Deleting parameter learning folder

There was an extra folder with the Matlab/Octave parameter learning files. These were out of date, so I deleted them and kept the versions in the sourcecoades directory.

1 files changed, 0 insertions, 111 deletions

diff --git a/BNW_parameter_learning/writeParameters.m b/BNW_parameter_learning/writeParameters.m
deleted file mode 100644
index 42b2a4ef..00000000
--- a/BNW_parameter_learning/writeParameters.m
+++ /dev/null
@@ -1,111 +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.
-%
-% The file is called ???parameters.txt where ??? is the prefix in BNW
-%    for the network.
-%
-% writeParameters is called by runBN_intial.m
-
-
-%%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
-