1 files changed, 106 insertions, 0 deletions
diff --git a/sourcecodes/parameter_learning/code_backup/writeParameters.m b/sourcecodes/parameter_learning/code_backup/writeParameters.m
new file mode 100644
index 00000000..0790a8e2
--- /dev/null
+++ b/sourcecodes/parameter_learning/code_backup/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
+