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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
% Simplifying output to read in and make table on webpage.
%%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);
predict = marginal_nodes_no_ev(bnet,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;
%%% 'i' in the line below is correct: m and s are had original node labeling
CPD=struct(bnet.CPD{nodeid});
no_gaussians = size(CPD.Wsum)(1);
if no_gaussians == 1
%%%line = 'Continuous node\n';
%%%fprintf(fileID,line);
adj_mu = CPD.mean*s(i)+m(i);
adj_sigma = s(i)*sqrt(CPD.cov);
fprintf(fileID,'Mean\tSt Dev\n');
fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma);
else
%%%%line = 'Continuous node modeled as a mixture of %i Gaussian distributions\n';
%%%%fprintf(fileID,line,no_gaussians);
fprintf(fileID,'Mean\tSt Dev\tWeight\n');
Wsum = sum(CPD.Wsum);
for j=1:no_gaussians
adj_mu = CPD.mean(j)*s(i)+m(i);
adj_sigma = s(i)*sqrt(CPD.cov(j));
weight = CPD.Wsum(j)/Wsum;
fprintf(fileID,'%6.4f\t%6.4f\t%6.4f\n',adj_mu,adj_sigma,weight);
end
fprintf(fileID,'\n');
end
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
fprintf(fileID,'State\tProbability\n');
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
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