Separating Octave and php calculations

1 files changed, 11 insertions, 6 deletions

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;