Separating Octave and php calculations

1 files changed, 16 insertions, 44 deletions

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