;
-for i = 1:nnodes,
-%%% The name and X- and Y-positions of each node
-    fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i)));
-end
-
-%Get the number of parents and children for each node.
-num_par = zeros(1,nnodes);
-%For parents, sum down columns
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(j,i) == 1,
-            num_par(i) = num_par(i) + 1;
-        end
-    end
-end
-num_child = zeros(1,nnodes);
-for i = 1:nnodes,
-    for j = 1:nnodes,
-        if bnet.dag(i,j) == 1,
-            num_child(i) = num_child(i) + 1;
-        end
-    end
-end
-
-for i = 1:nnodes,
-    %%% The name and type of each node (1=continuous, the number of states
-    %%% if it is discrete
-    fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i));
-    %%% The size of the node, I am going to keep them 
-    %%% 250(width) by 150(height) for now
-    %Could modify this to change the width based on the length of the node
-    %name
-    fprintf(fileID,'%i\t%i\n',250,150);
-    %%% The number of parents of the node, and the parents
-    if num_par(i) == 0;
-        %%% If no parents:
-        fprintf(fileID,'%i\n',num_par(i));
-    else
-        parents = zeros(1,num_par(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(j,i) == 1,
-             parents(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_par(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_par(i),parents(1,:));
-    end
-    
-    
-    %%% The number of children of the node, and the children
-    if num_child(i) == 0;
-        %%% If no children:
-        fprintf(fileID,'%i\n',num_child(i));
-    else
-        children = zeros(1,num_child(i));
-        k = 1;
-        for j = 1:nnodes,
-           if bnet.dag(i,j) == 1,
-             children(1,k) = j;
-             k = k + 1;
-           end
-        end
-        format = '%i\t';
-        for j = 1:num_child(i)-1,
-            format = strcat(format,'%i\t');
-        end
-        format = strcat(format,'%i\n');
-        %%%If there are parents:
-        fprintf(fileID,format,num_child(i),children(1,:));
-    end
-    
-    predict = marginal_nodes(engine,i);
-    if isempty(evidence{i})
-      if bnet.node_sizes(i) ~= 1,
-        for j = 1:bnet.node_sizes(i),
-            %%%For discrete nodes, the state and the percent of that state
-            fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j));
-        end;
-      else
-        [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i));
-        %%%For continuous nodes, print x and the pdf of a normal curve.
-        for j = 1:101,
-            %%Undo standardization
-            x_vals(j,1) = x_vals(j,1)*stdevs{i}+means{i};
-            fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1));
-        end;
-      end;
-    else
-      if bnet.node_sizes(i) == 1,
-	fprintf(fileID,'%6.4f\t%6.4f\n',ev_dat(i)*stdevs{i}+means{i},1);
-      else
-	fprintf(fileID,'%6.4f\t%6.4f\n',ev_dat(i),1);
-      endif 
-    end
-    
-end
-
-fclose(fileID);
-end
-
-
-
-
-
-
-
-
-
-function [x_dim, y_dim] = canvasSize(nnodes,x,y)
-%canvasSize Function to calculate the size of the canvas to
-%           build the network structure
-
-
-%I am going to assume that the node size will be
-% height = 150, width = 250
-% so there will be a node spacing of 
-% 200 (in y-dim) and 300 (in x-dim).
-y_space = 200;
-x_space = 300;
-
-%Set default minimum x and y dimensions
-x_dim = 1200;
-y_dim = 1200;
-
-%get unique y values
-y_unique = unique(y);
-size_y = size(y_unique,2);
-y_dim_temp = size_y*y_space;
-
-%get the maximum nodes in any layer
-size_x = zeros(1,size_y);
-for i = 1:size_y,
-    for j = 1:nnodes,
-        if y_unique(i) == y(j),
-            size_x(1,i) = size_x(1,i) + 1;
-        end;
-    end;
-end;
-size_x = max(size_x);
-x_dim_temp = size_x*x_space;
-
-if x_dim_temp > x_dim,
-    x_dim = x_dim_temp;
-end;
-
-if y_dim_temp > y_dim,
-    y_dim = y_dim_temp;
-end;
-end
-
-function [x_vals,y_vals] = calcGaussian(mu,Sigma,maxval,minval)
-%Function to calculate 101 points of Gaussian function to use in plotting
-% Gets the probability density of the mean value and 50 evenly spaced
-% points up to 3Sigma below the mean and 50 evenly space points up to
-% 3Sigma above the mean.
-%maxval
-%minval
-x_vals = zeros(101,1);
-y_vals = zeros(101,1);
-
-%x_vals(1,1) = mu - 3*Sigma;
-x_vals(1,1) = minval - 1;
-gap=((maxval+1)-(minval - 1))/100;
-%x_vals(1,1) = 0;%mu - 3*Sigma;
-for i = 1:100,
-   % x_vals(i+1,1) = x_vals(1,1) + i*6*Sigma/100;
-    x_vals(i+1,1) = x_vals(i,1) + gap;
- %x_vals(i+1,1) = x_vals(i,1) + 1/100;
-end
-
-for i = 1:101,
-    y_vals(i,1) = normpdf(x_vals(i,1),mu,Sigma);
-end
-
-end
diff --git a/sourcecodes/parameter_learning/code_backup/getParams.m b/sourcecodes/parameter_learning/code_backup/getParams.m
deleted file mode 100644
index 31f84ffb..00000000
--- a/sourcecodes/parameter_learning/code_backup/getParams.m
+++ /dev/null
@@ -1,22 +0,0 @@
-function [ bnet ] = getParams( bnet, cases )
-%getParams Code to initialize CPT and do parameter learning.
-%This will be very basic for now.  I can add more options later.
-
-dnodes = bnet.dnodes;
-cnodes = bnet.cnodes;
-nnodes = size(dnodes,2)+size(cnodes,2);
-
-%make dnodes tabular_CPT
-for i = 1:size(dnodes,2)
-    bnet.CPD{dnodes(i)} = tabular_CPD(bnet,dnodes(i));
-end
-
-for i = 1:size(cnodes,2)
-    bnet.CPD{cnodes(i)} = gaussian_CPD(bnet,cnodes(i));
-end
-
-bnet = learn_params(bnet,cases);
-
-
-end
-
diff --git a/sourcecodes/parameter_learning/code_backup/parameterLearning.m b/sourcecodes/parameter_learning/code_backup/parameterLearning.m
deleted file mode 100644
index 872e94b1..00000000
--- a/sourcecodes/parameter_learning/code_backup/parameterLearning.m
+++ /dev/null
@@ -1,17 +0,0 @@
-function [ bnet ] = parameterLearning( bnet,cases,engine_name )
-%parameterLearning Do parameter learning and inference
-
-%engine is an optional argument
-if nargin < 3
-    engine_name = 'jtree_inf_engine';
-end
-
-
-%First do parameter learning with all the data
-[bnet] = getParams(bnet,cases);
-
-
-
-
-end
-
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
diff --git a/sourcecodes/parameter_learning/code_backup/prepareInput.m~ b/sourcecodes/parameter_learning/code_backup/prepareInput.m~
deleted file mode 100644
index 9fc0f97f..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} = "This was determined to be discrete because there are few (<4) different 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} = "This was determined to be continuous because there are a large number of different 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} = "This 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} = "This variable was determined to be discrete.";
-       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
diff --git a/sourcecodes/parameter_learning/code_backup/readInput.m b/sourcecodes/parameter_learning/code_backup/readInput.m
deleted file mode 100644
index 891d7f36..00000000
--- a/sourcecodes/parameter_learning/code_backup/readInput.m
+++ /dev/null
@@ -1,63 +0,0 @@
-function [ labels, cases, bnet, node_sizes, data,labelsold] = readInput( dfile, sfile, nnodes, std_flag )
-    %readInput is to be used when reading in a network with a known structure
-    %   
-    %Input:
-	%   dfile  = name of the file containing the data (required)
-    %   sfile = name of the file containing the structure (required)
-    %   nnodes = number of nodes in the network (required)
-    %   std_flag = flag for whether or not to standardize the data.
-    %   (optional-- Default is FALSE)
-    %
-    %   See readInputData.m and readInputStructure.m for description of the
-    %       format of the dfile and sfile, respectively. 
-    %
-    %Output:
-    %   labels = cell array with the names of the nodes.
-    %   cases = cell array with the data.
-    %   bnet = BNT bayesian network with the input structure.
-
-if nargin < 4
-    std_flag = false(1);
-end
-
-    
-% read in the file with the data
-[labelsold,node_sizes,cases, data] = readInputData(dfile,nnodes);
-
-
-% read in the file with the structure
-[dag] = readInputStructure(sfile,labelsold);
-
-
-% check the ordering of the nodes and reorder if necessary
-[labels,cases,dag,node_sizes,ord_flag] = checkStructure(labelsold,cases,dag,node_sizes);
-
-dcount = 0;
-for i = 1:nnodes
-    if node_sizes(i) ~= 1
-        dcount = dcount + 1;
-    end
-end
-discrete = zeros(1,dcount);
-dcount = 0;
-for i = 1:nnodes
-    if node_sizes(i) ~= 1
-        dcount = dcount + 1;
-        discrete(dcount) = i;
-    end
-end
-
-bnet = mk_bnet(dag,node_sizes,'discrete',discrete,'names',labels);
-
-%bnet.dag
-
-checkDiscreteNodes(bnet,cases);
-
-% standardize continuous data to have a mean = 0 and std = 1
-if (std_flag)
-    [cases] = standardizeData(labels,node_sizes,cases);
-end
-        
-
-end
-%  end of readInput.m
diff --git a/sourcecodes/parameter_learning/code_backup/readInputData.m b/sourcecodes/parameter_learning/code_backup/readInputData.m
deleted file mode 100644
index 706e2751..00000000
--- a/sourcecodes/parameter_learning/code_backup/readInputData.m
+++ /dev/null
@@ -1,75 +0,0 @@
-function  [ labels , node_sizes, cases, data] = readInputData( dfile , nnodes )
-	%  readColData  reads data from a file containing data in columns
-	%               that have text titles, and possibly other header text
-	%   
-	%  Input:
-	%     dfile  = name of the file containing the data.(required)
-	%     nnodes  = number of columns in the data file.  (required)
-    %
-    %   Function assumes the following format for the input file:
-    %       1) First line has labels for each of the nodes.  There cannot
-    %              be spaces in any node label.
-    %       2) The next line is the "node_sizes" of the nodes.  If the 
-    %           nodes are discrete, this number will be equal to the number
-    %           of states.  If the nodes are continuous, they should be 
-    %           equal to 1.  The function assumes that any nodes with
-    %           node_size = 1 is continuous.
-    %       3) The rest of the file is numeric data.  The data in the input
-    %               data has the number of columns equal to the number of 
-    %               nodes in the network and the number of rows equal to
-    %               the number of samples.
-    %
-    %
-	%  Output:
-	%     labels = cell array with node (column) labels.
-    %     node_sizes  =  vector with the size of each node
-    %     cases = cell array with the data.  The cases array is transposed
-    %       in comparison with the input data to agree with the format of
-    %       cell data used in BNT.
-
-%  open file for input, include error handling
-fin = fopen(dfile,'r');
-if fin < 0
-   error(['Could not open ',dfile,' for input']);
-end
-
-% Read in first line to get the node labels.
-labels = cell(1,nnodes);
-buffer = fgetl(fin);    %get header line as a string
-for j=1:nnodes
-    [next,buffer] = strtok(buffer);
-    labels{j} = next;
-end
-
-%  Read in the data.  Use the vetorized fscanf function to load all
-%  numerical values into one vector.  Then reshape this vector into a
-%  matrix.
-
-data = fscanf(fin,'%f');  %  Load the numerical values into one long vector
-
-
-
-
-nd = length(data);        %  total number of data points
-nr = nd/nnodes;            %  number of rows; check (next statement) to make sure
-if nr ~= round(nd/nnodes)
-   fprintf(1,'\ndata: nrow = %f\tncol = %d\n',nr,nnodes);
-   fprintf(1,'number of data points = %d does not equal nrow*ncol\n',nd);
-   error('data is not rectangular')
-end
-
-data = reshape(data,nnodes,nr)';   %  have to transpose the reshaped array
-
-
-node_sizes = zeros(1,nnodes);
-for j = 1:nnodes
-    node_sizes(j) = data(1,j);
-end
-
-nr = nr - 1;
-data(1,:) = [];
-cases = cell(nnodes,nr);
-cases(:,:) = num2cell(data');
-
-end
-%  end of readInputData.m
\ No newline at end of file
diff --git a/sourcecodes/parameter_learning/code_backup/readInputStructure.m b/sourcecodes/parameter_learning/code_backup/readInputStructure.m
deleted file mode 100644
index 6b3cbece..00000000
--- a/sourcecodes/parameter_learning/code_backup/readInputStructure.m
+++ /dev/null
@@ -1,72 +0,0 @@
-function [ dag ] = readInputStructure( sfile, labels )
-%readInputStructure Read in file with structure information
-    %   
-    %Input:
-	%     sfile  = name of the file containing the data (required)
-	%     labels = cell array with node labels. (required)
-    %     nnodes  = number of columns in the data file. (required)  
-    %
-    %   Function assumes the following format for the structure input file:
-    %       1) The first line has node labels.  These must be the same as 
-    %           in the input data file.  They cannot contain spaces.
-    %       2) The remainder of the file contains the structure of the dag.
-    %           The structure of a graph is a N-by-N matrix, where N is the
-    %           number of nodes.  There are 1's in the matrix representing
-    %           parent-child relationships.  For each 1, the row indicates
-    %           the parent and the column indicates the child.  For
-    %           example, a 1 in the (2,3) position of the matrix indicates
-    %           that there is an arc pointing from node 2 to node 3.
-    %        
-    %
-	%  Output:
-    %     dag = matrix with the structure.
-%
-%   Read in first line of the structure file
-%  open file for input, include error handling
-fin = fopen(sfile,'r');
-if fin < 0
-   error(['Could not open ',sfile,' for input']);
-end
-
-nnodes = size(labels,2);
-% Read in first line to get the node labels.
-labels_test = cell(1,nnodes);
-buffer = fgetl(fin);    %get header line as a string
-for j=1:nnodes
-    [next,buffer] = strtok(buffer);
-    labels_test{j} = next;  
-end
-    
-for j=1:nnodes
-    if labels_test{j} ~= labels{j}
-        fprintf(['Label of node ',j,' is not consistent in input and structure files'])
-    end
-end
-
-data = fscanf(fin,'%f');
-  
-nd = length(data);        %  total number of data points
-nr = nd/nnodes;            %  number of rows; check (next statement) to make sure
-if nr ~= round(nd/nnodes)
-   fprintf(1,'\ndata: nrow = %f\tncol = %d\n',nr,nnodes);
-   fprintf(1,'number of data points = %d does not equal nrow*ncol\n',nd);
-   error('Structure file does not have the correct dimensions (1)')
-end
-% check to make sure that structure is square
-if nr ~= nnodes
-    error('Structure file does not have the correct dimensions (2)')
-end
-
-data = reshape(data,nnodes,nr)';   %  have to transpose the reshaped array
-
-
-dag = zeros(nnodes,nnodes);
-for i = 1:size(data,1)
-    for j = 1:size(data,2)
-        dag(i,j) = data(i,j);
-    end
-end
-
-
-end
-%  end of readInputStructure.m
diff --git a/sourcecodes/parameter_learning/code_backup/runBN_initial.m b/sourcecodes/parameter_learning/code_backup/runBN_initial.m
deleted file mode 100644
index 0deff1b5..00000000
--- a/sourcecodes/parameter_learning/code_backup/runBN_initial.m
+++ /dev/null
@@ -1,57 +0,0 @@
-function runBN_initial(pre)
-sfile=strcat(pre,'structure_input.txt');
-dfile=strcat(pre,'continuous_input.txt');
-
-nnodefile=strcat(pre,'nnode.txt');
-fnnode = fopen(nnodefile,'r');
-nnodes = fscanf(fnnode,'%d');
-
-
-mapfilename=strcat(pre,'mapdata.txt');
-mapvalfilename=strcat(pre,'map.txt');
-
-mapfile = fopen(mapfilename,'w');
-
-mapval = fopen(mapvalfilename,'w');
-
-
-Std_flag=true;
-[labels,cases,bnet,node_sizes,data,labelsold]=readInput(dfile,sfile,nnodes,Std_flag);
-s=std(data,0,1);
-m=mean(data);
-
-for i=1:nnodes
-  fprintf(mapval,'%s\t%f\t%f\n',labelsold{i},s(i),m(i));
-end
-
-fprintf(mapfile,'%s',labels{1});
-for i=2:nnodes
-  fprintf(mapfile,'\t%s',labels{i});
-end
-fprintf(mapfile,'\n');
-fclose(mapval);
-fclose(mapfile);
-
-%Need to rearrange the means and stdevs to match the new labeling.
-means = cell(1,nnodes);
-stdevs = cell(1,nnodes);
-for i = 1:nnodes
-    for j = 1:nnodes
-       if strcmp(labels{i},labelsold{j})
-          means{i} = m(j);
-          stdevs{i} = s(j);
-          break
-       end
-    end
-end
-
-
-[bnet]=parameterLearning(bnet,cases);
-
-filename=strcat(pre,'net_figure.txt');
-
-drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means);
-
-writeParameters(pre,nnodes,bnet,labels,cases,labelsold,s,m);
-
-end
diff --git a/sourcecodes/parameter_learning/code_backup/standardizeData.m b/sourcecodes/parameter_learning/code_backup/standardizeData.m
deleted file mode 100644
index db5e04c7..00000000
--- a/sourcecodes/parameter_learning/code_backup/standardizeData.m
+++ /dev/null
@@ -1,25 +0,0 @@
-function [ cases ] = standardizeData( labels, node_sizes, cases )
-%standardizeData standardizes continuous nodes so they have a mean = 0
-%   and standard deviation = 1
-
-
-nnodes = size(labels,2);
-
-%fprintf(['Standardizing data for continuous nodes\n'])
-for i = 1:nnodes
-    if node_sizes(i) == 1
-        temp = cell2num(cases(i,:));
-        [temp] = standardize(temp);
-        cases(i,:) = num2cell(temp);
-    end
-end
-
-%write standardized data to file
-%fprintf(['Standardized data is written to file standardized_data.txt\n'])
-%fout = 'standardized_data.txt';
-%txt = sprintf([repmat('%s\t',1,size(labels,2))],labels{:});
-%dlmwrite(fout,txt,'');
-%dlmwrite(fout,cell2num(cases'),'-append','delimiter','\t');
-
-end
-
diff --git a/sourcecodes/parameter_learning/code_backup/writeParameters.m b/sourcecodes/parameter_learning/code_backup/writeParameters.m
deleted file mode 100644
index 0790a8e2..00000000
--- a/sourcecodes/parameter_learning/code_backup/writeParameters.m
+++ /dev/null
@@ -1,106 +0,0 @@
-function [] = writeParameters(pre,nnodes,bnet,labels,cases,labelsold,s,m)
-%Writes a file that contains the parameters of the network with no evidence.
-
-
-%%Get the types of the nodes.
-typefile = strcat(pre,'type.txt');
-ftype = fopen(typefile,'r');
-types = cell(1,nnodes);
-buffer = fgetl(ftype);
-buffer = fgetl(ftype);
-for j = 1:nnodes
-    [next,buffer] = strtok(buffer);
-    types{j} = uint16(str2num(next));
-end
-
-max_states = 0;
-disc_nodes = 0;
-for j = 1:nnodes
-  if types{j} > max_states
-    max_states = types{j};
-  end
-  if types{j} > 1
-    disc_nodes = disc_nodes + 1;
-  end
-end
-
-%Add 1 to max_states to account for node name
-max_states = max_states + 1;
-
-%%Get mapping of discrete levels.
-levelfile = strcat(pre,'nlevels.txt');
-flevels = fopen(levelfile,'r');
-levels = cell(disc_nodes,max_states);
-ndisc_nodes = 0;
-for i=1:disc_nodes
-    ndisc_nodes = ndisc_nodes + 1;
-    buffer = fgetl(flevels);
-     for j = 1:max_states
-       [next,buffer] = strtok(buffer);
-       if j == 1
-          levels{i,j} = next;
-       else
-%          levels{i,j} = uint16(str2num(next));
-          levels{i,j} = next;
-       end        
-       if length(buffer) < 1
-        break
-       end
-     end
-end
-
-
-evidence = cell(1,nnodes);
-engine = jtree_inf_engine(bnet);
-[engine,loglik] = enter_evidence(engine,evidence);
-
-%Open output file.
-filename = strcat(pre,'parameters.txt');
-fileID = fopen(filename,'w');
-
-for i = 1:nnodes
-    for j = 1:nnodes
-	if strcmp(labelsold{i},labels{j});
-            nodeid = j;
-            break
-        end
-    end
-    predict = marginal_nodes(engine,nodeid);
-    %%%Print the name of the node
-    fprintf(fileID,'%s\n',labels{nodeid});
-    %%%Print the type of node
-    if bnet.node_sizes(nodeid) == 1;
-        line = 'Continuous node\n';
-        fprintf(fileID,line);
-        %%% 'i' in the line below is correct: m and s are had original node labeling
-        adj_mu = predict.mu*s(i)+m(i);
-        adj_sigma = s(i)*predict.Sigma;
-	fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma);
-    else
-        line = 'Discrete node with %i states\n';
-        fprintf(fileID,line,bnet.node_sizes(nodeid));
-        %line = 'Probability of each state\n';
-        %fprintf(fileID,line);
-        nodeid2 = 0;
-        for k = 1:ndisc_nodes,
-           if strcmp(levels{k,1},labels{nodeid}),
-	      nodeid2 = k;
-              break
-           end
-        end
-        for j = 1:bnet.node_sizes(nodeid),
-            %%%For discrete nodes, the state and the percent of that state
-%		  fprintf(fileID,'%i\t%6.4f\n',levels{nodeid2,j+1},predict.T(j));
-		  fprintf(fileID,'%s\t%6.4f\n',levels{nodeid2,j+1},predict.T(j));
-        end;
-        fprintf(fileID,'\n')
-
-    end
-end
-
-
-
-fclose(fileID);
-
-end
-
diff --git a/sourcecodes/parameter_learning/code_backup/writeParameters_ev.m b/sourcecodes/parameter_learning/code_backup/writeParameters_ev.m
deleted file mode 100644
index fc24e2e5..00000000
--- a/sourcecodes/parameter_learning/code_backup/writeParameters_ev.m
+++ /dev/null
@@ -1,151 +0,0 @@
-function [] = writeParameters_ev(pre,bnet,nnodes,labels,cases,stdevs,means,selectvar,selectdata)
-%Writes a file that contains the parameters of the network after entering evidence.
-
-%Read in original node labels to get node IDs.
-infile = strcat(pre,'continuous_input.txt');
-fin = fopen(infile,'r');
-labelsold = cell(1,nnodes);
-buffer = fgetl(fin);
-for j = 1:nnodes
-    [next,buffer] = strtok(buffer);
-    labelsold{j} = next;
-end
-fclose(fin);
-
-
-evidence = cell(1,nnodes);
-engine = jtree_inf_engine(bnet);
-
-m = size(selectvar,1);
-
-%%Get the types of the nodes.
-typefile = strcat(pre,'type.txt');
-ftype = fopen(typefile,'r');
-types = cell(1,nnodes);
-buffer = fgetl(ftype);
-buffer = fgetl(ftype);
-for j = 1:nnodes
-   [next,buffer] = strtok(buffer);
-   types{j} = uint16(str2num(next));
-end
-
-max_states = 0;
-disc_nodes = 0;
-for j = 1:nnodes
-  if types{j} > max_states
-       max_states = types{j};
-  end
-  if types{j} > 1
-    disc_nodes = disc_nodes + 1;
-  end
-end
-
-%Add 1 to max_states to account for node name
-max_states = max_states + 1;
-
-%%Get mapping of discrete levels.
-levelfile = strcat(pre,'nlevels.txt');
-flevels = fopen(levelfile,'r');
-levels = cell(disc_nodes,max_states);
-ndisc_nodes = 0;
-for i=1:disc_nodes
-	ndisc_nodes = ndisc_nodes + 1;
-buffer = fgetl(flevels);
-for j = 1:max_states
-	  [next,buffer] = strtok(buffer);
-       if j == 1
-	 levels{i,j} = next;
-       else
-%	 levels{i,j} = uint16(str2num(next));
-	 levels{i,j} = next;
-       end
-       if length(buffer) < 1
-        break
-       end
-     end
-end
-
-
-ev_dat = zeros(1,nnodes);
-for i = 1:m,
-    di=selectvar(i,1);
-    ev_dat(di)=selectdata(i,1);
-%Need to standardize evidence for continuous nodes.
-    if bnet.node_sizes(di) == 1,
-        ev_dat(di) = (ev_dat(di) - means{di})/stdevs{di};
-    end
-    evidence{di} = ev_dat(di);
-end
-
-[engine,loglik]=enter_evidence(engine,evidence);
-
-%Open output file.
-filename = strcat(pre,'parameters_ev.txt');
-fileID = fopen(filename,'w');
-
-for i = 1:nnodes
-    for j = 1:nnodes
-	if strcmp(labelsold{i},labels{j});
-            nodeid = j;
-            break
-        end
-    end
-    %%%Print the name of the node
-    fprintf(fileID,'%s\n',labels{nodeid});
-    predict = marginal_nodes(engine,nodeid);
-    if isempty(evidence{nodeid})
-       %%%Print the type of node
-       if bnet.node_sizes(nodeid) == 1;
-           line = 'Continuous parameters considering evidence:\n';
-           fprintf(fileID,line);
-           %line = 'Mean and standard deviation of Gaussian distribution\n';
-           %fprintf(fileID,line);
-	     adj_mu = predict.mu*stdevs{nodeid}+means{nodeid};
-             adj_sigma = stdevs{nodeid}*predict.Sigma;
-             fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma);
-       else
-           line = 'Probability of states considering evidence:\n';
-           fprintf(fileID,line);
-           nodeid2 = 0;
-           for k = 1:ndisc_nodes,
-	     if strcmp(levels{k,1},labels{nodeid}),
-                nodeid2 = k;
-                break
-             end
-            end
-	    for j = 1:bnet.node_sizes(nodeid),
-		%%%For discrete nodes, the state and the percent of that state
-%		fprintf(fileID,'%i\t%6.4f\n',levels{nodeid2,j+1},predict.T(j));
-		fprintf(fileID,'%s\t%6.4f\n',levels{nodeid2,j+1},predict.T(j));
-           end;
-           fprintf(fileID,'\n')
-       end
-   else
-       if bnet.node_sizes(nodeid) == 1;
-          line = 'Evidence was observed for this node. The observed value was:\n';
-          fprintf(fileID,line);  
-          adj_mu = ev_dat(nodeid)*stdevs{nodeid}+means{nodeid};
-          fprintf(fileID,'%6.4f\n\n',adj_mu);
-       else
-          nodeid2 = 0;
-          for k = 1:ndisc_nodes,
-	    if strcmp(levels{k,1},labels{nodeid}),
-               nodeid2 = k;
-               break
-            end
-          end
-	 line = 'Evidence was observed for this node. The observed state was:\n';
-         fprintf(fileID,line);
-         state_ev =   uint16(ev_dat(nodeid));
-%         fprintf(fileID,'%i\n\n',levels{nodeid2,state_ev+1});
-         fprintf(fileID,'%s\n\n',levels{nodeid2,state_ev+1});
-       end
-   end
-end
-
-
-
-fclose(fileID);
-
-end
-
diff --git a/sourcecodes/parameter_learning/code_backup/writeParameters_int.m b/sourcecodes/parameter_learning/code_backup/writeParameters_int.m
deleted file mode 100644
index ed92d593..00000000
--- a/sourcecodes/parameter_learning/code_backup/writeParameters_int.m
+++ /dev/null
@@ -1,186 +0,0 @@
-function [] = writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,selectvar,selectdata)
-%Writes a file that contains the parameters of the network after intervention.
-
-
-%First read input file to get node labels to get node IDs.
-infile = strcat(pre,'continuous_input.txt');
-fin = fopen(infile,'r');
-labelsold = cell(1,nnodes);
-buffer = fgetl(fin);
-for j = 1:nnodes
-    [next,buffer] = strtok(buffer);
-    labelsold{j} = next;
-end
-
-evidence = cell(1,nnodes);
-engine = jtree_inf_engine(bnet);
-
-m = size(selectvar,1);
-
-%%Get the types of the nodes.
-typefile = strcat(pre,'type.txt');
-ftype = fopen(typefile,'r');
-types = cell(1,nnodes);
-buffer = fgetl(ftype);
-buffer = fgetl(ftype);
-for j = 1:nnodes
-   [next,buffer] = strtok(buffer);
-   types{j} = uint16(str2num(next));
-end
-
-max_states = 0;
-disc_nodes = 0;
-for j = 1:nnodes
-  if types{j} > max_states
-       max_states = types{j};
-  end
-  if types{j} > 1
-    disc_nodes = disc_nodes + 1;
-  end
-end
-
-%Add 1 to max_states to account for node name
-max_states = max_states + 1;
-
-%%Get mapping of discrete levels.
-levelfile = strcat(pre,'nlevels.txt');
-flevels = fopen(levelfile,'r');
-levels = cell(disc_nodes,max_states);
-ndisc_nodes = 0;
-for i=1:disc_nodes
-	ndisc_nodes = ndisc_nodes + 1;
-buffer = fgetl(flevels);
-for j = 1:max_states
-	  [next,buffer] = strtok(buffer);
-       if j == 1
-	 levels{i,j} = next;
-       else
-%	 levels{i,j} = uint16(str2num(next));
-	 levels{i,j} = next;
-       end
-       if length(buffer) < 1
-        break
-       end
-     end
-end
-
-
-ev_dat = zeros(1,nnodes);
-for i = 1:m,
-    di=selectvar(i,1);
-    ev_dat(di)=selectdata(i,1);
-%Need to standardize evidence for continuous nodes.
-    if bnet.node_sizes(di) == 1,
-      ev_dat(di) = (ev_dat(di) - means{di})/stdevs{di};
-    end
-    evidence{di} = ev_dat(di);
-end
-
-[engine,loglik]=enter_evidence(engine,evidence);
-
-%Get list of nodes that are children, grandchildren, etc. of intervened nodes
-%int_nodes contains the list of these children nodes
-int_nodes = zeros(1,nnodes);
-%new_nodes is just a temporary array to know when to keep looking
-new_nodes = zeros(1,nnodes);
-for i = 1:nnodes
-    if !isempty(evidence{i});
-        new_nodes(i) = 1;
-        int_nodes(i) = 1;
-    end
-end
-while sum(new_nodes) != 0
-   new_nodes_old = new_nodes;
-   new_nodes = zeros(1,nnodes);
-   for i = 1:nnodes
-      if new_nodes_old(i) == 1
-           for j = 1:nnodes
-              if int_nodes(j) == 0
-	        if bnet.dag(i,j) == 1,
-		     new_nodes(j) = 1;
-                end
-              end
-           end
-       end
-   end
-   for i = 1:nnodes
-      if new_nodes(i) == 1;
-        int_nodes(i) = 1;
-      end
-   end               
-end
-
-
-%Open output file.
-filename = strcat(pre,'parameters_ev.txt');
-fileID = fopen(filename,'w');
-
-for i = 1:nnodes
-    for j = 1:nnodes
-	if strcmp(labelsold{i},labels{j});
-            nodeid = j;
-            break
-        end
-    end
-    %check to see if this is a node impacted by intervention
-    if int_nodes(nodeid) == 1
-    %%%Print the name of the node
-    fprintf(fileID,'%s\n',labels{nodeid});
-    predict = marginal_nodes(engine,nodeid);
-    if isempty(evidence{nodeid})
-       %%%Print the type of node
-       if bnet.node_sizes(nodeid) == 1;
-           line = 'Continuous parameters considering intervention:\n';
-           fprintf(fileID,line);
-           %line = 'Mean and standard deviation of Gaussian distribution\n';
-           %fprintf(fileID,line);
-	   adj_mu = predict.mu*stdevs{nodeid}+means{nodeid};
-           adj_sigma = stdevs{nodeid}*predict.Sigma;
-           fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma);
-       else
-           line = 'Probability of states considering intervention:\n';
-           fprintf(fileID,line);
-           nodeid2 = 0;
-           for k = 1:ndisc_nodes,
-	     if strcmp(levels{k,1},labels{nodeid}),
-                nodeid2 = k;
-                break
-             end
-            end
-	    for j = 1:bnet.node_sizes(nodeid),
-		%%%For discrete nodes, the state and the percent of that state
-%		fprintf(fileID,'%i\t%6.4f\n',levels{nodeid2,j+1},predict.T(j));
-		fprintf(fileID,'%s\t%6.4f\n',levels{nodeid2,j+1},predict.T(j));
-           end;
-           fprintf(fileID,'\n')
-       end
-   else
-       if bnet.node_sizes(nodeid) == 1;
-          line = 'Intervention on this node assigned the following value:\n';
-          fprintf(fileID,line);  
-          adj_mu = ev_dat(nodeid)*stdevs{nodeid}+means{nodeid};
-          fprintf(fileID,'%6.4f\n\n',adj_mu);
-       else
-          nodeid2 = 0;
-          for k = 1:ndisc_nodes,
-	    if strcmp(levels{k,1},labels{nodeid}),
-               nodeid2 = k;
-               break
-            end
-          end
-	 line = 'Intervention on this node assigned the following state:\n';
-         fprintf(fileID,line);
-         state_ev =   uint16(ev_dat(nodeid));
-%         fprintf(fileID,'%i\n\n',levels{nodeid2,state_ev+1});
-         fprintf(fileID,'%s\n\n',levels{nodeid2,state_ev+1});
-       end
-   end
-   end
-end
-
-
-
-fclose(fileID);
-
-end
-
diff --git a/sourcecodes/parameter_learning/getParams.m b/sourcecodes/parameter_learning/getParams.m
deleted file mode 100644
index 31f84ffb..00000000
--- a/sourcecodes/parameter_learning/getParams.m
+++ /dev/null
@@ -1,22 +0,0 @@
-function [ bnet ] = getParams( bnet, cases )
-%getParams Code to initialize CPT and do parameter learning.
-%This will be very basic for now.  I can add more options later.
-
-dnodes = bnet.dnodes;
-cnodes = bnet.cnodes;
-nnodes = size(dnodes,2)+size(cnodes,2);
-
-%make dnodes tabular_CPT
-for i = 1:size(dnodes,2)
-    bnet.CPD{dnodes(i)} = tabular_CPD(bnet,dnodes(i));
-end
-
-for i = 1:size(cnodes,2)
-    bnet.CPD{cnodes(i)} = gaussian_CPD(bnet,cnodes(i));
-end
-
-bnet = learn_params(bnet,cases);
-
-
-end
-
diff --git a/sourcecodes/parameter_learning/kfoldCrossValid.m b/sourcecodes/parameter_learning/kfoldCrossValid.m
index 6306c511..02f34866 100644
--- a/sourcecodes/parameter_learning/kfoldCrossValid.m
+++ b/sourcecodes/parameter_learning/kfoldCrossValid.m
@@ -149,6 +149,9 @@ for i = 1:ncases
      fprintf(fileID,'%6.4f\t%6.4f\n',kfoldPredictions(i,:));
 end
 
+fflush(fileID);
+fclose(fileID);
+
 end
 
 
@@ -283,6 +286,9 @@ for i = 1:ncases
      fprintf(fileID,'%6.4f\n',kfoldPredictions(i,end));
 end
 
+fflush(fileID);
+fclose(fileID);
+
 end
 
 
diff --git a/sourcecodes/parameter_learning/looCrossValid.m b/sourcecodes/parameter_learning/looCrossValid.m
index 67b04409..840e486e 100644
--- a/sourcecodes/parameter_learning/looCrossValid.m
+++ b/sourcecodes/parameter_learning/looCrossValid.m
@@ -126,6 +126,10 @@ for i = 1:ncases
      fprintf(fileID,'%6.4f\t%6.4f\n',loopredictions(i,:));
 end
 
+fflush(fileID);
+fclose(fileID);
+
+
 end
 
 
@@ -247,4 +251,7 @@ for i = 1:ncases
      fprintf(fileID,'%6.4f\n',loopredictions(i,end));
 end
 
+fflush(fileID);
+fclose(fileID);
+
 end
diff --git a/sourcecodes/parameter_learning/modifyEdges.m b/sourcecodes/parameter_learning/modifyEdges.m
new file mode 100644
index 00000000..2b336710
--- /dev/null
+++ b/sourcecodes/parameter_learning/modifyEdges.m
@@ -0,0 +1,133 @@
+function  [ ] = modifyEdges( pre_old, pre_new )
+   %  This function will allow users to add or delete edges from the network.
+   %
+   %  Input: 
+   %   1) pre_oldstructure_input.txt
+   %    Original structure file.
+   %   2) pre_olddel_edge.txt
+   %    A list of the edges that should be deleted.   
+   %   3) pre_oldadd_edge.txt
+   %    A list of the edges that should be added.
+   %
+   %  Output: 
+   %   pre_newstructure_input.txt-- structure file with edges added/deleted.
+   %
+
+
+%  open file for input, include error handling
+dfile=strcat(pre_old,'structure_input.txt');
+
+fin = fopen(dfile,'r');
+if fin < 0
+   error(['Could not open ',dfile,' for input']);
+end
+
+% 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"));
+labels = cell(1,nnodes);
+for j=1:nnodes
+    [next,buffer] = strtok(buffer);
+    labels{j} = next;
+end
+
+% Read in the edges
+edges = cell(nnodes,nnodes);
+for i = 1:nnodes
+    buffer = fgetl(fin);
+    for j = 1:nnodes
+         [next,buffer] = strtok(buffer);
+         edges{i,j} = next;
+    end
+end
+
+
+%  open file with edges to be deleted
+dedgefile=strcat(pre_old,'del_edge.txt');
+fin2 = fopen(dedgefile,'r');
+ndel=fskipl(fin2,Inf) - 1;
+frewind(fin2);
+
+delfrom = cell(1,ndel);
+delto = cell(1,ndel);
+
+buffer = fgetl(fin2);    %get header line
+for j=1:ndel
+  buffer = fgetl(fin2);    %get line with actual variables
+  [next,buffer] = strtok(buffer);
+  delfrom{j} = next;
+  [next,buffer] = strtok(buffer);
+  delto{j} = next;
+end
+
+%get index of edges to delete
+idel_from = [];
+idel_to = [];
+for j = 1:ndel
+  for k = 1:nnodes
+    if strcmp(labels{k},delfrom{j})
+      idel_from = [idel_from;k];
+    endif
+    if strcmp(labels{k},delto{j})
+      idel_to = [idel_to;k];
+    endif
+  end
+end
+
+for i = 1:ndel
+  edges(idel_from(i),idel_to(i)) = "0";
+end
+
+fclose(fin2);
+
+%  open file with edges to be added
+aedgefile=strcat(pre_old,'add_edge.txt');
+fin3 = fopen(aedgefile,'r');
+nadd=fskipl(fin3,Inf) - 1;
+frewind(fin3);
+
+addfrom = cell(1,nadd);
+addto = cell(1,nadd);
+
+buffer = fgetl(fin3);    %get header line
+for j=1:nadd
+  buffer = fgetl(fin3);    %get line with actual variables
+  [next,buffer] = strtok(buffer);
+  addfrom{j} = next;
+  [next,buffer] = strtok(buffer);
+  addto{j} = next;
+end
+
+%get index of edges to added
+iadd_from = [];
+iadd_to = [];
+for j = 1:nadd
+  for k = 1:nnodes
+    if strcmp(labels{k},addfrom{j})
+      iadd_from = [iadd_from;k];
+    endif
+    if strcmp(labels{k},addto{j})
+      iadd_to = [iadd_to;k];
+    endif
+  end
+end
+
+for i = 1:nadd
+  edges(iadd_from(i),iadd_to(i)) = "1";
+end
+
+
+
+
+
+outfile = strcat(pre_new,'structure_input.txt');
+fout = fopen(outfile,'w');
+fprintf(fout,'%s\t',labels{1:end-1});
+fprintf(fout,'%s\n',labels{end});
+for i = 1:nnodes
+      fprintf(fout,'%s\t',edges{i,1:end-1});
+      fprintf(fout,'%s\n',edges{i,end});
+end
+fclose(fout);
+
+end
diff --git a/sourcecodes/parameter_learning/prepareInput.m b/sourcecodes/parameter_learning/prepareInput.m
index 5268f872..3147d54e 100644
--- a/sourcecodes/parameter_learning/prepareInput.m
+++ b/sourcecodes/parameter_learning/prepareInput.m
@@ -1,4 +1,5 @@
 function  [ ] = prepareInput( pre )
+   % Jan. 2019: Modifying to allow for missing data.
    %   
    %  This function takes files that are uploaded to BNW and creates output
    %    files that can be used for structure and parameter learning.
@@ -74,6 +75,11 @@ for i = 1:ncases
     end
 end
 
+% Remove all rows that have missing data from data file
+remove_count = sum(any(strcmp(data,"NA"),2));
+data(any(strcmp(data,"NA"),2),:)=[];
+ncases = ncases - remove_count;
+
 % 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);
@@ -242,6 +248,7 @@ 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,'%i cases(rows) have been removed because they contained NA (missing data).\n',remove_count);
 fprintf(dout,'The variable names are:\n');
 fprintf(dout,'%s\t',labels{1:end-1});
 fprintf(dout,'%s\n\n',labels{end});
diff --git a/sourcecodes/parameter_learning/removeNodes.m b/sourcecodes/parameter_learning/removeNodes.m
new file mode 100644
index 00000000..8740d999
--- /dev/null
+++ b/sourcecodes/parameter_learning/removeNodes.m
@@ -0,0 +1,94 @@
+function  [ ] = removeNodes( pre_old, pre_new )
+   %  This function will allow users to delete variables from uploaded input file.
+   %      For example, if an input file contains 20 variables, but the user is
+   %      only interested in using 10 of these variables in a particular model,
+   %      they can use this function to delete the variable.
+   %
+   %
+   %  Input: 
+   %   1) pre_oldcontinuous_input_orig.txt
+   %    This is the original 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.
+   %   2) pre_olddel_var.txt
+   %    This is a list of the names of the variables that should be delete.   
+   %
+   %  Output: 
+   %   pre_newcontinuous_input_orig.txt-- input file with variables deleted.
+   %
+
+
+%  open file for input, include error handling
+dfile=strcat(pre_old,'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
+
+
+%  open file for input, include error handling
+dvarfile=strcat(pre_old,'del_var.txt');
+fin2 = fopen(dvarfile,'r');
+if fin2 < 0
+   dellabels = {};
+   ndel = 0;
+else
+  buffer = fgetl(fin2);    %get header line as a string
+  ndel = numel(strfind(buffer," ")) + 1;
+  dellabels = cell(1,ndel);
+  for j=1:ndel
+    [next,buffer] = strtok(buffer);
+    dellabels{j} = next;
+  end
+end
+
+%get index of variables to delete
+delindex = [];
+for j=1:nnodes
+  for k = 1:ndel
+    if strcmp(labels{j},dellabels{k})
+      delindex = [delindex;j];
+    endif
+  end
+end
+
+labels(:,[delindex])=[];
+data(:,[delindex])=[];
+
+outfile = strcat(pre_new,'continuous_input_orig.txt');
+fout = fopen(outfile,'w');
+fprintf(fout,'%s\t',labels{1:end-1});
+fprintf(fout,'%s\n',labels{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
diff --git a/sourcecodes/parameter_learning/testSetPredictions.m b/sourcecodes/parameter_learning/testSetPredictions.m
index 0fff197d..23a50868 100644
--- a/sourcecodes/parameter_learning/testSetPredictions.m
+++ b/sourcecodes/parameter_learning/testSetPredictions.m
@@ -273,6 +273,8 @@ for i = 1:ntestcases
      fprintf(fileID,'%6.4f\t%6.4f\n',predictions(i,:));
 end
 
+fflush(fileID);
+fclose(fileID);
 
 end
 
@@ -353,7 +355,8 @@ for i = 1:ntestcases
      fprintf(fileID,'%6.4f\n',predictions(i,end));
 end
 
-
+fflush(fileID);
+fclose(fileID);