BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

21 files changed, 2263 insertions, 0 deletions

diff --git a/sourcecodes/parameter_learning/Predictmultiple.m b/sourcecodes/parameter_learning/Predictmultiple.m
new file mode 100644
index 00000000..692c6b24
--- /dev/null
+++ b/sourcecodes/parameter_learning/Predictmultiple.m
@@ -0,0 +1,57 @@
+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');
+
+
+
+%nnodes=5;
+Std_flag=true;
+[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag);
+
+
+%name
+%labels
+%map
+
+[bnet]=parameterLearning(bnet,cases);
+%[predict_mean,predict_sd,q_sq]=looCrossValid(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');
+
+filename=strcat(pre,'net_figure_new.txt');
+
+drawFigureM(nnodes,bnet,labels,filename,cases,select_var_new,select_var_data_new);
+
+%quit force;
+%marginal_nodes(engine,2)
+%marginal_nodes(engine,3)
+%marginal_nodes(engine,4)
+%marginal_nodes(engine,5)
+%evidence{1}=2;
+%[engine,loglik]=enter_evidence(engine,evidence)
+%marginal_nodes(engine,1)
+%marginal_nodes(engine,2)
+%marginal_nodes(engine,3)
+%marginal_nodes(engine,4)
+%marginal_nodes(engine,5)
+%evidence{2}=0.6;
+%evidence{1}=[];
+%[engine,loglik]=enter_evidence(engine,evidence);
+%marginal_nodes(engine,3);
+%marginal_nodes(engine,4);
+%marginal_nodes(engine,5);
+end
\ No newline at end of file
diff --git a/sourcecodes/parameter_learning/Predictmultipleintrvention.m b/sourcecodes/parameter_learning/Predictmultipleintrvention.m
new file mode 100644
index 00000000..d3d509cb
--- /dev/null
+++ b/sourcecodes/parameter_learning/Predictmultipleintrvention.m
@@ -0,0 +1,73 @@
+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');
+
+%nnodes=5;
+Std_flag=true;
+[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag);
+[bnet]=parameterLearning(bnet,cases);
+
+%[predict_mean,predict_sd,q_sq]=looCrossValid(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');
+
+filename=strcat(pre,'net_figure_new.txt');
+
+drawFigureM(nnodes,bnet,labels,filename,cases,select_var_new,select_var_data_new);
+
+%quit force;
+%marginal_nodes(engine,2)
+%marginal_nodes(engine,3)
+%marginal_nodes(engine,4)
+%marginal_nodes(engine,5)
+%evidence{1}=2;
+%[engine,loglik]=enter_evidence(engine,evidence)
+%marginal_nodes(engine,1)
+%marginal_nodes(engine,2)
+%marginal_nodes(engine,3)
+%marginal_nodes(engine,4)
+%marginal_nodes(engine,5)
+%evidence{2}=0.6;
+%evidence{1}=[];
+%[engine,loglik]=enter_evidence(engine,evidence);
+%marginal_nodes(engine,3);
+%marginal_nodes(engine,4);
+%marginal_nodes(engine,5);
+end
\ No newline at end of file
diff --git a/sourcecodes/parameter_learning/checkDiscreteNodes.m b/sourcecodes/parameter_learning/checkDiscreteNodes.m
new file mode 100644
index 00000000..c9d0692c
--- /dev/null
+++ b/sourcecodes/parameter_learning/checkDiscreteNodes.m
@@ -0,0 +1,37 @@
+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/checkStructure.m b/sourcecodes/parameter_learning/checkStructure.m
new file mode 100644
index 00000000..5931c187
--- /dev/null
+++ b/sourcecodes/parameter_learning/checkStructure.m
@@ -0,0 +1,78 @@
+function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, cases, dag, node_sizes)
+    %checkStructure Check to see if nodes are sorted correctly.  They must be
+    %   in topological order (i.e., parents before children) before parameter
+    %   learning can take place.
+    %
+    %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/drawFigure.m b/sourcecodes/parameter_learning/drawFigure.m
new file mode 100644
index 00000000..76979b3e
--- /dev/null
+++ b/sourcecodes/parameter_learning/drawFigure.m
@@ -0,0 +1,383 @@
+function [] = drawFigure(nnodes,bnet,labels,filename,cases,selectvar,selectdata)
+%drawFigure writes the parameters and data that are needed to draw the
+%structure of a Bayesian network.
+
+if nargin < 6,
+    drawFigureNoEv(nnodes,bnet,labels,filename,cases);
+else
+    drawFigureEv(nnodes,bnet,labels,filename,cases,selectvar,selectdata);
+end;
+
+end
+
+
+
+function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,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,
+            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)
+%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,
+            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/drawFigureM.m b/sourcecodes/parameter_learning/drawFigureM.m
new file mode 100644
index 00000000..95a27634
--- /dev/null
+++ b/sourcecodes/parameter_learning/drawFigureM.m
@@ -0,0 +1,258 @@
+function [] = drawFigureM(nnodes,bnet,labels,filename,cases,selectvar,selectdata)
+%drawFigure writes the parameters and data that are needed to draw the
+%structure of a Bayesian network.
+
+
+%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
+
+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 parents, sum down columns
+for i = 1:m,
+    di=selectvar(i,1);
+    ev_dat(di)=selectdata(i,1);   
+    
+    evidence{di}=ev_dat(di);
+    fprintf(fileID,'%i\t',di);
+end
+fprintf(fileID,'\n');
+
+%ev_dat
+
+% select_var = selectvar(1,1)
+% 
+% select_var_data = selectdata(1,1)
+% 
+% 
+% evidence{select_var}=select_var_data;
+
+[engine,loglik]=enter_evidence(engine,evidence);
+
+%Open the file, and write the nodes to a file.
+
+
+%%%%Evidence node
+
+%%% 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,
+            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',ev_dat(i),1);
+    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/getParams.m b/sourcecodes/parameter_learning/getParams.m
new file mode 100644
index 00000000..31f84ffb
--- /dev/null
+++ b/sourcecodes/parameter_learning/getParams.m
@@ -0,0 +1,22 @@
+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/parameterLearning.m b/sourcecodes/parameter_learning/parameterLearning.m
new file mode 100644
index 00000000..872e94b1
--- /dev/null
+++ b/sourcecodes/parameter_learning/parameterLearning.m
@@ -0,0 +1,17 @@
+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/readInput.m b/sourcecodes/parameter_learning/readInput.m
new file mode 100644
index 00000000..9d4959b8
--- /dev/null
+++ b/sourcecodes/parameter_learning/readInput.m
@@ -0,0 +1,70 @@
+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);
+%draw_graph(dag,labels);
+%if ord_flag == 1
+%    fprintf(['Order of nodes was changed to agree with topological order\n'])
+%end
+%fprintf(['The structure of the network should be correctly displayed in a figure\n'])
+
+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
\ No newline at end of file
diff --git a/sourcecodes/parameter_learning/readInputData.m b/sourcecodes/parameter_learning/readInputData.m
new file mode 100644
index 00000000..706e2751
--- /dev/null
+++ b/sourcecodes/parameter_learning/readInputData.m
@@ -0,0 +1,75 @@
+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/readInputStructure.m b/sourcecodes/parameter_learning/readInputStructure.m
new file mode 100644
index 00000000..6b3cbece
--- /dev/null
+++ b/sourcecodes/parameter_learning/readInputStructure.m
@@ -0,0 +1,72 @@
+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/runBN_initial.m b/sourcecodes/parameter_learning/runBN_initial.m
new file mode 100644
index 00000000..789b4260
--- /dev/null
+++ b/sourcecodes/parameter_learning/runBN_initial.m
@@ -0,0 +1,98 @@
+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');
+
+
+%nnodes=5;
+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%d\t%f\t%f\n',labelsold{i},node_sizes(i),s(i),m(i));
+end
+
+
+% for j=1:nnodes
+%     [next,buffer] = strtok(buffer);
+%     name{j}=next;
+%     for i=1:nnodes    
+%         if strcmp(name{j},labels{i})
+%             map{j}=i;
+%             fprintf(mapfile,'%d\t',i);
+%         end
+%      end
+% end
+%name
+%labels
+%map
+fprintf(mapfile,'%s',labels{1});
+for i=2:nnodes
+  fprintf(mapfile,'\t%s',labels{i});
+end
+fprintf(mapfile,'\n');
+
+[bnet]=parameterLearning(bnet,cases);
+%[predict_mean,predict_sd,q_sq]=looCrossValid(bnet,cases);
+%engine=jtree_inf_engine(bnet);
+%evidence=cell(1,nnodes);
+
+%varfile='var.txt';
+%fvar = fopen(varfile,'r');
+%select_var = fscanf(fvar,'%d');
+%select_var=map{select_var};
+%varfiled='vardata.txt';
+%fvard = fopen(varfiled,'r');
+%select_var_data = fscanf(fvard,'%f');
+
+%evidence{select_var}=select_var_data;
+%[engine,loglik]=enter_evidence(engine,evidence);
+
+%outdata='prediction.txt';
+%fout = fopen(outdata,'w');
+
+%for ii = 1:nnodes 
+ % i=map{ii};
+ % data=marginal_nodes(engine,i);
+ % fprintf(fout,'%d\t%d\t%f\t%f\t%f\n',ii,data.domain,data.T,data.mu,data.Sigma);
+  %fprintf(1,'%d\n',i);
+% end
+
+filename=strcat(pre,'net_figure.txt');
+drawFigure(nnodes,bnet,labels,filename,cases);
+
+%quit force;
+%marginal_nodes(engine,2)
+%marginal_nodes(engine,3)
+%marginal_nodes(engine,4)
+%marginal_nodes(engine,5)
+%evidence{1}=2;
+%[engine,loglik]=enter_evidence(engine,evidence)
+%marginal_nodes(engine,1)
+%marginal_nodes(engine,2)
+%marginal_nodes(engine,3)
+%marginal_nodes(engine,4)
+%marginal_nodes(engine,5)
+%evidence{2}=0.6;
+%evidence{1}=[];
+%[engine,loglik]=enter_evidence(engine,evidence);
+%marginal_nodes(engine,3);
+%marginal_nodes(engine,4);
+%marginal_nodes(engine,5);
+fclose(mapval);
+fclose(mapfile);
+end
\ No newline at end of file
diff --git a/sourcecodes/parameter_learning/standardizeData.m b/sourcecodes/parameter_learning/standardizeData.m
new file mode 100644
index 00000000..61ea280e
--- /dev/null
+++ b/sourcecodes/parameter_learning/standardizeData.m
@@ -0,0 +1,25 @@
+function [ cases ] = standardizeData( labels, node_sizes, cases )
+%standardizeData standardizes continuous nodes so they have a mean = 0
+%   and standard deviation = 1
+%   Detailed explanation goes here
+
+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/test/Agbcontinuous_input.txt b/sourcecodes/parameter_learning/test/Agbcontinuous_input.txt
new file mode 100644
index 00000000..bcd9b88c
--- /dev/null
+++ b/sourcecodes/parameter_learning/test/Agbcontinuous_input.txt
@@ -0,0 +1,103 @@
+GenotypeA	GenotypeB	Gene1	Gene2	Gene3	Gene4
+2	2	1	1	1	1
+1	1	0.0735451012188	0.807744827105	-0.141557122166	0.871977046116
+2	1	0.0783291492541	0.784023461068	0.501395957396	1.20598627055
+2	1	0.786243065384	0.978600201012	1.10615045137	0.91427570527
+2	1	-0.133165253244	1.09368397217	0.943147613583	1.28625182746
+2	1	0.849732696834	0.701697179341	1.1597647359	1.10898527576
+2	1	0.117358779641	1.27641582521	1.07600246132	0.837957699405
+1	2	0.260845541489	0.126507267356	0.134953769296	1.05166904426
+2	1	0.277734881926	1.07193390309	0.282304188176	1.11305323003
+1	2	0.23482774261	1.22992089679	0.0929753295409	1.16341704702
+1	2	0.948183366714	0.133267523413	-0.218162396333	0.906540379337
+2	1	-0.116613369121	1.14203606435	0.109901766308	0.729527128692
+1	1	-0.179825551391	0.10619275827	0.937787825497	1.03567603615
+2	1	0.293022973091	1.08721255661	-0.175473007708	0.887010836361
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diff --git a/sourcecodes/parameter_learning/test/Agbmap.txt b/sourcecodes/parameter_learning/test/Agbmap.txt
new file mode 100644
index 00000000..bb1dd64f
--- /dev/null
+++ b/sourcecodes/parameter_learning/test/Agbmap.txt
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diff --git a/sourcecodes/parameter_learning/test/Agbmapdata.txt b/sourcecodes/parameter_learning/test/Agbmapdata.txt
new file mode 100644
index 00000000..fa8be0bd
--- /dev/null
+++ b/sourcecodes/parameter_learning/test/Agbmapdata.txt
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diff --git a/sourcecodes/parameter_learning/test/Agbnet_figure.txt b/sourcecodes/parameter_learning/test/Agbnet_figure.txt
new file mode 100644
index 00000000..8243b7d9
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diff --git a/sourcecodes/parameter_learning/test/Agbnet_figure.txt.bk b/sourcecodes/parameter_learning/test/Agbnet_figure.txt.bk
new file mode 100644
index 00000000..8243b7d9
--- /dev/null
+++ b/sourcecodes/parameter_learning/test/Agbnet_figure.txt.bk
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diff --git a/sourcecodes/parameter_learning/test/Agbnnode.txt b/sourcecodes/parameter_learning/test/Agbnnode.txt
new file mode 100644
index 00000000..1e8b3149
--- /dev/null
+++ b/sourcecodes/parameter_learning/test/Agbnnode.txt
@@ -0,0 +1 @@
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diff --git a/sourcecodes/parameter_learning/test/Agbstructure_input.txt b/sourcecodes/parameter_learning/test/Agbstructure_input.txt
new file mode 100644
index 00000000..10766f0c
--- /dev/null
+++ b/sourcecodes/parameter_learning/test/Agbstructure_input.txt
@@ -0,0 +1,7 @@
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diff --git a/sourcecodes/parameter_learning/test/octave-core b/sourcecodes/parameter_learning/test/octave-core
new file mode 100644
index 00000000..678fda92
--- /dev/null
+++ b/sourcecodes/parameter_learning/test/octave-core
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