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Diffstat (limited to 'BNW_parameter_learning/writeParameters_int.m')
| -rw-r--r-- | BNW_parameter_learning/writeParameters_int.m | 190 |
1 files changed, 0 insertions, 190 deletions
diff --git a/BNW_parameter_learning/writeParameters_int.m b/BNW_parameter_learning/writeParameters_int.m deleted file mode 100644 index 69dcbb93..00000000 --- a/BNW_parameter_learning/writeParameters_int.m +++ /dev/null @@ -1,190 +0,0 @@ -function [] = writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,selectvar,selectdata) -%Writes a file that contains the parameters of the network after intervention. -% -% The file is called ???parameters_ev.txt where ??? is the prefix in BNW -% for the network. -% -% writeParameters is called by Predictmultipleintervention.m - -%First read input file to get 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 - -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); - -%Get list of nodes that are children, grandchildren, etc. of intervened nodes -%int_nodes contains the list of these children nodes -int_nodes = zeros(1,nnodes); -%new_nodes is just a temporary array to know when to keep looking -new_nodes = zeros(1,nnodes); -for i = 1:nnodes - if !isempty(evidence{i}); - new_nodes(i) = 1; - int_nodes(i) = 1; - end -end -while sum(new_nodes) != 0 - new_nodes_old = new_nodes; - new_nodes = zeros(1,nnodes); - for i = 1:nnodes - if new_nodes_old(i) == 1 - for j = 1:nnodes - if int_nodes(j) == 0 - if bnet.dag(i,j) == 1, - new_nodes(j) = 1; - end - end - end - end - end - for i = 1:nnodes - if new_nodes(i) == 1; - int_nodes(i) = 1; - end - end -end - - -%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 - %check to see if this is a node impacted by intervention - if int_nodes(nodeid) == 1 - %%%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 intervention:\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 intervention:\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 = 'Intervention on this node assigned the following value:\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 = 'Intervention on this node assigned the following state:\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 -end - - - -fclose(fileID); - -end - |