diff options
| author | ziejd2 | 2019-01-28 16:37:57 -0600 |
|---|---|---|
| committer | ziejd2 | 2019-01-28 16:37:57 -0600 |
| commit | 995f6673b5e725d6907ccd4f8e25033e8524f116 (patch) | |
| tree | 5df08d9503394eab1190a01a640f3c338e8c74d6 /sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m | |
| parent | bb9d93322abf825368dedaafc2376ef8fdfc1e2f (diff) | |
| download | BNW-995f6673b5e725d6907ccd4f8e25033e8524f116.tar.gz | |
Deleting old versions of files
Diffstat (limited to 'sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m')
| -rw-r--r-- | sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m | 95 |
1 files changed, 0 insertions, 95 deletions
diff --git a/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m b/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m deleted file mode 100644 index e9f741f2..00000000 --- a/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m +++ /dev/null @@ -1,95 +0,0 @@ -function Predictmultipleintrvention(pre) -dfile=strcat(pre,'structure_input.txt'); -sfile=dfile; -dfile=strcat(pre,'continuous_input.txt'); -nnodefile=strcat(pre,'nnode.txt'); - -fnnode = fopen(nnodefile,'r'); -nnodes = fscanf(fnnode,'%d'); - -fvarnamefile=strcat(pre,'varname.txt'); - -varfile = fopen(fvarnamefile,'r'); - -Std_flag=true; -[labels,cases,bnet]=readInput(dfile,sfile,nnodes,Std_flag); - -[bnet]=parameterLearning(bnet,cases); - -fvarfile=strcat(pre,'var.txt'); -fvar = fopen(fvarfile,'r'); -select_var_new = fscanf(fvar,'%d'); - -nm = numel(select_var_new); - -varlabels = cell(1,nm); -varbuffer = fgetl(varfile); %get header line as a string -for j=1:nm - [varnext,varbuffer] = strtok(varbuffer); - varlabels{j} = varnext; - for i=1:nnodes - if strcmp(varlabels{j},labels{i}) - select_var_new(j)=i; - end - end - -end - - - - -fvardfile=strcat(pre,'vardata.txt'); - -fvard = fopen(fvardfile,'r'); - -select_var_data_new = fscanf(fvard,'%f'); - -means_orig = cell(1,nnodes); -stdevs_orig = cell(1,nnodes); -labels_orig = cell(1,nnodes); -%Read in original means and standard deviations -mapfile = strcat(pre,'map.txt'); -fmap = fopen(mapfile,'r'); -for i=1:nnodes - buffer = fgetl(mapfile); - temp = cell(1,3); - for j=1:3 - [next,buffer] = strtok(buffer); - temp{j} = next; - end - labels_orig{i} = temp{1}; - means_orig{i} = str2num(temp{3}); - stdevs_orig{i} = str2num(temp{2}); -end -fclose(fmap); - -%Need to map the means and stdevs to the correct labels -means = cell(1,nnodes); -stdevs = cell(1,nnodes); -%Read in labels in new order. -labelsnew = cell(1,nnodes); -mapdatafile = strcat(pre,'mapdata.txt'); -fmapdata = fopen(mapdatafile,'r'); -buffer = fgetl(fmapdata); -for i = 1:nnodes - [next,buffer ] = strtok(buffer); - labelsnew{i} = next; -end -fclose(fmapdata); -for i = 1:nnodes - for j = 1:nnodes - if strcmp(labelsnew{i},labels_orig{j}) - means{i} = means_orig{j}; - stdevs{i} = stdevs_orig{j}; - break - end - end -end - -filename=strcat(pre,'net_figure_new.txt'); - -drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,select_var_new,select_var_data_new); - -writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,select_var_new,select_var_data_new); - -end |
