diff options
Diffstat (limited to 'sourcecodes/parameter_learning/writeParameters_int.m')
| -rw-r--r-- | sourcecodes/parameter_learning/writeParameters_int.m | 29 |
1 files changed, 17 insertions, 12 deletions
diff --git a/sourcecodes/parameter_learning/writeParameters_int.m b/sourcecodes/parameter_learning/writeParameters_int.m index 69dcbb93..98e4edf1 100644 --- a/sourcecodes/parameter_learning/writeParameters_int.m +++ b/sourcecodes/parameter_learning/writeParameters_int.m @@ -5,6 +5,7 @@ function [] = writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,sele % for the network. % % writeParameters is called by Predictmultipleintervention.m +% Modifying to simplify output to make a table on webpage. %First read input file to get node labels to get node IDs. infile = strcat(pre,'continuous_input.txt'); @@ -129,21 +130,22 @@ for i = 1:nnodes %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}); + fprintf(fileID,'%s considering intervention\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 = '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; + adj_sigma = stdevs{nodeid}*sqrt(predict.Sigma); + fprintf(fileID,"Mean\tSt Dev\n"); fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma); else - line = 'Probability of states considering intervention:\n'; - fprintf(fileID,line); + %line = 'Probability of states considering intervention:\n'; + %fprintf(fileID,line); nodeid2 = 0; for k = 1:ndisc_nodes, if strcmp(levels{k,1},labels{nodeid}), @@ -151,6 +153,7 @@ for i = 1:nnodes 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)); @@ -160,10 +163,11 @@ for i = 1:nnodes end else if bnet.node_sizes(nodeid) == 1; - line = 'Intervention on this node assigned the following value:\n'; - fprintf(fileID,line); + %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); + fprintf(fileID,"Fixed by\tValue\n"); + fprintf(fileID,'Intervention\t%6.4f\n\n',adj_mu); else nodeid2 = 0; for k = 1:ndisc_nodes, @@ -172,11 +176,12 @@ for i = 1:nnodes break end end - line = 'Intervention on this node assigned the following state:\n'; - fprintf(fileID,line); + %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}); + fprintf(fileID,"Fixed by\tValue\n"); + fprintf(fileID,'Intervention\t%s\n\n',levels{nodeid2,state_ev+1}); end end end |