GENENET8 update

39 files changed, 784 insertions, 3098 deletions

diff --git a/sourcecodes/parameter_learning/Predictmultiple.m b/sourcecodes/parameter_learning/Predictmultiple.m
index a7236e35..2abd1df9 100644
--- a/sourcecodes/parameter_learning/Predictmultiple.m
+++ b/sourcecodes/parameter_learning/Predictmultiple.m
@@ -79,7 +79,7 @@ end
 
 filename=strcat(pre,'net_figure_new.txt');
 
-drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new);
+drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new,pre);
 
 writeParameters_ev(pre,bnet,nnodes,labels,cases,stdevs,means,select_var_new,select_var_data_new);
 
diff --git a/sourcecodes/parameter_learning/Predictmultipleintervention.m b/sourcecodes/parameter_learning/Predictmultipleintervention.m
index 7e6140a9..b4ad58b8 100644
--- a/sourcecodes/parameter_learning/Predictmultipleintervention.m
+++ b/sourcecodes/parameter_learning/Predictmultipleintervention.m
@@ -100,7 +100,7 @@ end
 
 filename=strcat(pre,'net_figure_new.txt');
 
-drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new);
+drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new,pre);
 
 writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,select_var_new,select_var_data_new);
 
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/looCrossValid.m b/sourcecodes/parameter_learning/code_backup/looCrossValid.m
deleted file mode 100644
index 21d6560d..00000000
--- a/sourcecodes/parameter_learning/code_backup/looCrossValid.m
+++ /dev/null
@@ -1,241 +0,0 @@
-function looCrossValid(pre,predict_label)
-% This function will peform leave-one-out cross-validation.
-% This requires the specification of the name of the variable
-%   that you want to predict.
-% For now, I will assume that the variable is a discrete variable.
-
-
-
-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');
-
-Std_flag=true;
-[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag);
-
-for i=1:nnodes
-    if strcmp(labels(i),predict_label)
-       predict_node = i;
-    end
-end
-
-predict_cases = bnet.node_sizes(predict_node);
-
-if predict_cases == 1
-	looCV_continuous(pre,predict_label,nnodes,labels,cases,bnet,predict_node,predict_cases);
-else
-	looCV_discrete(pre,predict_label,nnodes,labels,cases,bnet,predict_node,predict_cases);
-endif
-
-end
-
-function looCV_continuous(pre,predict_label,nnodes,labels,cases,bnet,predict_node,predict_cases)
-
-ncases = size(cases,2);
-
-%%Read in original means and standard deviations to report output as
-%% untransformed values.
-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
-
-
-loopredictions=zeros(size(cases,2),2);
-
-%t=cputime;
-%First get loo predictions
-for i =1:ncases
-%  i
-  current_data = cases(:,i);
-  cases_new = cases;
-  cases_new(:,i) = [];
-  evidence = current_data;
-  evidence{predict_node} = {};
-  [bnet]=parameterLearning(bnet,cases_new);
-  engine = jtree_inf_engine(bnet);
-  [engine,loglik] = enter_evidence(engine,evidence);
-  predict = marginal_nodes(engine,predict_node);
-  adj_mu = predict.mu*stdevs{predict_node}+means{predict_node};
-  adj_sigma = stdevs{predict_node}*predict.Sigma;
-  loopredictions(i,1) = adj_mu;
-  loopredictions(i,2) = adj_sigma;
-end
-%e=cputime-t;
-
-
-%Open output file.
-filename = strcat(pre,'looCV.txt');
-fileID = fopen(filename,'w');
-
-fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
-
-
-%%Print the predictions
-fprintf(fileID,'Predicted mean and standard deviation for each case:\n');
-fprintf(fileID,'Mean\tStDev\n');
-for i = 1:ncases
-     fprintf(fileID,'%i\t',i);
-     fprintf(fileID,'%6.4f\t%6.4f\n',loopredictions(i,:));
-end
-
-end
-
-
-function looCV_discrete(pre,predict_label,nnodes,labels,cases,bnet,predict_node,predict_cases)
-
-ncases = size(cases,2);
-
-%This next section just gets the original names of the levels.
-% so they can be written to the output file.
-%%Get the maximum_number of states so array will be big enough
-%%Add 1 because the input includes the node name
-max_states = max(bnet.node_sizes) + 1;
-disc_nodes = size(bnet.dnodes,2);
-
-%%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
-
-pred_levels = cell(1,predict_cases);
-for i = 1:disc_nodes
-   if strcmp(levels{i,1},predict_label);
-	for j = 1:predict_cases
-	    pred_levels{j} = levels{i,j+1};
-	end
-        break
-   end
-end
-
-loopredictions=zeros(size(cases,2),predict_cases);
-
-%t=cputime;
-%First get loo predictions
-for i =1:ncases
-%  i
-  current_data = cases(:,i);
-  cases_new = cases;
-  cases_new(:,i) = [];
-  evidence = current_data;
-  evidence{predict_node} = {};
-
-  [bnet]=parameterLearning(bnet,cases_new);
-  engine = jtree_inf_engine(bnet);
-  [engine,loglik] = enter_evidence(engine,evidence);
-  predict = marginal_nodes(engine,predict_node);
-  for j = 1:predict_cases
-	loopredictions(i,j) = predict.T(j);
-  end
-end
-%e=cputime-t;
-
-%Now compare with actual outcomes
-actual_states = zeros(1,predict_cases);
-for i=1:ncases
-    for j = 1:predict_cases
-	if cell2mat(cases(predict_node,i)) == j
-           actual_states(j) = actual_states(j) + 1;
-	end
-    end
-end
-
-actual_states;
-pred_states = zeros(1,ncases);
-
-for i=1:ncases
-    max_state = 1;
-    for j = 2:predict_cases
-       if loopredictions(i,j) > loopredictions(i,max_state)
-           max_state = j;
-	end
-    end
-    pred_states(i) = max_state;
-end
-
-correct = 0;
-for i=1:ncases
-    if pred_states(i) == cell2mat(cases(predict_node,i))
-	correct = correct + 1;
-    end
-end
-
-accuracy = correct/ncases;
-
-%Open output file.
-filename = strcat(pre,'looCV.txt');
-fileID = fopen(filename,'w');
-
-fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
-
-
-%%Print the accuracy
-fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy);
-
-%%Print the predictions
-fprintf(fileID,'Predicted likelihood of each state for each case:\n');
-fprintf(fileID,'%s\t','Case');
-fprintf(fileID,'%s\t',pred_levels{1:end-1});
-fprintf(fileID,'%s\n',pred_levels{end});
-for i = 1:ncases
-     fprintf(fileID,'%i\t',i);
-     fprintf(fileID,'%6.4f\t',loopredictions(i,1:end-1));
-     fprintf(fileID,'%6.4f\n',loopredictions(i,end));
-end
-
-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/createJSON.m b/sourcecodes/parameter_learning/createJSON.m
index 65758356..e6fd7ea2 100644
--- a/sourcecodes/parameter_learning/createJSON.m
+++ b/sourcecodes/parameter_learning/createJSON.m
@@ -12,6 +12,10 @@ function  [ ] = createJSON( pre )
    %
 
 
+nnodefile=strcat(pre,'nnode.txt');
+fnnode = fopen(nnodefile,'r');
+nnodes = fscanf(fnnode,'%d');
+
 %  open file for input, include error handling
 dfile=strcat(pre,'structure_input.txt');
 
@@ -22,7 +26,6 @@ end
 
 % Read in first line to get the number of nodes and the node labels.
 buffer = strtrim(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);
@@ -40,8 +43,14 @@ for i = 1:nnodes
 end
 
 
-% open file to read in model averaging scores
+
+%Check to see if a file with model averaging scores exists
 dfile2=strcat(pre,'structure_input_temp.txt');
+%if it does not exist, then just open the other structure file again
+if exist(dfile2, 'file') != 2
+   dfile2 = dfile;
+   frewind(dfile2);
+end
 
 fin2 = fopen(dfile2,'r');
 if fin2 < 0
diff --git a/sourcecodes/parameter_learning/createSVG.m b/sourcecodes/parameter_learning/createSVG.m
index f7cc9b64..2d190b68 100644
--- a/sourcecodes/parameter_learning/createSVG.m
+++ b/sourcecodes/parameter_learning/createSVG.m
@@ -16,6 +16,10 @@ function  [ ] = createSVG( pre )
    %
 
 
+nnodefile=strcat(pre,'nnode.txt');
+fnnode = fopen(nnodefile,'r');
+nnodes = fscanf(fnnode,'%d');
+
 %  open file for input, include error handling
 dfile=strcat(pre,'structure_input.txt');
 
@@ -26,19 +30,20 @@ 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")) + 1;
+
 labels = cell(1,nnodes);
 for j=1:nnodes
+    j, buffer
     [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
+	 i, j, buffer
          [next,buffer] = strtok(buffer);
          edges{i,j} = next;
     end
@@ -48,6 +53,13 @@ end
 % open file to read in model averaging scores
 dfile2=strcat(pre,'structure_input_temp.txt');
 
+%If it does not exist, then just read in the regular structure file
+%%   as the model averaging score file
+if exist(dfile2, 'file') != 2
+   dfile2 = dfile;
+   frewind(dfile2);
+end
+
 if (exist(dfile2) == 0)
   scores = cell(nnodes,nnodes);
   for i = 1:nnodes
@@ -107,11 +119,11 @@ end
 
 outfile = strcat(pre,'graphviz_svg.txt');
 fout = fopen(outfile,'w');
-fprintf(fout,"digraph G {\n");
+fprintf(fout,"digraph \"\" {\n");
 %fprintf(fout,"size=\"10,10\"; ratio = fill;\n");
 fprintf(fout,"size=\"10,10\"; remincross = true;\n");
-fprintf(fout,"node [shape=rectangle, width=1.0, fontsize=22];\n");
-fprintf(fout,"edge [fontsize=16];\n");
+fprintf(fout,"node [shape=box, fontsize=18, margin=\"0.05,0\", height=0.3, rounded=true];\n");
+fprintf(fout,"edge [fontsize=12];\n");
 for i = 1:nedges
   fprintf(fout,"\"%s\" -> \"%s\" [ label=\"%3.2f\", penwidth=\"%3.2f\" ];\n",labels{sources(i)},labels{targets(i)},scores1(i),2*scores1(i));
 end
@@ -121,11 +133,11 @@ fclose(fout);
 
 outfile2 = strcat(pre,'graphviz_svg_no_edge.txt');
 fout2 = fopen(outfile2,'w');
-fprintf(fout2,"digraph G {\n");
+fprintf(fout2,"digraph \"\" {\n");
 %fprintf(fout,"size=\"10,10\"; ratio = fill;\n");
 fprintf(fout2,"size=\"10,10\"; remincross = true;\n");
-fprintf(fout2,"node [shape=rectangle, width=1.0, fontsize=22];\n");
-fprintf(fout2,"edge [fontsize=16];\n");
+fprintf(fout2,"node [shape=box, fontsize=18, margin=\"0.05,0\", height=0.3,rounded=true];\n");
+fprintf(fout2,"edge [fontsize=12];\n");
 for i = 1:nedges
   fprintf(fout,"\"%s\" -> \"%s\" [ penwidth=\"%3.2f\" ];\n",labels{sources(i)},labels{targets(i)},2*scores1(i));
 end
diff --git a/sourcecodes/parameter_learning/drawFigure.m b/sourcecodes/parameter_learning/drawFigure.m
index c48f5b2d..8255f6b9 100644
--- a/sourcecodes/parameter_learning/drawFigure.m
+++ b/sourcecodes/parameter_learning/drawFigure.m
@@ -1,4 +1,4 @@
-function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means)
+function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means,pre)
 %drawFigure writes the parameters and data that are needed to draw the
 %structure of a Bayesian network for BNW.
 % This is the function that is called to create the initial
@@ -11,6 +11,10 @@ function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means)
 % 
 % drawFigure is called by runBN_intial.m
 %
+% This was modified to show the original distributions of continuous nodes 
+%  as mixtures of Gaussian distributions instead of marginalizing the nodes.
+%  JZ, 7-15-2019
+
 
 A=cell2mat(cases');
 Amax=max(A);
@@ -26,6 +30,11 @@ fileID = fopen(filename,'w');
 %%% The number of nodes
 fprintf(fileID,'%i\n',nnodes);
 
+%Open file to write violin plot data
+
+violin_file = strcat(pre,'violin_orig.txt');
+vfile = fopen(violin_file,'w');
+
 %Get canvas size
 
 labels_temp = cellstr(labels);
@@ -68,7 +77,6 @@ for i = 1:nnodes,
     end
 end
 
-
 for i = 1:nnodes,
     %%% The name and type of each node (1=continuous, the number of states
     %%% if it is discrete
@@ -131,22 +139,67 @@ for i = 1:nnodes,
             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,
+	%% The name of the node for the violin plot data
+	if bnet.node_sizes(i) == 1;
+	    fprintf(vfile,'Data for new node\n');
+	    fprintf(vfile,'%s\n',labels{i});
+	end
+
+        %The next line is marginal node method for continuous variables.
+        %%[x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i));
+        %Instead get mixture of Gaussian distributions.
+	s=struct(bnet.CPD{i});
+	no_gaussians = size(s.Wsum)(1);
+        Wsum = sum(s.Wsum);
+        weights = zeros(no_gaussians,1);
+	for j=1:no_gaussians,
+            weights(j) = s.Wsum(j)/Wsum;
+        end;
+
+
+	%Get random samples from normals to make violin plots
+        for j=1:no_gaussians
+                normrnd_count = int16(1000*weights(j));
+                if normrnd_count > 0
+		    violin_data = normrnd(s.mean(j),sqrt(s.cov(j)),normrnd_count,1);
+		%violin_data = normrnd(s.mean(j),sqrt(s.cov(j)),1000,1);
+		    for k = 1:size(violin_data)
+		      fprintf(vfile,'%6.4f\n',violin_data(k));
+		    end
+		end
+	end
+        
+
+        all_y = zeros(101,1);
+        for j=1:no_gaussians
+            [x_vals,y_vals] = calcGaussian(s.mean(j),sqrt(s.cov(j)),Amax(i),Amin(i));
+            y_vals = y_vals*weights(j);
+            all_y = all_y + y_vals;
+	end;
+        
+        %%%For continuous nodes, print x and the pdf of curve.
+	for j = 1:101,
+            %Write data to make violin plots
+            %no_lines = int16(100*y_vals(j,1));
+            %if no_lines > 0;
+	    %	for k = 1:no_lines
+	    %	    fprintf(vfile,'%6.4f\n',x_vals(j,1));
+            %    end
+            %end
             %%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));
+            %%fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1));
+            fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),all_y(j,1));
         end;
-    end;
+
+end
+
 end
 %fprintf(fileID,'%s\t %\n',labels_temp{:});
 
 
 fclose(fileID);
+fclose(vfile);
 
 end
 
diff --git a/sourcecodes/parameter_learning/drawFigureM.m b/sourcecodes/parameter_learning/drawFigureM.m
index 820f06dd..4a666ada 100644
--- a/sourcecodes/parameter_learning/drawFigureM.m
+++ b/sourcecodes/parameter_learning/drawFigureM.m
@@ -1,4 +1,4 @@
-function [] = drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata)
+function [] = drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata,pre)
 %drawFigureM writes the parameters and data that are needed to draw the
 %structure of a Bayesian network after adding evidence or intervention
 %It creates the net_figure_new file after evidence/intervetion.
@@ -51,6 +51,11 @@ y = 1 - y;
 y = y - min(y);
 [x_dim,y_dim] = canvasSize(nnodes,x,y);
 
+%Open file to write violin plot data
+violin_file = strcat(pre,'violin_evidence.txt');
+vfile = fopen(violin_file,'w');
+
+
 %%% The dimensions of the canvas for the javascript code
 fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim);
 x = x*x_dim;
@@ -141,7 +146,22 @@ for i = 1:nnodes,
             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));
+        %% The name of the node for the violin plot data
+        if bnet.node_sizes(i) == 1;
+            fprintf(vfile,'Data for new node\n');
+            fprintf(vfile,'%s\n',labels{i});
+        end
+
+        %Get random samples from normals to make violin plots
+        violin_data = normrnd(predict.mu,sqrt(predict.Sigma),1000,1);
+        %violin_data = normrnd(s.mean(j),sqrt(s.cov(j)),1000,1);
+        for k = 1:size(violin_data)
+            fprintf(vfile,'%6.4f\n',violin_data(k));
+        end
+
+
+
+        [x_vals,y_vals] = calcGaussian(predict.mu,sqrt(predict.Sigma),Amax(i),Amin(i));
         %%%For continuous nodes, print x and the pdf of a normal curve.
         for j = 1:101,
             %%Undo standardization
@@ -160,6 +180,7 @@ for i = 1:nnodes,
 end
 
 fclose(fileID);
+fclose(vfile);
 end
 
 
diff --git a/sourcecodes/parameter_learning/kfoldCrossValid.m b/sourcecodes/parameter_learning/kfoldCrossValid.m
index 02f34866..40b72c61 100644
--- a/sourcecodes/parameter_learning/kfoldCrossValid.m
+++ b/sourcecodes/parameter_learning/kfoldCrossValid.m
@@ -117,7 +117,7 @@ end
 filename = strcat(pre,'kfoldCV.txt');
 fileID = fopen(filename,'w');
 
-fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
+%fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
 
 %Calculate RMSEP (root mean square error of prediction) and q^2
 %First, calculate TSS (total sum of squares) and
@@ -136,19 +136,22 @@ end
 rmsep = sqrt(press/ncases);
 q_squared = 1 - press/tss;
 
-%% Print rmseq and q^2
-fprintf(fileID,'RMS error of predictions: %6.4f\n',rmsep);
-fprintf(fileID,'Q^2 of predictions: %6.4f\n\n',q_squared);
+%% Print rmsep and q^2
+%fprintf(fileID,'RMS error of predictions: %6.4f\n',rmsep);
+%fprintf(fileID,'Q^2 of predictions: %6.4f\n\n',q_squared);
 
 
 %%Print the predictions
-fprintf(fileID,'Predicted mean and standard deviation for each case:\n');
+%%fprintf(fileID,'Predicted mean and standard deviation for each case:\n');
 fprintf(fileID,'CaseRow\tFoldNumber\tActualValue\tPredictionMean\tPredictionStDev\n');
 for i = 1:ncases
      fprintf(fileID,'%i\t%i\t%6.4f\t',i,kfold_index(i),case_adj(i));
      fprintf(fileID,'%6.4f\t%6.4f\n',kfoldPredictions(i,:));
 end
 
+%%Print rmse and q^2
+fprintf(fileID,'k-fold CV for %s; RMSE= %6.4f; Q^2= %6.4f\n',predict_label,rmsep,q_squared);
+
 fflush(fileID);
 fclose(fileID);
 
@@ -267,14 +270,14 @@ accuracy = correct/ncases;
 filename = strcat(pre,'kfoldCV.txt');
 fileID = fopen(filename,'w');
 
-fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
+%fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
 
 
 %%Print the accuracy
-fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy);
+%fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy);
 
 %%Print the predictions
-fprintf(fileID,'Predicted likelihood of each state for each case:\n');
+%fprintf(fileID,'Predicted likelihood of each state for each case:\n');
 fprintf(fileID,'%s\t%s\t%s\t','CaseRow','FoldNumber','ActualState');
 fprintf(fileID,'%s\t',pred_levels{1:end-1});
 fprintf(fileID,'%s\n',pred_levels{end});
@@ -286,6 +289,8 @@ for i = 1:ncases
      fprintf(fileID,'%6.4f\n',kfoldPredictions(i,end));
 end
 
+fprintf(fileID,'k-fold CV for %s; Fraction of accurate predictions= %6.4f\n',predict_label,accuracy);
+
 fflush(fileID);
 fclose(fileID);
 
diff --git a/sourcecodes/parameter_learning/looCrossValid.m b/sourcecodes/parameter_learning/looCrossValid.m
index 840e486e..8cfffe79 100644
--- a/sourcecodes/parameter_learning/looCrossValid.m
+++ b/sourcecodes/parameter_learning/looCrossValid.m
@@ -2,7 +2,7 @@ function looCrossValid(pre,predict_label)
 % This function will peform leave-one-out cross-validation.
 % This requires the specification of the name of the variable
 %   that you want to predict.
-
+% Modifying to output a table.  Sept 2020
 
 
 sfile=strcat(pre,'structure_input.txt');
@@ -93,7 +93,7 @@ end
 filename = strcat(pre,'looCV.txt');
 fileID = fopen(filename,'w');
 
-fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
+%fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
 
 %Calculate RMSEP (root mean square error of prediction) and q^2
 %First, calculate TSS (total sum of squares) and
@@ -113,18 +113,17 @@ end
 rmsep = sqrt(press/ncases);
 q_squared = 1 - press/tss;
 
-%% Print rmseq and q^2
-fprintf(fileID,'RMS error of predictions: %6.4f\n',rmsep);
-fprintf(fileID,'Q^2 of predictions: %6.4f\n\n',q_squared);
 
 
 %%Print the predictions
-fprintf(fileID,'Predicted mean and standard deviation for each case:\n');
+%%fprintf(fileID,'Predicted mean and standard deviation for each case:\n');
 fprintf(fileID,'CaseRow\tActualValue\tPredictionMean\tPredictionStDev\n');
 for i = 1:ncases
      fprintf(fileID,'%i\t%6.4f\t',i,case_adj(i));
      fprintf(fileID,'%6.4f\t%6.4f\n',loopredictions(i,:));
 end
+%% Print rmse and q^2
+fprintf(fileID,'LOOCV predictions of %s; RMSE= %6.4f; Q^2= %6.4f\n',predict_label,rmsep,q_squared);
 
 fflush(fileID);
 fclose(fileID);
@@ -232,14 +231,13 @@ accuracy = correct/ncases;
 filename = strcat(pre,'looCV.txt');
 fileID = fopen(filename,'w');
 
-fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
+%fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
 
 
 %%Print the accuracy
-fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy);
+%fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy);
 
 %%Print the predictions
-fprintf(fileID,'Predicted likelihood of each state for each case:\n');
 fprintf(fileID,'%s\t%s\t','CaseRow','ActualState');
 fprintf(fileID,'%s\t',pred_levels{1:end-1});
 fprintf(fileID,'%s\n',pred_levels{end});
@@ -251,6 +249,8 @@ for i = 1:ncases
      fprintf(fileID,'%6.4f\n',loopredictions(i,end));
 end
 
+fprintf(fileID,'LOOCV predictions of %s; Fraction of accurate predictions= %6.4f\n',predict_label,accuracy);
+
 fflush(fileID);
 fclose(fileID);
 
diff --git a/sourcecodes/parameter_learning/modifyEdges.m b/sourcecodes/parameter_learning/modifyEdges.m
index 2b336710..6542d9fb 100644
--- a/sourcecodes/parameter_learning/modifyEdges.m
+++ b/sourcecodes/parameter_learning/modifyEdges.m
@@ -2,132 +2,147 @@ 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.
+   %  pre_oldstructure_input.txt-- old structure file that is just used to make sure the node order is consistent
+   %  pre_oldmodify_edge.txt-- file with network structure after modification
    %
    %  Output: 
    %   pre_newstructure_input.txt-- structure file with edges added/deleted.
+   %   pre_newstructure_input_temp.txt-- structure file with model averaging scores.
+   %  September 16, 2020: I am only writing the file with model averaging scores
+   %         if no edges were added to the network.
    %
 
 
 %  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.
+
+%  open file for input, include error handling
+mfile=strcat(pre_old,'modify_edge.txt');
+fin2 = fopen(mfile,'r');
+if fin2 < 0
+   error(['Could not open ',mfile,' for input']);
+end
+% Read in first line to get the number of nodes
+nnodes = str2num(fgetl(fin2));
+
+% Read in first line of old structure file to get 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
+%Read in network structure information
+buffer = fgetl(fin2);
+buffer = buffer(2:end-1);
+buffer = strrep(buffer,"\"","");
+labels2 = cell(1,nnodes);
+for j=1:nnodes
+    [next,buffer] = strtok(buffer,",");
+    labels2{j} = next;
 end
 
+nedges = str2num(fgetl(fin2));
+buffer = fgetl(fin2);
+buffer = buffer(2:end-1);
+buffer = strrep(buffer,"\"","");
+sources = cell(1,nedges);
+for j=1:nedges
+    [next,buffer] = strtok(buffer,",");
+    sources{j} = str2num(next);
+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);
+buffer = buffer(2:end-1);
+buffer = strrep(buffer,"\"","");
+targets = cell(1,nedges);
+for j=1:nedges
+    [next,buffer] = strtok(buffer,",");
+    targets{j} = str2num(next);
+end
 
-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;
+buffer = fgetl(fin2);
+buffer = buffer(2:end-1);
+weights = cell(1,nedges);
+for j=1:nedges
+    [next,buffer] = strtok(buffer,",");
+    weights{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];
+
+%label_map is the index in "labels" that corresponds to each label in "labels2"
+label_map = cell(1,nnodes);
+for i = 1:nnodes
+  current = labels2{i};
+  for j = 1:nnodes
+    if strcmp(current,labels{j})
+      label_map{i} = j;
     endif
   end
 end
 
-for i = 1:ndel
-  edges(idel_from(i),idel_to(i)) = "0";
-end
 
-fclose(fin2);
+edges_out = cell(nnodes,nnodes);
+scores_out = cell(nnodes,nnodes);
 
-%  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);
+for i = 1:nedges
+  source_i = label_map{sources{i}};
+  target_i = label_map{targets{i}};
+  scores_out(source_i,target_i) = weights{i};
+  edges_out(source_i,target_i) = "1";
+end
 
-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
+tf = cellfun('isempty',edges_out);
+edges_out(tf) = {"0"};
+scores_out(tf) = {"0"};
 
-%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
+test_score = 1;
+for i=1:nnodes
+  for j=1:nnodes
+     if strcmp(scores_out{i,j},"1.1")
+       test_score = 0;
+     end
   end
 end
 
-for i = 1:nadd
-  edges(iadd_from(i),iadd_to(i)) = "1";
-end
 
-
-
-
-
-outfile = strcat(pre_new,'structure_input.txt');
+if test_score == 1
+outfile = strcat(pre_new,'structure_input_temp.txt');
 fout = fopen(outfile,'w');
 fprintf(fout,'%s\t',labels{1:end-1});
-fprintf(fout,'%s\n',labels{end});
+fprintf(fout,'%s\t\n',labels{end});
 for i = 1:nnodes
-      fprintf(fout,'%s\t',edges{i,1:end-1});
-      fprintf(fout,'%s\n',edges{i,end});
+      fprintf(fout,'%s\t',scores_out{i,1:end-1});
+      fprintf(fout,'%s\t\n',scores_out{i,end});
 end
 fclose(fout);
+end
+
+
+
+outfile2 = strcat(pre_new,'structure_input.txt');
+fout2 = fopen(outfile2,'w');
+fprintf(fout2,'%s\t',labels{1:end-1});
+fprintf(fout2,'%s\t\n',labels{end});
+for i = 1:nnodes
+      fprintf(fout2,'%s\t',edges_out{i,1:end-1});
+      fprintf(fout2,'%s\t\n',edges_out{i,end});
+end
+fclose(fout2);
+
+
+
+
+
+
+
 
 end
diff --git a/sourcecodes/parameter_learning/normpdf.m b/sourcecodes/parameter_learning/normpdf.m
new file mode 100644
index 00000000..2b154f02
--- /dev/null
+++ b/sourcecodes/parameter_learning/normpdf.m
@@ -0,0 +1,50 @@
+function p = normpdf(x,m,s);
+% Normal probability density function
+%
+% pdf = normpdf(x,m,s);
+%
+% Computes the PDF of a the normal distribution 
+%    with mean m and standard deviation s
+%    default: m=0; s=1;
+% x,m,s must be matrices of same size, or any one can be a scalar. 
+%
+% see also: NORMCDF, NORMINV 
+
+% Reference(s):
+
+%	Version 1.28   Date: 23.Sep.2002
+%	Copyright (c) 2000-2002 by  Alois Schloegl <a.schloegl@ieee.org>	
+
+%    This program is free software; you can redistribute it and/or modify
+%    it under the terms of the GNU General Public License as published by
+%    the Free Software Foundation; either version 2 of the License, or
+%    (at your option) any later version.
+%
+%    This program is distributed in the hope that it will be useful,
+%    but WITHOUT ANY WARRANTY; without even the implied warranty of
+%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+%    GNU General Public License for more details.
+%
+%    You should have received a copy of the GNU General Public License
+%    along with this program; if not, write to the Free Software
+%    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
+
+if nargin==1,
+        m=0;s=1;
+elseif nargin==2,
+        s=1;
+end;        
+
+% allocate output memory and check size of argument
+z = (x-m)./s;		% if this line causes an error, input arguments do not fit. 
+
+%p = ((2*pi)^(-1/2))*exp(-z.^2/2)./s;
+SQ2PI = 2.5066282746310005024157652848110;
+p = exp(-z.^2/2)./(s*SQ2PI);
+
+p((x==m) & (s==0)) = inf;
+
+p(isinf(z)~=0) = 0;
+
+p(isnan(x) | isnan(m) | isnan(s) | (s<0)) = nan;
+
diff --git a/sourcecodes/parameter_learning/normrnd.m b/sourcecodes/parameter_learning/normrnd.m
new file mode 100644
index 00000000..0267ddf6
--- /dev/null
+++ b/sourcecodes/parameter_learning/normrnd.m
@@ -0,0 +1,130 @@
+## Copyright (C) 2012 Rik Wehbring
+## Copyright (C) 1995-2012 Kurt Hornik
+##
+## This file is part of Octave.
+##
+## Octave is free software; you can redistribute it and/or modify it
+## under the terms of the GNU General Public License as published by
+## the Free Software Foundation; either version 3 of the License, or (at
+## your option) any later version.
+##
+## Octave is distributed in the hope that it will be useful, but
+## WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+## General Public License for more details.
+##
+## You should have received a copy of the GNU General Public License
+## along with Octave; see the file COPYING.  If not, see
+## <http://www.gnu.org/licenses/>.
+
+## -*- texinfo -*-
+## @deftypefn  {Function File} {} normrnd (@var{mu}, @var{sigma})
+## @deftypefnx {Function File} {} normrnd (@var{mu}, @var{sigma}, @var{r})
+## @deftypefnx {Function File} {} normrnd (@var{mu}, @var{sigma}, @var{r}, @var{c}, @dots{})
+## @deftypefnx {Function File} {} normrnd (@var{mu}, @var{sigma}, [@var{sz}])
+## Return a matrix of random samples from the normal distribution with
+## parameters mean @var{mu} and standard deviation @var{sigma}.
+##
+## When called with a single size argument, return a square matrix with
+## the dimension specified.  When called with more than one scalar argument the
+## first two arguments are taken as the number of rows and columns and any
+## further arguments specify additional matrix dimensions.  The size may also
+## be specified with a vector of dimensions @var{sz}.
+## 
+## If no size arguments are given then the result matrix is the common size of
+## @var{mu} and @var{sigma}.
+## @end deftypefn
+
+## Author: KH <Kurt.Hornik@wu-wien.ac.at>
+## Description: Random deviates from the normal distribution
+
+function rnd = normrnd (mu, sigma, varargin)
+
+  if (nargin < 2)
+    print_usage ();
+  endif
+
+  if (!isscalar (mu) || !isscalar (sigma))
+    [retval, mu, sigma] = common_size (mu, sigma);
+    if (retval > 0)
+      error ("normrnd: mu and sigma must be of common size or scalars");
+    endif
+  endif
+
+  if (iscomplex (mu) || iscomplex (sigma))
+    error ("normrnd: MU and SIGMA must not be complex");
+  endif
+
+  if (nargin == 2)
+    sz = size (mu);
+  elseif (nargin == 3)
+    if (isscalar (varargin{1}) && varargin{1} >= 0)
+      sz = [varargin{1}, varargin{1}];
+    elseif (isrow (varargin{1}) && all (varargin{1} >= 0))
+      sz = varargin{1};
+    else
+      error ("normrnd: dimension vector must be row vector of non-negative integers");
+    endif
+  elseif (nargin > 3)
+    if (any (cellfun (@(x) (!isscalar (x) || x < 0), varargin)))
+      error ("normrnd: dimensions must be non-negative integers");
+    endif
+    sz = [varargin{:}];
+  endif
+
+  if (!isscalar (mu) && !isequal (size (mu), sz))
+    error ("normrnd: mu and sigma must be scalar or of size SZ");
+  endif
+
+  if (isa (mu, "single") || isa (sigma, "single"))
+    cls = "single";
+  else
+    cls = "double";
+  endif
+
+  if (isscalar (mu) && isscalar (sigma))
+    if (!isnan (mu) && !isinf (mu) && (sigma > 0) && (sigma < Inf))
+      rnd =  mu + sigma * randn (sz);
+    else
+      rnd = NaN (sz, cls);
+    endif
+  else
+    rnd = mu + sigma .* randn (sz);
+    k = isnan (mu) | isinf (mu) | !(sigma > 0) | !(sigma < Inf);
+    rnd(k) = NaN;
+  endif
+
+endfunction
+
+
+%!assert(size (normrnd (1,2)), [1, 1]);
+%!assert(size (normrnd (ones(2,1), 2)), [2, 1]);
+%!assert(size (normrnd (ones(2,2), 2)), [2, 2]);
+%!assert(size (normrnd (1, 2*ones(2,1))), [2, 1]);
+%!assert(size (normrnd (1, 2*ones(2,2))), [2, 2]);
+%!assert(size (normrnd (1, 2, 3)), [3, 3]);
+%!assert(size (normrnd (1, 2, [4 1])), [4, 1]);
+%!assert(size (normrnd (1, 2, 4, 1)), [4, 1]);
+
+%% Test class of input preserved
+%!assert(class (normrnd (1, 2)), "double");
+%!assert(class (normrnd (single(1), 2)), "single");
+%!assert(class (normrnd (single([1 1]), 2)), "single");
+%!assert(class (normrnd (1, single(2))), "single");
+%!assert(class (normrnd (1, single([2 2]))), "single");
+
+%% Test input validation
+%!error normrnd ()
+%!error normrnd (1)
+%!error normrnd (ones(3),ones(2))
+%!error normrnd (ones(2),ones(3))
+%!error normrnd (i, 2)
+%!error normrnd (2, i)
+%!error normrnd (1,2, -1)
+%!error normrnd (1,2, ones(2))
+%!error normrnd (1, 2, [2 -1 2])
+%!error normrnd (1,2, 1, ones(2))
+%!error normrnd (1,2, 1, -1)
+%!error normrnd (ones(2,2), 2, 3)
+%!error normrnd (ones(2,2), 2, [3, 2])
+%!error normrnd (ones(2,2), 2, 2, 3)
diff --git a/sourcecodes/parameter_learning/prepareInput.m b/sourcecodes/parameter_learning/prepareInput.m
index 3147d54e..39368582 100644
--- a/sourcecodes/parameter_learning/prepareInput.m
+++ b/sourcecodes/parameter_learning/prepareInput.m
@@ -1,6 +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.
    %  It replaces php code that was previously in bn_file_load_gom.php.
@@ -32,12 +31,13 @@ function  [ ] = prepareInput( pre )
    %           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
+   %    3) ???input_table.txt Similar to above file, but in tabular format.
+   %    4) ???nlevels.txt: The states of discrete variables.
+   %    5) ???name.txt: The names of the variables as uploaded.
+   %    6) ???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
+   %    7/8) ???nnode.txt and ???nrows.txt: number of nodes and cases
+   %    9-13) ???ban.txt, ???white.txt, ???k.txt, ???thr.txt, and
    %          ???parent.txt: Files with default values for structure learning. 
    %
    % It is called by the run_prep_input script in the 'sourcecodes' directory.
@@ -54,6 +54,7 @@ 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.
@@ -65,6 +66,8 @@ for j=1:nnodes
     labels{j} = next;
 end
 
+
+
 % Read in the data
 data = cell(ncases,nnodes);
 for i = 1:ncases
@@ -75,11 +78,25 @@ for i = 1:ncases
     end
 end
 
+%Read one more line of the file to make sure that
+% it does not contain a final line of data
+buffer = fgetl(fin);
+if length(buffer) > 1
+    ncases = ncases + 1;
+    for j = 1:nnodes
+         [next,buffer] = strtok(buffer);
+         data{ncases,j} = next;
+    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);
@@ -89,36 +106,54 @@ for j = 1:nnodes
    levels{j} = size(states{j},1);
 end
 
+
+
+% Remove any variables that have a single variable
+remove_labels = cell(1,nnodes);
+remove = [];
+for j = 1:nnodes
+  if levels{j} == 1
+    remove_labels{j} = labels{j};
+    remove(end+1) = j;
+  end
+end
+
+nnodes = nnodes - size(remove,2);
+data(:,remove)=[];
+labels(remove) = [];
+levels(remove) = [];
+states(:,remove) = [];
+
 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.";
+        reason{j} = "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.";
+       reason{j} = "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.";
+       reason{j} = "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.";
+       reason{j} = "Default classification";
        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(.).";
+              reason{j} = "At least one value contained a period(.).";
               levels{j} = 1;
            end
            k++;
@@ -129,6 +164,8 @@ for j = 1:nnodes
     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;
@@ -175,7 +212,6 @@ if max_disc > min_cont
   
 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');
@@ -241,14 +277,24 @@ for i = 1:nnodes
 end
 fclose(fout);
 
-
 %Print a file with a short description of the input.
 descfile = strcat(pre,'input_desc.txt');
 dout = fopen(descfile,'w');
+tablefile = strcat(pre,'input_table.txt');
+dout_table = fopen(tablefile,'w');
 fprintf(dout,['As loaded, the input file had the following properties:\n\n']);
-dout = fopen(descfile,'a');
+if size(remove,1) > 0
+	fprintf(dout,['The following variables were removed because they contained only one value:\n']);
+	for j = 1:size(remove_labels,2)
+		if remove_labels{j}!= 1
+		  fprintf(dout,'%s\n',remove_labels{j});
+		end
+	end
+	fprintf(dout,['\n']);
+end
 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_table,'Variable\tType\tStates/Mean (St Dev)\tReason for classification\n');
 fprintf(dout,'The variable names are:\n');
 fprintf(dout,'%s\t',labels{1:end-1});
 fprintf(dout,'%s\n\n',labels{end});
@@ -259,16 +305,20 @@ for i=1:nnodes
        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))
+       fprintf(dout,'It has a mean of %6.3f and a standard deviation of %6.3f\n\n',mean(column),std(column));
+       fprintf(dout_table,'%s\tContinuous\t%6.3f (%6.3f)\t%s\n',labels{i},mean(column),std(column),reason{i});
     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});
+       fprintf(dout_table,'%s\tDiscrete\t%i\t%s\n',labels{i},levels{i},reason{i});
     end
 end
-fclose(fout);
+fprintf(dout_table,'There are %i variables and %i cases; %i variables and %i rows were removed.\n',size(labels,2),ncases,size(remove,1),remove_count);
+fclose(dout);
+fclose(dout_table);
 
 outfile = strcat(pre,'continuous_input.txt');
 fout = fopen(outfile,'w');
diff --git a/sourcecodes/parameter_learning/removeNodes.m b/sourcecodes/parameter_learning/removeNodes.m
index 8740d999..45f09cb7 100644
--- a/sourcecodes/parameter_learning/removeNodes.m
+++ b/sourcecodes/parameter_learning/removeNodes.m
@@ -59,27 +59,34 @@ 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;
+  buffer = fgetl(fin2);    %get first line as a string
+  if buffer < 0
+    dellabels = {};
+    ndel = 0;
+  else
+    ndel = numel(strfind(buffer," ")) + 1;
+    dellabels = cell(1,ndel);
+    for j=1:ndel
+      [next,buffer] = strtok(buffer);
+      dellabels{j} = next;
+    end
   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
+if ndel > 0
+  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])=[];
+   labels(:,[delindex])=[];
+   data(:,[delindex])=[];
+end
 
 outfile = strcat(pre_new,'continuous_input_orig.txt');
 fout = fopen(outfile,'w');
diff --git a/sourcecodes/parameter_learning/runBN_initial.m b/sourcecodes/parameter_learning/runBN_initial.m
index 16ce06dc..d87e468b 100644
--- a/sourcecodes/parameter_learning/runBN_initial.m
+++ b/sourcecodes/parameter_learning/runBN_initial.m
@@ -63,7 +63,7 @@ end
 
 filename=strcat(pre,'net_figure.txt');
 
-drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means);
+drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means,pre);
 
 writeParameters(pre,nnodes,bnet,labels,cases,labelsold,s,m);
 
diff --git a/sourcecodes/parameter_learning/testSetPredictions.m b/sourcecodes/parameter_learning/testSetPredictions.m
index 23a50868..978326b0 100644
--- a/sourcecodes/parameter_learning/testSetPredictions.m
+++ b/sourcecodes/parameter_learning/testSetPredictions.m
@@ -4,7 +4,7 @@ function  [ ] = testSetPredictions( pre )
    %   in the uploaded data file.
    %      
    %
-   %  Input: ???ts_input.txt
+   %  Input: ???ts_upload.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 that you want to predict.
@@ -157,7 +157,6 @@ for i=1:nnodes
      end
   else
      for j=1:ntestcases
-	labels{i}
         if !strcmp(data_test{j,i},"NA")
 	    for jj = 1:size(levels,1)
 		if strcmp(labels{i},levels{jj,1})
@@ -179,7 +178,7 @@ end
 predict_cases = bnet.node_sizes(predict_node);
 
 if predict_cases == 1
-      ts_continuous(pre,bnet,nnodes,predict_label,predict_node,data_test,means,stdevs)
+      ts_continuous(pre,bnet,nnodes,predict_label,predict_node,data_test,means,stdevs);
 else
       pred_levels = cell(1,predict_cases);
       for i = 1:disc_nodes
@@ -190,7 +189,7 @@ else
             break
          end
       end
-      ts_discrete(pre,bnet,nnodes,predict_label,predict_node,data_test,pred_levels,predict_cases)
+      ts_discrete(pre,bnet,nnodes,predict_label,predict_node,data_test,pred_levels,predict_cases);
 end
 
 %delete(dfile)
@@ -224,7 +223,7 @@ end
 filename = strcat(pre,'ts_output.txt');
 fileID = fopen(filename,'w');
 
-fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
+%fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
 
 
 %Calculate RMSEP (root mean square error of prediction) and q^2
@@ -235,14 +234,11 @@ actual_values = [];
 predictions_removeNA = [];
 for i=1:ntestcases
    if !strcmp(data_test(i,predict_node),'NA')
-        actual_values = [actual_values, cell2num(data_test(i,predict_node))]
-	predictions_removeNA = [predictions_removeNA,predictions(i,1)]
+        actual_values = [actual_values, cell2num(data_test(i,predict_node))];
+	predictions_removeNA = [predictions_removeNA,predictions(i,1)];
    end
 end
 
-size(actual_values)
-size(predictions_removeNA)
-
 average = mean(actual_values);
 average = average*stdevs{predict_node}+means{predict_node};
 tss = 0;
@@ -256,12 +252,12 @@ rmsep = sqrt(press/length(actual_values));
 q_squared = 1 - press/tss;
 
 %% Print rmseq and q^2
-fprintf(fileID,'RMS error of predictions: %6.4f\n',rmsep);
-fprintf(fileID,'Q^2 of predictions: %6.4f\n\n',q_squared);
+%fprintf(fileID,'RMS error of predictions: %6.4f\n',rmsep);
+%fprintf(fileID,'Q^2 of predictions: %6.4f\n\n',q_squared);
 
 
 %%Print the predictions
-fprintf(fileID,'Predicted mean and standard deviation for each case:\n');
+%fprintf(fileID,'Predicted mean and standard deviation for each case:\n');
 fprintf(fileID,'CaseRow\tActualValue\tPredictionMean\tPredictionStDev\n');
 for i = 1:ntestcases
      if strcmp(data_test{i,predict_node},"NA")
@@ -273,6 +269,8 @@ for i = 1:ntestcases
      fprintf(fileID,'%6.4f\t%6.4f\n',predictions(i,:));
 end
 
+fprintf(fileID,'Test set predictions for %s; RMSE= %6.4f; Q^2= %6.4f\n',predict_label,rmsep,q_squared);
+
 fflush(fileID);
 fclose(fileID);
 
@@ -333,13 +331,13 @@ accuracy = correct/length(pred_states);
 filename = strcat(pre,'ts_output.txt');
 fileID = fopen(filename,'w');
 
-fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
+%fprintf(fileID,'Variable that was predicted: %s\n\n',predict_label);
 
 %%Print the accuracy
-fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy);
+%fprintf(fileID,'Fraction of accurate predictions: %6.4f\n\n',accuracy);
 
 %%Print the predictions
-fprintf(fileID,'Predicted likelihood of each state for each case:\n');
+%fprintf(fileID,'Predicted likelihood of each state for each case:\n');
 fprintf(fileID,'%s\t%s\t','CaseRow','ActualState');
 fprintf(fileID,'%s\t',pred_levels{1:end-1});
 fprintf(fileID,'%s\n',pred_levels{end});
@@ -355,6 +353,8 @@ for i = 1:ntestcases
      fprintf(fileID,'%6.4f\n',predictions(i,end));
 end
 
+fprintf(fileID,'Test set predictions of %s; Fraction of accurate predictions= %6.4f\n',predict_label,accuracy);
+
 fflush(fileID);
 fclose(fileID);
 
diff --git a/sourcecodes/parameter_learning/writeParameters.m b/sourcecodes/parameter_learning/writeParameters.m
index 6efafe8a..cb119c76 100644
--- a/sourcecodes/parameter_learning/writeParameters.m
+++ b/sourcecodes/parameter_learning/writeParameters.m
@@ -5,7 +5,7 @@ function [] = writeParameters(pre,nnodes,bnet,labels,cases,labelsold,s,m)
 %    for the network.
 %
 % writeParameters is called by runBN_intial.m
-
+% Simplifying output to read in and make table on webpage.
 
 %%Get the types of the nodes.
 typefile = strcat(pre,'type.txt');
@@ -76,15 +76,32 @@ for i = 1:nnodes
     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);
+	CPD=struct(bnet.CPD{nodeid});
+	no_gaussians = size(CPD.Wsum)(1);
+	if no_gaussians == 1
+            %%%line = 'Continuous node\n';
+            %%%fprintf(fileID,line);
+            adj_mu = CPD.mean*s(i)+m(i);
+            adj_sigma = s(i)*sqrt(CPD.cov);
+	    fprintf(fileID,'Mean\tSt Dev\n');
+	    fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma);
+	else
+        %%%%line = 'Continuous node modeled as a mixture of %i Gaussian distributions\n';
+        %%%%fprintf(fileID,line,no_gaussians);
+	fprintf(fileID,'Mean\tSt Dev\tWeight\n');
+	Wsum = sum(CPD.Wsum);
+	for j=1:no_gaussians
+            adj_mu = CPD.mean(j)*s(i)+m(i);
+            adj_sigma = s(i)*sqrt(CPD.cov(j));
+	    weight = CPD.Wsum(j)/Wsum;
+	    fprintf(fileID,'%6.4f\t%6.4f\t%6.4f\n',adj_mu,adj_sigma,weight);
+	end
+        fprintf(fileID,'\n');
+        end
     else
-        line = 'Discrete node with %i states\n';
-        fprintf(fileID,line,bnet.node_sizes(nodeid));
+        %%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;
@@ -94,6 +111,7 @@ for i = 1:nnodes
               break
            end
         end
+	fprintf(fileID,'State\tProbability\n');
         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));
diff --git a/sourcecodes/parameter_learning/writeParameters_ev.m b/sourcecodes/parameter_learning/writeParameters_ev.m
index 1f07c745..13524f26 100644
--- a/sourcecodes/parameter_learning/writeParameters_ev.m
+++ b/sourcecodes/parameter_learning/writeParameters_ev.m
@@ -97,21 +97,22 @@ for i = 1:nnodes
         end
     end
     %%%Print the name of the node
-    fprintf(fileID,'%s\n',labels{nodeid});
+    fprintf(fileID,'%s considering evidence\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 = '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;
+             adj_sigma = stdevs{nodeid}*sqrt(predict.Sigma);
+	    fprintf(fileID,'Mean\tSt Dev\n');
              fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma);
        else
-           line = 'Probability of states considering evidence:\n';
-           fprintf(fileID,line);
+           %line = 'Probability of states considering evidence:\n';
+           %fprintf(fileID,line);
            nodeid2 = 0;
            for k = 1:ndisc_nodes,
 	     if strcmp(levels{k,1},labels{nodeid}),
@@ -119,6 +120,7 @@ for i = 1:nnodes
                 break
              end
             end
+	    fprintf(fileID,'State\tProbability\n');
 	    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));
@@ -128,10 +130,11 @@ for i = 1:nnodes
        end
    else
        if bnet.node_sizes(nodeid) == 1;
-          line = 'Evidence was observed for this node. The observed value was:\n';
-          fprintf(fileID,line);  
+          %%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);
+	  fprintf(fileID,'Observed as\tValue\n');
+          fprintf(fileID,'Evidence\t%6.4f\n\n',adj_mu);
        else
           nodeid2 = 0;
           for k = 1:ndisc_nodes,
@@ -140,11 +143,12 @@ for i = 1:nnodes
                break
             end
           end
-	 line = 'Evidence was observed for this node. The observed state was:\n';
-         fprintf(fileID,line);
+	 %%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});
+	  fprintf(fileID,'Observed as\tValue\n');
+         fprintf(fileID,'Evidence\t %s\n\n',levels{nodeid2,state_ev+1});
        end
    end
 end
diff --git a/sourcecodes/parameter_learning/writeParameters_int.m b/sourcecodes/parameter_learning/writeParameters_int.m
index 69dcbb93..98e4edf1 100644
--- a/sourcecodes/parameter_learning/writeParameters_int.m
+++ b/sourcecodes/parameter_learning/writeParameters_int.m
@@ -5,6 +5,7 @@ function [] = writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,sele
 %    for the network.
 %
 % writeParameters is called by Predictmultipleintervention.m
+% Modifying to simplify output to make a table on webpage.
 
 %First read input file to get node labels to get node IDs.
 infile = strcat(pre,'continuous_input.txt');
@@ -129,21 +130,22 @@ for i = 1:nnodes
     %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});
+    fprintf(fileID,'%s considering intervention\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 = '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;
+           adj_sigma = stdevs{nodeid}*sqrt(predict.Sigma);
+	   fprintf(fileID,"Mean\tSt Dev\n");
            fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma);
        else
-           line = 'Probability of states considering intervention:\n';
-           fprintf(fileID,line);
+           %line = 'Probability of states considering intervention:\n';
+           %fprintf(fileID,line);
            nodeid2 = 0;
            for k = 1:ndisc_nodes,
 	     if strcmp(levels{k,1},labels{nodeid}),
@@ -151,6 +153,7 @@ for i = 1:nnodes
                 break
              end
             end
+	    fprintf(fileID,"State\tProbability\n")
 	    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));
@@ -160,10 +163,11 @@ for i = 1:nnodes
        end
    else
        if bnet.node_sizes(nodeid) == 1;
-          line = 'Intervention on this node assigned the following value:\n';
-          fprintf(fileID,line);  
+          %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);
+	  fprintf(fileID,"Fixed by\tValue\n");
+          fprintf(fileID,'Intervention\t%6.4f\n\n',adj_mu);
        else
           nodeid2 = 0;
           for k = 1:ndisc_nodes,
@@ -172,11 +176,12 @@ for i = 1:nnodes
                break
             end
           end
-	 line = 'Intervention on this node assigned the following state:\n';
-         fprintf(fileID,line);
+	 %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});
+	 fprintf(fileID,"Fixed by\tValue\n");
+         fprintf(fileID,'Intervention\t%s\n\n',levels{nodeid2,state_ev+1});
        end
    end
    end
diff --git a/sourcecodes/parameter_learning/writeSettings.m b/sourcecodes/parameter_learning/writeSettings.m
new file mode 100644
index 00000000..0c5ad865
--- /dev/null
+++ b/sourcecodes/parameter_learning/writeSettings.m
@@ -0,0 +1,200 @@
+function writeSettings(pre)
+  %function to write tables of settings used in structure learning
+  % so they can be reviewed by users
+
+
+parfile = strcat(pre,'parent.txt');
+paropen = fopen(parfile,'r');
+par_val = fscanf(paropen,'%i');
+
+kfile = strcat(pre,'k.txt');
+kopen = fopen(kfile,'r');
+k_val = fscanf(kopen,'%i');
+
+thrfile = strcat(pre,'thr.txt');
+thropen = fopen(thrfile,'r');
+thr_val = fscanf(thropen,'%f');
+
+filename = strcat(pre,'slsettings.txt');
+fileID = fopen(filename,'w');
+fprintf(fileID,'Structure learning settings\n');
+fprintf(fileID,'Parameter\tValue\n');
+fprintf(fileID,'Maximum number of parents\t%i\n',par_val);
+fprintf(fileID,'Number of structures for model averaging\t%i\n',k_val);
+fprintf(fileID,'Model averaging selection threshold\t%6.3f\n',thr_val);
+fprintf(fileID,'\n');
+
+tierfile = strcat(pre,'tier.txt');
+if exist(tierfile,'file') == 2
+	tieropen = fopen(tierfile,'r');
+	tiers = fscanf(tieropen,'%s');
+	tiers = strsplit(",",tiers);
+	tiers(size(tiers,2)) = [];
+	fprintf(fileID,'Tier assignments\n');
+	fprintf(fileID,'Variable\tTier\n');
+	notiers = str2num(tiers{1});
+	i = 1;
+        for j = 1:notiers
+		i = i + 1;
+		tiername = tiers{i};
+		i = i + 1;
+		novars = str2num(tiers{i});
+                if novars == 0
+                   fprintf(fileID,'No variables\t%s\n',tiername);
+                else
+		   for k = 1:novars
+		       i = i + 1;
+		       fprintf(fileID,'%s\t%s\n',tiers{i},tiername);
+                   end
+                end
+        end
+fprintf(fileID,"\n");
+end
+
+
+descfile = strcat(pre,'tierdesc.txt');
+%format of ???tierdesc.txt is:
+%% Assume that there are n tiers:
+%% For Tier i:
+%%   within tier interactions allowed: 
+%%       (i-1)*n+1 true/false for yes,
+%%       (i-1)*n+2 true/false for no;  (not needed, opposite of above)
+%%   Tier j (for 1 to n, with i != j)
+%%       Can j be parent of Tier i
+%%       Can j be child of Tier i
+if exist(descfile,'file') == 2
+        %%I am going to assume that if ???tierdesc.txt exists, then
+        %%%  ???tiers.txt exists and notiers has been defined.
+        within = cell(notiers,1);
+        parents = cell(notiers,notiers);
+        children = cell(notiers,notiers);
+        within(:) = 0;
+        parents(:) = 0;
+        children(:) = 0;
+	descopen = fopen(descfile,'r');
+	desc = fscanf(descopen,'%s');
+	desc = strsplit(",",desc);
+	desc(size(desc,2)) = [];
+        for i = 1:notiers
+            %%Pull out the section of desc array for this tiers
+            desc_current = desc((i-1)*notiers*2+1:i*notiers*2);
+	    for j = 1:notiers
+		if j < i
+                   if strcmp(desc_current{j*2+1},'true')
+                       parents{i,j} = j;
+		   end
+                   if strcmp(desc_current{j*2+2},'true')
+                       children{i,j} = j;
+		   end
+		end
+                if i == j
+                   if strcmp(desc_current{1},'true')
+                      within{i,1} = 1;
+		   end
+		end
+		if j > i
+                   if strcmp(desc_current{(j-1)*2+1},'true')
+                       parents{i,j} = j;
+		   end
+                   if strcmp(desc_current{(j-1)*2+2},'true')
+                       children{i,j} = j;
+		   end
+		end
+	end
+	end
+        fprintf(fileID,"Within tier interactions\n");
+        fprintf(fileID,"Tier\tAre within tier interaction allowed?\n");
+        for i = 1:notiers
+            i1 = sprintf("%i",i);
+            if within{i,1} == 1
+               fprintf(fileID,"Tier%s\tYes\n",i1);
+            else
+               fprintf(fileID,"Tier%s\tNo\n",i1);
+            end
+        end
+        fprintf(fileID,'\n');          
+	fprintf(fileID,'Allowed parents\n');
+	fprintf(fileID,'Tier\tOther tiers containing potential parents\n');
+        for i=1:notiers
+	    lineout = '';
+            for j=1:notiers
+               if parents{i,j} != 0
+                   j1 = sprintf("%i",parents{i,j});
+                   lineout = strcat(lineout,j1,',',{' '});
+               end
+            end
+	    col1 = sprintf("%i",i);
+            col1 = strcat("Tier",col1);
+            if size(lineout,1) > 0
+	       fprintf(fileID,'%s\t%s\n',col1,lineout{1,1}(1:end-2));
+            else 
+	       fprintf(fileID,'%s\tNone\n',col1);
+            end
+        end
+        fprintf(fileID,'\n');          
+	fprintf(fileID,'Allowed children\n');
+	fprintf(fileID,'Tier\tOther tiers containing potential children\n');
+        for i=1:notiers
+	    lineout = '';
+            for j=1:notiers
+               if children{i,j} != 0
+                   j1 = sprintf("%i",children{i,j});
+                   lineout = strcat(lineout,j1,',',{' '});
+               end
+            end
+	    col1 = sprintf("%i",i);
+            col1 = strcat("Tier",col1);
+            if size(lineout,1) > 0
+	       fprintf(fileID,'%s\t%s\n',col1,lineout{1,1}(1:end-2));
+            else 
+	       fprintf(fileID,'%s\tNone\n',col1);
+            end
+        end
+end
+
+banfile = strcat(pre,'ban.txt');
+if exist(banfile,'file') == 2
+	banopen = fopen(banfile,'r');
+	ban = fscanf(banopen,'%s');
+	ban = strsplit(",",ban);
+        ban(end) = [];
+        nban = size(ban,2)/2;
+        if nban > 0
+           fprintf(fileID,'\n');          
+           fprintf(fileID,'Banned edges\n');
+	   fprintf(fileID,'From\tTo\n');
+           for j=1:nban
+              j1 = (j-1)*2 + 1;
+              j2 = (j-1)*2 + 2;
+	      fprintf(fileID,"%s\t%s\n",ban{1,j1},ban{1,j2})
+           end
+        end
+end
+
+
+
+whitefile = strcat(pre,'white.txt');
+if exist(whitefile,'file') == 2
+	whiteopen = fopen(whitefile,'r');
+	white = fscanf(whiteopen,'%s');
+	white = strsplit(",",white);
+        white(end) = [];
+        nwhite = size(white,2)/2;
+        if nwhite > 0
+           fprintf(fileID,'\n');          
+           fprintf(fileID,'Required edges\n');
+	   fprintf(fileID,'From\tTo\n');
+           for j=1:nwhite
+              j1 = (j-1)*2 + 1;
+              j2 = (j-1)*2 + 2;
+	      fprintf(fileID,"%s\t%s\n",white{1,j1},white{1,j2})
+           end
+        end
+end
+
+
+
+
+fclose(fileID);
+
+end
\ No newline at end of file