Separating Octave and php calculations

11 files changed, 876 insertions, 186 deletions

diff --git a/BNW_parameter_learning/Predictmultiple.m b/BNW_parameter_learning/Predictmultiple.m
index c14b516c..781c64a4 100644
--- a/BNW_parameter_learning/Predictmultiple.m
+++ b/BNW_parameter_learning/Predictmultiple.m
@@ -1,57 +1,72 @@
 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');

-                            

+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(1,4);

+    for j=1:4

+        [next,buffer] = strtok(buffer);

+        temp{j} = next;

+    end

+    labels_orig{i} = temp{1};

+    means_orig{i} = str2num(temp{4});

+    stdevs_orig{i} = str2num(temp{3});

+end

+fclose(fmap);

+

+%Need to map the means and stdevs to the correct labels

+means = cell(1,nnodes);

+stdevs = cell(1,nnodes);

+%Read in labels in new order.

+labelsnew = cell(1,nnodes);

+mapdatafile = strcat(pre,'mapdata.txt');

+fmapdata = fopen(mapdatafile,'r');

+buffer = fgetl(fmapdata);

+for i = 1:nnodes

+    [next,buffer ] = strtok(buffer);

+    labelsnew{i} = next;

+end

+fclose(fmapdata);

+for i = 1:nnodes

+    for j = 1:nnodes

+       if strcmp(labelsnew{i},labels_orig{j})

+          means{i} = means_orig{j};

+          stdevs{i} = stdevs_orig{j};

+          break

+       end

+    end

+end

+

+

 filename=strcat(pre,'net_figure_new.txt');

 

-drawFigureM(nnodes,bnet,labels,filename,cases,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
+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/BNW_parameter_learning/Predictmultipleintrvention.m b/BNW_parameter_learning/Predictmultipleintrvention.m
index 4675b846..eaec60dc 100644
--- a/BNW_parameter_learning/Predictmultipleintrvention.m
+++ b/BNW_parameter_learning/Predictmultipleintrvention.m
@@ -11,17 +11,13 @@ 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);

 

+[bnet]=parameterLearning(bnet,cases);

 

 fvarfile=strcat(pre,'var.txt');

-fvar = fopen(fvarfile,'r');

-                            

+fvar = fopen(fvarfile,'r');                           

 select_var_new = fscanf(fvar,'%d');

 

 nm = numel(select_var_new);

@@ -48,26 +44,52 @@ fvard = fopen(fvardfile,'r');
 

 select_var_data_new = fscanf(fvard,'%f');

 

+means_orig = cell(1,nnodes);

+stdevs_orig = cell(1,nnodes);

+labels_orig = cell(1,nnodes);

+%Read in original means and standard deviations

+mapfile = strcat(pre,'map.txt');

+fmap = fopen(mapfile,'r');

+for i=1:nnodes

+    buffer = fgetl(mapfile);

+    temp = cell(1,4);

+    for j=1:4

+        [next,buffer] = strtok(buffer);

+        temp{j} = next;

+    end

+    labels_orig{i} = temp{1};

+    means_orig{i} = str2num(temp{4});

+    stdevs_orig{i} = str2num(temp{3});

+end

+fclose(fmap);

+

+%Need to map the means and stdevs to the correct labels

+means = cell(1,nnodes);

+stdevs = cell(1,nnodes);

+%Read in labels in new order.

+labelsnew = cell(1,nnodes);

+mapdatafile = strcat(pre,'mapdata.txt');

+fmapdata = fopen(mapdatafile,'r');

+buffer = fgetl(fmapdata);

+for i = 1:nnodes

+    [next,buffer ] = strtok(buffer);

+    labelsnew{i} = next;

+end

+fclose(fmapdata);

+for i = 1:nnodes

+    for j = 1:nnodes

+       if strcmp(labelsnew{i},labels_orig{j})

+          means{i} = means_orig{j};

+          stdevs{i} = stdevs_orig{j};

+          break

+       end

+    end

+end

+

 filename=strcat(pre,'net_figure_new.txt');

 

-drawFigureM(nnodes,bnet,labels,filename,cases,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
+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/BNW_parameter_learning/drawFigure.m b/BNW_parameter_learning/drawFigure.m
index a5de0f2d..fa963a4b 100644
--- a/BNW_parameter_learning/drawFigure.m
+++ b/BNW_parameter_learning/drawFigure.m
@@ -1,18 +1,19 @@
-function [] = drawFigure(nnodes,bnet,labels,filename,cases,selectvar,selectdata)

+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 < 6,

-    drawFigureNoEv(nnodes,bnet,labels,filename,cases);

+

+if nargin < 8,

+    drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means);

 else

-    drawFigureEv(nnodes,bnet,labels,filename,cases,selectvar,selectdata);

+    drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata);

 end;

 

 end

 

 

 

-function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,selectvar,selectdata)

+function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata)

 %Function to use if there is no entered evidence. 

 %         

 %

@@ -152,6 +153,8 @@ for i = 1:nnodes,
         [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;

@@ -172,7 +175,7 @@ end
 

 

 

-function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases)

+function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means)

 %Function to use if there is no entered evidence. 

 %         

 %

@@ -302,6 +305,8 @@ for i = 1:nnodes,
         [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;

diff --git a/BNW_parameter_learning/drawFigureM.m b/BNW_parameter_learning/drawFigureM.m
index bca223ce..9aa77d35 100644
--- a/BNW_parameter_learning/drawFigureM.m
+++ b/BNW_parameter_learning/drawFigureM.m
@@ -1,21 +1,6 @@
-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

+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');

 

@@ -30,49 +15,35 @@ 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);   

-    

+%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');

-

-%ev_dat

 

-% select_var = selectvar(1,1)

-% 

-% select_var_data = selectdata(1,1)

-% 

-% 

-% evidence{select_var}=select_var_data;

+fprintf(fileID,'\n');

 

 [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)

-

+fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim);

 x = x*x_dim;

 y = y*y_dim;

 for i = 1:nnodes,

@@ -99,7 +70,6 @@ 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

@@ -162,23 +132,25 @@ 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));

         %%%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

-      fprintf(fileID,'%6.4f\t%6.4f\n',ev_dat(i),1);

+      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

-%fprintf(fileID,'%s\t %\n',labels_temp{:});

-

 

 fclose(fileID);

-

 end

 

 

diff --git a/BNW_parameter_learning/prepareInput.m b/BNW_parameter_learning/prepareInput.m
new file mode 100644
index 00000000..28a06c15
--- /dev/null
+++ b/BNW_parameter_learning/prepareInput.m
@@ -0,0 +1,281 @@
+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
+
+%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;
+        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;
+    % If there are more than twenty unique values,
+    %      I will assume that the node is continuous.
+    elseif levels{j} > 20;
+       levels{j} = 1;
+    % Otherwise, I will scan through the individual values.
+    % If any of the values contain a '.', I will assume it is continuous.
+    else
+       period_test = 0;
+       column = data(:,j);
+       k = 1;
+       while period_test == 0 
+           period_test = sum(cell2mat(strfind(column(k),".")));
+           if period_test != 0;
+              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;
+  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 = {};
+  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}};
+    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});
+       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,'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/BNW_parameter_learning/readInput.m b/BNW_parameter_learning/readInput.m
index c2c16331..2be0af29 100644
--- a/BNW_parameter_learning/readInput.m
+++ b/BNW_parameter_learning/readInput.m
@@ -25,19 +25,12 @@ end
 [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

@@ -67,4 +60,4 @@ end
         

 

 end

-%  end of readInput.m
\ No newline at end of file
+%  end of readInput.m

diff --git a/BNW_parameter_learning/runBN_initial.m b/BNW_parameter_learning/runBN_initial.m
index c3f2a34b..43caf402 100644
--- a/BNW_parameter_learning/runBN_initial.m
+++ b/BNW_parameter_learning/runBN_initial.m
@@ -15,84 +15,43 @@ 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);

+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');

+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);

-%[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
+

+drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means);

+

+writeParameters(pre,nnodes,bnet,labels,cases,labelsold,s,m);

+

+end

diff --git a/BNW_parameter_learning/standardizeData.m b/BNW_parameter_learning/standardizeData.m
index ba4ef5dd..449aa440 100644
--- a/BNW_parameter_learning/standardizeData.m
+++ b/BNW_parameter_learning/standardizeData.m
@@ -1,7 +1,7 @@
 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);

 

diff --git a/BNW_parameter_learning/writeParameters.m b/BNW_parameter_learning/writeParameters.m
new file mode 100644
index 00000000..0790a8e2
--- /dev/null
+++ b/BNW_parameter_learning/writeParameters.m
@@ -0,0 +1,106 @@
+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/BNW_parameter_learning/writeParameters_ev.m b/BNW_parameter_learning/writeParameters_ev.m
new file mode 100644
index 00000000..fc24e2e5
--- /dev/null
+++ b/BNW_parameter_learning/writeParameters_ev.m
@@ -0,0 +1,151 @@
+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/BNW_parameter_learning/writeParameters_int.m b/BNW_parameter_learning/writeParameters_int.m
new file mode 100644
index 00000000..ed92d593
--- /dev/null
+++ b/BNW_parameter_learning/writeParameters_int.m
@@ -0,0 +1,186 @@
+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
+