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function [] = writeParameters_ev(pre,bnet,nnodes,labels,cases,stdevs,means,selectvar,selectdata)
%Writes a file that contains the parameters of the network after entering evidence.
%
% The file is called ???parameters_ev.txt where ??? is the prefix in BNW
% for the network.
%
% It is called by Predictmultiple.m
%Read in original node labels to get node IDs.
infile = strcat(pre,'continuous_input.txt');
fin = fopen(infile,'r');
labelsold = cell(1,nnodes);
buffer = fgetl(fin);
for j = 1:nnodes
[next,buffer] = strtok(buffer);
labelsold{j} = next;
end
fclose(fin);
evidence = cell(1,nnodes);
engine = jtree_inf_engine(bnet);
m = size(selectvar,1);
%%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
ev_dat = zeros(1,nnodes);
for i = 1:m,
di=selectvar(i,1);
ev_dat(di)=selectdata(i,1);
%Need to standardize 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);
end
[engine,loglik]=enter_evidence(engine,evidence);
%Open output file.
filename = strcat(pre,'parameters_ev.txt');
fileID = fopen(filename,'w');
for i = 1:nnodes
for j = 1:nnodes
if strcmp(labelsold{i},labels{j});
nodeid = j;
break
end
end
%%%Print the name of the node
fprintf(fileID,'%s\n',labels{nodeid});
predict = marginal_nodes(engine,nodeid);
if isempty(evidence{nodeid})
%%%Print the type of node
if bnet.node_sizes(nodeid) == 1;
line = 'Continuous parameters considering evidence:\n';
fprintf(fileID,line);
%line = 'Mean and standard deviation of Gaussian distribution\n';
%fprintf(fileID,line);
adj_mu = predict.mu*stdevs{nodeid}+means{nodeid};
adj_sigma = stdevs{nodeid}*predict.Sigma;
fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma);
else
line = 'Probability of states considering evidence:\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
else
if bnet.node_sizes(nodeid) == 1;
line = 'Evidence was observed for this node. The observed value was:\n';
fprintf(fileID,line);
adj_mu = ev_dat(nodeid)*stdevs{nodeid}+means{nodeid};
fprintf(fileID,'%6.4f\n\n',adj_mu);
else
nodeid2 = 0;
for k = 1:ndisc_nodes,
if strcmp(levels{k,1},labels{nodeid}),
nodeid2 = k;
break
end
end
line = 'Evidence was observed for this node. The observed state was:\n';
fprintf(fileID,line);
state_ev = uint16(ev_dat(nodeid));
% fprintf(fileID,'%i\n\n',levels{nodeid2,state_ev+1});
fprintf(fileID,'%s\n\n',levels{nodeid2,state_ev+1});
end
end
end
fclose(fileID);
end
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