From 74b673ba4a706085201a5610b938ff98f08f641d Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Wed, 25 Apr 2018 16:43:19 -0500 Subject: Bug fixes, code comments, and minor changes --- BNW_parameter_learning/Predictmultiple.m | 22 +- .../Predictmultipleintervention.m | 107 + BNW_parameter_learning/checkDiscreteNodes.m | 2 + BNW_parameter_learning/checkStructure.m | 8 +- BNW_parameter_learning/drawFigure.m | 191 +- BNW_parameter_learning/drawFigureM.m | 11 +- BNW_parameter_learning/parameterLearning.m | 31 + BNW_parameter_learning/prepareInput.m | 21 +- BNW_parameter_learning/readInput.m | 3 + BNW_parameter_learning/readInputData.m | 4 + BNW_parameter_learning/readInputStructure.m | 3 + BNW_parameter_learning/runBN_initial.m | 15 +- BNW_parameter_learning/standardizeData.m | 9 +- BNW_parameter_learning/writeParameters.m | 5 + BNW_parameter_learning/writeParameters_ev.m | 6 + BNW_parameter_learning/writeParameters_int.m | 6 +- sourcecodes/BNW_workflow_net1.htm | 10 +- sourcecodes/BNW_workflow_sci.htm | 6 + sourcecodes/add_evd.php | 7 +- sourcecodes/add_evd_example.php | 9 +- sourcecodes/add_inv.php | 7 +- sourcecodes/add_inv_example.php | 7 +- sourcecodes/bn_file_load_gom.php | 22 +- .../BNT/potentials/@cgpot/marginalize_pot.m | 2 +- sourcecodes/bnt-master/KPMstats/clg_Mstep.m | 8 +- sourcecodes/bnt-master/graph/findroot.m | 2 +- sourcecodes/bnt-master/graph/findroot.m~ | 24 + sourcecodes/create_tiers_gom.php | 3 + sourcecodes/data/example1/Bqxmap.txt | 16 +- sourcecodes/data/example1/Bqxparameters.txt | 34 + sourcecodes/data/example2/hQGmap.txt | 16 +- sourcecodes/data/example2/hQGparameters.txt | 34 + sourcecodes/data/example_chl/bWRmap.txt | 10 +- sourcecodes/data/example_chl/bWRnet_figure.txt | 808 +++--- sourcecodes/data/example_chl/bWRparameters.txt | 21 + sourcecodes/data/example_chr2_spleen/cuLmap.txt | 28 +- .../data/example_chr2_spleen/cuLnet_figure.txt | 2626 ++++++++++---------- .../data/example_chr2_spleen/cuLparameters.txt | 57 + sourcecodes/data/example_sci/Llumap.txt | 10 +- sourcecodes/data/example_sci/Llunet_figure.txt | 808 +++--- sourcecodes/data/example_sci/Lluparameters.txt | 21 + sourcecodes/data/example_time_series/TEbmap.txt | 20 +- .../data/example_time_series/TEbnet_figure.txt | 1818 +++++++------- .../data/example_time_series/TEbparameters.txt | 41 + sourcecodes/data/examplecar15node/MtXmap.txt | 30 +- sourcecodes/data/examplecar15node/eIAmap.txt | 30 +- .../data/examplecar15node/eIAparameters.txt | 82 + sourcecodes/data/old/example_sci_bk/Lluban.txt | 15 + .../old/example_sci_bk/Llucontinuous_input.txt | 503 ++++ .../data/old/example_sci_bk/Llugraphviz.txt | 10 + sourcecodes/data/old/example_sci_bk/Lluk.txt | 1 + sourcecodes/data/old/example_sci_bk/Llumap.txt | 5 + sourcecodes/data/old/example_sci_bk/Llumapdata.txt | 1 + sourcecodes/data/old/example_sci_bk/Lluname.txt | 1 + .../data/old/example_sci_bk/Llunet_figure.txt | 433 ++++ .../data/old/example_sci_bk/Llunet_figure_new.txt | 433 ++++ sourcecodes/data/old/example_sci_bk/Llunlevels.txt | 2 + sourcecodes/data/old/example_sci_bk/Llunnode.txt | 1 + sourcecodes/data/old/example_sci_bk/Llunrows.txt | 1 + sourcecodes/data/old/example_sci_bk/Lluparent.txt | 1 + .../data/old/example_sci_bk/Llustructure_input.txt | 6 + .../old/example_sci_bk/Llustructure_input_temp.txt | 6 + .../data/old/example_sci_bk/Llustructure_old.txt | 5 + sourcecodes/data/old/example_sci_bk/Lluthr.txt | 1 + sourcecodes/data/old/example_sci_bk/Llutier.txt | 1 + sourcecodes/data/old/example_sci_bk/Llutype.txt | 2 + sourcecodes/data/old/example_sci_bk/Lluvar.txt | 1 + sourcecodes/data/old/example_sci_bk/Lluvardata.txt | 1 + sourcecodes/data/old/example_sci_bk/Lluvarname.txt | 1 + sourcecodes/data/old/example_sci_bk/Lluwhite.txt | 1 + .../example_sci_bk/old/Llurun_evidencemodified.sh | 38 + .../example_sci_bk/old/Llurun_initialstructure.sh | 38 + sourcecodes/data/old/examplecar/OVIban.txt | 1 + .../data/old/examplecar/OVIcontinuous_input.txt | 1004 ++++++++ sourcecodes/data/old/examplecar/OVIgraphviz.txt | 70 + sourcecodes/data/old/examplecar/OVIk.txt | 1 + sourcecodes/data/old/examplecar/OVImap.txt | 19 + sourcecodes/data/old/examplecar/OVImapdata.txt | 1 + sourcecodes/data/old/examplecar/OVIname.txt | 1 + sourcecodes/data/old/examplecar/OVInet_figure.txt | 237 ++ .../data/old/examplecar/OVInet_figure_new.txt | 138 + sourcecodes/data/old/examplecar/OVInnode.txt | 1 + sourcecodes/data/old/examplecar/OVInrows.txt | 1 + sourcecodes/data/old/examplecar/OVIparent.txt | 1 + .../data/old/examplecar/OVIstructure_input.txt | 20 + .../old/examplecar/OVIstructure_input_temp.txt | 20 + .../data/old/examplecar/OVIstructure_old.txt | 19 + sourcecodes/data/old/examplecar/OVIthr.txt | 1 + sourcecodes/data/old/examplecar/OVItype.txt | 2 + sourcecodes/data/old/examplecar/OVIvar.txt | 1 + sourcecodes/data/old/examplecar/OVIvardata.txt | 1 + sourcecodes/data/old/examplecar/OVIvarname.txt | 1 + sourcecodes/data/old/examplecar/OVIwhite.txt | 1 + .../old/examplecar/old/OVIrun_evidencemodified.sh | 38 + .../old/examplecar/old/OVIrun_initialstructure.sh | 38 + sourcecodes/data/old/examplezoo/fSfban.txt | 261 ++ .../data/old/examplezoo/fSfcontinuous_input.txt | 104 + sourcecodes/data/old/examplezoo/fSfgraphviz.txt | 61 + sourcecodes/data/old/examplezoo/fSfk.txt | 1 + sourcecodes/data/old/examplezoo/fSfmap.txt | 17 + sourcecodes/data/old/examplezoo/fSfmapdata.txt | 1 + sourcecodes/data/old/examplezoo/fSfname.txt | 1 + sourcecodes/data/old/examplezoo/fSfnet_figure.txt | 130 + sourcecodes/data/old/examplezoo/fSfnnode.txt | 1 + sourcecodes/data/old/examplezoo/fSfnrows.txt | 1 + sourcecodes/data/old/examplezoo/fSfparent.txt | 1 + .../data/old/examplezoo/fSfstructure_input.txt | 18 + .../old/examplezoo/fSfstructure_input_temp.txt | 18 + .../data/old/examplezoo/fSfstructure_old.txt | 16 + sourcecodes/data/old/examplezoo/fSfthr.txt | 1 + sourcecodes/data/old/examplezoo/fSftier.txt | 1 + sourcecodes/data/old/examplezoo/fSftype.txt | 2 + sourcecodes/data/old/examplezoo/fSfwhite.txt | 1 + .../old/examplezoo/old/fSfrun_initialstructure.sh | 38 + .../data/old/examplezoo/standardized_data.txt | 102 + sourcecodes/execute_bn_gom.php | 9 - sourcecodes/faq.php | 6 +- sourcecodes/help.php | 48 +- sourcecodes/home.php | 8 +- sourcecodes/k-best/index.html | 2 +- sourcecodes/network_layout_evd.php | 14 +- sourcecodes/network_layout_evd_2.php | 17 +- sourcecodes/network_layout_evd_2_example.php | 21 +- sourcecodes/network_layout_evd_example.php | 14 +- sourcecodes/network_layout_inv.php | 20 +- sourcecodes/network_layout_inv_2.php | 32 +- sourcecodes/network_layout_inv_2_example.php | 23 +- sourcecodes/network_layout_inv_example.php | 18 +- sourcecodes/parameter_learning/Predictmultiple.m | 22 +- .../Predictmultipleintervention.m | 107 + .../parameter_learning/checkDiscreteNodes.m | 2 + sourcecodes/parameter_learning/checkStructure.m | 8 +- .../code_backup/Predictmultiple.m | 72 + .../code_backup/Predictmultipleintrvention.m | 95 + .../code_backup/checkDiscreteNodes.m | 37 + .../code_backup/checkStructure.m | 78 + .../parameter_learning/code_backup/drawFigure.m | 390 +++ .../parameter_learning/code_backup/drawFigure.m~ | 388 +++ .../parameter_learning/code_backup/drawFigureM.m | 230 ++ .../parameter_learning/code_backup/getParams.m | 22 + .../code_backup/parameterLearning.m | 17 + .../parameter_learning/code_backup/prepareInput.m | 294 +++ .../parameter_learning/code_backup/prepareInput.m~ | 294 +++ .../parameter_learning/code_backup/readInput.m | 63 + .../parameter_learning/code_backup/readInputData.m | 75 + .../code_backup/readInputStructure.m | 72 + .../parameter_learning/code_backup/runBN_initial.m | 57 + .../code_backup/standardizeData.m | 25 + .../code_backup/writeParameters.m | 106 + .../code_backup/writeParameters_ev.m | 151 ++ .../code_backup/writeParameters_int.m | 186 ++ sourcecodes/parameter_learning/drawFigure.m | 191 +- sourcecodes/parameter_learning/drawFigureM.m | 11 +- sourcecodes/parameter_learning/parameterLearning.m | 31 + sourcecodes/parameter_learning/prepareInput.m | 21 +- sourcecodes/parameter_learning/readInput.m | 3 + sourcecodes/parameter_learning/readInputData.m | 4 + .../parameter_learning/readInputStructure.m | 3 + sourcecodes/parameter_learning/runBN_initial.m | 15 +- sourcecodes/parameter_learning/standardizeData.m | 9 +- sourcecodes/parameter_learning/writeParameters.m | 5 + .../parameter_learning/writeParameters_ev.m | 6 + .../parameter_learning/writeParameters_int.m | 6 +- sourcecodes/run_octave_inv | 2 +- sourcecodes/upload_structure_file.php | 270 +- 165 files changed, 10637 insertions(+), 3925 deletions(-) create mode 100644 BNW_parameter_learning/Predictmultipleintervention.m create mode 100644 sourcecodes/bnt-master/graph/findroot.m~ create mode 100644 sourcecodes/data/example1/Bqxparameters.txt create mode 100644 sourcecodes/data/example2/hQGparameters.txt create mode 100644 sourcecodes/data/example_chl/bWRparameters.txt create mode 100644 sourcecodes/data/example_chr2_spleen/cuLparameters.txt create mode 100644 sourcecodes/data/example_sci/Lluparameters.txt create mode 100644 sourcecodes/data/example_time_series/TEbparameters.txt create mode 100644 sourcecodes/data/examplecar15node/eIAparameters.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluban.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llucontinuous_input.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llugraphviz.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluk.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llumap.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llumapdata.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluname.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llunet_figure.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llunet_figure_new.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llunlevels.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llunnode.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llunrows.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluparent.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llustructure_input.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llustructure_input_temp.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llustructure_old.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluthr.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llutier.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Llutype.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluvar.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluvardata.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluvarname.txt create mode 100644 sourcecodes/data/old/example_sci_bk/Lluwhite.txt create mode 100644 sourcecodes/data/old/example_sci_bk/old/Llurun_evidencemodified.sh create mode 100644 sourcecodes/data/old/example_sci_bk/old/Llurun_initialstructure.sh create mode 100644 sourcecodes/data/old/examplecar/OVIban.txt create mode 100644 sourcecodes/data/old/examplecar/OVIcontinuous_input.txt create mode 100644 sourcecodes/data/old/examplecar/OVIgraphviz.txt create mode 100644 sourcecodes/data/old/examplecar/OVIk.txt create mode 100644 sourcecodes/data/old/examplecar/OVImap.txt create mode 100644 sourcecodes/data/old/examplecar/OVImapdata.txt create mode 100644 sourcecodes/data/old/examplecar/OVIname.txt create mode 100644 sourcecodes/data/old/examplecar/OVInet_figure.txt create mode 100644 sourcecodes/data/old/examplecar/OVInet_figure_new.txt create mode 100644 sourcecodes/data/old/examplecar/OVInnode.txt create mode 100644 sourcecodes/data/old/examplecar/OVInrows.txt create mode 100644 sourcecodes/data/old/examplecar/OVIparent.txt create mode 100644 sourcecodes/data/old/examplecar/OVIstructure_input.txt create mode 100644 sourcecodes/data/old/examplecar/OVIstructure_input_temp.txt create mode 100644 sourcecodes/data/old/examplecar/OVIstructure_old.txt create mode 100644 sourcecodes/data/old/examplecar/OVIthr.txt create mode 100644 sourcecodes/data/old/examplecar/OVItype.txt create mode 100644 sourcecodes/data/old/examplecar/OVIvar.txt create mode 100644 sourcecodes/data/old/examplecar/OVIvardata.txt create mode 100644 sourcecodes/data/old/examplecar/OVIvarname.txt create mode 100644 sourcecodes/data/old/examplecar/OVIwhite.txt create mode 100644 sourcecodes/data/old/examplecar/old/OVIrun_evidencemodified.sh create mode 100644 sourcecodes/data/old/examplecar/old/OVIrun_initialstructure.sh create mode 100644 sourcecodes/data/old/examplezoo/fSfban.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfcontinuous_input.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfgraphviz.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfk.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfmap.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfmapdata.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfname.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfnet_figure.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfnnode.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfnrows.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfparent.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfstructure_input.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfstructure_input_temp.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfstructure_old.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfthr.txt create mode 100644 sourcecodes/data/old/examplezoo/fSftier.txt create mode 100644 sourcecodes/data/old/examplezoo/fSftype.txt create mode 100644 sourcecodes/data/old/examplezoo/fSfwhite.txt create mode 100644 sourcecodes/data/old/examplezoo/old/fSfrun_initialstructure.sh create mode 100644 sourcecodes/data/old/examplezoo/standardized_data.txt create mode 100644 sourcecodes/parameter_learning/Predictmultipleintervention.m create mode 100644 sourcecodes/parameter_learning/code_backup/Predictmultiple.m create mode 100644 sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m create mode 100644 sourcecodes/parameter_learning/code_backup/checkDiscreteNodes.m create mode 100644 sourcecodes/parameter_learning/code_backup/checkStructure.m create mode 100644 sourcecodes/parameter_learning/code_backup/drawFigure.m create mode 100644 sourcecodes/parameter_learning/code_backup/drawFigure.m~ create mode 100644 sourcecodes/parameter_learning/code_backup/drawFigureM.m create mode 100644 sourcecodes/parameter_learning/code_backup/getParams.m create mode 100644 sourcecodes/parameter_learning/code_backup/parameterLearning.m create mode 100644 sourcecodes/parameter_learning/code_backup/prepareInput.m create mode 100644 sourcecodes/parameter_learning/code_backup/prepareInput.m~ create mode 100644 sourcecodes/parameter_learning/code_backup/readInput.m create mode 100644 sourcecodes/parameter_learning/code_backup/readInputData.m create mode 100644 sourcecodes/parameter_learning/code_backup/readInputStructure.m create mode 100644 sourcecodes/parameter_learning/code_backup/runBN_initial.m create mode 100644 sourcecodes/parameter_learning/code_backup/standardizeData.m create mode 100644 sourcecodes/parameter_learning/code_backup/writeParameters.m create mode 100644 sourcecodes/parameter_learning/code_backup/writeParameters_ev.m create mode 100644 sourcecodes/parameter_learning/code_backup/writeParameters_int.m diff --git a/BNW_parameter_learning/Predictmultiple.m b/BNW_parameter_learning/Predictmultiple.m index 781c64a4..019488a3 100644 --- a/BNW_parameter_learning/Predictmultiple.m +++ b/BNW_parameter_learning/Predictmultiple.m @@ -1,4 +1,18 @@ function Predictmultiple(pre) +% Predictmultiple is used when predicting the impact of entering +% evidence on the network. The 'multiple' part refers to +% it working when evidence for multiple nodes is entered. +% +% The input is 'pre'-- the prefix for the network and data +% in BNW. It reads information from several files from BNW. +% +% The output is ???net_figure_new.txt. It also calls +% writeParameters_ev to write the parameter file. +% +% It is called by the run_octave_evd file in the 'sourcecodes' directory. + + + dfile=strcat(pre,'structure_input.txt'); sfile=dfile; dfile=strcat(pre,'continuous_input.txt'); @@ -28,14 +42,14 @@ mapfile = strcat(pre,'map.txt'); fmap = fopen(mapfile,'r'); for i=1:nnodes buffer = fgetl(mapfile); - temp = cell(1,4); - for j=1:4 + 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{4}); - stdevs_orig{i} = str2num(temp{3}); + means_orig{i} = str2num(temp{3}); + stdevs_orig{i} = str2num(temp{2}); end fclose(fmap); diff --git a/BNW_parameter_learning/Predictmultipleintervention.m b/BNW_parameter_learning/Predictmultipleintervention.m new file mode 100644 index 00000000..7e6140a9 --- /dev/null +++ b/BNW_parameter_learning/Predictmultipleintervention.m @@ -0,0 +1,107 @@ +function Predictmultipleintervention(pre) +% Predictmultipleintervention is used when predicting the impact of +% intervention on the network. The 'multiple' part refers to +% it working when intervention for multiple nodes is entered. +% +% The input is 'pre'-- the prefix for the network and data +% in BNW. It reads information from several files from BNW. +% +% The output is ???net_figure_new.txt. It also calls +% writeParameters_int to write the parameter file. +% +% It is called by the run_octave_inv file in the 'sourcecodes' directory. + +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 diff --git a/BNW_parameter_learning/checkDiscreteNodes.m b/BNW_parameter_learning/checkDiscreteNodes.m index 7b2a695f..919128e4 100644 --- a/BNW_parameter_learning/checkDiscreteNodes.m +++ b/BNW_parameter_learning/checkDiscreteNodes.m @@ -7,7 +7,9 @@ function [ ] = checkDiscreteNodes( bnet, cases) % bnet: BNT bnet % cases: cell array of data % +% checkDiscreteNodes is called by readInput.m % + node_sizes = bnet.node_sizes; dnodes = bnet.dnodes; ndisc = size(dnodes,2); diff --git a/BNW_parameter_learning/checkStructure.m b/BNW_parameter_learning/checkStructure.m index fc72e4b7..104e39e1 100644 --- a/BNW_parameter_learning/checkStructure.m +++ b/BNW_parameter_learning/checkStructure.m @@ -1,7 +1,7 @@ function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, cases, dag, node_sizes) - %checkStructure Check to see if nodes are sorted correctly. They must be + %checkStructure Check to see if nodes are sorted correctly. Nodes must be % in topological order (i.e., parents before children) before parameter - % learning can take place. + % learning can take place. This function performs this sorting. % %Input and output have the same meaning. The output has just been %topologically ordered. @@ -9,6 +9,10 @@ function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, c % cases = cell array with the data. % dag = matrix with the strucutre of the network. % node_sizes = vector with the size of each node. +% +% checkStructure is called by readInput.m + + %make connections array %count how big you need the connections array to be diff --git a/BNW_parameter_learning/drawFigure.m b/BNW_parameter_learning/drawFigure.m index fa963a4b..599d8f3b 100644 --- a/BNW_parameter_learning/drawFigure.m +++ b/BNW_parameter_learning/drawFigure.m @@ -1,186 +1,17 @@ -function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) +function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means) %drawFigure writes the parameters and data that are needed to draw the -%structure of a Bayesian network. - - -if nargin < 8, - drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means); -else - drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata); -end; - -end - - - -function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) -%Function to use if there is no entered evidence. -% +%structure of a Bayesian network for BNW. +% This is the function that is called to create the initial +% net_figure file for the network (before evidence or intervention). % -%Before each printed line, I will have a line that starts with %%% -% that describes what will be on that line - -%Create an empty evidence cell array. - -%val=cases; -%for i = 1:nnodes -% val(i,1)=val(i,2); - -%end - -A=cell2mat(cases'); -Amax=max(A); -Amin=min(A); - - -evidence = cell(1,nnodes); -engine = jtree_inf_engine(bnet); - -evidence{selectvar}=selectdata; - -[engine,loglik]=enter_evidence(engine,evidence); - -%Open the file, and write the nodes to a file. -fileID = fopen(filename,'w'); - -%%%%Evidence node -fprintf(fileID,'%i\n',selectvar); -%%% The number of nodes -fprintf(fileID,'%i\n',nnodes); -%Get canvas size -labels_temp = cellstr(labels); -[x,y] = make_layout(bnet.dag); - -x = x - min(x); -y = 1 - y; -y = y - min(y); - -[x_dim,y_dim] = canvasSize(nnodes,x,y); - -%%% The dimensions of the canvas for the javascript code -fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim) - -x = x*x_dim; -y = y*y_dim; -for i = 1:nnodes, -%%% The name and X- and Y-positions of each node - fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i))); -end - -%Get the number of parents and children for each node. -num_par = zeros(1,nnodes); -%For parents, sum down columns -for i = 1:nnodes, - for j = 1:nnodes, - if bnet.dag(j,i) == 1, - num_par(i) = num_par(i) + 1; - end - end -end -num_child = zeros(1,nnodes); -for i = 1:nnodes, - for j = 1:nnodes, - if bnet.dag(i,j) == 1, - num_child(i) = num_child(i) + 1; - end - end -end - - -for i = 1:nnodes, - %%% The name and type of each node (1=continuous, the number of states - %%% if it is discrete - fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i)); - %%% The size of the node, I am going to keep them - %%% 250(width) by 150(height) for now - %Could modify this to change the width based on the length of the node - %name - fprintf(fileID,'%i\t%i\n',250,150); - %%% The number of parents of the node, and the parents - if num_par(i) == 0; - %%% If no parents: - fprintf(fileID,'%i\n',num_par(i)); - else - parents = zeros(1,num_par(i)); - k = 1; - for j = 1:nnodes, - if bnet.dag(j,i) == 1, - parents(1,k) = j; - k = k + 1; - end - end - format = '%i\t'; - for j = 1:num_par(i)-1, - format = strcat(format,'%i\t'); - end - format = strcat(format,'%i\n'); - %%%If there are parents: - fprintf(fileID,format,num_par(i),parents(1,:)); - end - - - %%% The number of children of the node, and the children - if num_child(i) == 0; - %%% If no children: - fprintf(fileID,'%i\n',num_child(i)); - else - children = zeros(1,num_child(i)); - k = 1; - for j = 1:nnodes, - if bnet.dag(i,j) == 1, - children(1,k) = j; - k = k + 1; - end - end - format = '%i\t'; - for j = 1:num_child(i)-1, - format = strcat(format,'%i\t'); - end - format = strcat(format,'%i\n'); - %%%If there are parents: - fprintf(fileID,format,num_child(i),children(1,:)); - end - - predict = marginal_nodes(engine,i); - if isempty(evidence{i}) - if bnet.node_sizes(i) ~= 1, - for j = 1:bnet.node_sizes(i), - %%%For discrete nodes, the state and the percent of that state - fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j)); - end; - else - - [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i)); - %%%For continuous nodes, print x and the pdf of a normal curve. - for j = 1:101, - %%Undo standardization - xvals(j,1) = xvals(j,1)*stdevs{i}+means{i} - fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1)); - end; - end; - else - fprintf(fileID,'%6.4f\t%6.4f\n',selectdata,1); - end - -end -%fprintf(fileID,'%s\t %\n',labels_temp{:}); - - -fclose(fileID); - -end - - - - - - -function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means) -%Function to use if there is no entered evidence. -% % -%Before each printed line, I will have a line that starts with %%% -% that describes what will be on that line +% The output is the file specified by 'filename'. +% For BNW, this file is called: ???net_figure.txt +% where ??? is the prefix. +% +% drawFigure is called by runBN_intial.m +% + A=cell2mat(cases'); Amax=max(A); Amin=min(A); diff --git a/BNW_parameter_learning/drawFigureM.m b/BNW_parameter_learning/drawFigureM.m index 9aa77d35..0e0d9b6e 100644 --- a/BNW_parameter_learning/drawFigureM.m +++ b/BNW_parameter_learning/drawFigureM.m @@ -1,6 +1,15 @@ function [] = drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) %drawFigureM writes the parameters and data that are needed to draw the -%structure of a Bayesian network after added evidence or intervention +%structure of a Bayesian network after adding evidence or intervention +%It creates the net_figure_new file after evidence/intervetion. +% +% The output file is specified by 'filename'. +% For BNW, the file is named ???net_figure_new.txt +% where ??? is the prefix. +% +% drawFigureM is called by Predictmultiple.m and Predictmultipleintervention.m + + fileID = fopen(filename,'w'); diff --git a/BNW_parameter_learning/parameterLearning.m b/BNW_parameter_learning/parameterLearning.m index d4b67c87..414ffe6c 100644 --- a/BNW_parameter_learning/parameterLearning.m +++ b/BNW_parameter_learning/parameterLearning.m @@ -1,5 +1,14 @@ function [ bnet ] = parameterLearning( bnet,cases,engine_name ) %parameterLearning Do parameter learning and inference +% It returns the bnet with parameters learned from the data in cases. +% +% This is very basic now. It could be modified to use different engine +% types in the future. Now, I always use the 'jtree_inf_engine'. +% +% +% parameterLearning is called by runBN_initial.m, +% Predictmultiple.m, and Predictmultipleintervention.m + %engine is an optional argument if nargin < 3 @@ -15,3 +24,25 @@ end end +function [ bnet ] = getParams( bnet, cases ) +%getParams Code to initialize CPT and do parameter learning. +%This will be very basic for now. I can add more options later. +% + +dnodes = bnet.dnodes; +cnodes = bnet.cnodes; +nnodes = size(dnodes,2)+size(cnodes,2); + +%make dnodes tabular_CPT +for i = 1:size(dnodes,2) + bnet.CPD{dnodes(i)} = tabular_CPD(bnet,dnodes(i)); +end + +for i = 1:size(cnodes,2) + bnet.CPD{cnodes(i)} = gaussian_CPD(bnet,cnodes(i)); +end + +bnet = learn_params(bnet,cases); + + +end diff --git a/BNW_parameter_learning/prepareInput.m b/BNW_parameter_learning/prepareInput.m index 28a06c15..84d2ae17 100644 --- a/BNW_parameter_learning/prepareInput.m +++ b/BNW_parameter_learning/prepareInput.m @@ -39,6 +39,8 @@ function [ ] = prepareInput( pre ) % 8-12) ???ban.txt, ???white.txt, ???k.txt, ???thr.txt, and % ???parent.txt: Files with default values for structure learning. % + % It is called by the run_prep_input script in the 'sourcecodes' directory. + % open file for input, include error handling dfile=strcat(pre,'continuous_input_orig.txt'); @@ -81,28 +83,36 @@ for j = 1:nnodes levels{j} = size(states{j},1); end +reason = cell(1,nnodes); %Now do some checks to see if nodes are discrete or continuous for j = 1:nnodes % If there are 3 or less unique values, I will assume that the node is discrete. if levels{j} < 4; + reason{j} = "It was determined to be discrete because there are a small number (<4) of possible values."; continue % If there are as many unique values as a third of the number of cases, % I will assume that the node is continuous. elseif levels{j} > ncases/3; levels{j} = 1; + reason{j} = "It was determined to be continuous because there are a large number of possible values compared to the number of cases."; + continue % If there are more than twenty unique values, % I will assume that the node is continuous. elseif levels{j} > 20; levels{j} = 1; + reason{j} = "It was determined to be continuous because there are many (>20) possible values."; + continue % Otherwise, I will scan through the individual values. % If any of the values contain a '.', I will assume it is continuous. else + reason{j} = "It was determined to be discrete by default."; period_test = 0; column = data(:,j); k = 1; while period_test == 0 period_test = sum(cell2mat(strfind(column(k),"."))); if period_test != 0; + reason{j} = "This variable was determined to be continuous because there were several possible values and at least one value contained a period(.)."; levels{j} = 1; end k++; @@ -131,6 +141,7 @@ if max_disc > min_cont labels_old = labels; data_old = data; states_old = states; + reason_old = reason; new_order = {}; for i=1:nnodes if levels_old{i} > 1 @@ -145,10 +156,12 @@ if max_disc > min_cont labels = {}; levels = {}; states = {}; + reason = {}; for i =1:nnodes labels{i} = labels_old{new_order{i}}; levels{i} = levels_old{new_order{i}}; states{i} = states_old{new_order{i}}; + reason{i} = reason_old{new_order{i}}; for j=1:ncases data{j,i} = data_old{j,new_order{i}}; end @@ -228,19 +241,21 @@ descfile = strcat(pre,'input_desc.txt'); dout = fopen(descfile,'w'); fprintf(dout,['As loaded, the input file had the following properties:\n\n']); dout = fopen(descfile,'a'); -fprintf(dout,'There are %i variables and %i cases(rows)\n',size(labels,2),ncases); +fprintf(dout,'There are %i variables and %i cases(rows).\n',size(labels,2),ncases); fprintf(dout,'The variable names are:\n'); fprintf(dout,'%s\t',labels{1:end-1}); fprintf(dout,'%s\n\n',labels{end}); for i=1:nnodes if levels{i} == 1 - fprintf(dout,'%s is a continuous variable\n',labels{i}); + fprintf(dout,'%s is a continuous variable.\n',labels{i}); + fprintf(dout,'%s\n',reason{i}); column = str2double(data(:,i)); colmean = mean(column); colstd = std(column); fprintf(dout,'It has a mean of %6.3f and a standard deviation of %6.3f\n\n',mean(column),std(column)) else - fprintf(dout,'%s is a discrete variable with %i states\n',labels{i},levels{i}); + fprintf(dout,'%s is a discrete variable with %i states.\n',labels{i},levels{i}); + fprintf(dout,'%s\n',reason{i}); fprintf(dout,'The states are: '); fprintf(dout,'%s ',states{i}{1:end-1}); fprintf(dout,'%s\n\n',states{i}{end}); diff --git a/BNW_parameter_learning/readInput.m b/BNW_parameter_learning/readInput.m index 2be0af29..f291b06d 100644 --- a/BNW_parameter_learning/readInput.m +++ b/BNW_parameter_learning/readInput.m @@ -15,6 +15,9 @@ function [ labels, cases, bnet, node_sizes, data,labelsold] = readInput( dfile, % labels = cell array with the names of the nodes. % cases = cell array with the data. % bnet = BNT bayesian network with the input structure. + % + % readInput is called by runBN_initial.m + if nargin < 4 std_flag = false(1); diff --git a/BNW_parameter_learning/readInputData.m b/BNW_parameter_learning/readInputData.m index 2df158f9..d92ec015 100644 --- a/BNW_parameter_learning/readInputData.m +++ b/BNW_parameter_learning/readInputData.m @@ -26,6 +26,10 @@ function [ labels , node_sizes, cases, data] = readInputData( dfile , nnodes ) % cases = cell array with the data. The cases array is transposed % in comparison with the input data to agree with the format of % cell data used in BNT. + % + % readInputData is called by readInput.m + + % open file for input, include error handling fin = fopen(dfile,'r'); diff --git a/BNW_parameter_learning/readInputStructure.m b/BNW_parameter_learning/readInputStructure.m index 72c39f46..9d5e2056 100644 --- a/BNW_parameter_learning/readInputStructure.m +++ b/BNW_parameter_learning/readInputStructure.m @@ -21,6 +21,9 @@ function [ dag ] = readInputStructure( sfile, labels ) % Output: % dag = matrix with the structure. % +% readInputStructure is called by runBN_initial.m + + % Read in first line of the structure file % open file for input, include error handling fin = fopen(sfile,'r'); diff --git a/BNW_parameter_learning/runBN_initial.m b/BNW_parameter_learning/runBN_initial.m index 43caf402..614accc5 100644 --- a/BNW_parameter_learning/runBN_initial.m +++ b/BNW_parameter_learning/runBN_initial.m @@ -1,4 +1,17 @@ function runBN_initial(pre) +% runBN_initial is used to create the net_figure file +% for a network without entered evidence or intervention. +% +% The input is 'pre'-- the prefix for the network and data +% in BNW. It uses this identifier to read several files from +% BNW. +% +% The output is ???net_figure.txt. It also calls writeParameters +% to write the parameter file. +% +% runBN_initial is called by run_octave in the 'sourcecodes' directory. +% + sfile=strcat(pre,'structure_input.txt'); dfile=strcat(pre,'continuous_input.txt'); @@ -21,7 +34,7 @@ s=std(data,0,1); m=mean(data); for i=1:nnodes - fprintf(mapval,'%s\t%d\t%f\t%f\n',labelsold{i},node_sizes(i),s(i),m(i)); + fprintf(mapval,'%s\t%f\t%f\n',labelsold{i},s(i),m(i)); end fprintf(mapfile,'%s',labels{1}); diff --git a/BNW_parameter_learning/standardizeData.m b/BNW_parameter_learning/standardizeData.m index 449aa440..b7673a1a 100644 --- a/BNW_parameter_learning/standardizeData.m +++ b/BNW_parameter_learning/standardizeData.m @@ -1,7 +1,8 @@ function [ cases ] = standardizeData( labels, node_sizes, cases ) %standardizeData standardizes continuous nodes so they have a mean = 0 % and standard deviation = 1 - +% +% standardizeData is called by readInput.m nnodes = size(labels,2); @@ -14,12 +15,6 @@ for i = 1:nnodes end end -%write standardized data to file -%fprintf(['Standardized data is written to file standardized_data.txt\n']) -%fout = 'standardized_data.txt'; -%txt = sprintf([repmat('%s\t',1,size(labels,2))],labels{:}); -%dlmwrite(fout,txt,''); -%dlmwrite(fout,cell2num(cases'),'-append','delimiter','\t'); end diff --git a/BNW_parameter_learning/writeParameters.m b/BNW_parameter_learning/writeParameters.m index 0790a8e2..42b2a4ef 100644 --- a/BNW_parameter_learning/writeParameters.m +++ b/BNW_parameter_learning/writeParameters.m @@ -1,5 +1,10 @@ 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 %%Get the types of the nodes. diff --git a/BNW_parameter_learning/writeParameters_ev.m b/BNW_parameter_learning/writeParameters_ev.m index fc24e2e5..1f07c745 100644 --- a/BNW_parameter_learning/writeParameters_ev.m +++ b/BNW_parameter_learning/writeParameters_ev.m @@ -1,5 +1,11 @@ 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'); diff --git a/BNW_parameter_learning/writeParameters_int.m b/BNW_parameter_learning/writeParameters_int.m index ed92d593..69dcbb93 100644 --- a/BNW_parameter_learning/writeParameters_int.m +++ b/BNW_parameter_learning/writeParameters_int.m @@ -1,6 +1,10 @@ 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'); diff --git a/sourcecodes/BNW_workflow_net1.htm b/sourcecodes/BNW_workflow_net1.htm index e82f1f41..d69ddf84 100644 --- a/sourcecodes/BNW_workflow_net1.htm +++ b/sourcecodes/BNW_workflow_net1.htm @@ -291,8 +291,10 @@ ul

This tutorial provides -an overview of using BNW to build a Bayesian network model from a dataset and use the network to make predictions. The dataset used in this tutorial is a synthetic example of a genetic dataset that has a total of 8 variables. Two of the variables are genotypes labeled Geno1 and Geno2, and the remaining 6 variables are gene expression levels or other quantitative traits that are labeled Trait1 to Trait6. The dataset is available here.

The data file is formatted according to the guidelines on the BNW help page. The first row of the file contains the names of the variables and the remaining rows contain the data for each sample of the dataset. The genotypes (Geno1 and Geno2), which are the only discrete variables in the network, are the leftmost variables in the input file and are integer values (1 and 2) for all of the samples. The quantitative traits are continuous variables, and, therefore, all contain a "." in at least one of the samples.

+line-height:115%;font-family:"Arial","sans-serif"'> +**Recent updates to BNW may result in slight differences between what is described/shown below and what would currently be experienced in BNW.**

+This tutorial provides +an overview of using BNW to build a Bayesian network model from a dataset and use the network to make predictions. The dataset used in this tutorial is a synthetic example of a genetic dataset that has a total of 8 variables. Two of the variables are genotypes labeled Geno1 and Geno2, and the remaining 6 variables are gene expression levels or other quantitative traits that are labeled Trait1 to Trait6. The dataset is available here.

The data file is formatted according to the guidelines on the BNW help page. The first row of the file contains the names of the variables and the remaining rows contain the data for each sample of the dataset. The genotypes (Geno1 and Geno2), which are the only discrete variables in the network, are the leftmost variables in the input file and are integer values (1 and 2) for all of the samples. The quantitative traits are continuous variables, and, therefore, all contain a "." in at least one of the samples.

1. Structure learning using default options

@@ -322,7 +324,7 @@ normal'>In order to test if edges present the single best scoring network are conserved across high scoring networks. We can modify the structure learning settings to get identify the structures of many high scoring networks and perform model averaging over these structures. To do this, return to the BNW home page, select Learn a network model from data, and upload the datafile. Instead of using the default settings, select Go to structure learning settings and the BNW structural constraint interface. A more detailed overview of use of the structural constraint interface is provided in another tutorial, but, here, we will investigate the impact of modifying some of the structure learning settings shown below:


+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>In order to test if edges present the single best scoring network are conserved across high scoring networks. We can modify the structure learning settings to get identify the structures of many high scoring networks and perform model averaging over these structures. To do this, return to the BNW home page, select Learn a network model from data, and upload the datafile. Instead of using the default settings, select Go to structure learning settings and the BNW structural constraint interface. A more detailed overview of use of the structural constraint interface is provided in another tutorial, but, here, we will investigate the impact of modifying some of the structure learning settings shown below:


@@ -356,7 +358,7 @@ normal'>
To make predictions with the network, we will use the structure learned after model averaging of the top 100 highest scoring networks. First, we will use the model to compare the expected values for nodes in the network based on observed genotypes. For these predictions, we will keep the prediction in evidence mode. The difference between evidence and intervention modes is discussed in the BNW FAQ page. To use the model to make predictions based on Geno1, click on one of the blue bars in the Geno1 node and enter 1 or 2 to indicate which genotype value should be used to predict the values of the other network nodes. In the figure below, Geno1 is outlined in red and state 2 has a 100% probability, indicating that the value of this node has been entered as evidence. The red lines in the figure show the predicted distributions of the nodes after this evidence is known and can be compared with the blue lines which show the distributions for variables using the original data.


+line-height:115%;font-family:"Arial","sans-serif";mso-no-proof:yes'>
To make predictions with the network, we will use the structure learned after model averaging of the top 100 highest scoring networks. First, we will use the model to compare the expected values for nodes in the network based on observed genotypes. For these predictions, we will keep the prediction in evidence mode. The difference between evidence and intervention modes is discussed in the BNW FAQ page. To use the model to make predictions based on Geno1, click on one of the blue bars in the Geno1 node and enter 1 or 2 to indicate which genotype value should be used to predict the values of the other network nodes. In the figure below, Geno1 is outlined in red and state 2 has a 100% probability, indicating that the value of this node has been entered as evidence. The red lines in the figure show the predicted distributions of the nodes after this evidence is known and can be compared with the blue lines which show the distributions for variables using the original data.


diff --git a/sourcecodes/BNW_workflow_sci.htm b/sourcecodes/BNW_workflow_sci.htm index 73451b4c..8adbc7b7 100644 --- a/sourcecodes/BNW_workflow_sci.htm +++ b/sourcecodes/BNW_workflow_sci.htm @@ -315,6 +315,12 @@ ul

+

+**Recent updates to BNW may result in slight differences between what is described/shown below and what would currently be experienced in BNW.** +

+

1. A genetic network linking genotype and phenotype

diff --git a/sourcecodes/add_evd.php b/sourcecodes/add_evd.php index 191dc244..92aca69f 100644 --- a/sourcecodes/add_evd.php +++ b/sourcecodes/add_evd.php @@ -2,7 +2,7 @@ $keyval=trim($_GET['My_key']); -include("restructuremap.php"); +//include("restructuremap.php"); function discretemap($textdata,$sym,$dmapdata) @@ -104,11 +104,6 @@ for($j=0;$j<$nn;$j++) } $dt=$type_d[$s]; - -//if($dt==1) -// $textdata=reversemap($sym,$textdata,$keyval); -//else -// $textdata=discretemap($textdata,$sym,$dmapdata); if($dt!=1) $textdata=discretemap($textdata,$sym,$dmapdata); diff --git a/sourcecodes/add_evd_example.php b/sourcecodes/add_evd_example.php index 03846663..a38d4474 100644 --- a/sourcecodes/add_evd_example.php +++ b/sourcecodes/add_evd_example.php @@ -2,7 +2,7 @@ $keyval=trim($_GET['My_key']); -include("restructuremap.php"); +//include("restructuremap.php"); function discretemap($textdata,$sym,$dmapdata) @@ -104,11 +104,6 @@ for($j=0;$j<$nn;$j++) } $dt=$type_d[$s]; - -//if($dt==1) -// $textdata=reversemap($sym,$textdata,$keyval); -//else -// $textdata=discretemap($textdata,$sym,$dmapdata); if($dt!=1) $textdata=discretemap($textdata,$sym,$dmapdata); @@ -189,7 +184,7 @@ else } - + // $file1="./data/".$keyval."run_evidencemodified.sh"; // $initiallines=file_get_contents("./data/temp_evidence_file"); // $all_lines="$initiallines"."$keyval\nfi\nexit"; diff --git a/sourcecodes/add_inv.php b/sourcecodes/add_inv.php index d9ec7cbb..c2953f85 100644 --- a/sourcecodes/add_inv.php +++ b/sourcecodes/add_inv.php @@ -1,5 +1,5 @@ @@ -51,18 +51,6 @@ if($searchID!="") - - - +

n0 root = i ; diff --git a/sourcecodes/bnt-master/graph/findroot.m~ b/sourcecodes/bnt-master/graph/findroot.m~ new file mode 100644 index 00000000..d242a3a3 --- /dev/null +++ b/sourcecodes/bnt-master/graph/findroot.m~ @@ -0,0 +1,24 @@ +function root = findroot(bnet, cliques) + +%% findroot is to find the strong root in a clique tree assume it has one +%% in the tree. For a clique tree constructed from a strongly triangulated +%% graph, an interface clique that contains all discrete parents +%% and at least one continuous node from a connected continuous component +%% is for sure to be available as a guaranteed strong root. +%% -By Wei Sun, George Mason University, 4/17/2010. + +%% We choose the interface clique that contains the max number +%% of interface nodes to be the strong root. +n0 = 0 ; +for i=1:length(cliques) + % check hybrid cliques + hc = intersect(cliques{i}, bnet.cnodes) ; + hd = intersect(cliques{i}, bnet.dnodes) ; + if ~isempty(hd) & ~isempty(hc) + nd = length(hd) ; + if nd > n0 + root = i ; + n0 = nd ; + end + end +end diff --git a/sourcecodes/create_tiers_gom.php b/sourcecodes/create_tiers_gom.php index 0f332eef..be8e2fc2 100644 --- a/sourcecodes/create_tiers_gom.php +++ b/sourcecodes/create_tiers_gom.php @@ -90,6 +90,9 @@ $runtime=exe_time($keyval,$parent_number,$k_number);
  • How to use this page diff --git a/sourcecodes/data/example1/Bqxmap.txt b/sourcecodes/data/example1/Bqxmap.txt index 032b8928..dc9ba9be 100644 --- a/sourcecodes/data/example1/Bqxmap.txt +++ b/sourcecodes/data/example1/Bqxmap.txt @@ -1,8 +1,8 @@ -Geno1 1 0.473175 1.335000 -Geno2 2 0.496318 1.570000 -Trait1 1 0.668009 -0.023903 -Trait2 1 0.796873 0.038552 -Trait3 2 1.036694 0.068708 -Trait4 1 0.984008 -0.010125 -Trait5 1 0.316471 0.008911 -Trait6 1 0.904542 -0.114267 +Geno1 0.473175 1.335000 +Geno2 0.496318 1.570000 +Trait1 0.668009 -0.023903 +Trait2 0.796873 0.038552 +Trait3 1.036694 0.068708 +Trait4 0.984008 -0.010125 +Trait5 0.316471 0.008911 +Trait6 0.904542 -0.114267 diff --git a/sourcecodes/data/example1/Bqxparameters.txt b/sourcecodes/data/example1/Bqxparameters.txt new file mode 100644 index 00000000..d1345709 --- /dev/null +++ b/sourcecodes/data/example1/Bqxparameters.txt @@ -0,0 +1,34 @@ +Geno1 +Discrete node with 2 states +1 0.6650 +2 0.3350 + +Geno2 +Discrete node with 2 states +1 0.4300 +2 0.5700 + +Trait1 +Continuous node +-0.0239 0.6720 + +Trait2 +Continuous node +0.0386 0.8009 + +Trait3 +Continuous node +0.0687 1.0420 + +Trait4 +Continuous node +-0.0081 1.0133 + +Trait5 +Continuous node +0.0089 0.3181 + +Trait6 +Continuous node +-0.1143 0.9091 + diff --git a/sourcecodes/data/example2/hQGmap.txt b/sourcecodes/data/example2/hQGmap.txt index 032b8928..dc9ba9be 100644 --- a/sourcecodes/data/example2/hQGmap.txt +++ b/sourcecodes/data/example2/hQGmap.txt @@ -1,8 +1,8 @@ -Geno1 1 0.473175 1.335000 -Geno2 2 0.496318 1.570000 -Trait1 1 0.668009 -0.023903 -Trait2 1 0.796873 0.038552 -Trait3 2 1.036694 0.068708 -Trait4 1 0.984008 -0.010125 -Trait5 1 0.316471 0.008911 -Trait6 1 0.904542 -0.114267 +Geno1 0.473175 1.335000 +Geno2 0.496318 1.570000 +Trait1 0.668009 -0.023903 +Trait2 0.796873 0.038552 +Trait3 1.036694 0.068708 +Trait4 0.984008 -0.010125 +Trait5 0.316471 0.008911 +Trait6 0.904542 -0.114267 diff --git a/sourcecodes/data/example2/hQGparameters.txt b/sourcecodes/data/example2/hQGparameters.txt new file mode 100644 index 00000000..4cffac4a --- /dev/null +++ b/sourcecodes/data/example2/hQGparameters.txt @@ -0,0 +1,34 @@ +Geno1 +Discrete node with 2 states +1 0.6650 +2 0.3350 + +Geno2 +Discrete node with 2 states +1 0.4300 +2 0.5700 + +Trait1 +Continuous node +-0.0239 0.6720 + +Trait2 +Continuous node +0.0386 0.8009 + +Trait3 +Continuous node +0.0687 1.0419 + +Trait4 +Continuous node +-0.0081 1.0133 + +Trait5 +Continuous node +0.0089 0.3181 + +Trait6 +Continuous node +-0.1143 0.9091 + diff --git a/sourcecodes/data/example_chl/bWRmap.txt b/sourcecodes/data/example_chl/bWRmap.txt index b872d2f0..b24eae2f 100644 --- a/sourcecodes/data/example_chl/bWRmap.txt +++ b/sourcecodes/data/example_chl/bWRmap.txt @@ -1,5 +1,5 @@ -Ctrq3 2 0.487652 1.365854 -MAS 1 0.355012 0.479560 -Neutrophil 1 10.929756 15.048780 -Load 1 0.825354 4.077143 -Weight 1 0.088036 0.877077 +Ctrq3 0.487652 1.365854 +MAS 0.355012 0.479560 +Neutrophil 10.929756 15.048780 +Load 0.825354 4.077143 +Weight 0.088036 0.877077 diff --git a/sourcecodes/data/example_chl/bWRnet_figure.txt b/sourcecodes/data/example_chl/bWRnet_figure.txt index 1798da4f..6a3a1dbc 100644 --- a/sourcecodes/data/example_chl/bWRnet_figure.txt +++ b/sourcecodes/data/example_chl/bWRnet_figure.txt @@ -15,419 +15,419 @@ MAS 1 250 150 1 1 2 3 4 --2.2595 0.0292 --2.2129 0.0326 --2.1662 0.0362 --2.1196 0.0401 --2.0729 0.0443 --2.0263 0.0489 --1.9796 0.0539 --1.9330 0.0592 --1.8863 0.0648 --1.8397 0.0709 --1.7930 0.0774 --1.7464 0.0842 --1.6997 0.0915 --1.6531 0.0992 --1.6064 0.1072 --1.5598 0.1157 --1.5131 0.1246 --1.4665 0.1338 --1.4199 0.1434 --1.3732 0.1534 --1.3266 0.1636 --1.2799 0.1742 --1.2333 0.1850 --1.1866 0.1961 --1.1400 0.2074 --1.0933 0.2188 --1.0467 0.2303 --1.0000 0.2419 --0.9534 0.2535 --0.9067 0.2651 --0.8601 0.2766 --0.8134 0.2879 --0.7668 0.2991 --0.7201 0.3099 --0.6735 0.3205 --0.6268 0.3307 --0.5802 0.3404 --0.5335 0.3496 --0.4869 0.3583 --0.4402 0.3663 --0.3936 0.3737 --0.3470 0.3804 --0.3003 0.3864 --0.2537 0.3916 --0.2070 0.3959 --0.1604 0.3994 --0.1137 0.4021 --0.0671 0.4038 --0.0204 0.4047 -0.0262 0.4046 -0.0729 0.4037 -0.1195 0.4018 -0.1662 0.3991 -0.2128 0.3954 -0.2595 0.3910 -0.3061 0.3857 -0.3528 0.3797 -0.3994 0.3729 -0.4461 0.3654 -0.4927 0.3572 -0.5394 0.3485 -0.5860 0.3392 -0.6327 0.3294 -0.6793 0.3192 -0.7259 0.3086 -0.7726 0.2977 -0.8192 0.2865 -0.8659 0.2752 -0.9125 0.2637 -0.9592 0.2521 -1.0058 0.2405 -1.0525 0.2289 -1.0991 0.2173 -1.1458 0.2059 -1.1924 0.1947 -1.2391 0.1837 -1.2857 0.1729 -1.3324 0.1623 -1.3790 0.1521 -1.4257 0.1422 -1.4723 0.1326 -1.5190 0.1234 -1.5656 0.1146 -1.6123 0.1062 -1.6589 0.0982 -1.7056 0.0906 -1.7522 0.0834 -1.7989 0.0765 -1.8455 0.0701 -1.8921 0.0641 -1.9388 0.0585 -1.9854 0.0532 -2.0321 0.0483 -2.0787 0.0438 -2.1254 0.0396 -2.1720 0.0357 -2.2187 0.0321 -2.2653 0.0288 -2.3120 0.0258 -2.3586 0.0231 -2.4053 0.0206 +-0.3226 0.0292 +-0.3060 0.0326 +-0.2895 0.0362 +-0.2729 0.0401 +-0.2564 0.0443 +-0.2398 0.0489 +-0.2232 0.0539 +-0.2067 0.0592 +-0.1901 0.0648 +-0.1735 0.0709 +-0.1570 0.0774 +-0.1404 0.0842 +-0.1239 0.0915 +-0.1073 0.0992 +-0.0907 0.1072 +-0.0742 0.1157 +-0.0576 0.1246 +-0.0411 0.1338 +-0.0245 0.1434 +-0.0079 0.1534 +0.0086 0.1636 +0.0252 0.1742 +0.0417 0.1850 +0.0583 0.1961 +0.0749 0.2074 +0.0914 0.2188 +0.1080 0.2303 +0.1245 0.2419 +0.1411 0.2535 +0.1577 0.2651 +0.1742 0.2766 +0.1908 0.2879 +0.2073 0.2991 +0.2239 0.3099 +0.2405 0.3205 +0.2570 0.3307 +0.2736 0.3404 +0.2901 0.3496 +0.3067 0.3583 +0.3233 0.3663 +0.3398 0.3737 +0.3564 0.3804 +0.3729 0.3864 +0.3895 0.3916 +0.4061 0.3959 +0.4226 0.3994 +0.4392 0.4021 +0.4558 0.4038 +0.4723 0.4047 +0.4889 0.4046 +0.5054 0.4037 +0.5220 0.4018 +0.5386 0.3991 +0.5551 0.3954 +0.5717 0.3910 +0.5882 0.3857 +0.6048 0.3797 +0.6214 0.3729 +0.6379 0.3654 +0.6545 0.3572 +0.6710 0.3485 +0.6876 0.3392 +0.7042 0.3294 +0.7207 0.3192 +0.7373 0.3086 +0.7538 0.2977 +0.7704 0.2865 +0.7870 0.2752 +0.8035 0.2637 +0.8201 0.2521 +0.8366 0.2405 +0.8532 0.2289 +0.8698 0.2173 +0.8863 0.2059 +0.9029 0.1947 +0.9194 0.1837 +0.9360 0.1729 +0.9526 0.1623 +0.9691 0.1521 +0.9857 0.1422 +1.0022 0.1326 +1.0188 0.1234 +1.0354 0.1146 +1.0519 0.1062 +1.0685 0.0982 +1.0851 0.0906 +1.1016 0.0834 +1.1182 0.0765 +1.1347 0.0701 +1.1513 0.0641 +1.1679 0.0585 +1.1844 0.0532 +1.2010 0.0483 +1.2175 0.0438 +1.2341 0.0396 +1.2507 0.0357 +1.2672 0.0321 +1.2838 0.0288 +1.3003 0.0258 +1.3169 0.0231 +1.3335 0.0206 Load 1 250 150 2 1 2 0 --3.0935 0.0030 --3.0388 0.0036 --2.9842 0.0043 --2.9295 0.0050 --2.8748 0.0059 --2.8202 0.0069 --2.7655 0.0081 --2.7108 0.0094 --2.6562 0.0110 --2.6015 0.0127 --2.5469 0.0147 --2.4922 0.0169 --2.4375 0.0194 --2.3829 0.0222 --2.3282 0.0253 --2.2735 0.0287 --2.2189 0.0326 --2.1642 0.0368 --2.1095 0.0415 --2.0549 0.0466 --2.0002 0.0522 --1.9456 0.0583 --1.8909 0.0649 --1.8362 0.0720 --1.7816 0.0797 --1.7269 0.0879 --1.6722 0.0966 --1.6176 0.1059 --1.5629 0.1158 --1.5083 0.1261 --1.4536 0.1370 --1.3989 0.1484 --1.3443 0.1602 --1.2896 0.1724 --1.2349 0.1850 --1.1803 0.1979 --1.1256 0.2111 --1.0709 0.2244 --1.0163 0.2379 --0.9616 0.2514 --0.9070 0.2649 --0.8523 0.2782 --0.7976 0.2914 --0.7430 0.3042 --0.6883 0.3166 --0.6336 0.3285 --0.5790 0.3398 --0.5243 0.3504 --0.4697 0.3603 --0.4150 0.3693 --0.3603 0.3774 --0.3057 0.3845 --0.2510 0.3905 --0.1963 0.3954 --0.1417 0.3992 --0.0870 0.4017 --0.0323 0.4031 -0.0223 0.4032 -0.0770 0.4021 -0.1316 0.3997 -0.1863 0.3962 -0.2410 0.3915 -0.2956 0.3857 -0.3503 0.3788 -0.4050 0.3709 -0.4596 0.3620 -0.5143 0.3523 -0.5689 0.3418 -0.6236 0.3306 -0.6783 0.3188 -0.7329 0.3065 -0.7876 0.2937 -0.8423 0.2807 -0.8969 0.2674 -0.9516 0.2539 -1.0063 0.2404 -1.0609 0.2269 -1.1156 0.2135 -1.1702 0.2003 -1.2249 0.1874 -1.2796 0.1747 -1.3342 0.1624 -1.3889 0.1505 -1.4436 0.1391 -1.4982 0.1281 -1.5529 0.1176 -1.6075 0.1077 -1.6622 0.0983 -1.7169 0.0894 -1.7715 0.0811 -1.8262 0.0734 -1.8809 0.0662 -1.9355 0.0595 -1.9902 0.0533 -2.0449 0.0476 -2.0995 0.0424 -2.1542 0.0377 -2.2088 0.0333 -2.2635 0.0294 -2.3182 0.0259 -2.3728 0.0227 +1.5239 0.0030 +1.5690 0.0036 +1.6142 0.0043 +1.6593 0.0050 +1.7044 0.0059 +1.7495 0.0069 +1.7946 0.0081 +1.8397 0.0094 +1.8849 0.0110 +1.9300 0.0127 +1.9751 0.0147 +2.0202 0.0169 +2.0653 0.0194 +2.1104 0.0222 +2.1556 0.0253 +2.2007 0.0287 +2.2458 0.0326 +2.2909 0.0368 +2.3360 0.0415 +2.3811 0.0466 +2.4263 0.0522 +2.4714 0.0583 +2.5165 0.0649 +2.5616 0.0720 +2.6067 0.0797 +2.6518 0.0879 +2.6970 0.0966 +2.7421 0.1059 +2.7872 0.1158 +2.8323 0.1261 +2.8774 0.1370 +2.9225 0.1484 +2.9676 0.1602 +3.0128 0.1724 +3.0579 0.1850 +3.1030 0.1979 +3.1481 0.2111 +3.1932 0.2244 +3.2383 0.2379 +3.2835 0.2514 +3.3286 0.2649 +3.3737 0.2782 +3.4188 0.2914 +3.4639 0.3042 +3.5090 0.3166 +3.5542 0.3285 +3.5993 0.3398 +3.6444 0.3504 +3.6895 0.3603 +3.7346 0.3693 +3.7797 0.3774 +3.8249 0.3845 +3.8700 0.3905 +3.9151 0.3954 +3.9602 0.3992 +4.0053 0.4017 +4.0504 0.4031 +4.0956 0.4032 +4.1407 0.4021 +4.1858 0.3997 +4.2309 0.3962 +4.2760 0.3915 +4.3211 0.3857 +4.3663 0.3788 +4.4114 0.3709 +4.4565 0.3620 +4.5016 0.3523 +4.5467 0.3418 +4.5918 0.3306 +4.6370 0.3188 +4.6821 0.3065 +4.7272 0.2937 +4.7723 0.2807 +4.8174 0.2674 +4.8625 0.2539 +4.9077 0.2404 +4.9528 0.2269 +4.9979 0.2135 +5.0430 0.2003 +5.0881 0.1874 +5.1332 0.1747 +5.1784 0.1624 +5.2235 0.1505 +5.2686 0.1391 +5.3137 0.1281 +5.3588 0.1176 +5.4039 0.1077 +5.4491 0.0983 +5.4942 0.0894 +5.5393 0.0811 +5.5844 0.0734 +5.6295 0.0662 +5.6746 0.0595 +5.7198 0.0533 +5.7649 0.0476 +5.8100 0.0424 +5.8551 0.0377 +5.9002 0.0333 +5.9453 0.0294 +5.9905 0.0259 +6.0356 0.0227 Neutrophil 1 250 150 2 1 2 1 5 --2.3403 0.0245 --2.2876 0.0277 --2.2349 0.0313 --2.1823 0.0353 --2.1296 0.0396 --2.0770 0.0444 --2.0243 0.0496 --1.9716 0.0552 --1.9190 0.0613 --1.8663 0.0679 --1.8136 0.0750 --1.7610 0.0825 --1.7083 0.0906 --1.6556 0.0992 --1.6030 0.1083 --1.5503 0.1180 --1.4977 0.1281 --1.4450 0.1386 --1.3923 0.1496 --1.3397 0.1611 --1.2870 0.1729 --1.2343 0.1851 --1.1817 0.1975 --1.1290 0.2102 --1.0764 0.2231 --1.0237 0.2361 --0.9710 0.2491 --0.9184 0.2621 --0.8657 0.2750 --0.8130 0.2878 --0.7604 0.3002 --0.7077 0.3124 --0.6550 0.3240 --0.6024 0.3352 --0.5497 0.3458 --0.4971 0.3557 --0.4444 0.3648 --0.3917 0.3731 --0.3391 0.3806 --0.2864 0.3870 --0.2337 0.3925 --0.1811 0.3969 --0.1284 0.4002 --0.0758 0.4025 --0.0231 0.4035 -0.0296 0.4035 -0.0822 0.4022 -0.1349 0.3999 -0.1876 0.3964 -0.2402 0.3919 -0.2929 0.3863 -0.3456 0.3797 -0.3982 0.3722 -0.4509 0.3637 -0.5035 0.3545 -0.5562 0.3445 -0.6089 0.3339 -0.6615 0.3226 -0.7142 0.3109 -0.7669 0.2987 -0.8195 0.2862 -0.8722 0.2735 -0.9248 0.2605 -0.9775 0.2475 -1.0302 0.2345 -1.0828 0.2215 -1.1355 0.2086 -1.1882 0.1960 -1.2408 0.1835 -1.2935 0.1714 -1.3462 0.1597 -1.3988 0.1483 -1.4515 0.1373 -1.5041 0.1268 -1.5568 0.1167 -1.6095 0.1072 -1.6621 0.0981 -1.7148 0.0896 -1.7675 0.0816 -1.8201 0.0741 -1.8728 0.0670 -1.9254 0.0605 -1.9781 0.0545 -2.0308 0.0489 -2.0834 0.0438 -2.1361 0.0391 -2.1888 0.0348 -2.2414 0.0308 -2.2941 0.0273 -2.3468 0.0241 -2.3994 0.0212 -2.4521 0.0186 -2.5047 0.0163 -2.5574 0.0142 -2.6101 0.0123 -2.6627 0.0107 -2.7154 0.0093 -2.7681 0.0080 -2.8207 0.0069 -2.8734 0.0059 -2.9260 0.0050 +-10.5298 0.0245 +-9.9542 0.0277 +-9.3786 0.0313 +-8.8030 0.0353 +-8.2274 0.0396 +-7.6518 0.0444 +-7.0762 0.0496 +-6.5006 0.0552 +-5.9250 0.0613 +-5.3494 0.0679 +-4.7738 0.0750 +-4.1982 0.0825 +-3.6226 0.0906 +-3.0470 0.0992 +-2.4714 0.1083 +-1.8958 0.1180 +-1.3202 0.1281 +-0.7446 0.1386 +-0.1690 0.1496 +0.4066 0.1611 +0.9821 0.1729 +1.5577 0.1851 +2.1333 0.1975 +2.7089 0.2102 +3.2845 0.2231 +3.8601 0.2361 +4.4357 0.2491 +5.0113 0.2621 +5.5869 0.2750 +6.1625 0.2878 +6.7381 0.3002 +7.3137 0.3124 +7.8893 0.3240 +8.4649 0.3352 +9.0405 0.3458 +9.6161 0.3557 +10.1917 0.3648 +10.7673 0.3731 +11.3429 0.3806 +11.9185 0.3870 +12.4940 0.3925 +13.0696 0.3969 +13.6452 0.4002 +14.2208 0.4025 +14.7964 0.4035 +15.3720 0.4035 +15.9476 0.4022 +16.5232 0.3999 +17.0988 0.3964 +17.6744 0.3919 +18.2500 0.3863 +18.8256 0.3797 +19.4012 0.3722 +19.9768 0.3637 +20.5524 0.3545 +21.1280 0.3445 +21.7036 0.3339 +22.2792 0.3226 +22.8548 0.3109 +23.4304 0.2987 +24.0060 0.2862 +24.5815 0.2735 +25.1571 0.2605 +25.7327 0.2475 +26.3083 0.2345 +26.8839 0.2215 +27.4595 0.2086 +28.0351 0.1960 +28.6107 0.1835 +29.1863 0.1714 +29.7619 0.1597 +30.3375 0.1483 +30.9131 0.1373 +31.4887 0.1268 +32.0643 0.1167 +32.6399 0.1072 +33.2155 0.0981 +33.7911 0.0896 +34.3667 0.0816 +34.9423 0.0741 +35.5179 0.0670 +36.0934 0.0605 +36.6690 0.0545 +37.2446 0.0489 +37.8202 0.0438 +38.3958 0.0391 +38.9714 0.0348 +39.5470 0.0308 +40.1226 0.0273 +40.6982 0.0241 +41.2738 0.0212 +41.8494 0.0186 +42.4250 0.0163 +43.0006 0.0142 +43.5762 0.0123 +44.1518 0.0107 +44.7274 0.0093 +45.3030 0.0080 +45.8786 0.0069 +46.4542 0.0059 +47.0298 0.0050 Weight 1 250 150 2 1 4 0 --2.7996 0.0076 --2.7425 0.0089 --2.6853 0.0105 --2.6281 0.0122 --2.5709 0.0142 --2.5137 0.0164 --2.4565 0.0190 --2.3993 0.0218 --2.3421 0.0250 --2.2850 0.0286 --2.2278 0.0326 --2.1706 0.0370 --2.1134 0.0419 --2.0562 0.0473 --1.9990 0.0531 --1.9418 0.0596 --1.8846 0.0665 --1.8275 0.0741 --1.7703 0.0822 --1.7131 0.0909 --1.6559 0.1002 --1.5987 0.1101 --1.5415 0.1206 --1.4843 0.1316 --1.4271 0.1432 --1.3700 0.1553 --1.3128 0.1678 --1.2556 0.1808 --1.1984 0.1941 --1.1412 0.2076 --1.0840 0.2215 --1.0268 0.2354 --0.9696 0.2494 --0.9125 0.2633 --0.8553 0.2772 --0.7981 0.2907 --0.7409 0.3040 --0.6837 0.3168 --0.6265 0.3290 --0.5693 0.3406 --0.5121 0.3514 --0.4550 0.3614 --0.3978 0.3704 --0.3406 0.3784 --0.2834 0.3853 --0.2262 0.3910 --0.1690 0.3955 --0.1118 0.3988 --0.0546 0.4007 -0.0025 0.4013 -0.0597 0.4006 -0.1169 0.3985 -0.1741 0.3952 -0.2313 0.3906 -0.2885 0.3847 -0.3457 0.3778 -0.4029 0.3697 -0.4600 0.3605 -0.5172 0.3505 -0.5744 0.3396 -0.6316 0.3280 -0.6888 0.3157 -0.7460 0.3028 -0.8032 0.2896 -0.8603 0.2759 -0.9175 0.2621 -0.9747 0.2481 -1.0319 0.2342 -1.0891 0.2202 -1.1463 0.2064 -1.2035 0.1929 -1.2607 0.1796 -1.3178 0.1667 -1.3750 0.1542 -1.4322 0.1422 -1.4894 0.1306 -1.5466 0.1197 -1.6038 0.1092 -1.6610 0.0994 -1.7182 0.0901 -1.7753 0.0815 -1.8325 0.0734 -1.8897 0.0659 -1.9469 0.0590 -2.0041 0.0526 -2.0613 0.0468 -2.1185 0.0414 -2.1757 0.0366 -2.2328 0.0322 -2.2900 0.0283 -2.3472 0.0247 -2.4044 0.0215 -2.4616 0.0187 -2.5188 0.0162 -2.5760 0.0140 -2.6332 0.0120 -2.6903 0.0103 -2.7475 0.0088 -2.8047 0.0075 -2.8619 0.0064 -2.9191 0.0054 +0.6306 0.0076 +0.6356 0.0089 +0.6407 0.0105 +0.6457 0.0122 +0.6507 0.0142 +0.6558 0.0164 +0.6608 0.0190 +0.6659 0.0218 +0.6709 0.0250 +0.6759 0.0286 +0.6810 0.0326 +0.6860 0.0370 +0.6910 0.0419 +0.6961 0.0473 +0.7011 0.0531 +0.7061 0.0596 +0.7112 0.0665 +0.7162 0.0741 +0.7212 0.0822 +0.7263 0.0909 +0.7313 0.1002 +0.7363 0.1101 +0.7414 0.1206 +0.7464 0.1316 +0.7514 0.1432 +0.7565 0.1553 +0.7615 0.1678 +0.7665 0.1808 +0.7716 0.1941 +0.7766 0.2076 +0.7816 0.2215 +0.7867 0.2354 +0.7917 0.2494 +0.7967 0.2633 +0.8018 0.2772 +0.8068 0.2907 +0.8119 0.3040 +0.8169 0.3168 +0.8219 0.3290 +0.8270 0.3406 +0.8320 0.3514 +0.8370 0.3614 +0.8421 0.3704 +0.8471 0.3784 +0.8521 0.3853 +0.8572 0.3910 +0.8622 0.3955 +0.8672 0.3988 +0.8723 0.4007 +0.8773 0.4013 +0.8823 0.4006 +0.8874 0.3985 +0.8924 0.3952 +0.8974 0.3906 +0.9025 0.3847 +0.9075 0.3778 +0.9125 0.3697 +0.9176 0.3605 +0.9226 0.3505 +0.9276 0.3396 +0.9327 0.3280 +0.9377 0.3157 +0.9427 0.3028 +0.9478 0.2896 +0.9528 0.2759 +0.9579 0.2621 +0.9629 0.2481 +0.9679 0.2342 +0.9730 0.2202 +0.9780 0.2064 +0.9830 0.1929 +0.9881 0.1796 +0.9931 0.1667 +0.9981 0.1542 +1.0032 0.1422 +1.0082 0.1306 +1.0132 0.1197 +1.0183 0.1092 +1.0233 0.0994 +1.0283 0.0901 +1.0334 0.0815 +1.0384 0.0734 +1.0434 0.0659 +1.0485 0.0590 +1.0535 0.0526 +1.0585 0.0468 +1.0636 0.0414 +1.0686 0.0366 +1.0736 0.0322 +1.0787 0.0283 +1.0837 0.0247 +1.0888 0.0215 +1.0938 0.0187 +1.0988 0.0162 +1.1039 0.0140 +1.1089 0.0120 +1.1139 0.0103 +1.1190 0.0088 +1.1240 0.0075 +1.1290 0.0064 +1.1341 0.0054 diff --git a/sourcecodes/data/example_chl/bWRparameters.txt b/sourcecodes/data/example_chl/bWRparameters.txt new file mode 100644 index 00000000..6d6eabf6 --- /dev/null +++ b/sourcecodes/data/example_chl/bWRparameters.txt @@ -0,0 +1,21 @@ +Ctrq3 +Discrete node with 2 states +1 0.6341 +2 0.3659 + +MAS +Continuous node +0.4796 0.3499 + +Neutrophil +Continuous node +15.0488 10.8026 + +Load +Continuous node +4.0771 0.8165 + +Weight +Continuous node +0.8771 0.0875 + diff --git a/sourcecodes/data/example_chr2_spleen/cuLmap.txt b/sourcecodes/data/example_chr2_spleen/cuLmap.txt index 325f9a8d..9ca8e641 100644 --- a/sourcecodes/data/example_chr2_spleen/cuLmap.txt +++ b/sourcecodes/data/example_chr2_spleen/cuLmap.txt @@ -1,14 +1,14 @@ -rs3664317 2 0.501140 1.452055 -Oas1a 1 0.347301 8.281466 -Parp9 1 0.216211 9.804164 -Dhx58 1 0.302026 8.891247 -Cd1d1 1 0.268738 9.643644 -Irf7 1 0.548579 10.009315 -Oas3 1 0.548788 8.957397 -Pml 1 0.190985 11.075425 -Mx1 1 0.336795 7.483137 -Ifit1 1 0.569192 8.828521 -Trim25 1 0.176657 10.255192 -Oas1g 1 0.458134 8.632151 -Pglyrp3 1 0.123345 6.070877 -Ifih1 1 0.387383 9.298137 +rs3664317 0.501140 1.452055 +Oas1a 0.347301 8.281466 +Parp9 0.216211 9.804164 +Dhx58 0.302026 8.891247 +Cd1d1 0.268738 9.643644 +Irf7 0.548579 10.009315 +Oas3 0.548788 8.957397 +Pml 0.190985 11.075425 +Mx1 0.336795 7.483137 +Ifit1 0.569192 8.828521 +Trim25 0.176657 10.255192 +Oas1g 0.458134 8.632151 +Pglyrp3 0.123345 6.070877 +Ifih1 0.387383 9.298137 diff --git a/sourcecodes/data/example_chr2_spleen/cuLnet_figure.txt b/sourcecodes/data/example_chr2_spleen/cuLnet_figure.txt index 79f052a0..db0c7cf0 100644 --- a/sourcecodes/data/example_chr2_spleen/cuLnet_figure.txt +++ b/sourcecodes/data/example_chr2_spleen/cuLnet_figure.txt @@ -24,1364 +24,1364 @@ Ifih1 1 250 150 1 1 6 3 4 6 8 10 12 --3.1997 0.0023 --3.1378 0.0028 --3.0758 0.0034 --3.0138 0.0041 --2.9518 0.0050 --2.8899 0.0060 --2.8279 0.0071 --2.7659 0.0085 --2.7039 0.0101 --2.6420 0.0119 --2.5800 0.0140 --2.5180 0.0164 --2.4560 0.0192 --2.3941 0.0223 --2.3321 0.0259 --2.2701 0.0299 --2.2081 0.0343 --2.1462 0.0393 --2.0842 0.0449 --2.0222 0.0510 --1.9602 0.0578 --1.8983 0.0652 --1.8363 0.0733 --1.7743 0.0820 --1.7124 0.0914 --1.6504 0.1015 --1.5884 0.1124 --1.5264 0.1238 --1.4645 0.1359 --1.4025 0.1487 --1.3405 0.1620 --1.2785 0.1758 --1.2166 0.1900 --1.1546 0.2046 --1.0926 0.2195 --1.0306 0.2345 --0.9687 0.2496 --0.9067 0.2647 --0.8447 0.2795 --0.7827 0.2941 --0.7208 0.3082 --0.6588 0.3218 --0.5968 0.3347 --0.5348 0.3467 --0.4729 0.3578 --0.4109 0.3678 --0.3489 0.3766 --0.2870 0.3842 --0.2250 0.3903 --0.1630 0.3951 --0.1010 0.3984 --0.0391 0.4001 -0.0229 0.4003 -0.0849 0.3990 -0.1469 0.3961 -0.2088 0.3917 -0.2708 0.3859 -0.3328 0.3787 -0.3948 0.3702 -0.4567 0.3605 -0.5187 0.3497 -0.5807 0.3379 -0.6427 0.3252 -0.7046 0.3118 -0.7666 0.2978 -0.8286 0.2834 -0.8906 0.2685 -0.9525 0.2535 -1.0145 0.2384 -1.0765 0.2234 -1.1385 0.2084 -1.2004 0.1938 -1.2624 0.1794 -1.3244 0.1655 -1.3863 0.1521 -1.4483 0.1392 -1.5103 0.1269 -1.5723 0.1153 -1.6342 0.1043 -1.6962 0.0940 -1.7582 0.0844 -1.8202 0.0755 -1.8821 0.0672 -1.9441 0.0597 -2.0061 0.0527 -2.0681 0.0464 -2.1300 0.0407 -2.1920 0.0356 -2.2540 0.0310 -2.3160 0.0269 -2.3779 0.0232 -2.4399 0.0200 -2.5019 0.0171 -2.5639 0.0146 -2.6258 0.0124 -2.6878 0.0105 -2.7498 0.0089 -2.8117 0.0075 -2.8737 0.0063 -2.9357 0.0052 -2.9977 0.0043 +8.0586 0.0023 +8.0826 0.0028 +8.1066 0.0034 +8.1306 0.0041 +8.1546 0.0050 +8.1787 0.0060 +8.2027 0.0071 +8.2267 0.0085 +8.2507 0.0101 +8.2747 0.0119 +8.2987 0.0140 +8.3227 0.0164 +8.3467 0.0192 +8.3707 0.0223 +8.3947 0.0259 +8.4187 0.0299 +8.4427 0.0343 +8.4667 0.0393 +8.4908 0.0449 +8.5148 0.0510 +8.5388 0.0578 +8.5628 0.0652 +8.5868 0.0733 +8.6108 0.0820 +8.6348 0.0914 +8.6588 0.1015 +8.6828 0.1124 +8.7068 0.1238 +8.7308 0.1359 +8.7548 0.1487 +8.7788 0.1620 +8.8029 0.1758 +8.8269 0.1900 +8.8509 0.2046 +8.8749 0.2195 +8.8989 0.2345 +8.9229 0.2496 +8.9469 0.2647 +8.9709 0.2795 +8.9949 0.2941 +9.0189 0.3082 +9.0429 0.3218 +9.0669 0.3347 +9.0909 0.3467 +9.1150 0.3578 +9.1390 0.3678 +9.1630 0.3766 +9.1870 0.3842 +9.2110 0.3903 +9.2350 0.3951 +9.2590 0.3984 +9.2830 0.4001 +9.3070 0.4003 +9.3310 0.3990 +9.3550 0.3961 +9.3790 0.3917 +9.4030 0.3859 +9.4271 0.3787 +9.4511 0.3702 +9.4751 0.3605 +9.4991 0.3497 +9.5231 0.3379 +9.5471 0.3252 +9.5711 0.3118 +9.5951 0.2978 +9.6191 0.2834 +9.6431 0.2685 +9.6671 0.2535 +9.6911 0.2384 +9.7151 0.2234 +9.7392 0.2084 +9.7632 0.1938 +9.7872 0.1794 +9.8112 0.1655 +9.8352 0.1521 +9.8592 0.1392 +9.8832 0.1269 +9.9072 0.1153 +9.9312 0.1043 +9.9552 0.0940 +9.9792 0.0844 +10.0032 0.0755 +10.0272 0.0672 +10.0513 0.0597 +10.0753 0.0527 +10.0993 0.0464 +10.1233 0.0407 +10.1473 0.0356 +10.1713 0.0310 +10.1953 0.0269 +10.2193 0.0232 +10.2433 0.0200 +10.2673 0.0171 +10.2913 0.0146 +10.3153 0.0124 +10.3393 0.0105 +10.3634 0.0089 +10.3874 0.0075 +10.4114 0.0063 +10.4354 0.0052 +10.4594 0.0043 Cd1d1 1 250 150 2 1 2 3 6 8 9 --3.4620 0.0010 --3.3962 0.0012 --3.3304 0.0015 --3.2645 0.0019 --3.1987 0.0023 --3.1328 0.0029 --3.0670 0.0035 --3.0011 0.0043 --2.9353 0.0052 --2.8694 0.0064 --2.8036 0.0077 --2.7378 0.0092 --2.6719 0.0110 --2.6061 0.0131 --2.5402 0.0156 --2.4744 0.0184 --2.4085 0.0216 --2.3427 0.0253 --2.2768 0.0295 --2.2110 0.0342 --2.1452 0.0395 --2.0793 0.0455 --2.0135 0.0521 --1.9476 0.0594 --1.8818 0.0674 --1.8159 0.0762 --1.7501 0.0857 --1.6843 0.0960 --1.6184 0.1072 --1.5526 0.1190 --1.4867 0.1316 --1.4209 0.1449 --1.3550 0.1589 --1.2892 0.1734 --1.2233 0.1885 --1.1575 0.2039 --1.0917 0.2197 --1.0258 0.2357 --0.9600 0.2517 --0.8941 0.2677 --0.8283 0.2834 --0.7624 0.2987 --0.6966 0.3135 --0.6308 0.3276 --0.5649 0.3408 --0.4991 0.3530 --0.4332 0.3641 --0.3674 0.3739 --0.3015 0.3823 --0.2357 0.3891 --0.1698 0.3944 --0.1040 0.3980 --0.0382 0.3999 -0.0277 0.4000 -0.0935 0.3984 -0.1594 0.3951 -0.2252 0.3901 -0.2911 0.3835 -0.3569 0.3753 -0.4227 0.3658 -0.4886 0.3549 -0.5544 0.3428 -0.6203 0.3297 -0.6861 0.3158 -0.7520 0.3011 -0.8178 0.2858 -0.8837 0.2702 -0.9495 0.2543 -1.0153 0.2382 -1.0812 0.2223 -1.1470 0.2064 -1.2129 0.1909 -1.2787 0.1758 -1.3446 0.1612 -1.4104 0.1471 -1.4763 0.1337 -1.5421 0.1210 -1.6079 0.1090 -1.6738 0.0978 -1.7396 0.0873 -1.8055 0.0776 -1.8713 0.0687 -1.9372 0.0606 -2.0030 0.0532 -2.0688 0.0465 -2.1347 0.0404 -2.2005 0.0350 -2.2664 0.0302 -2.3322 0.0259 -2.3981 0.0222 -2.4639 0.0189 -2.5298 0.0160 -2.5956 0.0135 -2.6614 0.0113 -2.7273 0.0095 -2.7931 0.0079 -2.8590 0.0066 -2.9248 0.0054 -2.9907 0.0044 -3.0565 0.0036 -3.1223 0.0030 +8.7133 0.0010 +8.7310 0.0012 +8.7487 0.0015 +8.7663 0.0019 +8.7840 0.0023 +8.8017 0.0029 +8.8194 0.0035 +8.8371 0.0043 +8.8548 0.0052 +8.8725 0.0064 +8.8902 0.0077 +8.9079 0.0092 +8.9256 0.0110 +8.9433 0.0131 +8.9610 0.0156 +8.9787 0.0184 +8.9964 0.0216 +9.0141 0.0253 +9.0318 0.0295 +9.0495 0.0342 +9.0672 0.0395 +9.0849 0.0455 +9.1025 0.0521 +9.1202 0.0594 +9.1379 0.0674 +9.1556 0.0762 +9.1733 0.0857 +9.1910 0.0960 +9.2087 0.1072 +9.2264 0.1190 +9.2441 0.1316 +9.2618 0.1449 +9.2795 0.1589 +9.2972 0.1734 +9.3149 0.1885 +9.3326 0.2039 +9.3503 0.2197 +9.3680 0.2357 +9.3857 0.2517 +9.4034 0.2677 +9.4211 0.2834 +9.4387 0.2987 +9.4564 0.3135 +9.4741 0.3276 +9.4918 0.3408 +9.5095 0.3530 +9.5272 0.3641 +9.5449 0.3739 +9.5626 0.3823 +9.5803 0.3891 +9.5980 0.3944 +9.6157 0.3980 +9.6334 0.3999 +9.6511 0.4000 +9.6688 0.3984 +9.6865 0.3951 +9.7042 0.3901 +9.7219 0.3835 +9.7396 0.3753 +9.7573 0.3658 +9.7749 0.3549 +9.7926 0.3428 +9.8103 0.3297 +9.8280 0.3158 +9.8457 0.3011 +9.8634 0.2858 +9.8811 0.2702 +9.8988 0.2543 +9.9165 0.2382 +9.9342 0.2223 +9.9519 0.2064 +9.9696 0.1909 +9.9873 0.1758 +10.0050 0.1612 +10.0227 0.1471 +10.0404 0.1337 +10.0581 0.1210 +10.0758 0.1090 +10.0935 0.0978 +10.1111 0.0873 +10.1288 0.0776 +10.1465 0.0687 +10.1642 0.0606 +10.1819 0.0532 +10.1996 0.0465 +10.2173 0.0404 +10.2350 0.0350 +10.2527 0.0302 +10.2704 0.0259 +10.2881 0.0222 +10.3058 0.0189 +10.3235 0.0160 +10.3412 0.0135 +10.3589 0.0113 +10.3766 0.0095 +10.3943 0.0079 +10.4120 0.0066 +10.4297 0.0054 +10.4473 0.0044 +10.4650 0.0036 +10.4827 0.0030 Dhx58 1 250 150 2 1 2 5 5 6 12 13 14 --3.2125 0.0024 --3.1470 0.0029 --3.0814 0.0035 --3.0159 0.0043 --2.9503 0.0053 --2.8848 0.0064 --2.8192 0.0076 --2.7536 0.0092 --2.6881 0.0110 --2.6225 0.0130 --2.5570 0.0154 --2.4914 0.0182 --2.4258 0.0213 --2.3603 0.0249 --2.2947 0.0290 --2.2292 0.0336 --2.1636 0.0388 --2.0980 0.0446 --2.0325 0.0510 --1.9669 0.0581 --1.9014 0.0659 --1.8358 0.0745 --1.7702 0.0838 --1.7047 0.0938 --1.6391 0.1046 --1.5736 0.1162 --1.5080 0.1284 --1.4425 0.1414 --1.3769 0.1550 --1.3113 0.1692 --1.2458 0.1839 --1.1802 0.1990 --1.1147 0.2145 --1.0491 0.2302 --0.9835 0.2459 --0.9180 0.2617 --0.8524 0.2772 --0.7869 0.2924 --0.7213 0.3071 --0.6557 0.3212 --0.5902 0.3346 --0.5246 0.3469 --0.4591 0.3582 --0.3935 0.3683 --0.3280 0.3771 --0.2624 0.3844 --0.1968 0.3902 --0.1313 0.3944 --0.0657 0.3970 --0.0002 0.3978 -0.0654 0.3970 -0.1310 0.3944 -0.1965 0.3902 -0.2621 0.3845 -0.3276 0.3771 -0.3932 0.3684 -0.4588 0.3583 -0.5243 0.3470 -0.5899 0.3346 -0.6554 0.3213 -0.7210 0.3072 -0.7866 0.2925 -0.8521 0.2773 -0.9177 0.2617 -0.9832 0.2460 -1.0488 0.2302 -1.1143 0.2146 -1.1799 0.1991 -1.2455 0.1840 -1.3110 0.1693 -1.3766 0.1551 -1.4421 0.1415 -1.5077 0.1285 -1.5733 0.1162 -1.6388 0.1047 -1.7044 0.0939 -1.7699 0.0838 -1.8355 0.0745 -1.9011 0.0660 -1.9666 0.0582 -2.0322 0.0510 -2.0977 0.0446 -2.1633 0.0388 -2.2288 0.0337 -2.2944 0.0290 -2.3600 0.0250 -2.4255 0.0213 -2.4911 0.0182 -2.5566 0.0154 -2.6222 0.0130 -2.6878 0.0110 -2.7533 0.0092 -2.8189 0.0077 -2.8844 0.0064 -2.9500 0.0053 -3.0156 0.0043 -3.0811 0.0035 -3.1467 0.0029 -3.2122 0.0024 -3.2778 0.0019 -3.3434 0.0015 +7.9210 0.0024 +7.9408 0.0029 +7.9606 0.0035 +7.9804 0.0043 +8.0002 0.0053 +8.0200 0.0064 +8.0398 0.0076 +8.0596 0.0092 +8.0794 0.0110 +8.0992 0.0130 +8.1190 0.0154 +8.1388 0.0182 +8.1586 0.0213 +8.1784 0.0249 +8.1982 0.0290 +8.2180 0.0336 +8.2378 0.0388 +8.2576 0.0446 +8.2774 0.0510 +8.2972 0.0581 +8.3170 0.0659 +8.3368 0.0745 +8.3566 0.0838 +8.3764 0.0938 +8.3962 0.1046 +8.4160 0.1162 +8.4358 0.1284 +8.4556 0.1414 +8.4754 0.1550 +8.4952 0.1692 +8.5150 0.1839 +8.5348 0.1990 +8.5546 0.2145 +8.5744 0.2302 +8.5942 0.2459 +8.6140 0.2617 +8.6338 0.2772 +8.6536 0.2924 +8.6734 0.3071 +8.6932 0.3212 +8.7130 0.3346 +8.7328 0.3469 +8.7526 0.3582 +8.7724 0.3683 +8.7922 0.3771 +8.8120 0.3844 +8.8318 0.3902 +8.8516 0.3944 +8.8714 0.3970 +8.8912 0.3978 +8.9110 0.3970 +8.9308 0.3944 +8.9506 0.3902 +8.9704 0.3845 +8.9902 0.3771 +9.0100 0.3684 +9.0298 0.3583 +9.0496 0.3470 +9.0694 0.3346 +9.0892 0.3213 +9.1090 0.3072 +9.1288 0.2925 +9.1486 0.2773 +9.1684 0.2617 +9.1882 0.2460 +9.2080 0.2302 +9.2278 0.2146 +9.2476 0.1991 +9.2674 0.1840 +9.2872 0.1693 +9.3070 0.1551 +9.3268 0.1415 +9.3466 0.1285 +9.3664 0.1162 +9.3862 0.1047 +9.4060 0.0939 +9.4258 0.0838 +9.4456 0.0745 +9.4654 0.0660 +9.4852 0.0582 +9.5050 0.0510 +9.5248 0.0446 +9.5446 0.0388 +9.5644 0.0337 +9.5842 0.0290 +9.6040 0.0250 +9.6238 0.0213 +9.6436 0.0182 +9.6634 0.0154 +9.6832 0.0130 +9.7030 0.0110 +9.7228 0.0092 +9.7426 0.0077 +9.7624 0.0064 +9.7822 0.0053 +9.8020 0.0043 +9.8218 0.0035 +9.8416 0.0029 +9.8614 0.0024 +9.8812 0.0019 +9.9010 0.0015 Oas1g 1 250 150 2 1 4 0 --3.5192 0.0009 --3.4507 0.0011 --3.3821 0.0014 --3.3135 0.0018 --3.2449 0.0022 --3.1763 0.0028 --3.1077 0.0034 --3.0391 0.0042 --2.9705 0.0052 --2.9019 0.0063 --2.8334 0.0076 --2.7648 0.0092 --2.6962 0.0111 --2.6276 0.0133 --2.5590 0.0158 --2.4904 0.0187 --2.4218 0.0221 --2.3532 0.0260 --2.2847 0.0304 --2.2161 0.0353 --2.1475 0.0409 --2.0789 0.0472 --2.0103 0.0542 --1.9417 0.0619 --1.8731 0.0704 --1.8045 0.0797 --1.7359 0.0899 --1.6674 0.1008 --1.5988 0.1125 --1.5302 0.1251 --1.4616 0.1383 --1.3930 0.1523 --1.3244 0.1670 --1.2558 0.1822 --1.1872 0.1978 --1.1186 0.2138 --1.0501 0.2300 --0.9815 0.2464 --0.9129 0.2626 --0.8443 0.2787 --0.7757 0.2943 --0.7071 0.3094 --0.6385 0.3238 --0.5699 0.3373 --0.5014 0.3497 --0.4328 0.3609 --0.3642 0.3707 --0.2956 0.3791 --0.2270 0.3858 --0.1584 0.3909 --0.0898 0.3942 --0.0212 0.3956 -0.0474 0.3953 -0.1159 0.3931 -0.1845 0.3891 -0.2531 0.3834 -0.3217 0.3761 -0.3903 0.3672 -0.4589 0.3568 -0.5275 0.3451 -0.5961 0.3323 -0.6646 0.3184 -0.7332 0.3038 -0.8018 0.2884 -0.8704 0.2726 -0.9390 0.2565 -1.0076 0.2401 -1.0762 0.2238 -1.1448 0.2077 -1.2134 0.1918 -1.2819 0.1763 -1.3505 0.1613 -1.4191 0.1469 -1.4877 0.1332 -1.5563 0.1202 -1.6249 0.1080 -1.6935 0.0965 -1.7621 0.0859 -1.8307 0.0761 -1.8992 0.0671 -1.9678 0.0589 -2.0364 0.0514 -2.1050 0.0447 -2.1736 0.0387 -2.2422 0.0334 -2.3108 0.0286 -2.3794 0.0244 -2.4479 0.0208 -2.5165 0.0176 -2.5851 0.0148 -2.6537 0.0124 -2.7223 0.0103 -2.7909 0.0086 -2.8595 0.0071 -2.9281 0.0058 -2.9967 0.0048 -3.0652 0.0039 -3.1338 0.0032 -3.2024 0.0025 -3.2710 0.0020 -3.3396 0.0016 +7.0199 0.0009 +7.0513 0.0011 +7.0827 0.0014 +7.1141 0.0018 +7.1456 0.0022 +7.1770 0.0028 +7.2084 0.0034 +7.2398 0.0042 +7.2712 0.0052 +7.3027 0.0063 +7.3341 0.0076 +7.3655 0.0092 +7.3969 0.0111 +7.4284 0.0133 +7.4598 0.0158 +7.4912 0.0187 +7.5226 0.0221 +7.5541 0.0260 +7.5855 0.0304 +7.6169 0.0353 +7.6483 0.0409 +7.6797 0.0472 +7.7112 0.0542 +7.7426 0.0619 +7.7740 0.0704 +7.8054 0.0797 +7.8369 0.0899 +7.8683 0.1008 +7.8997 0.1125 +7.9311 0.1251 +7.9625 0.1383 +7.9940 0.1523 +8.0254 0.1670 +8.0568 0.1822 +8.0882 0.1978 +8.1197 0.2138 +8.1511 0.2300 +8.1825 0.2464 +8.2139 0.2626 +8.2454 0.2787 +8.2768 0.2943 +8.3082 0.3094 +8.3396 0.3238 +8.3710 0.3373 +8.4025 0.3497 +8.4339 0.3609 +8.4653 0.3707 +8.4967 0.3791 +8.5282 0.3858 +8.5596 0.3909 +8.5910 0.3942 +8.6224 0.3956 +8.6538 0.3953 +8.6853 0.3931 +8.7167 0.3891 +8.7481 0.3834 +8.7795 0.3761 +8.8110 0.3672 +8.8424 0.3568 +8.8738 0.3451 +8.9052 0.3323 +8.9366 0.3184 +8.9681 0.3038 +8.9995 0.2884 +9.0309 0.2726 +9.0623 0.2565 +9.0938 0.2401 +9.1252 0.2238 +9.1566 0.2077 +9.1880 0.1918 +9.2195 0.1763 +9.2509 0.1613 +9.2823 0.1469 +9.3137 0.1332 +9.3451 0.1202 +9.3766 0.1080 +9.4080 0.0965 +9.4394 0.0859 +9.4708 0.0761 +9.5023 0.0671 +9.5337 0.0589 +9.5651 0.0514 +9.5965 0.0447 +9.6279 0.0387 +9.6594 0.0334 +9.6908 0.0286 +9.7222 0.0244 +9.7536 0.0208 +9.7851 0.0176 +9.8165 0.0148 +9.8479 0.0124 +9.8793 0.0103 +9.9108 0.0086 +9.9422 0.0071 +9.9736 0.0058 +10.0050 0.0048 +10.0364 0.0039 +10.0679 0.0032 +10.0993 0.0025 +10.1307 0.0020 +10.1621 0.0016 Trim25 1 250 150 3 2 3 4 3 7 9 10 --3.4748 0.0009 --3.4127 0.0012 --3.3506 0.0014 --3.2885 0.0018 --3.2263 0.0022 --3.1642 0.0026 --3.1021 0.0032 --3.0400 0.0039 --2.9779 0.0047 --2.9158 0.0056 --2.8536 0.0068 --2.7915 0.0081 --2.7294 0.0096 --2.6673 0.0113 --2.6052 0.0133 --2.5431 0.0156 --2.4810 0.0183 --2.4188 0.0213 --2.3567 0.0247 --2.2946 0.0286 --2.2325 0.0329 --2.1704 0.0377 --2.1083 0.0431 --2.0461 0.0490 --1.9840 0.0556 --1.9219 0.0628 --1.8598 0.0706 --1.7977 0.0791 --1.7356 0.0883 --1.6735 0.0982 --1.6113 0.1087 --1.5492 0.1200 --1.4871 0.1319 --1.4250 0.1444 --1.3629 0.1575 --1.3008 0.1711 --1.2386 0.1851 --1.1765 0.1996 --1.1144 0.2144 --1.0523 0.2293 --0.9902 0.2444 --0.9281 0.2594 --0.8660 0.2743 --0.8038 0.2889 --0.7417 0.3031 --0.6796 0.3168 --0.6175 0.3299 --0.5554 0.3422 --0.4933 0.3535 --0.4311 0.3638 --0.3690 0.3730 --0.3069 0.3809 --0.2448 0.3875 --0.1827 0.3927 --0.1206 0.3964 --0.0585 0.3987 -0.0037 0.3993 -0.0658 0.3985 -0.1279 0.3961 -0.1900 0.3922 -0.2521 0.3868 -0.3142 0.3801 -0.3764 0.3720 -0.4385 0.3627 -0.5006 0.3522 -0.5627 0.3408 -0.6248 0.3284 -0.6869 0.3153 -0.7490 0.3015 -0.8112 0.2872 -0.8733 0.2725 -0.9354 0.2576 -0.9975 0.2426 -1.0596 0.2275 -1.1217 0.2126 -1.1839 0.1979 -1.2460 0.1835 -1.3081 0.1695 -1.3702 0.1559 -1.4323 0.1429 -1.4944 0.1304 -1.5565 0.1186 -1.6187 0.1075 -1.6808 0.0970 -1.7429 0.0872 -1.8050 0.0781 -1.8671 0.0696 -1.9292 0.0619 -1.9914 0.0548 -2.0535 0.0483 -2.1156 0.0424 -2.1777 0.0371 -2.2398 0.0323 -2.3019 0.0281 -2.3640 0.0243 -2.4262 0.0209 -2.4883 0.0180 -2.5504 0.0153 -2.6125 0.0131 -2.6746 0.0111 -2.7367 0.0094 +9.6413 0.0009 +9.6523 0.0012 +9.6633 0.0014 +9.6743 0.0018 +9.6852 0.0022 +9.6962 0.0026 +9.7072 0.0032 +9.7182 0.0039 +9.7291 0.0047 +9.7401 0.0056 +9.7511 0.0068 +9.7620 0.0081 +9.7730 0.0096 +9.7840 0.0113 +9.7950 0.0133 +9.8059 0.0156 +9.8169 0.0183 +9.8279 0.0213 +9.8389 0.0247 +9.8498 0.0286 +9.8608 0.0329 +9.8718 0.0377 +9.8828 0.0431 +9.8937 0.0490 +9.9047 0.0556 +9.9157 0.0628 +9.9266 0.0706 +9.9376 0.0791 +9.9486 0.0883 +9.9596 0.0982 +9.9705 0.1087 +9.9815 0.1200 +9.9925 0.1319 +10.0035 0.1444 +10.0144 0.1575 +10.0254 0.1711 +10.0364 0.1851 +10.0473 0.1996 +10.0583 0.2144 +10.0693 0.2293 +10.0803 0.2444 +10.0912 0.2594 +10.1022 0.2743 +10.1132 0.2889 +10.1242 0.3031 +10.1351 0.3168 +10.1461 0.3299 +10.1571 0.3422 +10.1681 0.3535 +10.1790 0.3638 +10.1900 0.3730 +10.2010 0.3809 +10.2119 0.3875 +10.2229 0.3927 +10.2339 0.3964 +10.2449 0.3987 +10.2558 0.3993 +10.2668 0.3985 +10.2778 0.3961 +10.2888 0.3922 +10.2997 0.3868 +10.3107 0.3801 +10.3217 0.3720 +10.3327 0.3627 +10.3436 0.3522 +10.3546 0.3408 +10.3656 0.3284 +10.3765 0.3153 +10.3875 0.3015 +10.3985 0.2872 +10.4095 0.2725 +10.4204 0.2576 +10.4314 0.2426 +10.4424 0.2275 +10.4534 0.2126 +10.4643 0.1979 +10.4753 0.1835 +10.4863 0.1695 +10.4972 0.1559 +10.5082 0.1429 +10.5192 0.1304 +10.5302 0.1186 +10.5411 0.1075 +10.5521 0.0970 +10.5631 0.0872 +10.5741 0.0781 +10.5850 0.0696 +10.5960 0.0619 +10.6070 0.0548 +10.6180 0.0483 +10.6289 0.0424 +10.6399 0.0371 +10.6509 0.0323 +10.6618 0.0281 +10.6728 0.0243 +10.6838 0.0209 +10.6948 0.0180 +10.7057 0.0153 +10.7167 0.0131 +10.7277 0.0111 +10.7387 0.0094 Pml 1 250 150 1 6 2 8 9 --4.0862 0.0001 --4.0165 0.0001 --3.9468 0.0002 --3.8770 0.0002 --3.8073 0.0003 --3.7375 0.0004 --3.6678 0.0005 --3.5980 0.0006 --3.5283 0.0008 --3.4586 0.0010 --3.3888 0.0013 --3.3191 0.0016 --3.2493 0.0020 --3.1796 0.0026 --3.1098 0.0032 --3.0401 0.0040 --2.9704 0.0049 --2.9006 0.0060 --2.8309 0.0073 --2.7611 0.0089 --2.6914 0.0107 --2.6217 0.0129 --2.5519 0.0154 --2.4822 0.0184 --2.4124 0.0218 --2.3427 0.0257 --2.2729 0.0302 --2.2032 0.0353 --2.1335 0.0411 --2.0637 0.0476 --1.9940 0.0548 --1.9242 0.0628 --1.8545 0.0716 --1.7847 0.0813 --1.7150 0.0918 --1.6453 0.1032 --1.5755 0.1155 --1.5058 0.1285 --1.4360 0.1424 --1.3663 0.1570 --1.2966 0.1722 --1.2268 0.1880 --1.1571 0.2043 --1.0873 0.2209 --1.0176 0.2377 --0.9478 0.2546 --0.8781 0.2713 --0.8084 0.2877 --0.7386 0.3036 --0.6689 0.3188 --0.5991 0.3332 --0.5294 0.3466 --0.4596 0.3587 --0.3899 0.3695 --0.3202 0.3787 --0.2504 0.3863 --0.1807 0.3922 --0.1109 0.3962 --0.0412 0.3983 -0.0286 0.3985 -0.0983 0.3967 -0.1680 0.3930 -0.2378 0.3875 -0.3075 0.3802 -0.3773 0.3713 -0.4470 0.3608 -0.5167 0.3489 -0.5865 0.3357 -0.6562 0.3215 -0.7260 0.3064 -0.7957 0.2906 -0.8655 0.2743 -0.9352 0.2576 -1.0049 0.2408 -1.0747 0.2240 -1.1444 0.2073 -1.2142 0.1910 -1.2839 0.1751 -1.3537 0.1597 -1.4234 0.1450 -1.4931 0.1310 -1.5629 0.1178 -1.6326 0.1054 -1.7024 0.0938 -1.7721 0.0831 -1.8418 0.0733 -1.9116 0.0643 -1.9813 0.0562 -2.0511 0.0488 -2.1208 0.0422 -2.1906 0.0363 -2.2603 0.0311 -2.3300 0.0265 -2.3998 0.0225 -2.4695 0.0190 -2.5393 0.0159 -2.6090 0.0133 -2.6788 0.0111 -2.7485 0.0092 -2.8182 0.0076 -2.8880 0.0062 +10.2950 0.0001 +10.3083 0.0001 +10.3217 0.0002 +10.3350 0.0002 +10.3483 0.0003 +10.3616 0.0004 +10.3749 0.0005 +10.3883 0.0006 +10.4016 0.0008 +10.4149 0.0010 +10.4282 0.0013 +10.4415 0.0016 +10.4549 0.0020 +10.4682 0.0026 +10.4815 0.0032 +10.4948 0.0040 +10.5081 0.0049 +10.5214 0.0060 +10.5348 0.0073 +10.5481 0.0089 +10.5614 0.0107 +10.5747 0.0129 +10.5880 0.0154 +10.6014 0.0184 +10.6147 0.0218 +10.6280 0.0257 +10.6413 0.0302 +10.6546 0.0353 +10.6680 0.0411 +10.6813 0.0476 +10.6946 0.0548 +10.7079 0.0628 +10.7212 0.0716 +10.7346 0.0813 +10.7479 0.0918 +10.7612 0.1032 +10.7745 0.1155 +10.7878 0.1285 +10.8012 0.1424 +10.8145 0.1570 +10.8278 0.1722 +10.8411 0.1880 +10.8544 0.2043 +10.8678 0.2209 +10.8811 0.2377 +10.8944 0.2546 +10.9077 0.2713 +10.9210 0.2877 +10.9344 0.3036 +10.9477 0.3188 +10.9610 0.3332 +10.9743 0.3466 +10.9876 0.3587 +11.0010 0.3695 +11.0143 0.3787 +11.0276 0.3863 +11.0409 0.3922 +11.0542 0.3962 +11.0676 0.3983 +11.0809 0.3985 +11.0942 0.3967 +11.1075 0.3930 +11.1208 0.3875 +11.1342 0.3802 +11.1475 0.3713 +11.1608 0.3608 +11.1741 0.3489 +11.1874 0.3357 +11.2008 0.3215 +11.2141 0.3064 +11.2274 0.2906 +11.2407 0.2743 +11.2540 0.2576 +11.2674 0.2408 +11.2807 0.2240 +11.2940 0.2073 +11.3073 0.1910 +11.3206 0.1751 +11.3340 0.1597 +11.3473 0.1450 +11.3606 0.1310 +11.3739 0.1178 +11.3872 0.1054 +11.4006 0.0938 +11.4139 0.0831 +11.4272 0.0733 +11.4405 0.0643 +11.4538 0.0562 +11.4671 0.0488 +11.4805 0.0422 +11.4938 0.0363 +11.5071 0.0311 +11.5204 0.0265 +11.5337 0.0225 +11.5471 0.0190 +11.5604 0.0159 +11.5737 0.0133 +11.5870 0.0111 +11.6003 0.0092 +11.6137 0.0076 +11.6270 0.0062 Oas3 1 250 150 3 2 3 7 1 9 --3.0744 0.0017 --3.0125 0.0021 --2.9506 0.0026 --2.8888 0.0032 --2.8269 0.0039 --2.7650 0.0048 --2.7031 0.0059 --2.6413 0.0072 --2.5794 0.0087 --2.5175 0.0104 --2.4556 0.0125 --2.3938 0.0149 --2.3319 0.0177 --2.2700 0.0209 --2.2081 0.0246 --2.1463 0.0289 --2.0844 0.0337 --2.0225 0.0391 --1.9606 0.0452 --1.8988 0.0520 --1.8369 0.0595 --1.7750 0.0679 --1.7132 0.0771 --1.6513 0.0871 --1.5894 0.0980 --1.5275 0.1098 --1.4657 0.1224 --1.4038 0.1358 --1.3419 0.1501 --1.2800 0.1651 --1.2182 0.1808 --1.1563 0.1971 --1.0944 0.2139 --1.0325 0.2311 --0.9707 0.2486 --0.9088 0.2662 --0.8469 0.2837 --0.7850 0.3011 --0.7232 0.3181 --0.6613 0.3345 --0.5994 0.3502 --0.5375 0.3650 --0.4757 0.3787 --0.4138 0.3911 --0.3519 0.4021 --0.2901 0.4116 --0.2282 0.4195 --0.1663 0.4255 --0.1044 0.4297 --0.0426 0.4320 -0.0193 0.4324 -0.0812 0.4308 -0.1431 0.4273 -0.2049 0.4220 -0.2668 0.4148 -0.3287 0.4059 -0.3906 0.3954 -0.4524 0.3835 -0.5143 0.3702 -0.5762 0.3558 -0.6381 0.3405 -0.6999 0.3243 -0.7618 0.3075 -0.8237 0.2903 -0.8856 0.2728 -0.9474 0.2552 -1.0093 0.2377 -1.0712 0.2204 -1.1331 0.2034 -1.1949 0.1869 -1.2568 0.1709 -1.3187 0.1557 -1.3805 0.1411 -1.4424 0.1273 -1.5043 0.1144 -1.5662 0.1023 -1.6280 0.0911 -1.6899 0.0807 -1.7518 0.0712 -1.8137 0.0626 -1.8755 0.0547 -1.9374 0.0476 -1.9993 0.0413 -2.0612 0.0356 -2.1230 0.0306 -2.1849 0.0262 -2.2468 0.0223 -2.3087 0.0189 -2.3705 0.0159 -2.4324 0.0134 -2.4943 0.0112 -2.5562 0.0093 -2.6180 0.0077 -2.6799 0.0064 -2.7418 0.0052 -2.8037 0.0043 -2.8655 0.0035 -2.9274 0.0028 -2.9893 0.0023 -3.0511 0.0018 -3.1130 0.0015 +7.2702 0.0017 +7.3042 0.0021 +7.3381 0.0026 +7.3721 0.0032 +7.4060 0.0039 +7.4400 0.0048 +7.4739 0.0059 +7.5079 0.0072 +7.5419 0.0087 +7.5758 0.0104 +7.6098 0.0125 +7.6437 0.0149 +7.6777 0.0177 +7.7116 0.0209 +7.7456 0.0246 +7.7795 0.0289 +7.8135 0.0337 +7.8475 0.0391 +7.8814 0.0452 +7.9154 0.0520 +7.9493 0.0595 +7.9833 0.0679 +8.0172 0.0771 +8.0512 0.0871 +8.0852 0.0980 +8.1191 0.1098 +8.1531 0.1224 +8.1870 0.1358 +8.2210 0.1501 +8.2549 0.1651 +8.2889 0.1808 +8.3228 0.1971 +8.3568 0.2139 +8.3908 0.2311 +8.4247 0.2486 +8.4587 0.2662 +8.4926 0.2837 +8.5266 0.3011 +8.5605 0.3181 +8.5945 0.3345 +8.6284 0.3502 +8.6624 0.3650 +8.6964 0.3787 +8.7303 0.3911 +8.7643 0.4021 +8.7982 0.4116 +8.8322 0.4195 +8.8661 0.4255 +8.9001 0.4297 +8.9340 0.4320 +8.9680 0.4324 +9.0020 0.4308 +9.0359 0.4273 +9.0699 0.4220 +9.1038 0.4148 +9.1378 0.4059 +9.1717 0.3954 +9.2057 0.3835 +9.2396 0.3702 +9.2736 0.3558 +9.3076 0.3405 +9.3415 0.3243 +9.3755 0.3075 +9.4094 0.2903 +9.4434 0.2728 +9.4773 0.2552 +9.5113 0.2377 +9.5452 0.2204 +9.5792 0.2034 +9.6132 0.1869 +9.6471 0.1709 +9.6811 0.1557 +9.7150 0.1411 +9.7490 0.1273 +9.7829 0.1144 +9.8169 0.1023 +9.8508 0.0911 +9.8848 0.0807 +9.9188 0.0712 +9.9527 0.0626 +9.9867 0.0547 +10.0206 0.0476 +10.0546 0.0413 +10.0885 0.0356 +10.1225 0.0306 +10.1565 0.0262 +10.1904 0.0223 +10.2244 0.0189 +10.2583 0.0159 +10.2923 0.0134 +10.3262 0.0112 +10.3602 0.0093 +10.3941 0.0077 +10.4281 0.0064 +10.4621 0.0052 +10.4960 0.0043 +10.5300 0.0035 +10.5639 0.0028 +10.5979 0.0023 +10.6318 0.0018 +10.6658 0.0015 Pglyrp3 1 250 150 4 3 6 7 8 0 --3.5690 0.0011 --3.5005 0.0014 --3.4321 0.0017 --3.3636 0.0021 --3.2951 0.0026 --3.2266 0.0032 --3.1581 0.0039 --3.0897 0.0047 --3.0212 0.0057 --2.9527 0.0069 --2.8842 0.0083 --2.8157 0.0100 --2.7472 0.0119 --2.6788 0.0141 --2.6103 0.0166 --2.5418 0.0196 --2.4733 0.0229 --2.4048 0.0267 --2.3364 0.0310 --2.2679 0.0359 --2.1994 0.0413 --2.1309 0.0474 --2.0624 0.0541 --1.9939 0.0614 --1.9255 0.0695 --1.8570 0.0783 --1.7885 0.0878 --1.7200 0.0981 --1.6515 0.1091 --1.5831 0.1208 --1.5146 0.1332 --1.4461 0.1462 --1.3776 0.1598 --1.3091 0.1739 --1.2406 0.1885 --1.1722 0.2034 --1.1037 0.2185 --1.0352 0.2337 --0.9667 0.2490 --0.8982 0.2640 --0.8298 0.2788 --0.7613 0.2931 --0.6928 0.3069 --0.6243 0.3199 --0.5558 0.3320 --0.4873 0.3431 --0.4189 0.3530 --0.3504 0.3617 --0.2819 0.3690 --0.2134 0.3748 --0.1449 0.3790 --0.0765 0.3817 --0.0080 0.3827 -0.0605 0.3821 -0.1290 0.3798 -0.1975 0.3759 -0.2660 0.3705 -0.3344 0.3635 -0.4029 0.3552 -0.4714 0.3455 -0.5399 0.3347 -0.6084 0.3228 -0.6769 0.3100 -0.7453 0.2964 -0.8138 0.2822 -0.8823 0.2675 -0.9508 0.2525 -1.0193 0.2373 -1.0877 0.2220 -1.1562 0.2069 -1.2247 0.1919 -1.2932 0.1773 -1.3617 0.1631 -1.4302 0.1493 -1.4986 0.1362 -1.5671 0.1236 -1.6356 0.1118 -1.7041 0.1006 -1.7726 0.0902 -1.8410 0.0804 -1.9095 0.0715 -1.9780 0.0632 -2.0465 0.0557 -2.1150 0.0489 -2.1835 0.0427 -2.2519 0.0371 -2.3204 0.0321 -2.3889 0.0277 -2.4574 0.0238 -2.5259 0.0203 -2.5943 0.0173 -2.6628 0.0147 -2.7313 0.0124 -2.7998 0.0104 -2.8683 0.0087 -2.9368 0.0072 -3.0052 0.0060 -3.0737 0.0050 -3.1422 0.0041 -3.2107 0.0033 -3.2792 0.0027 +5.6307 0.0011 +5.6391 0.0014 +5.6475 0.0017 +5.6560 0.0021 +5.6644 0.0026 +5.6729 0.0032 +5.6813 0.0039 +5.6898 0.0047 +5.6982 0.0057 +5.7067 0.0069 +5.7151 0.0083 +5.7236 0.0100 +5.7320 0.0119 +5.7405 0.0141 +5.7489 0.0166 +5.7574 0.0196 +5.7658 0.0229 +5.7743 0.0267 +5.7827 0.0310 +5.7911 0.0359 +5.7996 0.0413 +5.8080 0.0474 +5.8165 0.0541 +5.8249 0.0614 +5.8334 0.0695 +5.8418 0.0783 +5.8503 0.0878 +5.8587 0.0981 +5.8672 0.1091 +5.8756 0.1208 +5.8841 0.1332 +5.8925 0.1462 +5.9010 0.1598 +5.9094 0.1739 +5.9178 0.1885 +5.9263 0.2034 +5.9347 0.2185 +5.9432 0.2337 +5.9516 0.2490 +5.9601 0.2640 +5.9685 0.2788 +5.9770 0.2931 +5.9854 0.3069 +5.9939 0.3199 +6.0023 0.3320 +6.0108 0.3431 +6.0192 0.3530 +6.0277 0.3617 +6.0361 0.3690 +6.0446 0.3748 +6.0530 0.3790 +6.0614 0.3817 +6.0699 0.3827 +6.0783 0.3821 +6.0868 0.3798 +6.0952 0.3759 +6.1037 0.3705 +6.1121 0.3635 +6.1206 0.3552 +6.1290 0.3455 +6.1375 0.3347 +6.1459 0.3228 +6.1544 0.3100 +6.1628 0.2964 +6.1713 0.2822 +6.1797 0.2675 +6.1882 0.2525 +6.1966 0.2373 +6.2050 0.2220 +6.2135 0.2069 +6.2219 0.1919 +6.2304 0.1773 +6.2388 0.1631 +6.2473 0.1493 +6.2557 0.1362 +6.2642 0.1236 +6.2726 0.1118 +6.2811 0.1006 +6.2895 0.0902 +6.2980 0.0804 +6.3064 0.0715 +6.3149 0.0632 +6.3233 0.0557 +6.3317 0.0489 +6.3402 0.0427 +6.3486 0.0371 +6.3571 0.0321 +6.3655 0.0277 +6.3740 0.0238 +6.3824 0.0203 +6.3909 0.0173 +6.3993 0.0147 +6.4078 0.0124 +6.4162 0.0104 +6.4247 0.0087 +6.4331 0.0072 +6.4416 0.0060 +6.4500 0.0050 +6.4585 0.0041 +6.4669 0.0033 +6.4753 0.0027 Parp9 1 250 150 2 2 6 1 11 --2.7999 0.0080 --2.7399 0.0094 --2.6799 0.0111 --2.6199 0.0130 --2.5599 0.0152 --2.4999 0.0177 --2.4399 0.0205 --2.3799 0.0237 --2.3199 0.0272 --2.2599 0.0312 --2.1999 0.0357 --2.1398 0.0406 --2.0798 0.0461 --2.0198 0.0521 --1.9598 0.0587 --1.8998 0.0659 --1.8398 0.0737 --1.7798 0.0821 --1.7198 0.0912 --1.6598 0.1009 --1.5998 0.1112 --1.5398 0.1222 --1.4798 0.1337 --1.4198 0.1458 --1.3598 0.1585 --1.2997 0.1716 --1.2397 0.1851 --1.1797 0.1990 --1.1197 0.2132 --1.0597 0.2276 --0.9997 0.2420 --0.9397 0.2565 --0.8797 0.2709 --0.8197 0.2850 --0.7597 0.2988 --0.6997 0.3121 --0.6397 0.3249 --0.5797 0.3369 --0.5197 0.3482 --0.4596 0.3586 --0.3996 0.3679 --0.3396 0.3761 --0.2796 0.3832 --0.2196 0.3889 --0.1596 0.3934 --0.0996 0.3964 --0.0396 0.3981 -0.0204 0.3983 -0.0804 0.3971 -0.1404 0.3945 -0.2004 0.3905 -0.2604 0.3851 -0.3204 0.3785 -0.3805 0.3706 -0.4405 0.3617 -0.5005 0.3516 -0.5605 0.3406 -0.6205 0.3288 -0.6805 0.3163 -0.7405 0.3031 -0.8005 0.2894 -0.8605 0.2754 -0.9205 0.2611 -0.9805 0.2467 -1.0405 0.2322 -1.1005 0.2178 -1.1605 0.2035 -1.2206 0.1895 -1.2806 0.1759 -1.3406 0.1626 -1.4006 0.1498 -1.4606 0.1375 -1.5206 0.1258 -1.5806 0.1146 -1.6406 0.1041 -1.7006 0.0942 -1.7606 0.0849 -1.8206 0.0763 -1.8806 0.0683 -1.9406 0.0609 -2.0006 0.0541 -2.0607 0.0480 -2.1207 0.0423 -2.1807 0.0372 -2.2407 0.0326 -2.3007 0.0285 -2.3607 0.0247 -2.4207 0.0214 -2.4807 0.0185 -2.5407 0.0159 -2.6007 0.0137 -2.6607 0.0117 -2.7207 0.0099 -2.7807 0.0084 -2.8407 0.0071 -2.9008 0.0060 -2.9608 0.0050 -3.0208 0.0042 -3.0808 0.0035 -3.1408 0.0029 -3.2008 0.0024 +9.1988 0.0080 +9.2118 0.0094 +9.2247 0.0111 +9.2377 0.0130 +9.2507 0.0152 +9.2637 0.0177 +9.2766 0.0205 +9.2896 0.0237 +9.3026 0.0272 +9.3156 0.0312 +9.3285 0.0357 +9.3415 0.0406 +9.3545 0.0461 +9.3675 0.0521 +9.3804 0.0587 +9.3934 0.0659 +9.4064 0.0737 +9.4194 0.0821 +9.4323 0.0912 +9.4453 0.1009 +9.4583 0.1112 +9.4712 0.1222 +9.4842 0.1337 +9.4972 0.1458 +9.5102 0.1585 +9.5231 0.1716 +9.5361 0.1851 +9.5491 0.1990 +9.5621 0.2132 +9.5750 0.2276 +9.5880 0.2420 +9.6010 0.2565 +9.6140 0.2709 +9.6269 0.2850 +9.6399 0.2988 +9.6529 0.3121 +9.6659 0.3249 +9.6788 0.3369 +9.6918 0.3482 +9.7048 0.3586 +9.7178 0.3679 +9.7307 0.3761 +9.7437 0.3832 +9.7567 0.3889 +9.7697 0.3934 +9.7826 0.3964 +9.7956 0.3981 +9.8086 0.3983 +9.8216 0.3971 +9.8345 0.3945 +9.8475 0.3905 +9.8605 0.3851 +9.8734 0.3785 +9.8864 0.3706 +9.8994 0.3617 +9.9124 0.3516 +9.9253 0.3406 +9.9383 0.3288 +9.9513 0.3163 +9.9643 0.3031 +9.9772 0.2894 +9.9902 0.2754 +10.0032 0.2611 +10.0162 0.2467 +10.0291 0.2322 +10.0421 0.2178 +10.0551 0.2035 +10.0681 0.1895 +10.0810 0.1759 +10.0940 0.1626 +10.1070 0.1498 +10.1200 0.1375 +10.1329 0.1258 +10.1459 0.1146 +10.1589 0.1041 +10.1719 0.0942 +10.1848 0.0849 +10.1978 0.0763 +10.2108 0.0683 +10.2238 0.0609 +10.2367 0.0541 +10.2497 0.0480 +10.2627 0.0423 +10.2756 0.0372 +10.2886 0.0326 +10.3016 0.0285 +10.3146 0.0247 +10.3275 0.0214 +10.3405 0.0185 +10.3535 0.0159 +10.3665 0.0137 +10.3794 0.0117 +10.3924 0.0099 +10.4054 0.0084 +10.4184 0.0071 +10.4313 0.0060 +10.4443 0.0050 +10.4573 0.0042 +10.4703 0.0035 +10.4832 0.0029 +10.4962 0.0024 Oas1a 1 250 150 1 10 0 --3.0946 0.0034 --3.0304 0.0042 --2.9662 0.0051 --2.9019 0.0061 --2.8377 0.0073 --2.7735 0.0087 --2.7093 0.0104 --2.6450 0.0124 --2.5808 0.0146 --2.5166 0.0172 --2.4524 0.0201 --2.3881 0.0235 --2.3239 0.0273 --2.2597 0.0316 --2.1955 0.0364 --2.1312 0.0417 --2.0670 0.0477 --2.0028 0.0543 --1.9385 0.0616 --1.8743 0.0695 --1.8101 0.0782 --1.7459 0.0876 --1.6816 0.0977 --1.6174 0.1085 --1.5532 0.1201 --1.4890 0.1323 --1.4247 0.1452 --1.3605 0.1586 --1.2963 0.1727 --1.2320 0.1871 --1.1678 0.2020 --1.1036 0.2172 --1.0394 0.2325 --0.9751 0.2479 --0.9109 0.2633 --0.8467 0.2785 --0.7825 0.2933 --0.7182 0.3077 --0.6540 0.3214 --0.5898 0.3344 --0.5256 0.3465 --0.4613 0.3576 --0.3971 0.3675 --0.3329 0.3761 --0.2686 0.3834 --0.2044 0.3892 --0.1402 0.3935 --0.0760 0.3963 --0.0117 0.3974 -0.0525 0.3969 -0.1167 0.3947 -0.1809 0.3910 -0.2452 0.3857 -0.3094 0.3790 -0.3736 0.3708 -0.4378 0.3613 -0.5021 0.3507 -0.5663 0.3389 -0.6305 0.3263 -0.6948 0.3128 -0.7590 0.2986 -0.8232 0.2839 -0.8874 0.2689 -0.9517 0.2536 -1.0159 0.2382 -1.0801 0.2228 -1.1443 0.2075 -1.2086 0.1925 -1.2728 0.1779 -1.3370 0.1637 -1.4012 0.1500 -1.4655 0.1369 -1.5297 0.1245 -1.5939 0.1127 -1.6582 0.1016 -1.7224 0.0912 -1.7866 0.0816 -1.8508 0.0726 -1.9151 0.0644 -1.9793 0.0569 -2.0435 0.0501 -2.1077 0.0439 -2.1720 0.0383 -2.2362 0.0332 -2.3004 0.0288 -2.3646 0.0248 -2.4289 0.0213 -2.4931 0.0182 -2.5573 0.0155 -2.6216 0.0131 -2.6858 0.0111 -2.7500 0.0093 -2.8142 0.0078 -2.8785 0.0065 -2.9427 0.0054 -3.0069 0.0045 -3.0711 0.0037 -3.1354 0.0030 -3.1996 0.0025 -3.2638 0.0020 -3.3280 0.0016 +7.2067 0.0034 +7.2290 0.0042 +7.2513 0.0051 +7.2736 0.0061 +7.2959 0.0073 +7.3182 0.0087 +7.3405 0.0104 +7.3628 0.0124 +7.3851 0.0146 +7.4075 0.0172 +7.4298 0.0201 +7.4521 0.0235 +7.4744 0.0273 +7.4967 0.0316 +7.5190 0.0364 +7.5413 0.0417 +7.5636 0.0477 +7.5859 0.0543 +7.6082 0.0616 +7.6305 0.0695 +7.6528 0.0782 +7.6751 0.0876 +7.6974 0.0977 +7.7197 0.1085 +7.7420 0.1201 +7.7643 0.1323 +7.7867 0.1452 +7.8090 0.1586 +7.8313 0.1727 +7.8536 0.1871 +7.8759 0.2020 +7.8982 0.2172 +7.9205 0.2325 +7.9428 0.2479 +7.9651 0.2633 +7.9874 0.2785 +8.0097 0.2933 +8.0320 0.3077 +8.0543 0.3214 +8.0766 0.3344 +8.0989 0.3465 +8.1212 0.3576 +8.1436 0.3675 +8.1659 0.3761 +8.1882 0.3834 +8.2105 0.3892 +8.2328 0.3935 +8.2551 0.3963 +8.2774 0.3974 +8.2997 0.3969 +8.3220 0.3947 +8.3443 0.3910 +8.3666 0.3857 +8.3889 0.3790 +8.4112 0.3708 +8.4335 0.3613 +8.4558 0.3507 +8.4781 0.3389 +8.5004 0.3263 +8.5228 0.3128 +8.5451 0.2986 +8.5674 0.2839 +8.5897 0.2689 +8.6120 0.2536 +8.6343 0.2382 +8.6566 0.2228 +8.6789 0.2075 +8.7012 0.1925 +8.7235 0.1779 +8.7458 0.1637 +8.7681 0.1500 +8.7904 0.1369 +8.8127 0.1245 +8.8350 0.1127 +8.8573 0.1016 +8.8797 0.0912 +8.9020 0.0816 +8.9243 0.0726 +8.9466 0.0644 +8.9689 0.0569 +8.9912 0.0501 +9.0135 0.0439 +9.0358 0.0383 +9.0581 0.0332 +9.0804 0.0288 +9.1027 0.0248 +9.1250 0.0213 +9.1473 0.0182 +9.1696 0.0155 +9.1919 0.0131 +9.2142 0.0111 +9.2365 0.0093 +9.2589 0.0078 +9.2812 0.0065 +9.3035 0.0054 +9.3258 0.0045 +9.3481 0.0037 +9.3704 0.0030 +9.3927 0.0025 +9.4150 0.0020 +9.4373 0.0016 Ifit1 1 250 150 3 1 2 4 2 13 14 --2.8983 0.0063 --2.8370 0.0075 --2.7756 0.0089 --2.7142 0.0105 --2.6528 0.0124 --2.5915 0.0145 --2.5301 0.0169 --2.4687 0.0197 --2.4073 0.0228 --2.3460 0.0264 --2.2846 0.0303 --2.2232 0.0347 --2.1618 0.0397 --2.1005 0.0451 --2.0391 0.0511 --1.9777 0.0577 --1.9164 0.0649 --1.8550 0.0728 --1.7936 0.0812 --1.7322 0.0904 --1.6709 0.1002 --1.6095 0.1106 --1.5481 0.1217 --1.4867 0.1333 --1.4254 0.1456 --1.3640 0.1584 --1.3026 0.1717 --1.2412 0.1854 --1.1799 0.1995 --1.1185 0.2138 --1.0571 0.2284 --0.9957 0.2430 --0.9344 0.2576 --0.8730 0.2720 --0.8116 0.2862 --0.7502 0.3001 --0.6889 0.3134 --0.6275 0.3261 --0.5661 0.3381 --0.5047 0.3492 --0.4434 0.3593 --0.3820 0.3684 --0.3206 0.3763 --0.2592 0.3830 --0.1979 0.3883 --0.1365 0.3922 --0.0751 0.3948 --0.0137 0.3958 -0.0476 0.3954 -0.1090 0.3936 -0.1704 0.3902 -0.2318 0.3855 -0.2931 0.3795 -0.3545 0.3721 -0.4159 0.3635 -0.4773 0.3539 -0.5386 0.3432 -0.6000 0.3316 -0.6614 0.3192 -0.7228 0.3061 -0.7841 0.2925 -0.8455 0.2784 -0.9069 0.2641 -0.9682 0.2495 -1.0296 0.2349 -1.0910 0.2203 -1.1524 0.2059 -1.2137 0.1917 -1.2751 0.1778 -1.3365 0.1643 -1.3979 0.1513 -1.4592 0.1388 -1.5206 0.1268 -1.5820 0.1155 -1.6434 0.1047 -1.7047 0.0947 -1.7661 0.0852 -1.8275 0.0765 -1.8889 0.0683 -1.9502 0.0609 -2.0116 0.0540 -2.0730 0.0477 -2.1344 0.0420 -2.1957 0.0369 -2.2571 0.0322 -2.3185 0.0281 -2.3799 0.0244 -2.4412 0.0211 -2.5026 0.0181 -2.5640 0.0156 -2.6254 0.0133 -2.6867 0.0113 -2.7481 0.0096 -2.8095 0.0081 -2.8709 0.0068 -2.9322 0.0057 -2.9936 0.0048 -3.0550 0.0040 -3.1164 0.0033 -3.1777 0.0027 -3.2391 0.0023 +7.1788 0.0063 +7.2137 0.0075 +7.2487 0.0089 +7.2836 0.0105 +7.3185 0.0124 +7.3535 0.0145 +7.3884 0.0169 +7.4233 0.0197 +7.4583 0.0228 +7.4932 0.0264 +7.5281 0.0303 +7.5631 0.0347 +7.5980 0.0397 +7.6329 0.0451 +7.6679 0.0511 +7.7028 0.0577 +7.7377 0.0649 +7.7727 0.0728 +7.8076 0.0812 +7.8426 0.0904 +7.8775 0.1002 +7.9124 0.1106 +7.9474 0.1217 +7.9823 0.1333 +8.0172 0.1456 +8.0522 0.1584 +8.0871 0.1717 +8.1220 0.1854 +8.1570 0.1995 +8.1919 0.2138 +8.2268 0.2284 +8.2618 0.2430 +8.2967 0.2576 +8.3316 0.2720 +8.3666 0.2862 +8.4015 0.3001 +8.4364 0.3134 +8.4714 0.3261 +8.5063 0.3381 +8.5412 0.3492 +8.5762 0.3593 +8.6111 0.3684 +8.6460 0.3763 +8.6810 0.3830 +8.7159 0.3883 +8.7508 0.3922 +8.7858 0.3948 +8.8207 0.3958 +8.8556 0.3954 +8.8906 0.3936 +8.9255 0.3902 +8.9604 0.3855 +8.9954 0.3795 +9.0303 0.3721 +9.0652 0.3635 +9.1002 0.3539 +9.1351 0.3432 +9.1700 0.3316 +9.2050 0.3192 +9.2399 0.3061 +9.2748 0.2925 +9.3098 0.2784 +9.3447 0.2641 +9.3796 0.2495 +9.4146 0.2349 +9.4495 0.2203 +9.4844 0.2059 +9.5194 0.1917 +9.5543 0.1778 +9.5892 0.1643 +9.6242 0.1513 +9.6591 0.1388 +9.6940 0.1268 +9.7290 0.1155 +9.7639 0.1047 +9.7988 0.0947 +9.8338 0.0852 +9.8687 0.0765 +9.9036 0.0683 +9.9386 0.0609 +9.9735 0.0540 +10.0084 0.0477 +10.0434 0.0420 +10.0783 0.0369 +10.1133 0.0322 +10.1482 0.0281 +10.1831 0.0244 +10.2181 0.0211 +10.2530 0.0181 +10.2879 0.0156 +10.3229 0.0133 +10.3578 0.0113 +10.3927 0.0096 +10.4277 0.0081 +10.4626 0.0068 +10.4975 0.0057 +10.5325 0.0048 +10.5674 0.0040 +10.6023 0.0033 +10.6373 0.0027 +10.6722 0.0023 Mx1 1 250 150 2 4 12 0 --3.1709 0.0028 --3.0983 0.0035 --3.0258 0.0043 --2.9533 0.0053 --2.8808 0.0066 --2.8082 0.0081 --2.7357 0.0098 --2.6632 0.0119 --2.5907 0.0144 --2.5181 0.0173 --2.4456 0.0207 --2.3731 0.0246 --2.3006 0.0291 --2.2280 0.0342 --2.1555 0.0400 --2.0830 0.0465 --2.0105 0.0539 --1.9379 0.0621 --1.8654 0.0711 --1.7929 0.0811 --1.7204 0.0919 --1.6479 0.1037 --1.5753 0.1164 --1.5028 0.1300 --1.4303 0.1444 --1.3578 0.1595 --1.2852 0.1754 --1.2127 0.1918 --1.1402 0.2086 --1.0677 0.2258 --0.9951 0.2431 --0.9226 0.2604 --0.8501 0.2775 --0.7776 0.2941 --0.7050 0.3102 --0.6325 0.3254 --0.5600 0.3396 --0.4875 0.3526 --0.4149 0.3641 --0.3424 0.3742 --0.2699 0.3824 --0.1974 0.3889 --0.1248 0.3934 --0.0523 0.3959 -0.0202 0.3964 -0.0927 0.3948 -0.1653 0.3911 -0.2378 0.3855 -0.3103 0.3780 -0.3828 0.3688 -0.4554 0.3579 -0.5279 0.3455 -0.6004 0.3318 -0.6729 0.3170 -0.7455 0.3013 -0.8180 0.2849 -0.8905 0.2680 -0.9630 0.2508 -1.0356 0.2335 -1.1081 0.2162 -1.1806 0.1992 -1.2531 0.1826 -1.3257 0.1665 -1.3982 0.1510 -1.4707 0.1363 -1.5432 0.1223 -1.6158 0.1092 -1.6883 0.0970 -1.7608 0.0858 -1.8333 0.0754 -1.9059 0.0660 -1.9784 0.0574 -2.0509 0.0497 -2.1234 0.0428 -2.1959 0.0367 -2.2685 0.0312 -2.3410 0.0265 -2.4135 0.0223 -2.4860 0.0187 -2.5586 0.0156 -2.6311 0.0130 -2.7036 0.0107 -2.7761 0.0088 -2.8487 0.0072 -2.9212 0.0059 -2.9937 0.0047 -3.0662 0.0038 -3.1388 0.0031 -3.2113 0.0024 -3.2838 0.0019 -3.3563 0.0015 -3.4289 0.0012 -3.5014 0.0009 -3.5739 0.0007 -3.6464 0.0006 -3.7190 0.0004 -3.7915 0.0003 -3.8640 0.0002 -3.9365 0.0002 -4.0091 0.0001 -4.0816 0.0001 +6.4152 0.0028 +6.4396 0.0035 +6.4641 0.0043 +6.4885 0.0053 +6.5129 0.0066 +6.5373 0.0081 +6.5618 0.0098 +6.5862 0.0119 +6.6106 0.0144 +6.6350 0.0173 +6.6595 0.0207 +6.6839 0.0246 +6.7083 0.0291 +6.7327 0.0342 +6.7572 0.0400 +6.7816 0.0465 +6.8060 0.0539 +6.8304 0.0621 +6.8549 0.0711 +6.8793 0.0811 +6.9037 0.0919 +6.9281 0.1037 +6.9526 0.1164 +6.9770 0.1300 +7.0014 0.1444 +7.0259 0.1595 +7.0503 0.1754 +7.0747 0.1918 +7.0991 0.2086 +7.1236 0.2258 +7.1480 0.2431 +7.1724 0.2604 +7.1968 0.2775 +7.2213 0.2941 +7.2457 0.3102 +7.2701 0.3254 +7.2945 0.3396 +7.3190 0.3526 +7.3434 0.3641 +7.3678 0.3742 +7.3922 0.3824 +7.4167 0.3889 +7.4411 0.3934 +7.4655 0.3959 +7.4899 0.3964 +7.5144 0.3948 +7.5388 0.3911 +7.5632 0.3855 +7.5876 0.3780 +7.6121 0.3688 +7.6365 0.3579 +7.6609 0.3455 +7.6854 0.3318 +7.7098 0.3170 +7.7342 0.3013 +7.7586 0.2849 +7.7831 0.2680 +7.8075 0.2508 +7.8319 0.2335 +7.8563 0.2162 +7.8808 0.1992 +7.9052 0.1826 +7.9296 0.1665 +7.9540 0.1510 +7.9785 0.1363 +8.0029 0.1223 +8.0273 0.1092 +8.0517 0.0970 +8.0762 0.0858 +8.1006 0.0754 +8.1250 0.0660 +8.1494 0.0574 +8.1739 0.0497 +8.1983 0.0428 +8.2227 0.0367 +8.2471 0.0312 +8.2716 0.0265 +8.2960 0.0223 +8.3204 0.0187 +8.3449 0.0156 +8.3693 0.0130 +8.3937 0.0107 +8.4181 0.0088 +8.4426 0.0072 +8.4670 0.0059 +8.4914 0.0047 +8.5158 0.0038 +8.5403 0.0031 +8.5647 0.0024 +8.5891 0.0019 +8.6135 0.0015 +8.6380 0.0012 +8.6624 0.0009 +8.6868 0.0007 +8.7112 0.0006 +8.7357 0.0004 +8.7601 0.0003 +8.7845 0.0002 +8.8089 0.0002 +8.8334 0.0001 +8.8578 0.0001 Irf7 1 250 150 2 4 12 0 --3.9154 0.0002 --3.8440 0.0003 --3.7726 0.0004 --3.7012 0.0005 --3.6298 0.0006 --3.5583 0.0008 --3.4869 0.0010 --3.4155 0.0013 --3.3441 0.0016 --3.2727 0.0021 --3.2013 0.0026 --3.1299 0.0033 --3.0585 0.0040 --2.9871 0.0050 --2.9157 0.0061 --2.8443 0.0075 --2.7729 0.0091 --2.7015 0.0111 --2.6301 0.0133 --2.5587 0.0160 --2.4873 0.0191 --2.4159 0.0226 --2.3445 0.0267 --2.2730 0.0314 --2.2016 0.0367 --2.1302 0.0428 --2.0588 0.0495 --1.9874 0.0570 --1.9160 0.0654 --1.8446 0.0746 --1.7732 0.0846 --1.7018 0.0956 --1.6304 0.1074 --1.5590 0.1201 --1.4876 0.1336 --1.4162 0.1478 --1.3448 0.1628 --1.2734 0.1784 --1.2020 0.1946 --1.1306 0.2111 --1.0592 0.2279 --0.9878 0.2448 --0.9163 0.2617 --0.8449 0.2783 --0.7735 0.2946 --0.7021 0.3102 --0.6307 0.3250 --0.5593 0.3388 --0.4879 0.3514 --0.4165 0.3627 --0.3451 0.3725 --0.2737 0.3806 --0.2023 0.3870 --0.1309 0.3916 --0.0595 0.3942 -0.0119 0.3948 -0.0833 0.3935 -0.1547 0.3903 -0.2261 0.3851 -0.2975 0.3781 -0.3690 0.3694 -0.4404 0.3591 -0.5118 0.3473 -0.5832 0.3343 -0.6546 0.3201 -0.7260 0.3050 -0.7974 0.2892 -0.8688 0.2728 -0.9402 0.2561 -1.0116 0.2392 -1.0830 0.2223 -1.1544 0.2056 -1.2258 0.1891 -1.2972 0.1732 -1.3686 0.1577 -1.4400 0.1430 -1.5114 0.1290 -1.5828 0.1157 -1.6543 0.1033 -1.7257 0.0918 -1.7971 0.0812 -1.8685 0.0714 -1.9399 0.0625 -2.0113 0.0544 -2.0827 0.0472 -2.1541 0.0407 -2.2255 0.0349 -2.2969 0.0298 -2.3683 0.0253 -2.4397 0.0214 -2.5111 0.0180 -2.5825 0.0151 -2.6539 0.0125 -2.7253 0.0104 -2.7967 0.0086 -2.8681 0.0070 -2.9396 0.0057 -3.0110 0.0047 -3.0824 0.0038 -3.1538 0.0030 -3.2252 0.0024 +7.8614 0.0002 +7.9006 0.0003 +7.9398 0.0004 +7.9789 0.0005 +8.0181 0.0006 +8.0573 0.0008 +8.0965 0.0010 +8.1356 0.0013 +8.1748 0.0016 +8.2140 0.0021 +8.2531 0.0026 +8.2923 0.0033 +8.3315 0.0040 +8.3707 0.0050 +8.4098 0.0061 +8.4490 0.0075 +8.4882 0.0091 +8.5273 0.0111 +8.5665 0.0133 +8.6057 0.0160 +8.6449 0.0191 +8.6840 0.0226 +8.7232 0.0267 +8.7624 0.0314 +8.8015 0.0367 +8.8407 0.0428 +8.8799 0.0495 +8.9191 0.0570 +8.9582 0.0654 +8.9974 0.0746 +9.0366 0.0846 +9.0757 0.0956 +9.1149 0.1074 +9.1541 0.1201 +9.1933 0.1336 +9.2324 0.1478 +9.2716 0.1628 +9.3108 0.1784 +9.3499 0.1946 +9.3891 0.2111 +9.4283 0.2279 +9.4675 0.2448 +9.5066 0.2617 +9.5458 0.2783 +9.5850 0.2946 +9.6241 0.3102 +9.6633 0.3250 +9.7025 0.3388 +9.7417 0.3514 +9.7808 0.3627 +9.8200 0.3725 +9.8592 0.3806 +9.8983 0.3870 +9.9375 0.3916 +9.9767 0.3942 +10.0159 0.3948 +10.0550 0.3935 +10.0942 0.3903 +10.1334 0.3851 +10.1725 0.3781 +10.2117 0.3694 +10.2509 0.3591 +10.2901 0.3473 +10.3292 0.3343 +10.3684 0.3201 +10.4076 0.3050 +10.4467 0.2892 +10.4859 0.2728 +10.5251 0.2561 +10.5643 0.2392 +10.6034 0.2223 +10.6426 0.2056 +10.6818 0.1891 +10.7209 0.1732 +10.7601 0.1577 +10.7993 0.1430 +10.8385 0.1290 +10.8776 0.1157 +10.9168 0.1033 +10.9560 0.0918 +10.9951 0.0812 +11.0343 0.0714 +11.0735 0.0625 +11.1127 0.0544 +11.1518 0.0472 +11.1910 0.0407 +11.2302 0.0349 +11.2693 0.0298 +11.3085 0.0253 +11.3477 0.0214 +11.3869 0.0180 +11.4260 0.0151 +11.4652 0.0125 +11.5044 0.0104 +11.5435 0.0086 +11.5827 0.0070 +11.6219 0.0057 +11.6611 0.0047 +11.7002 0.0038 +11.7394 0.0030 +11.7786 0.0024 diff --git a/sourcecodes/data/example_chr2_spleen/cuLparameters.txt b/sourcecodes/data/example_chr2_spleen/cuLparameters.txt new file mode 100644 index 00000000..545d6e91 --- /dev/null +++ b/sourcecodes/data/example_chr2_spleen/cuLparameters.txt @@ -0,0 +1,57 @@ +rs3664317 +Discrete node with 2 states +1 0.5479 +2 0.4521 + +Oas1a +Continuous node +8.2815 0.3487 + +Parp9 +Continuous node +9.8042 0.2165 + +Dhx58 +Continuous node +8.8912 0.3029 + +Cd1d1 +Continuous node +9.6436 0.2679 + +Irf7 +Continuous node +10.0093 0.5542 + +Oas3 +Continuous node +8.9574 0.5062 + +Pml +Continuous node +11.0754 0.1911 + +Mx1 +Continuous node +7.4831 0.3389 + +Ifit1 +Continuous node +8.8285 0.5736 + +Trim25 +Continuous node +10.2552 0.1765 + +Oas1g +Continuous node +8.6322 0.4619 + +Pglyrp3 +Continuous node +6.0709 0.1286 + +Ifih1 +Continuous node +9.2981 0.3860 + diff --git a/sourcecodes/data/example_sci/Llumap.txt b/sourcecodes/data/example_sci/Llumap.txt index 958281d5..dfe8ca75 100644 --- a/sourcecodes/data/example_sci/Llumap.txt +++ b/sourcecodes/data/example_sci/Llumap.txt @@ -1,5 +1,5 @@ -Genotype 2 0.500305 1.514000 -Gene3 1 1.027261 -0.015284 -Gene2 1 1.160583 0.138097 -Phenotype 1 1.554104 0.069005 -Gene1 1 1.515414 0.174451 +Genotype 0.500305 1.514000 +Gene3 1.027261 -0.015284 +Gene2 1.160583 0.138097 +Phenotype 1.554104 0.069005 +Gene1 1.515414 0.174451 diff --git a/sourcecodes/data/example_sci/Llunet_figure.txt b/sourcecodes/data/example_sci/Llunet_figure.txt index bdc07cbd..2f383b1a 100644 --- a/sourcecodes/data/example_sci/Llunet_figure.txt +++ b/sourcecodes/data/example_sci/Llunet_figure.txt @@ -15,419 +15,419 @@ Gene1 1 250 150 1 1 1 3 --3.5013 0.0009 --3.4313 0.0012 --3.3613 0.0015 --3.2912 0.0019 --3.2212 0.0024 --3.1512 0.0030 --3.0811 0.0037 --3.0111 0.0046 --2.9411 0.0056 --2.8710 0.0069 --2.8010 0.0083 --2.7310 0.0101 --2.6609 0.0121 --2.5909 0.0145 --2.5209 0.0174 --2.4508 0.0206 --2.3808 0.0243 --2.3108 0.0286 --2.2407 0.0335 --2.1707 0.0389 --2.1007 0.0451 --2.0306 0.0520 --1.9606 0.0597 --1.8906 0.0682 --1.8205 0.0775 --1.7505 0.0876 --1.6805 0.0986 --1.6104 0.1105 --1.5404 0.1231 --1.4704 0.1366 --1.4003 0.1508 --1.3303 0.1657 --1.2603 0.1811 --1.1902 0.1971 --1.1202 0.2134 --1.0502 0.2300 --0.9801 0.2467 --0.9101 0.2633 --0.8401 0.2797 --0.7700 0.2956 --0.7000 0.3110 --0.6300 0.3256 --0.5599 0.3392 --0.4899 0.3517 --0.4199 0.3629 --0.3498 0.3726 --0.2798 0.3808 --0.2098 0.3873 --0.1397 0.3920 --0.0697 0.3948 -0.0003 0.3958 -0.0704 0.3948 -0.1404 0.3920 -0.2104 0.3872 -0.2805 0.3807 -0.3505 0.3726 -0.4205 0.3628 -0.4906 0.3516 -0.5606 0.3391 -0.6306 0.3254 -0.7007 0.3108 -0.7707 0.2955 -0.8407 0.2795 -0.9108 0.2631 -0.9808 0.2465 -1.0508 0.2299 -1.1209 0.2133 -1.1909 0.1969 -1.2609 0.1810 -1.3310 0.1655 -1.4010 0.1507 -1.4710 0.1365 -1.5411 0.1230 -1.6111 0.1103 -1.6811 0.0985 -1.7512 0.0875 -1.8212 0.0774 -1.8912 0.0681 -1.9613 0.0596 -2.0313 0.0520 -2.1013 0.0451 -2.1714 0.0389 -2.2414 0.0334 -2.3114 0.0286 -2.3815 0.0243 -2.4515 0.0206 -2.5215 0.0173 -2.5916 0.0145 -2.6616 0.0121 -2.7316 0.0101 -2.8017 0.0083 -2.8717 0.0068 -2.9417 0.0056 -3.0118 0.0046 -3.0818 0.0037 -3.1518 0.0030 -3.2219 0.0024 -3.2919 0.0019 -3.3619 0.0015 -3.4320 0.0012 -3.5020 0.0009 +-5.1315 0.0009 +-5.0254 0.0012 +-4.9193 0.0015 +-4.8131 0.0019 +-4.7070 0.0024 +-4.6009 0.0030 +-4.4947 0.0037 +-4.3886 0.0046 +-4.2825 0.0056 +-4.1764 0.0069 +-4.0702 0.0083 +-3.9641 0.0101 +-3.8580 0.0121 +-3.7518 0.0145 +-3.6457 0.0174 +-3.5396 0.0206 +-3.4334 0.0243 +-3.3273 0.0286 +-3.2212 0.0335 +-3.1151 0.0389 +-3.0089 0.0451 +-2.9028 0.0520 +-2.7967 0.0597 +-2.6905 0.0682 +-2.5844 0.0775 +-2.4783 0.0876 +-2.3722 0.0986 +-2.2660 0.1105 +-2.1599 0.1231 +-2.0538 0.1366 +-1.9476 0.1508 +-1.8415 0.1657 +-1.7354 0.1811 +-1.6292 0.1971 +-1.5231 0.2134 +-1.4170 0.2300 +-1.3109 0.2467 +-1.2047 0.2633 +-1.0986 0.2797 +-0.9925 0.2956 +-0.8863 0.3110 +-0.7802 0.3256 +-0.6741 0.3392 +-0.5680 0.3517 +-0.4618 0.3629 +-0.3557 0.3726 +-0.2496 0.3808 +-0.1434 0.3873 +-0.0373 0.3920 +0.0688 0.3948 +0.1749 0.3958 +0.2811 0.3948 +0.3872 0.3920 +0.4933 0.3872 +0.5995 0.3807 +0.7056 0.3726 +0.8117 0.3628 +0.9179 0.3516 +1.0240 0.3391 +1.1301 0.3254 +1.2362 0.3108 +1.3424 0.2955 +1.4485 0.2795 +1.5546 0.2631 +1.6608 0.2465 +1.7669 0.2299 +1.8730 0.2133 +1.9791 0.1969 +2.0853 0.1810 +2.1914 0.1655 +2.2975 0.1507 +2.4037 0.1365 +2.5098 0.1230 +2.6159 0.1103 +2.7221 0.0985 +2.8282 0.0875 +2.9343 0.0774 +3.0404 0.0681 +3.1466 0.0596 +3.2527 0.0520 +3.3588 0.0451 +3.4650 0.0389 +3.5711 0.0334 +3.6772 0.0286 +3.7833 0.0243 +3.8895 0.0206 +3.9956 0.0173 +4.1017 0.0145 +4.2079 0.0121 +4.3140 0.0101 +4.4201 0.0083 +4.5263 0.0068 +4.6324 0.0056 +4.7385 0.0046 +4.8446 0.0037 +4.9508 0.0030 +5.0569 0.0024 +5.1630 0.0019 +5.2692 0.0015 +5.3753 0.0012 +5.4814 0.0009 Gene2 1 250 150 1 2 2 4 5 --3.5862 0.0008 --3.5124 0.0010 --3.4385 0.0013 --3.3647 0.0016 --3.2909 0.0020 --3.2171 0.0026 --3.1432 0.0032 --3.0694 0.0041 --2.9956 0.0050 --2.9218 0.0062 --2.8479 0.0077 --2.7741 0.0094 --2.7003 0.0114 --2.6264 0.0138 --2.5526 0.0166 --2.4788 0.0199 --2.4050 0.0237 --2.3311 0.0281 --2.2573 0.0331 --2.1835 0.0388 --2.1097 0.0453 --2.0358 0.0526 --1.9620 0.0607 --1.8882 0.0696 --1.8143 0.0795 --1.7405 0.0903 --1.6667 0.1021 --1.5929 0.1147 --1.5190 0.1282 --1.4452 0.1426 --1.3714 0.1578 --1.2976 0.1736 --1.2237 0.1900 --1.1499 0.2069 --1.0761 0.2241 --1.0022 0.2414 --0.9284 0.2587 --0.8546 0.2758 --0.7808 0.2924 --0.7069 0.3084 --0.6331 0.3236 --0.5593 0.3377 --0.4855 0.3506 --0.4116 0.3621 --0.3378 0.3719 --0.2640 0.3800 --0.1901 0.3863 --0.1163 0.3905 --0.0425 0.3927 -0.0313 0.3929 -0.1052 0.3910 -0.1790 0.3870 -0.2528 0.3811 -0.3266 0.3732 -0.4005 0.3636 -0.4743 0.3524 -0.5481 0.3397 -0.6220 0.3258 -0.6958 0.3108 -0.7696 0.2949 -0.8434 0.2783 -0.9173 0.2613 -0.9911 0.2440 -1.0649 0.2267 -1.1387 0.2095 -1.2126 0.1925 -1.2864 0.1760 -1.3602 0.1601 -1.4341 0.1449 -1.5079 0.1304 -1.5817 0.1167 -1.6555 0.1039 -1.7294 0.0920 -1.8032 0.0811 -1.8770 0.0711 -1.9508 0.0620 -2.0247 0.0537 -2.0985 0.0464 -2.1723 0.0398 -2.2462 0.0340 -2.3200 0.0288 -2.3938 0.0243 -2.4676 0.0205 -2.5415 0.0171 -2.6153 0.0142 -2.6891 0.0117 -2.7629 0.0097 -2.8368 0.0079 -2.9106 0.0064 -2.9844 0.0052 -3.0583 0.0042 -3.1321 0.0034 -3.2059 0.0027 -3.2797 0.0021 -3.3536 0.0017 -3.4274 0.0013 -3.5012 0.0010 -3.5750 0.0008 -3.6489 0.0006 -3.7227 0.0005 -3.7965 0.0004 +-4.0240 0.0008 +-3.9383 0.0010 +-3.8526 0.0013 +-3.7669 0.0016 +-3.6813 0.0020 +-3.5956 0.0026 +-3.5099 0.0032 +-3.4242 0.0041 +-3.3385 0.0050 +-3.2528 0.0062 +-3.1672 0.0077 +-3.0815 0.0094 +-2.9958 0.0114 +-2.9101 0.0138 +-2.8244 0.0166 +-2.7387 0.0199 +-2.6531 0.0237 +-2.5674 0.0281 +-2.4817 0.0331 +-2.3960 0.0388 +-2.3103 0.0453 +-2.2246 0.0526 +-2.1390 0.0607 +-2.0533 0.0696 +-1.9676 0.0795 +-1.8819 0.0903 +-1.7962 0.1021 +-1.7106 0.1147 +-1.6249 0.1282 +-1.5392 0.1426 +-1.4535 0.1578 +-1.3678 0.1736 +-1.2821 0.1900 +-1.1965 0.2069 +-1.1108 0.2241 +-1.0251 0.2414 +-0.9394 0.2587 +-0.8537 0.2758 +-0.7680 0.2924 +-0.6824 0.3084 +-0.5967 0.3236 +-0.5110 0.3377 +-0.4253 0.3506 +-0.3396 0.3621 +-0.2539 0.3719 +-0.1683 0.3800 +-0.0826 0.3863 +0.0031 0.3905 +0.0888 0.3927 +0.1745 0.3929 +0.2601 0.3910 +0.3458 0.3870 +0.4315 0.3811 +0.5172 0.3732 +0.6029 0.3636 +0.6886 0.3524 +0.7742 0.3397 +0.8599 0.3258 +0.9456 0.3108 +1.0313 0.2949 +1.1170 0.2783 +1.2027 0.2613 +1.2883 0.2440 +1.3740 0.2267 +1.4597 0.2095 +1.5454 0.1925 +1.6311 0.1760 +1.7168 0.1601 +1.8024 0.1449 +1.8881 0.1304 +1.9738 0.1167 +2.0595 0.1039 +2.1452 0.0920 +2.2309 0.0811 +2.3165 0.0711 +2.4022 0.0620 +2.4879 0.0537 +2.5736 0.0464 +2.6593 0.0398 +2.7449 0.0340 +2.8306 0.0288 +2.9163 0.0243 +3.0020 0.0205 +3.0877 0.0171 +3.1734 0.0142 +3.2590 0.0117 +3.3447 0.0097 +3.4304 0.0079 +3.5161 0.0064 +3.6018 0.0052 +3.6875 0.0042 +3.7731 0.0034 +3.8588 0.0027 +3.9445 0.0021 +4.0302 0.0017 +4.1159 0.0013 +4.2016 0.0010 +4.2872 0.0008 +4.3729 0.0006 +4.4586 0.0005 +4.5443 0.0004 Gene3 1 250 150 2 1 3 1 5 --4.3571 0.0001 --4.2762 0.0001 --4.1954 0.0001 --4.1145 0.0001 --4.0336 0.0002 --3.9527 0.0002 --3.8719 0.0003 --3.7910 0.0004 --3.7101 0.0006 --3.6293 0.0008 --3.5484 0.0010 --3.4675 0.0013 --3.3866 0.0018 --3.3058 0.0023 --3.2249 0.0029 --3.1440 0.0037 --3.0632 0.0047 --2.9823 0.0059 --2.9014 0.0074 --2.8205 0.0092 --2.7397 0.0113 --2.6588 0.0139 --2.5779 0.0170 --2.4971 0.0206 --2.4162 0.0248 --2.3353 0.0297 --2.2544 0.0354 --2.1736 0.0419 --2.0927 0.0493 --2.0118 0.0576 --1.9310 0.0669 --1.8501 0.0773 --1.7692 0.0887 --1.6883 0.1012 --1.6075 0.1147 --1.5266 0.1292 --1.4457 0.1447 --1.3648 0.1610 --1.2840 0.1781 --1.2031 0.1958 --1.1222 0.2139 --1.0414 0.2323 --0.9605 0.2507 --0.8796 0.2688 --0.7987 0.2866 --0.7179 0.3036 --0.6370 0.3197 --0.5561 0.3345 --0.4753 0.3479 --0.3944 0.3597 --0.3135 0.3695 --0.2326 0.3773 --0.1518 0.3828 --0.0709 0.3861 -0.0100 0.3870 -0.0908 0.3855 -0.1717 0.3817 -0.2526 0.3756 -0.3335 0.3673 -0.4143 0.3570 -0.4952 0.3449 -0.5761 0.3311 -0.6569 0.3160 -0.7378 0.2996 -0.8187 0.2824 -0.8996 0.2645 -0.9804 0.2463 -1.0613 0.2279 -1.1422 0.2095 -1.2230 0.1915 -1.3039 0.1740 -1.3848 0.1570 -1.4657 0.1409 -1.5465 0.1256 -1.6274 0.1114 -1.7083 0.0981 -1.7891 0.0859 -1.8700 0.0747 -1.9509 0.0646 -2.0318 0.0555 -2.1126 0.0474 -2.1935 0.0402 -2.2744 0.0340 -2.3552 0.0285 -2.4361 0.0237 -2.5170 0.0197 -2.5979 0.0162 -2.6787 0.0132 -2.7596 0.0108 -2.8405 0.0087 -2.9213 0.0070 -3.0022 0.0056 -3.0831 0.0044 -3.1640 0.0035 -3.2448 0.0027 -3.3257 0.0021 -3.4066 0.0016 -3.4874 0.0013 -3.5683 0.0010 -3.6492 0.0007 -3.7301 0.0006 +-4.4912 0.0001 +-4.4081 0.0001 +-4.3250 0.0001 +-4.2419 0.0001 +-4.1589 0.0002 +-4.0758 0.0002 +-3.9927 0.0003 +-3.9096 0.0004 +-3.8266 0.0006 +-3.7435 0.0008 +-3.6604 0.0010 +-3.5773 0.0013 +-3.4942 0.0018 +-3.4112 0.0023 +-3.3281 0.0029 +-3.2450 0.0037 +-3.1619 0.0047 +-3.0789 0.0059 +-2.9958 0.0074 +-2.9127 0.0092 +-2.8296 0.0113 +-2.7466 0.0139 +-2.6635 0.0170 +-2.5804 0.0206 +-2.4973 0.0248 +-2.4143 0.0297 +-2.3312 0.0354 +-2.2481 0.0419 +-2.1650 0.0493 +-2.0820 0.0576 +-1.9989 0.0669 +-1.9158 0.0773 +-1.8327 0.0887 +-1.7496 0.1012 +-1.6666 0.1147 +-1.5835 0.1292 +-1.5004 0.1447 +-1.4173 0.1610 +-1.3343 0.1781 +-1.2512 0.1958 +-1.1681 0.2139 +-1.0850 0.2323 +-1.0020 0.2507 +-0.9189 0.2688 +-0.8358 0.2866 +-0.7527 0.3036 +-0.6697 0.3197 +-0.5866 0.3345 +-0.5035 0.3479 +-0.4204 0.3597 +-0.3373 0.3695 +-0.2543 0.3773 +-0.1712 0.3828 +-0.0881 0.3861 +-0.0050 0.3870 +0.0780 0.3855 +0.1611 0.3817 +0.2442 0.3756 +0.3273 0.3673 +0.4103 0.3570 +0.4934 0.3449 +0.5765 0.3311 +0.6596 0.3160 +0.7426 0.2996 +0.8257 0.2824 +0.9088 0.2645 +0.9919 0.2463 +1.0749 0.2279 +1.1580 0.2095 +1.2411 0.1915 +1.3242 0.1740 +1.4073 0.1570 +1.4903 0.1409 +1.5734 0.1256 +1.6565 0.1114 +1.7396 0.0981 +1.8226 0.0859 +1.9057 0.0747 +1.9888 0.0646 +2.0719 0.0555 +2.1549 0.0474 +2.2380 0.0402 +2.3211 0.0340 +2.4042 0.0285 +2.4872 0.0237 +2.5703 0.0197 +2.6534 0.0162 +2.7365 0.0132 +2.8195 0.0108 +2.9026 0.0087 +2.9857 0.0070 +3.0688 0.0056 +3.1519 0.0044 +3.2349 0.0035 +3.3180 0.0027 +3.4011 0.0021 +3.4842 0.0016 +3.5672 0.0013 +3.6503 0.0010 +3.7334 0.0007 +3.8165 0.0006 Phenotype 1 250 150 2 3 4 0 --3.5574 0.0010 --3.4806 0.0013 --3.4037 0.0016 --3.3268 0.0021 --3.2499 0.0026 --3.1731 0.0033 --3.0962 0.0042 --3.0193 0.0052 --2.9425 0.0065 --2.8656 0.0080 --2.7887 0.0098 --2.7118 0.0120 --2.6350 0.0146 --2.5581 0.0176 --2.4812 0.0212 --2.4044 0.0253 --2.3275 0.0300 --2.2506 0.0354 --2.1737 0.0416 --2.0969 0.0486 --2.0200 0.0564 --1.9431 0.0651 --1.8663 0.0748 --1.7894 0.0854 --1.7125 0.0970 --1.6356 0.1096 --1.5588 0.1230 --1.4819 0.1374 --1.4050 0.1526 --1.3282 0.1685 --1.2513 0.1850 --1.1744 0.2021 --1.0975 0.2195 --1.0207 0.2370 --0.9438 0.2545 --0.8669 0.2718 --0.7901 0.2887 --0.7132 0.3049 --0.6363 0.3202 --0.5594 0.3344 --0.4826 0.3473 --0.4057 0.3587 --0.3288 0.3684 --0.2520 0.3763 --0.1751 0.3822 --0.0982 0.3860 --0.0213 0.3877 -0.0555 0.3872 -0.1324 0.3846 -0.2093 0.3798 -0.2861 0.3731 -0.3630 0.3644 -0.4399 0.3539 -0.5168 0.3418 -0.5936 0.3283 -0.6705 0.3136 -0.7474 0.2979 -0.8242 0.2813 -0.9011 0.2643 -0.9780 0.2468 -1.0549 0.2293 -1.1317 0.2118 -1.2086 0.1945 -1.2855 0.1777 -1.3623 0.1614 -1.4392 0.1458 -1.5161 0.1310 -1.5930 0.1170 -1.6698 0.1039 -1.7467 0.0918 -1.8236 0.0806 -1.9004 0.0704 -1.9773 0.0612 -2.0542 0.0529 -2.1311 0.0454 -2.2079 0.0388 -2.2848 0.0329 -2.3617 0.0278 -2.4385 0.0234 -2.5154 0.0195 -2.5923 0.0162 -2.6692 0.0134 -2.7460 0.0110 -2.8229 0.0090 -2.8998 0.0073 -2.9766 0.0059 -3.0535 0.0047 -3.1304 0.0038 -3.2073 0.0030 -3.2841 0.0024 -3.3610 0.0019 -3.4379 0.0015 -3.5147 0.0011 -3.5916 0.0009 -3.6685 0.0007 -3.7454 0.0005 -3.8222 0.0004 -3.8991 0.0003 -3.9760 0.0002 -4.0528 0.0002 -4.1297 0.0001 +-5.4596 0.0010 +-5.3401 0.0013 +-5.2207 0.0016 +-5.1012 0.0021 +-4.9817 0.0026 +-4.8623 0.0033 +-4.7428 0.0042 +-4.6233 0.0052 +-4.5039 0.0065 +-4.3844 0.0080 +-4.2649 0.0098 +-4.1455 0.0120 +-4.0260 0.0146 +-3.9065 0.0176 +-3.7871 0.0212 +-3.6676 0.0253 +-3.5481 0.0300 +-3.4287 0.0354 +-3.3092 0.0416 +-3.1897 0.0486 +-3.0703 0.0564 +-2.9508 0.0651 +-2.8314 0.0748 +-2.7119 0.0854 +-2.5924 0.0970 +-2.4730 0.1096 +-2.3535 0.1230 +-2.2340 0.1374 +-2.1146 0.1526 +-1.9951 0.1685 +-1.8756 0.1850 +-1.7562 0.2021 +-1.6367 0.2195 +-1.5172 0.2370 +-1.3978 0.2545 +-1.2783 0.2718 +-1.1588 0.2887 +-1.0394 0.3049 +-0.9199 0.3202 +-0.8004 0.3344 +-0.6810 0.3473 +-0.5615 0.3587 +-0.4420 0.3684 +-0.3226 0.3763 +-0.2031 0.3822 +-0.0836 0.3860 +0.0358 0.3877 +0.1553 0.3872 +0.2748 0.3846 +0.3942 0.3798 +0.5137 0.3731 +0.6332 0.3644 +0.7526 0.3539 +0.8721 0.3418 +0.9916 0.3283 +1.1110 0.3136 +1.2305 0.2979 +1.3500 0.2813 +1.4694 0.2643 +1.5889 0.2468 +1.7084 0.2293 +1.8278 0.2118 +1.9473 0.1945 +2.0668 0.1777 +2.1862 0.1614 +2.3057 0.1458 +2.4252 0.1310 +2.5446 0.1170 +2.6641 0.1039 +2.7836 0.0918 +2.9030 0.0806 +3.0225 0.0704 +3.1420 0.0612 +3.2614 0.0529 +3.3809 0.0454 +3.5004 0.0388 +3.6198 0.0329 +3.7393 0.0278 +3.8588 0.0234 +3.9782 0.0195 +4.0977 0.0162 +4.2171 0.0134 +4.3366 0.0110 +4.4561 0.0090 +4.5755 0.0073 +4.6950 0.0059 +4.8145 0.0047 +4.9339 0.0038 +5.0534 0.0030 +5.1729 0.0024 +5.2923 0.0019 +5.4118 0.0015 +5.5313 0.0011 +5.6507 0.0009 +5.7702 0.0007 +5.8897 0.0005 +6.0091 0.0004 +6.1286 0.0003 +6.2481 0.0002 +6.3675 0.0002 +6.4870 0.0001 diff --git a/sourcecodes/data/example_sci/Lluparameters.txt b/sourcecodes/data/example_sci/Lluparameters.txt new file mode 100644 index 00000000..11583baa --- /dev/null +++ b/sourcecodes/data/example_sci/Lluparameters.txt @@ -0,0 +1,21 @@ +Genotype +Discrete node with 2 states +1 0.4860 +2 0.5140 + +Gene3 +Continuous node +-0.0150 1.0589 + +Gene2 +Continuous node +0.1381 1.1779 + +Phenotype +Continuous node +0.0693 1.5989 + +Gene1 +Continuous node +0.1745 1.5275 + diff --git a/sourcecodes/data/example_time_series/TEbmap.txt b/sourcecodes/data/example_time_series/TEbmap.txt index a9e0ef24..20f9b470 100644 --- a/sourcecodes/data/example_time_series/TEbmap.txt +++ b/sourcecodes/data/example_time_series/TEbmap.txt @@ -1,10 +1,10 @@ -Genotype 1 0.499053 1.462000 -TraitA_T1 1 1.306059 0.037189 -TraitA_T2 1 1.304164 -0.061147 -TraitA_T3 1 1.174305 -0.083449 -TraitB_T1 2 0.929161 -0.056347 -TraitB_T2 1 1.021691 -0.045299 -TraitB_T3 1 0.940946 0.012204 -TraitC_T1 1 0.907725 -0.001607 -TraitC_T2 1 0.923711 0.091657 -TraitC_T3 1 1.015189 0.017718 +Genotype 0.499053 1.462000 +TraitA_T1 1.306059 0.037189 +TraitA_T2 1.304164 -0.061147 +TraitA_T3 1.174305 -0.083449 +TraitB_T1 0.929161 -0.056347 +TraitB_T2 1.021691 -0.045299 +TraitB_T3 0.940946 0.012204 +TraitC_T1 0.907725 -0.001607 +TraitC_T2 0.923711 0.091657 +TraitC_T3 1.015189 0.017718 diff --git a/sourcecodes/data/example_time_series/TEbnet_figure.txt b/sourcecodes/data/example_time_series/TEbnet_figure.txt index 7b46ba95..1cceadd2 100644 --- a/sourcecodes/data/example_time_series/TEbnet_figure.txt +++ b/sourcecodes/data/example_time_series/TEbnet_figure.txt @@ -14,422 +14,422 @@ TraitC_T2 1 250 150 0 0 --4.0699 0.0001 --3.9911 0.0002 --3.9123 0.0002 --3.8335 0.0003 --3.7548 0.0004 --3.6760 0.0005 --3.5972 0.0007 --3.5185 0.0009 --3.4397 0.0012 --3.3609 0.0015 --3.2821 0.0020 --3.2034 0.0025 --3.1246 0.0032 --3.0458 0.0041 --2.9670 0.0052 --2.8883 0.0065 --2.8095 0.0081 --2.7307 0.0101 --2.6520 0.0124 --2.5732 0.0152 --2.4944 0.0185 --2.4156 0.0224 --2.3369 0.0269 --2.2581 0.0322 --2.1793 0.0382 --2.1006 0.0451 --2.0218 0.0529 --1.9430 0.0617 --1.8642 0.0716 --1.7855 0.0824 --1.7067 0.0944 --1.6279 0.1074 --1.5492 0.1215 --1.4704 0.1366 --1.3916 0.1526 --1.3128 0.1695 --1.2341 0.1871 --1.1553 0.2052 --1.0765 0.2238 --0.9978 0.2425 --0.9190 0.2612 --0.8402 0.2796 --0.7614 0.2975 --0.6827 0.3147 --0.6039 0.3308 --0.5251 0.3456 --0.4464 0.3588 --0.3676 0.3703 --0.2888 0.3799 --0.2100 0.3873 --0.1313 0.3924 --0.0525 0.3952 -0.0263 0.3956 -0.1050 0.3936 -0.1838 0.3892 -0.2626 0.3826 -0.3414 0.3737 -0.4201 0.3629 -0.4989 0.3502 -0.5777 0.3358 -0.6564 0.3202 -0.7352 0.3033 -0.8140 0.2857 -0.8928 0.2674 -0.9715 0.2487 -1.0503 0.2300 -1.1291 0.2114 -1.2078 0.1930 -1.2866 0.1753 -1.3654 0.1581 -1.4442 0.1418 -1.5229 0.1264 -1.6017 0.1120 -1.6805 0.0986 -1.7592 0.0863 -1.8380 0.0751 -1.9168 0.0649 -1.9956 0.0558 -2.0743 0.0476 -2.1531 0.0404 -2.2319 0.0341 -2.3106 0.0286 -2.3894 0.0238 -2.4682 0.0197 -2.5470 0.0163 -2.6257 0.0133 -2.7045 0.0108 -2.7833 0.0087 -2.8620 0.0070 -2.9408 0.0056 -3.0196 0.0045 -3.0984 0.0035 -3.1771 0.0028 -3.2559 0.0021 -3.3347 0.0017 -3.4134 0.0013 -3.4922 0.0010 -3.5710 0.0007 -3.6498 0.0006 -3.7285 0.0004 -3.8073 0.0003 +-3.6677 0.0001 +-3.5949 0.0002 +-3.5222 0.0002 +-3.4494 0.0003 +-3.3767 0.0004 +-3.3039 0.0005 +-3.2311 0.0007 +-3.1584 0.0009 +-3.0856 0.0012 +-3.0129 0.0015 +-2.9401 0.0020 +-2.8673 0.0025 +-2.7946 0.0032 +-2.7218 0.0041 +-2.6490 0.0052 +-2.5763 0.0065 +-2.5035 0.0081 +-2.4308 0.0101 +-2.3580 0.0124 +-2.2852 0.0152 +-2.2125 0.0185 +-2.1397 0.0224 +-2.0669 0.0269 +-1.9942 0.0322 +-1.9214 0.0382 +-1.8487 0.0451 +-1.7759 0.0529 +-1.7031 0.0617 +-1.6304 0.0716 +-1.5576 0.0824 +-1.4848 0.0944 +-1.4121 0.1074 +-1.3393 0.1215 +-1.2666 0.1366 +-1.1938 0.1526 +-1.1210 0.1695 +-1.0483 0.1871 +-0.9755 0.2052 +-0.9027 0.2238 +-0.8300 0.2425 +-0.7572 0.2612 +-0.6845 0.2796 +-0.6117 0.2975 +-0.5389 0.3147 +-0.4662 0.3308 +-0.3934 0.3456 +-0.3206 0.3588 +-0.2479 0.3703 +-0.1751 0.3799 +-0.1024 0.3873 +-0.0296 0.3924 +0.0432 0.3952 +0.1159 0.3956 +0.1887 0.3936 +0.2614 0.3892 +0.3342 0.3826 +0.4070 0.3737 +0.4797 0.3629 +0.5525 0.3502 +0.6253 0.3358 +0.6980 0.3202 +0.7708 0.3033 +0.8435 0.2857 +0.9163 0.2674 +0.9891 0.2487 +1.0618 0.2300 +1.1346 0.2114 +1.2074 0.1930 +1.2801 0.1753 +1.3529 0.1581 +1.4256 0.1418 +1.4984 0.1264 +1.5712 0.1120 +1.6439 0.0986 +1.7167 0.0863 +1.7895 0.0751 +1.8622 0.0649 +1.9350 0.0558 +2.0077 0.0476 +2.0805 0.0404 +2.1533 0.0341 +2.2260 0.0286 +2.2988 0.0238 +2.3716 0.0197 +2.4443 0.0163 +2.5171 0.0133 +2.5898 0.0108 +2.6626 0.0087 +2.7354 0.0070 +2.8081 0.0056 +2.8809 0.0045 +2.9537 0.0035 +3.0264 0.0028 +3.0992 0.0021 +3.1719 0.0017 +3.2447 0.0013 +3.3175 0.0010 +3.3902 0.0007 +3.4630 0.0006 +3.5357 0.0004 +3.6085 0.0003 TraitC_T1 1 250 150 0 0 --3.8413 0.0003 --3.7581 0.0004 --3.6749 0.0005 --3.5917 0.0007 --3.5086 0.0009 --3.4254 0.0012 --3.3422 0.0016 --3.2591 0.0021 --3.1759 0.0028 --3.0927 0.0036 --3.0095 0.0046 --2.9264 0.0059 --2.8432 0.0074 --2.7600 0.0093 --2.6768 0.0116 --2.5937 0.0144 --2.5105 0.0178 --2.4273 0.0218 --2.3441 0.0265 --2.2610 0.0320 --2.1778 0.0384 --2.0946 0.0457 --2.0114 0.0540 --1.9283 0.0635 --1.8451 0.0741 --1.7619 0.0859 --1.6787 0.0989 --1.5956 0.1131 --1.5124 0.1284 --1.4292 0.1448 --1.3460 0.1623 --1.2629 0.1806 --1.1797 0.1995 --1.0965 0.2190 --1.0133 0.2388 --0.9302 0.2585 --0.8470 0.2781 --0.7638 0.2970 --0.6806 0.3151 --0.5975 0.3320 --0.5143 0.3475 --0.4311 0.3612 --0.3479 0.3729 --0.2648 0.3824 --0.1816 0.3894 --0.0984 0.3939 --0.0152 0.3957 -0.0679 0.3949 -0.1511 0.3914 -0.2343 0.3852 -0.3174 0.3766 -0.4006 0.3657 -0.4838 0.3527 -0.5670 0.3379 -0.6501 0.3215 -0.7333 0.3038 -0.8165 0.2851 -0.8997 0.2657 -0.9828 0.2460 -1.0660 0.2262 -1.1492 0.2066 -1.2324 0.1874 -1.3155 0.1689 -1.3987 0.1511 -1.4819 0.1343 -1.5651 0.1186 -1.6482 0.1040 -1.7314 0.0905 -1.8146 0.0783 -1.8978 0.0673 -1.9809 0.0574 -2.0641 0.0486 -2.1473 0.0409 -2.2305 0.0342 -2.3136 0.0284 -2.3968 0.0234 -2.4800 0.0192 -2.5632 0.0156 -2.6463 0.0126 -2.7295 0.0101 -2.8127 0.0081 -2.8959 0.0064 -2.9790 0.0050 -3.0622 0.0039 -3.1454 0.0030 -3.2286 0.0023 -3.3117 0.0018 -3.3949 0.0014 -3.4781 0.0010 -3.5613 0.0008 -3.6444 0.0006 -3.7276 0.0004 -3.8108 0.0003 -3.8940 0.0002 -3.9771 0.0002 -4.0603 0.0001 -4.1435 0.0001 -4.2266 0.0001 -4.3098 0.0000 -4.3930 0.0000 -4.4762 0.0000 +-3.4884 0.0003 +-3.4129 0.0004 +-3.3374 0.0005 +-3.2619 0.0007 +-3.1864 0.0009 +-3.1109 0.0012 +-3.0354 0.0016 +-2.9599 0.0021 +-2.8844 0.0028 +-2.8089 0.0036 +-2.7334 0.0046 +-2.6579 0.0059 +-2.5824 0.0074 +-2.5069 0.0093 +-2.4314 0.0116 +-2.3559 0.0144 +-2.2804 0.0178 +-2.2049 0.0218 +-2.1294 0.0265 +-2.0539 0.0320 +-1.9784 0.0384 +-1.9029 0.0457 +-1.8274 0.0540 +-1.7519 0.0635 +-1.6764 0.0741 +-1.6009 0.0859 +-1.5254 0.0989 +-1.4499 0.1131 +-1.3744 0.1284 +-1.2989 0.1448 +-1.2234 0.1623 +-1.1479 0.1806 +-1.0724 0.1995 +-0.9969 0.2190 +-0.9214 0.2388 +-0.8459 0.2585 +-0.7704 0.2781 +-0.6949 0.2970 +-0.6194 0.3151 +-0.5439 0.3320 +-0.4684 0.3475 +-0.3929 0.3612 +-0.3174 0.3729 +-0.2419 0.3824 +-0.1664 0.3894 +-0.0909 0.3939 +-0.0154 0.3957 +0.0601 0.3949 +0.1356 0.3914 +0.2111 0.3852 +0.2866 0.3766 +0.3620 0.3657 +0.4375 0.3527 +0.5130 0.3379 +0.5885 0.3215 +0.6640 0.3038 +0.7395 0.2851 +0.8150 0.2657 +0.8905 0.2460 +0.9660 0.2262 +1.0415 0.2066 +1.1170 0.1874 +1.1925 0.1689 +1.2680 0.1511 +1.3435 0.1343 +1.4190 0.1186 +1.4945 0.1040 +1.5700 0.0905 +1.6455 0.0783 +1.7210 0.0673 +1.7965 0.0574 +1.8720 0.0486 +1.9475 0.0409 +2.0230 0.0342 +2.0985 0.0284 +2.1740 0.0234 +2.2495 0.0192 +2.3250 0.0156 +2.4005 0.0126 +2.4760 0.0101 +2.5515 0.0081 +2.6270 0.0064 +2.7025 0.0050 +2.7780 0.0039 +2.8535 0.0030 +2.9290 0.0023 +3.0045 0.0018 +3.0800 0.0014 +3.1555 0.0010 +3.2310 0.0008 +3.3065 0.0006 +3.3820 0.0004 +3.4575 0.0003 +3.5330 0.0002 +3.6085 0.0002 +3.6840 0.0001 +3.7595 0.0001 +3.8350 0.0001 +3.9105 0.0000 +3.9860 0.0000 +4.0615 0.0000 TraitB_T3 1 250 150 0 0 --3.8488 0.0003 --3.7734 0.0004 --3.6979 0.0005 --3.6224 0.0006 --3.5469 0.0008 --3.4714 0.0011 --3.3959 0.0014 --3.3205 0.0017 --3.2450 0.0022 --3.1695 0.0028 --3.0940 0.0036 --3.0185 0.0045 --2.9430 0.0056 --2.8676 0.0069 --2.7921 0.0085 --2.7166 0.0105 --2.6411 0.0128 --2.5656 0.0155 --2.4902 0.0187 --2.4147 0.0225 --2.3392 0.0268 --2.2637 0.0318 --2.1882 0.0375 --2.1127 0.0440 --2.0373 0.0513 --1.9618 0.0596 --1.8863 0.0687 --1.8108 0.0788 --1.7353 0.0899 --1.6598 0.1020 --1.5844 0.1151 --1.5089 0.1291 --1.4334 0.1440 --1.3579 0.1597 --1.2824 0.1762 --1.2070 0.1933 --1.1315 0.2108 --1.0560 0.2286 --0.9805 0.2466 --0.9050 0.2645 --0.8295 0.2821 --0.7541 0.2992 --0.6786 0.3155 --0.6031 0.3309 --0.5276 0.3451 --0.4521 0.3579 --0.3766 0.3691 --0.3012 0.3785 --0.2257 0.3860 --0.1502 0.3914 --0.0747 0.3947 -0.0008 0.3958 -0.0762 0.3946 -0.1517 0.3913 -0.2272 0.3858 -0.3027 0.3783 -0.3782 0.3689 -0.4537 0.3577 -0.5291 0.3448 -0.6046 0.3306 -0.6801 0.3152 -0.7556 0.2988 -0.8311 0.2817 -0.9066 0.2641 -0.9820 0.2462 -1.0575 0.2283 -1.1330 0.2104 -1.2085 0.1929 -1.2840 0.1758 -1.3595 0.1594 -1.4349 0.1437 -1.5104 0.1288 -1.5859 0.1148 -1.6614 0.1018 -1.7369 0.0897 -1.8123 0.0786 -1.8878 0.0685 -1.9633 0.0594 -2.0388 0.0512 -2.1143 0.0439 -2.1898 0.0374 -2.2652 0.0317 -2.3407 0.0267 -2.4162 0.0224 -2.4917 0.0186 -2.5672 0.0155 -2.6427 0.0127 -2.7181 0.0104 -2.7936 0.0085 -2.8691 0.0069 -2.9446 0.0056 -3.0201 0.0044 -3.0955 0.0035 -3.1710 0.0028 -3.2465 0.0022 -3.3220 0.0017 -3.3975 0.0014 -3.4730 0.0010 -3.5484 0.0008 -3.6239 0.0006 -3.6994 0.0005 +-3.6093 0.0003 +-3.5383 0.0004 +-3.4673 0.0005 +-3.3963 0.0006 +-3.3252 0.0008 +-3.2542 0.0011 +-3.1832 0.0014 +-3.1122 0.0017 +-3.0411 0.0022 +-2.9701 0.0028 +-2.8991 0.0036 +-2.8281 0.0045 +-2.7570 0.0056 +-2.6860 0.0069 +-2.6150 0.0085 +-2.5440 0.0105 +-2.4729 0.0128 +-2.4019 0.0155 +-2.3309 0.0187 +-2.2599 0.0225 +-2.1888 0.0268 +-2.1178 0.0318 +-2.0468 0.0375 +-1.9758 0.0440 +-1.9047 0.0513 +-1.8337 0.0596 +-1.7627 0.0687 +-1.6917 0.0788 +-1.6206 0.0899 +-1.5496 0.1020 +-1.4786 0.1151 +-1.4076 0.1291 +-1.3365 0.1440 +-1.2655 0.1597 +-1.1945 0.1762 +-1.1235 0.1933 +-1.0524 0.2108 +-0.9814 0.2286 +-0.9104 0.2466 +-0.8394 0.2645 +-0.7683 0.2821 +-0.6973 0.2992 +-0.6263 0.3155 +-0.5553 0.3309 +-0.4842 0.3451 +-0.4132 0.3579 +-0.3422 0.3691 +-0.2712 0.3785 +-0.2001 0.3860 +-0.1291 0.3914 +-0.0581 0.3947 +0.0129 0.3958 +0.0839 0.3946 +0.1550 0.3913 +0.2260 0.3858 +0.2970 0.3783 +0.3680 0.3689 +0.4391 0.3577 +0.5101 0.3448 +0.5811 0.3306 +0.6521 0.3152 +0.7232 0.2988 +0.7942 0.2817 +0.8652 0.2641 +0.9362 0.2462 +1.0073 0.2283 +1.0783 0.2104 +1.1493 0.1929 +1.2203 0.1758 +1.2914 0.1594 +1.3624 0.1437 +1.4334 0.1288 +1.5044 0.1148 +1.5755 0.1018 +1.6465 0.0897 +1.7175 0.0786 +1.7885 0.0685 +1.8596 0.0594 +1.9306 0.0512 +2.0016 0.0439 +2.0726 0.0374 +2.1437 0.0317 +2.2147 0.0267 +2.2857 0.0224 +2.3567 0.0186 +2.4278 0.0155 +2.4988 0.0127 +2.5698 0.0104 +2.6408 0.0085 +2.7119 0.0069 +2.7829 0.0056 +2.8539 0.0044 +2.9249 0.0035 +2.9960 0.0028 +3.0670 0.0022 +3.1380 0.0017 +3.2090 0.0014 +3.2801 0.0010 +3.3511 0.0008 +3.4221 0.0006 +3.4931 0.0005 TraitB_T1 1 250 150 0 0 --3.7153 0.0004 --3.6359 0.0006 --3.5565 0.0008 --3.4772 0.0010 --3.3978 0.0013 --3.3184 0.0018 --3.2390 0.0023 --3.1596 0.0029 --3.0802 0.0037 --3.0009 0.0047 --2.9215 0.0059 --2.8421 0.0074 --2.7627 0.0093 --2.6833 0.0114 --2.6039 0.0141 --2.5246 0.0172 --2.4452 0.0209 --2.3658 0.0252 --2.2864 0.0302 --2.2070 0.0360 --2.1277 0.0427 --2.0483 0.0502 --1.9689 0.0587 --1.8895 0.0683 --1.8101 0.0789 --1.7307 0.0906 --1.6514 0.1034 --1.5720 0.1173 --1.4926 0.1322 --1.4132 0.1481 --1.3338 0.1649 --1.2544 0.1824 --1.1751 0.2006 --1.0957 0.2192 --1.0163 0.2381 --0.9369 0.2570 --0.8575 0.2756 --0.7781 0.2938 --0.6988 0.3112 --0.6194 0.3277 --0.5400 0.3429 --0.4606 0.3565 --0.3812 0.3685 --0.3019 0.3784 --0.2225 0.3863 --0.1431 0.3918 --0.0637 0.3950 -0.0157 0.3957 -0.0951 0.3940 -0.1744 0.3899 -0.2538 0.3834 -0.3332 0.3747 -0.4126 0.3640 -0.4920 0.3513 -0.5714 0.3370 -0.6507 0.3213 -0.7301 0.3045 -0.8095 0.2867 -0.8889 0.2683 -0.9683 0.2495 -1.0477 0.2306 -1.1270 0.2118 -1.2064 0.1934 -1.2858 0.1754 -1.3652 0.1582 -1.4446 0.1417 -1.5239 0.1262 -1.6033 0.1117 -1.6827 0.0982 -1.7621 0.0859 -1.8415 0.0746 -1.9209 0.0644 -2.0002 0.0553 -2.0796 0.0471 -2.1590 0.0399 -2.2384 0.0336 -2.3178 0.0281 -2.3972 0.0234 -2.4765 0.0194 -2.5559 0.0159 -2.6353 0.0130 -2.7147 0.0105 -2.7941 0.0085 -2.8735 0.0068 -2.9528 0.0054 -3.0322 0.0043 -3.1116 0.0034 -3.1910 0.0026 -3.2704 0.0020 -3.3497 0.0016 -3.4291 0.0012 -3.5085 0.0009 -3.5879 0.0007 -3.6673 0.0005 -3.7467 0.0004 -3.8260 0.0003 -3.9054 0.0002 -3.9848 0.0002 -4.0642 0.0001 -4.1436 0.0001 -4.2230 0.0001 +-3.5085 0.0004 +-3.4347 0.0006 +-3.3609 0.0008 +-3.2872 0.0010 +-3.2134 0.0013 +-3.1397 0.0018 +-3.0659 0.0023 +-2.9921 0.0029 +-2.9184 0.0037 +-2.8446 0.0047 +-2.7709 0.0059 +-2.6971 0.0074 +-2.6234 0.0093 +-2.5496 0.0114 +-2.4758 0.0141 +-2.4021 0.0172 +-2.3283 0.0209 +-2.2546 0.0252 +-2.1808 0.0302 +-2.1070 0.0360 +-2.0333 0.0427 +-1.9595 0.0502 +-1.8858 0.0587 +-1.8120 0.0683 +-1.7382 0.0789 +-1.6645 0.0906 +-1.5907 0.1034 +-1.5170 0.1173 +-1.4432 0.1322 +-1.3694 0.1481 +-1.2957 0.1649 +-1.2219 0.1824 +-1.1482 0.2006 +-1.0744 0.2192 +-1.0006 0.2381 +-0.9269 0.2570 +-0.8531 0.2756 +-0.7794 0.2938 +-0.7056 0.3112 +-0.6319 0.3277 +-0.5581 0.3429 +-0.4843 0.3565 +-0.4106 0.3685 +-0.3368 0.3784 +-0.2631 0.3863 +-0.1893 0.3918 +-0.1155 0.3950 +-0.0418 0.3957 +0.0320 0.3940 +0.1057 0.3899 +0.1795 0.3834 +0.2533 0.3747 +0.3270 0.3640 +0.4008 0.3513 +0.4745 0.3370 +0.5483 0.3213 +0.6221 0.3045 +0.6958 0.2867 +0.7696 0.2683 +0.8433 0.2495 +0.9171 0.2306 +0.9909 0.2118 +1.0646 0.1934 +1.1384 0.1754 +1.2121 0.1582 +1.2859 0.1417 +1.3596 0.1262 +1.4334 0.1117 +1.5072 0.0982 +1.5809 0.0859 +1.6547 0.0746 +1.7284 0.0644 +1.8022 0.0553 +1.8760 0.0471 +1.9497 0.0399 +2.0235 0.0336 +2.0972 0.0281 +2.1710 0.0234 +2.2448 0.0194 +2.3185 0.0159 +2.3923 0.0130 +2.4660 0.0105 +2.5398 0.0085 +2.6136 0.0068 +2.6873 0.0054 +2.7611 0.0043 +2.8348 0.0034 +2.9086 0.0026 +2.9824 0.0020 +3.0561 0.0016 +3.1299 0.0012 +3.2036 0.0009 +3.2774 0.0007 +3.3511 0.0005 +3.4249 0.0004 +3.4987 0.0003 +3.5724 0.0002 +3.6462 0.0002 +3.7199 0.0001 +3.7937 0.0001 +3.8675 0.0001 Genotype 2 250 150 0 @@ -440,524 +440,524 @@ TraitA_T3 1 250 150 1 5 0 --3.6094 0.0007 --3.5392 0.0008 --3.4689 0.0011 --3.3987 0.0013 --3.3285 0.0017 --3.2582 0.0021 --3.1880 0.0027 --3.1177 0.0033 --3.0475 0.0041 --2.9773 0.0050 --2.9070 0.0062 --2.8368 0.0075 --2.7665 0.0092 --2.6963 0.0111 --2.6261 0.0133 --2.5558 0.0159 --2.4856 0.0189 --2.4154 0.0224 --2.3451 0.0264 --2.2749 0.0310 --2.2046 0.0362 --2.1344 0.0421 --2.0642 0.0486 --1.9939 0.0559 --1.9237 0.0641 --1.8534 0.0730 --1.7832 0.0828 --1.7130 0.0934 --1.6427 0.1049 --1.5725 0.1172 --1.5022 0.1304 --1.4320 0.1443 --1.3618 0.1589 --1.2915 0.1742 --1.2213 0.1900 --1.1510 0.2062 --1.0808 0.2227 --1.0106 0.2394 --0.9403 0.2561 --0.8701 0.2727 --0.7999 0.2889 --0.7296 0.3046 --0.6594 0.3195 --0.5891 0.3336 --0.5189 0.3467 --0.4487 0.3585 --0.3784 0.3688 --0.3082 0.3777 --0.2379 0.3849 --0.1677 0.3903 --0.0975 0.3939 --0.0272 0.3956 -0.0430 0.3954 -0.1133 0.3933 -0.1835 0.3893 -0.2537 0.3834 -0.3240 0.3759 -0.3942 0.3666 -0.4645 0.3559 -0.5347 0.3438 -0.6049 0.3306 -0.6752 0.3162 -0.7454 0.3011 -0.8156 0.2853 -0.8859 0.2690 -0.9561 0.2524 -1.0264 0.2357 -1.0966 0.2190 -1.1668 0.2025 -1.2371 0.1864 -1.3073 0.1707 -1.3776 0.1556 -1.4478 0.1411 -1.5180 0.1273 -1.5883 0.1144 -1.6585 0.1022 -1.7288 0.0909 -1.7990 0.0805 -1.8692 0.0709 -1.9395 0.0622 -2.0097 0.0542 -2.0799 0.0471 -2.1502 0.0407 -2.2204 0.0350 -2.2907 0.0299 -2.3609 0.0255 -2.4311 0.0216 -2.5014 0.0182 -2.5716 0.0153 -2.6419 0.0128 -2.7121 0.0106 -2.7823 0.0088 -2.8526 0.0072 -2.9228 0.0059 -2.9931 0.0048 -3.0633 0.0039 -3.1335 0.0032 -3.2038 0.0025 -3.2740 0.0020 -3.3443 0.0016 -3.4145 0.0013 +-4.3220 0.0007 +-4.2395 0.0008 +-4.1570 0.0011 +-4.0746 0.0013 +-3.9921 0.0017 +-3.9096 0.0021 +-3.8271 0.0027 +-3.7446 0.0033 +-3.6621 0.0041 +-3.5797 0.0050 +-3.4972 0.0062 +-3.4147 0.0075 +-3.3322 0.0092 +-3.2497 0.0111 +-3.1673 0.0133 +-3.0848 0.0159 +-3.0023 0.0189 +-2.9198 0.0224 +-2.8373 0.0264 +-2.7548 0.0310 +-2.6724 0.0362 +-2.5899 0.0421 +-2.5074 0.0486 +-2.4249 0.0559 +-2.3424 0.0641 +-2.2600 0.0730 +-2.1775 0.0828 +-2.0950 0.0934 +-2.0125 0.1049 +-1.9300 0.1172 +-1.8475 0.1304 +-1.7651 0.1443 +-1.6826 0.1589 +-1.6001 0.1742 +-1.5176 0.1900 +-1.4351 0.2062 +-1.3526 0.2227 +-1.2702 0.2394 +-1.1877 0.2561 +-1.1052 0.2727 +-1.0227 0.2889 +-0.9402 0.3046 +-0.8578 0.3195 +-0.7753 0.3336 +-0.6928 0.3467 +-0.6103 0.3585 +-0.5278 0.3688 +-0.4453 0.3777 +-0.3629 0.3849 +-0.2804 0.3903 +-0.1979 0.3939 +-0.1154 0.3956 +-0.0329 0.3954 +0.0495 0.3933 +0.1320 0.3893 +0.2145 0.3834 +0.2970 0.3759 +0.3795 0.3666 +0.4620 0.3559 +0.5444 0.3438 +0.6269 0.3306 +0.7094 0.3162 +0.7919 0.3011 +0.8744 0.2853 +0.9568 0.2690 +1.0393 0.2524 +1.1218 0.2357 +1.2043 0.2190 +1.2868 0.2025 +1.3693 0.1864 +1.4517 0.1707 +1.5342 0.1556 +1.6167 0.1411 +1.6992 0.1273 +1.7817 0.1144 +1.8642 0.1022 +1.9466 0.0909 +2.0291 0.0805 +2.1116 0.0709 +2.1941 0.0622 +2.2766 0.0542 +2.3590 0.0471 +2.4415 0.0407 +2.5240 0.0350 +2.6065 0.0299 +2.6890 0.0255 +2.7715 0.0216 +2.8539 0.0182 +2.9364 0.0153 +3.0189 0.0128 +3.1014 0.0106 +3.1839 0.0088 +3.2663 0.0072 +3.3488 0.0059 +3.4313 0.0048 +3.5138 0.0039 +3.5963 0.0032 +3.6788 0.0025 +3.7612 0.0020 +3.8437 0.0016 +3.9262 0.0013 TraitA_T2 1 250 150 1 5 0 --3.5730 0.0007 --3.5043 0.0009 --3.4357 0.0012 --3.3670 0.0015 --3.2984 0.0019 --3.2298 0.0023 --3.1611 0.0029 --3.0925 0.0036 --3.0239 0.0044 --2.9552 0.0054 --2.8866 0.0066 --2.8180 0.0079 --2.7493 0.0096 --2.6807 0.0115 --2.6121 0.0138 --2.5434 0.0164 --2.4748 0.0194 --2.4062 0.0229 --2.3375 0.0269 --2.2689 0.0314 --2.2003 0.0365 --2.1316 0.0423 --2.0630 0.0487 --1.9943 0.0559 --1.9257 0.0638 --1.8571 0.0725 --1.7884 0.0820 --1.7198 0.0923 --1.6512 0.1035 --1.5825 0.1154 --1.5139 0.1281 --1.4453 0.1416 --1.3766 0.1558 --1.3080 0.1705 --1.2394 0.1859 --1.1707 0.2016 --1.1021 0.2177 --1.0335 0.2340 --0.9648 0.2503 --0.8962 0.2666 --0.8276 0.2825 --0.7589 0.2981 --0.6903 0.3131 --0.6216 0.3272 --0.5530 0.3405 --0.4844 0.3526 --0.4157 0.3635 --0.3471 0.3730 --0.2785 0.3810 --0.2098 0.3873 --0.1412 0.3919 --0.0726 0.3948 --0.0039 0.3958 -0.0647 0.3950 -0.1333 0.3923 -0.2020 0.3879 -0.2706 0.3818 -0.3392 0.3740 -0.4079 0.3647 -0.4765 0.3539 -0.5451 0.3419 -0.6138 0.3288 -0.6824 0.3147 -0.7511 0.2998 -0.8197 0.2844 -0.8883 0.2684 -0.9570 0.2522 -1.0256 0.2359 -1.0942 0.2196 -1.1629 0.2034 -1.2315 0.1876 -1.3001 0.1723 -1.3688 0.1574 -1.4374 0.1432 -1.5060 0.1296 -1.5747 0.1168 -1.6433 0.1048 -1.7119 0.0936 -1.7806 0.0832 -1.8492 0.0736 -1.9178 0.0648 -1.9865 0.0568 -2.0551 0.0495 -2.1238 0.0430 -2.1924 0.0372 -2.2610 0.0320 -2.3297 0.0274 -2.3983 0.0233 -2.4669 0.0198 -2.5356 0.0167 -2.6042 0.0141 -2.6728 0.0118 -2.7415 0.0098 -2.8101 0.0081 -2.8787 0.0067 -2.9474 0.0055 -3.0160 0.0045 -3.0846 0.0037 -3.1533 0.0030 -3.2219 0.0024 -3.2905 0.0019 +-4.7209 0.0007 +-4.6314 0.0009 +-4.5418 0.0012 +-4.4523 0.0015 +-4.3628 0.0019 +-4.2733 0.0023 +-4.1838 0.0029 +-4.0943 0.0036 +-4.0048 0.0044 +-3.9153 0.0054 +-3.8258 0.0066 +-3.7362 0.0079 +-3.6467 0.0096 +-3.5572 0.0115 +-3.4677 0.0138 +-3.3782 0.0164 +-3.2887 0.0194 +-3.1992 0.0229 +-3.1097 0.0269 +-3.0201 0.0314 +-2.9306 0.0365 +-2.8411 0.0423 +-2.7516 0.0487 +-2.6621 0.0559 +-2.5726 0.0638 +-2.4831 0.0725 +-2.3936 0.0820 +-2.3041 0.0923 +-2.2145 0.1035 +-2.1250 0.1154 +-2.0355 0.1281 +-1.9460 0.1416 +-1.8565 0.1558 +-1.7670 0.1705 +-1.6775 0.1859 +-1.5880 0.2016 +-1.4985 0.2177 +-1.4089 0.2340 +-1.3194 0.2503 +-1.2299 0.2666 +-1.1404 0.2825 +-1.0509 0.2981 +-0.9614 0.3131 +-0.8719 0.3272 +-0.7824 0.3405 +-0.6929 0.3526 +-0.6033 0.3635 +-0.5138 0.3730 +-0.4243 0.3810 +-0.3348 0.3873 +-0.2453 0.3919 +-0.1558 0.3948 +-0.0663 0.3958 +0.0232 0.3950 +0.1127 0.3923 +0.2023 0.3879 +0.2918 0.3818 +0.3813 0.3740 +0.4708 0.3647 +0.5603 0.3539 +0.6498 0.3419 +0.7393 0.3288 +0.8288 0.3147 +0.9183 0.2998 +1.0079 0.2844 +1.0974 0.2684 +1.1869 0.2522 +1.2764 0.2359 +1.3659 0.2196 +1.4554 0.2034 +1.5449 0.1876 +1.6344 0.1723 +1.7239 0.1574 +1.8135 0.1432 +1.9030 0.1296 +1.9925 0.1168 +2.0820 0.1048 +2.1715 0.0936 +2.2610 0.0832 +2.3505 0.0736 +2.4400 0.0648 +2.5295 0.0568 +2.6191 0.0495 +2.7086 0.0430 +2.7981 0.0372 +2.8876 0.0320 +2.9771 0.0274 +3.0666 0.0233 +3.1561 0.0198 +3.2456 0.0167 +3.3352 0.0141 +3.4247 0.0118 +3.5142 0.0098 +3.6037 0.0081 +3.6932 0.0067 +3.7827 0.0055 +3.8722 0.0045 +3.9617 0.0037 +4.0512 0.0030 +4.1408 0.0024 +4.2303 0.0019 TraitA_T1 1 250 150 1 5 2 9 10 --4.4430 0.0000 --4.3666 0.0000 --4.2903 0.0000 --4.2139 0.0001 --4.1375 0.0001 --4.0611 0.0001 --3.9847 0.0002 --3.9083 0.0002 --3.8320 0.0003 --3.7556 0.0004 --3.6792 0.0005 --3.6028 0.0007 --3.5264 0.0009 --3.4500 0.0011 --3.3737 0.0015 --3.2973 0.0019 --3.2209 0.0024 --3.1445 0.0030 --3.0681 0.0039 --2.9918 0.0048 --2.9154 0.0060 --2.8390 0.0075 --2.7626 0.0093 --2.6862 0.0114 --2.6098 0.0139 --2.5335 0.0168 --2.4571 0.0203 --2.3807 0.0243 --2.3043 0.0290 --2.2279 0.0344 --2.1515 0.0406 --2.0752 0.0475 --1.9988 0.0554 --1.9224 0.0642 --1.8460 0.0740 --1.7696 0.0848 --1.6932 0.0965 --1.6169 0.1093 --1.5405 0.1231 --1.4641 0.1378 --1.3877 0.1534 --1.3113 0.1698 --1.2350 0.1869 --1.1586 0.2044 --1.0822 0.2224 --1.0058 0.2406 --0.9294 0.2587 --0.8530 0.2767 --0.7767 0.2941 --0.7003 0.3109 --0.6239 0.3268 --0.5475 0.3415 --0.4711 0.3548 --0.3947 0.3666 --0.3184 0.3765 --0.2420 0.3845 --0.1656 0.3905 --0.0892 0.3942 --0.0128 0.3957 -0.0636 0.3950 -0.1399 0.3920 -0.2163 0.3868 -0.2927 0.3794 -0.3691 0.3701 -0.4455 0.3590 -0.5218 0.3461 -0.5982 0.3319 -0.6746 0.3164 -0.7510 0.2999 -0.8274 0.2826 -0.9038 0.2648 -0.9801 0.2467 -1.0565 0.2285 -1.1329 0.2105 -1.2093 0.1927 -1.2857 0.1755 -1.3621 0.1588 -1.4384 0.1430 -1.5148 0.1280 -1.5912 0.1139 -1.6676 0.1007 -1.7440 0.0886 -1.8204 0.0775 -1.8967 0.0674 -1.9731 0.0583 -2.0495 0.0501 -2.1259 0.0428 -2.2023 0.0364 -2.2786 0.0307 -2.3550 0.0258 -2.4314 0.0216 -2.5078 0.0179 -2.5842 0.0148 -2.6606 0.0122 -2.7369 0.0099 -2.8133 0.0081 -2.8897 0.0065 -2.9661 0.0052 -3.0425 0.0042 -3.1189 0.0033 -3.1952 0.0026 +-5.7657 0.0000 +-5.6659 0.0000 +-5.5661 0.0000 +-5.4664 0.0001 +-5.3666 0.0001 +-5.2669 0.0001 +-5.1671 0.0002 +-5.0673 0.0002 +-4.9676 0.0003 +-4.8678 0.0004 +-4.7681 0.0005 +-4.6683 0.0007 +-4.5685 0.0009 +-4.4688 0.0011 +-4.3690 0.0015 +-4.2693 0.0019 +-4.1695 0.0024 +-4.0697 0.0030 +-3.9700 0.0039 +-3.8702 0.0048 +-3.7705 0.0060 +-3.6707 0.0075 +-3.5709 0.0093 +-3.4712 0.0114 +-3.3714 0.0139 +-3.2717 0.0168 +-3.1719 0.0203 +-3.0721 0.0243 +-2.9724 0.0290 +-2.8726 0.0344 +-2.7729 0.0406 +-2.6731 0.0475 +-2.5733 0.0554 +-2.4736 0.0642 +-2.3738 0.0740 +-2.2741 0.0848 +-2.1743 0.0965 +-2.0745 0.1093 +-1.9748 0.1231 +-1.8750 0.1378 +-1.7753 0.1534 +-1.6755 0.1698 +-1.5757 0.1869 +-1.4760 0.2044 +-1.3762 0.2224 +-1.2765 0.2406 +-1.1767 0.2587 +-1.0769 0.2767 +-0.9772 0.2941 +-0.8774 0.3109 +-0.7776 0.3268 +-0.6779 0.3415 +-0.5781 0.3548 +-0.4784 0.3666 +-0.3786 0.3765 +-0.2788 0.3845 +-0.1791 0.3905 +-0.0793 0.3942 +0.0204 0.3957 +0.1202 0.3950 +0.2200 0.3920 +0.3197 0.3868 +0.4195 0.3794 +0.5192 0.3701 +0.6190 0.3590 +0.7188 0.3461 +0.8185 0.3319 +0.9183 0.3164 +1.0180 0.2999 +1.1178 0.2826 +1.2176 0.2648 +1.3173 0.2467 +1.4171 0.2285 +1.5168 0.2105 +1.6166 0.1927 +1.7164 0.1755 +1.8161 0.1588 +1.9159 0.1430 +2.0156 0.1280 +2.1154 0.1139 +2.2152 0.1007 +2.3149 0.0886 +2.4147 0.0775 +2.5144 0.0674 +2.6142 0.0583 +2.7140 0.0501 +2.8137 0.0428 +2.9135 0.0364 +3.0132 0.0307 +3.1130 0.0258 +3.2128 0.0216 +3.3125 0.0179 +3.4123 0.0148 +3.5120 0.0122 +3.6118 0.0099 +3.7116 0.0081 +3.8113 0.0065 +3.9111 0.0052 +4.0108 0.0042 +4.1106 0.0033 +4.2104 0.0026 TraitB_T2 1 250 150 2 5 8 1 10 --3.6634 0.0006 --3.5878 0.0008 --3.5122 0.0010 --3.4365 0.0013 --3.3609 0.0017 --3.2853 0.0021 --3.2097 0.0027 --3.1340 0.0034 --3.0584 0.0043 --2.9828 0.0053 --2.9072 0.0066 --2.8315 0.0081 --2.7559 0.0100 --2.6803 0.0122 --2.6046 0.0148 --2.5290 0.0178 --2.4534 0.0214 --2.3778 0.0255 --2.3021 0.0302 --2.2265 0.0357 --2.1509 0.0419 --2.0753 0.0489 --1.9996 0.0568 --1.9240 0.0655 --1.8484 0.0752 --1.7727 0.0858 --1.6971 0.0975 --1.6215 0.1100 --1.5459 0.1235 --1.4702 0.1379 --1.3946 0.1532 --1.3190 0.1692 --1.2434 0.1858 --1.1677 0.2029 --1.0921 0.2204 --1.0165 0.2381 --0.9408 0.2557 --0.8652 0.2732 --0.7896 0.2902 --0.7140 0.3066 --0.6383 0.3222 --0.5627 0.3366 --0.4871 0.3498 --0.4115 0.3615 --0.3358 0.3715 --0.2602 0.3797 --0.1846 0.3859 --0.1089 0.3901 --0.0333 0.3921 -0.0423 0.3920 -0.1179 0.3897 -0.1936 0.3853 -0.2692 0.3788 -0.3448 0.3704 -0.4205 0.3602 -0.4961 0.3483 -0.5717 0.3350 -0.6473 0.3204 -0.7230 0.3047 -0.7986 0.2882 -0.8742 0.2711 -0.9498 0.2536 -1.0255 0.2359 -1.1011 0.2183 -1.1767 0.2008 -1.2524 0.1838 -1.3280 0.1672 -1.4036 0.1513 -1.4792 0.1362 -1.5549 0.1219 -1.6305 0.1085 -1.7061 0.0960 -1.7817 0.0845 -1.8574 0.0740 -1.9330 0.0644 -2.0086 0.0558 -2.0843 0.0480 -2.1599 0.0411 -2.2355 0.0350 -2.3111 0.0296 -2.3868 0.0250 -2.4624 0.0209 -2.5380 0.0174 -2.6136 0.0144 -2.6893 0.0119 -2.7649 0.0097 -2.8405 0.0079 -2.9162 0.0064 -2.9918 0.0052 -3.0674 0.0041 -3.1430 0.0033 -3.2187 0.0026 -3.2943 0.0021 -3.3699 0.0016 -3.4455 0.0013 -3.5212 0.0010 -3.5968 0.0008 -3.6724 0.0006 -3.7481 0.0004 -3.8237 0.0003 -3.8993 0.0003 +-3.7882 0.0006 +-3.7109 0.0008 +-3.6337 0.0010 +-3.5564 0.0013 +-3.4791 0.0017 +-3.4019 0.0021 +-3.3246 0.0027 +-3.2473 0.0034 +-3.1700 0.0043 +-3.0928 0.0053 +-3.0155 0.0066 +-2.9382 0.0081 +-2.8610 0.0100 +-2.7837 0.0122 +-2.7064 0.0148 +-2.6292 0.0178 +-2.5519 0.0214 +-2.4746 0.0255 +-2.3974 0.0302 +-2.3201 0.0357 +-2.2428 0.0419 +-2.1656 0.0489 +-2.0883 0.0568 +-2.0110 0.0655 +-1.9338 0.0752 +-1.8565 0.0858 +-1.7792 0.0975 +-1.7020 0.1100 +-1.6247 0.1235 +-1.5474 0.1379 +-1.4702 0.1532 +-1.3929 0.1692 +-1.3156 0.1858 +-1.2384 0.2029 +-1.1611 0.2204 +-1.0838 0.2381 +-1.0065 0.2557 +-0.9293 0.2732 +-0.8520 0.2902 +-0.7747 0.3066 +-0.6975 0.3222 +-0.6202 0.3366 +-0.5429 0.3498 +-0.4657 0.3615 +-0.3884 0.3715 +-0.3111 0.3797 +-0.2339 0.3859 +-0.1566 0.3901 +-0.0793 0.3921 +-0.0021 0.3920 +0.0752 0.3897 +0.1525 0.3853 +0.2297 0.3788 +0.3070 0.3704 +0.3843 0.3602 +0.4615 0.3483 +0.5388 0.3350 +0.6161 0.3204 +0.6933 0.3047 +0.7706 0.2882 +0.8479 0.2711 +0.9251 0.2536 +1.0024 0.2359 +1.0797 0.2183 +1.1569 0.2008 +1.2342 0.1838 +1.3115 0.1672 +1.3888 0.1513 +1.4660 0.1362 +1.5433 0.1219 +1.6206 0.1085 +1.6978 0.0960 +1.7751 0.0845 +1.8524 0.0740 +1.9296 0.0644 +2.0069 0.0558 +2.0842 0.0480 +2.1614 0.0411 +2.2387 0.0350 +2.3160 0.0296 +2.3932 0.0250 +2.4705 0.0209 +2.5478 0.0174 +2.6250 0.0144 +2.7023 0.0119 +2.7796 0.0097 +2.8568 0.0079 +2.9341 0.0064 +3.0114 0.0052 +3.0886 0.0041 +3.1659 0.0033 +3.2432 0.0026 +3.3204 0.0021 +3.3977 0.0016 +3.4750 0.0013 +3.5523 0.0010 +3.6295 0.0008 +3.7068 0.0006 +3.7841 0.0004 +3.8613 0.0003 +3.9386 0.0003 TraitC_T3 1 250 150 2 8 9 0 --4.6954 0.0000 --4.6103 0.0000 --4.5253 0.0000 --4.4402 0.0000 --4.3551 0.0000 --4.2701 0.0001 --4.1850 0.0001 --4.1000 0.0001 --4.0149 0.0002 --3.9298 0.0002 --3.8448 0.0003 --3.7597 0.0005 --3.6746 0.0006 --3.5896 0.0008 --3.5045 0.0011 --3.4195 0.0015 --3.3344 0.0019 --3.2493 0.0025 --3.1643 0.0033 --3.0792 0.0042 --2.9942 0.0054 --2.9091 0.0069 --2.8240 0.0087 --2.7390 0.0109 --2.6539 0.0135 --2.5688 0.0167 --2.4838 0.0205 --2.3987 0.0250 --2.3137 0.0303 --2.2286 0.0364 --2.1435 0.0435 --2.0585 0.0516 --1.9734 0.0608 --1.8884 0.0711 --1.8033 0.0826 --1.7182 0.0953 --1.6332 0.1091 --1.5481 0.1242 --1.4630 0.1403 --1.3780 0.1575 --1.2929 0.1755 --1.2079 0.1943 --1.1228 0.2136 --1.0377 0.2331 --0.9527 0.2527 --0.8676 0.2721 --0.7825 0.2910 --0.6975 0.3090 --0.6124 0.3259 --0.5274 0.3413 --0.4423 0.3550 --0.3572 0.3667 --0.2722 0.3762 --0.1871 0.3833 --0.1021 0.3878 --0.0170 0.3897 -0.0681 0.3889 -0.1531 0.3854 -0.2382 0.3793 -0.3233 0.3708 -0.4083 0.3599 -0.4934 0.3470 -0.5784 0.3322 -0.6635 0.3159 -0.7486 0.2983 -0.8336 0.2797 -0.9187 0.2605 -1.0038 0.2410 -1.0888 0.2214 -1.1739 0.2019 -1.2589 0.1829 -1.3440 0.1646 -1.4291 0.1471 -1.5141 0.1305 -1.5992 0.1150 -1.6842 0.1007 -1.7693 0.0875 -1.8544 0.0755 -1.9394 0.0648 -2.0245 0.0551 -2.1096 0.0466 -2.1946 0.0391 -2.2797 0.0326 -2.3647 0.0270 -2.4498 0.0222 -2.5349 0.0182 -2.6199 0.0147 -2.7050 0.0119 -2.7900 0.0095 -2.8751 0.0075 -2.9602 0.0060 -3.0452 0.0047 -3.1303 0.0036 -3.2154 0.0028 -3.3004 0.0022 -3.3855 0.0016 -3.4705 0.0012 -3.5556 0.0009 -3.6407 0.0007 -3.7257 0.0005 -3.8108 0.0004 +-4.7490 0.0000 +-4.6626 0.0000 +-4.5763 0.0000 +-4.4899 0.0000 +-4.4036 0.0000 +-4.3172 0.0001 +-4.2309 0.0001 +-4.1445 0.0001 +-4.0582 0.0002 +-3.9718 0.0002 +-3.8855 0.0003 +-3.7991 0.0005 +-3.7127 0.0006 +-3.6264 0.0008 +-3.5400 0.0011 +-3.4537 0.0015 +-3.3673 0.0019 +-3.2810 0.0025 +-3.1946 0.0033 +-3.1083 0.0042 +-3.0219 0.0054 +-2.9356 0.0069 +-2.8492 0.0087 +-2.7629 0.0109 +-2.6765 0.0135 +-2.5901 0.0167 +-2.5038 0.0205 +-2.4174 0.0250 +-2.3311 0.0303 +-2.2447 0.0364 +-2.1584 0.0435 +-2.0720 0.0516 +-1.9857 0.0608 +-1.8993 0.0711 +-1.8130 0.0826 +-1.7266 0.0953 +-1.6403 0.1091 +-1.5539 0.1242 +-1.4675 0.1403 +-1.3812 0.1575 +-1.2948 0.1755 +-1.2085 0.1943 +-1.1221 0.2136 +-1.0358 0.2331 +-0.9494 0.2527 +-0.8631 0.2721 +-0.7767 0.2910 +-0.6904 0.3090 +-0.6040 0.3259 +-0.5177 0.3413 +-0.4313 0.3550 +-0.3449 0.3667 +-0.2586 0.3762 +-0.1722 0.3833 +-0.0859 0.3878 +0.0005 0.3897 +0.0868 0.3889 +0.1732 0.3854 +0.2595 0.3793 +0.3459 0.3708 +0.4322 0.3599 +0.5186 0.3470 +0.6049 0.3322 +0.6913 0.3159 +0.7777 0.2983 +0.8640 0.2797 +0.9504 0.2605 +1.0367 0.2410 +1.1231 0.2214 +1.2094 0.2019 +1.2958 0.1829 +1.3821 0.1646 +1.4685 0.1471 +1.5548 0.1305 +1.6412 0.1150 +1.7275 0.1007 +1.8139 0.0875 +1.9003 0.0755 +1.9866 0.0648 +2.0730 0.0551 +2.1593 0.0466 +2.2457 0.0391 +2.3320 0.0326 +2.4184 0.0270 +2.5047 0.0222 +2.5911 0.0182 +2.6774 0.0147 +2.7638 0.0119 +2.8501 0.0095 +2.9365 0.0075 +3.0229 0.0060 +3.1092 0.0047 +3.1956 0.0036 +3.2819 0.0028 +3.3683 0.0022 +3.4546 0.0016 +3.5410 0.0012 +3.6273 0.0009 +3.7137 0.0007 +3.8000 0.0005 +3.8864 0.0004 diff --git a/sourcecodes/data/example_time_series/TEbparameters.txt b/sourcecodes/data/example_time_series/TEbparameters.txt new file mode 100644 index 00000000..14ddb1ac --- /dev/null +++ b/sourcecodes/data/example_time_series/TEbparameters.txt @@ -0,0 +1,41 @@ +Genotype +Discrete node with 2 states +1 0.5380 +2 0.4620 + +TraitA_T1 +Continuous node +0.0372 1.3165 + +TraitA_T2 +Continuous node +-0.0611 1.3146 + +TraitA_T3 +Continuous node +-0.0834 1.1837 + +TraitB_T1 +Continuous node +-0.0563 0.9366 + +TraitB_T2 +Continuous node +-0.0453 1.0389 + +TraitB_T3 +Continuous node +0.0122 0.9485 + +TraitC_T1 +Continuous node +-0.0016 0.9150 + +TraitC_T2 +Continuous node +0.0917 0.9311 + +TraitC_T3 +Continuous node +0.0177 1.0392 + diff --git a/sourcecodes/data/examplecar15node/MtXmap.txt b/sourcecodes/data/examplecar15node/MtXmap.txt index af7330d6..8099a6e7 100644 --- a/sourcecodes/data/examplecar15node/MtXmap.txt +++ b/sourcecodes/data/examplecar15node/MtXmap.txt @@ -1,15 +1,15 @@ -Voltage_at_plug 2 0.827795 2.021000 -Car_cranks 2 0.500234 1.504000 -Spark_plugs 2 0.820310 2.042000 -Spark_quality 2 0.816376 2.014000 -Distributer 3 0.500241 1.497000 -Spark_timing 2 0.810222 2.014000 -Car_starts 2 0.500054 1.486000 -Headlights 3 0.810946 2.032000 -Alternator 3 0.500214 1.494000 -Charging_system 2 0.500234 1.496000 -Battery_voltage 3 0.806400 2.037000 -Battery_age 3 0.561294 1.708000 -Main_fuse 3 0.500150 1.490000 -Starter_system 3 0.499674 1.476000 -Starter_motor 2 0.500201 1.493000 +Voltage_at_plug 0.827795 2.021000 +Car_cranks 0.500234 1.504000 +Spark_plugs 0.820310 2.042000 +Spark_quality 0.816376 2.014000 +Distributer 0.500241 1.497000 +Spark_timing 0.810222 2.014000 +Car_starts 0.500054 1.486000 +Headlights 0.810946 2.032000 +Alternator 0.500214 1.494000 +Charging_system 0.500234 1.496000 +Battery_voltage 0.806400 2.037000 +Battery_age 0.561294 1.708000 +Main_fuse 0.500150 1.490000 +Starter_system 0.499674 1.476000 +Starter_motor 0.500201 1.493000 diff --git a/sourcecodes/data/examplecar15node/eIAmap.txt b/sourcecodes/data/examplecar15node/eIAmap.txt index e96a904a..9f705391 100644 --- a/sourcecodes/data/examplecar15node/eIAmap.txt +++ b/sourcecodes/data/examplecar15node/eIAmap.txt @@ -1,15 +1,15 @@ -Voltage_at_plug 2 0.908185 1.905000 -Car_cranks 2 0.500106 1.488000 -Spark_plugs 2 0.778947 2.543000 -Spark_quality 2 0.853627 1.757000 -Distributer 3 0.099549 1.990000 -Spark_timing 2 0.357390 2.880000 -Car_starts 2 0.469352 1.327000 -Headlights 3 0.917301 1.951000 -Alternator 3 0.044699 1.998000 -Charging_system 2 0.500246 1.502000 -Battery_voltage 3 0.915330 2.003000 -Battery_age 3 0.500554 1.236000 -Main_fuse 3 0.121613 1.985000 -Starter_system 3 0.494126 1.578000 -Starter_motor 2 0.063151 1.996000 +Voltage_at_plug 0.908185 1.905000 +Car_cranks 0.500106 1.488000 +Spark_plugs 0.778947 2.543000 +Spark_quality 0.853627 1.757000 +Distributer 0.099549 1.990000 +Spark_timing 0.357390 2.880000 +Car_starts 0.469352 1.327000 +Headlights 0.917301 1.951000 +Alternator 0.044699 1.998000 +Charging_system 0.500246 1.502000 +Battery_voltage 0.915330 2.003000 +Battery_age 0.500554 1.236000 +Main_fuse 0.121613 1.985000 +Starter_system 0.494126 1.578000 +Starter_motor 0.063151 1.996000 diff --git a/sourcecodes/data/examplecar15node/eIAparameters.txt b/sourcecodes/data/examplecar15node/eIAparameters.txt new file mode 100644 index 00000000..57373374 --- /dev/null +++ b/sourcecodes/data/examplecar15node/eIAparameters.txt @@ -0,0 +1,82 @@ +Voltage_at_plug +Discrete node with 3 states +1 0.4646 +2 0.1681 +3 0.3674 + +Car_cranks +Discrete node with 2 states +1 0.5109 +2 0.4891 + +Spark_plugs +Discrete node with 3 states +1 0.1791 +2 0.0990 +3 0.7219 + +Spark_quality +Discrete node with 3 states +1 0.5114 +2 0.2233 +3 0.2652 + +Distributer +Discrete node with 2 states +1 0.0098 +2 0.9902 + +Spark_timing +Discrete node with 3 states +1 0.0088 +2 0.0982 +3 0.8930 + +Car_starts +Discrete node with 2 states +1 0.7903 +2 0.2097 + +Headlights +Discrete node with 3 states +1 0.4470 +2 0.1573 +3 0.3957 + +Alternator +Discrete node with 2 states +1 0.0020 +2 0.9980 + +Charging_system +Discrete node with 2 states +1 0.4988 +2 0.5012 + +Battery_voltage +Discrete node with 3 states +1 0.4181 +2 0.1633 +3 0.4186 + +Battery_age +Discrete node with 3 states +1 0.7989 +2 0.1661 +3 0.0350 + +Main_fuse +Discrete node with 2 states +1 0.0149 +2 0.9851 + +Starter_system +Discrete node with 2 states +1 0.4211 +2 0.5789 + +Starter_motor +Discrete node with 2 states +1 0.0039 +2 0.9961 + diff --git a/sourcecodes/data/old/example_sci_bk/Lluban.txt b/sourcecodes/data/old/example_sci_bk/Lluban.txt new file mode 100644 index 00000000..38108f00 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluban.txt @@ -0,0 +1,15 @@ +From To +Gene1 Genotype +Gene2 Genotype +Gene3 Genotype +Phenotype Genotype +Gene1 Genotype +Gene2 Genotype +Gene3 Genotype +Phenotype Gene1 +Phenotype Gene2 +Phenotype Gene3 +Phenotype Genotype +Phenotype Gene1 +Phenotype Gene2 +Phenotype Gene3 diff --git a/sourcecodes/data/old/example_sci_bk/Llucontinuous_input.txt b/sourcecodes/data/old/example_sci_bk/Llucontinuous_input.txt new file mode 100644 index 00000000..79295d7e --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llucontinuous_input.txt @@ -0,0 +1,503 @@ +Genotype Gene3 Gene2 Phenotype Gene1 +2 1 1 1 1 +2 -1.0008 -0.44837 0.21808 -1.196 +1 -0.29368 -0.53043 -1.0893 -0.10136 +2 0.70835 0.72886 -0.18098 -0.42907 +1 0.63703 0.86428 1.7013 0.1425 +1 -1.2402 -0.69712 -1.7002 -0.71634 +1 -1.1952 0.27155 -0.79413 -1.0965 +2 0.62914 0.94265 1.8385 0.6135 +1 -1.91 -2.379 -3.1094 -1.0924 +2 -0.83436 -0.90968 -1.3598 1.335 +1 0.15685 0.4766 1.2891 -1.3309 +2 0.7047 1.0623 1.9811 1.2821 +1 0.59747 -0.023123 0.88555 -0.8844 +1 -1.0557 -1.5548 -1.3016 -0.77687 +2 1.5206 2.0289 0.90366 1.8089 +1 0.65525 -0.47226 -1.1824 0.41863 +2 -0.39262 0.382 -0.64829 0.40947 +2 0.14966 0.22972 0.33797 0.69647 +1 -1.8836 -0.68534 -0.99941 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b/sourcecodes/data/old/example_sci_bk/Llugraphviz.txt new file mode 100644 index 00000000..88bbd3ba --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llugraphviz.txt @@ -0,0 +1,10 @@ +digraph G { +size="10,10"; ratio = fill; +node [shape=square,width=1.5]; +Genotype -> Gene3; +Genotype -> Gene1; +Gene3 -> Phenotype; +Gene2 -> Gene3; +Gene2 -> Phenotype; +Gene1 -> Gene2; +} \ No newline at end of file diff --git a/sourcecodes/data/old/example_sci_bk/Lluk.txt b/sourcecodes/data/old/example_sci_bk/Lluk.txt new file mode 100644 index 00000000..83b33d23 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluk.txt @@ -0,0 +1 @@ +1000 diff --git a/sourcecodes/data/old/example_sci_bk/Llumap.txt b/sourcecodes/data/old/example_sci_bk/Llumap.txt new file mode 100644 index 00000000..958281d5 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llumap.txt @@ -0,0 +1,5 @@ +Genotype 2 0.500305 1.514000 +Gene3 1 1.027261 -0.015284 +Gene2 1 1.160583 0.138097 +Phenotype 1 1.554104 0.069005 +Gene1 1 1.515414 0.174451 diff --git a/sourcecodes/data/old/example_sci_bk/Llumapdata.txt b/sourcecodes/data/old/example_sci_bk/Llumapdata.txt new file mode 100644 index 00000000..d684ea06 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llumapdata.txt @@ -0,0 +1 @@ +Genotype Gene1 Gene2 Gene3 Phenotype diff --git a/sourcecodes/data/old/example_sci_bk/Lluname.txt b/sourcecodes/data/old/example_sci_bk/Lluname.txt new file mode 100644 index 00000000..f3a1f5bf --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluname.txt @@ -0,0 +1 @@ +Genotype Gene3 Gene2 Phenotype Gene1 diff --git a/sourcecodes/data/old/example_sci_bk/Llunet_figure.txt b/sourcecodes/data/old/example_sci_bk/Llunet_figure.txt new file mode 100644 index 00000000..bdc07cbd --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llunet_figure.txt @@ -0,0 +1,433 @@ +5 +1200 1200 +Genotype 0 0 +Gene1 120 200 +Gene2 0 400 +Gene3 120 600 +Phenotype 0 800 +Genotype 2 +250 150 +0 +2 2 4 +1 0.4860 +2 0.5140 +Gene1 1 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+-1.7894 0.1381 +-1.7125 0.1597 +-1.6356 0.1831 +-1.5588 0.2081 +-1.4819 0.2343 +-1.4050 0.2615 +-1.3282 0.2893 +-1.2513 0.3173 +-1.1744 0.3448 +-1.0975 0.3714 +-1.0207 0.3966 +-0.9438 0.4197 +-0.8669 0.4402 +-0.7901 0.4576 +-0.7132 0.4716 +-0.6363 0.4816 +-0.5594 0.4875 +-0.4826 0.4891 +-0.4057 0.4864 +-0.3288 0.4794 +-0.2520 0.4684 +-0.1751 0.4535 +-0.0982 0.4353 +-0.0213 0.4140 +0.0555 0.3904 +0.1324 0.3648 +0.2093 0.3379 +0.2861 0.3102 +0.3630 0.2822 +0.4399 0.2545 +0.5168 0.2275 +0.5936 0.2016 +0.6705 0.1770 +0.7474 0.1541 +0.8242 0.1329 +0.9011 0.1136 +0.9780 0.0963 +1.0549 0.0809 +1.1317 0.0673 +1.2086 0.0556 +1.2855 0.0455 +1.3623 0.0368 +1.4392 0.0296 +1.5161 0.0236 +1.5930 0.0186 +1.6698 0.0146 +1.7467 0.0113 +1.8236 0.0087 +1.9004 0.0066 +1.9773 0.0050 +2.0542 0.0037 +2.1311 0.0028 +2.2079 0.0020 +2.2848 0.0015 +2.3617 0.0011 +2.4385 0.0008 +2.5154 0.0005 +2.5923 0.0004 +2.6692 0.0003 +2.7460 0.0002 +2.8229 0.0001 +2.8998 0.0001 +2.9766 0.0001 +3.0535 0.0000 +3.1304 0.0000 +3.2073 0.0000 +3.2841 0.0000 +3.3610 0.0000 +3.4379 0.0000 +3.5147 0.0000 +3.5916 0.0000 +3.6685 0.0000 +3.7454 0.0000 +3.8222 0.0000 +3.8991 0.0000 +3.9760 0.0000 +4.0528 0.0000 +4.1297 0.0000 diff --git a/sourcecodes/data/old/example_sci_bk/Llunlevels.txt b/sourcecodes/data/old/example_sci_bk/Llunlevels.txt new file mode 100644 index 00000000..714e56bd --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llunlevels.txt @@ -0,0 +1,2 @@ +Genotype 1 2 + diff --git a/sourcecodes/data/old/example_sci_bk/Llunnode.txt b/sourcecodes/data/old/example_sci_bk/Llunnode.txt new file mode 100644 index 00000000..7ed6ff82 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llunnode.txt @@ -0,0 +1 @@ +5 diff --git a/sourcecodes/data/old/example_sci_bk/Llunrows.txt b/sourcecodes/data/old/example_sci_bk/Llunrows.txt new file mode 100644 index 00000000..c15fb720 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llunrows.txt @@ -0,0 +1 @@ +501 diff --git a/sourcecodes/data/old/example_sci_bk/Lluparent.txt b/sourcecodes/data/old/example_sci_bk/Lluparent.txt new file mode 100644 index 00000000..b8626c4c --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluparent.txt @@ -0,0 +1 @@ +4 diff --git a/sourcecodes/data/old/example_sci_bk/Llustructure_input.txt b/sourcecodes/data/old/example_sci_bk/Llustructure_input.txt new file mode 100644 index 00000000..bcf72d97 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llustructure_input.txt @@ -0,0 +1,6 @@ +Genotype Gene3 Gene2 Phenotype Gene1 +0 1 0 0 1 +0 0 0 1 0 +0 1 0 1 0 +0 0 0 0 0 +0 0 1 0 0 diff --git a/sourcecodes/data/old/example_sci_bk/Llustructure_input_temp.txt b/sourcecodes/data/old/example_sci_bk/Llustructure_input_temp.txt new file mode 100644 index 00000000..95aa7a5d --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llustructure_input_temp.txt @@ -0,0 +1,6 @@ +Genotype Gene3 Gene2 Phenotype Gene1 +0.000000 0.999929 0.183248 0.000431 1.000000 +0.000000 0.000000 0.000000 1.000000 0.000000 +0.000000 0.935752 0.000000 1.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.053385 0.881001 0.156255 0.000000 diff --git a/sourcecodes/data/old/example_sci_bk/Llustructure_old.txt b/sourcecodes/data/old/example_sci_bk/Llustructure_old.txt new file mode 100644 index 00000000..5b464577 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llustructure_old.txt @@ -0,0 +1,5 @@ +Genotype 0.4860 0.5140 +Gene3 0.0001 0.0001 0.0001 0.0001 0.0002 0.0002 0.0003 0.0004 0.0006 0.0008 0.0010 0.0013 0.0018 0.0023 0.0029 0.0037 0.0047 0.0059 0.0074 0.0092 0.0113 0.0139 0.0170 0.0206 0.0248 0.0297 0.0354 0.0419 0.0493 0.0576 0.0669 0.0773 0.0887 0.1012 0.1147 0.1292 0.1447 0.1610 0.1781 0.1958 0.2139 0.2323 0.2507 0.2688 0.2866 0.3036 0.3197 0.3345 0.3479 0.3597 0.3695 0.3773 0.3828 0.3861 0.3870 0.3855 0.3817 0.3756 0.3673 0.3570 0.3449 0.3311 0.3160 0.2996 0.2824 0.2645 0.2463 0.2279 0.2095 0.1915 0.1740 0.1570 0.1409 0.1256 0.1114 0.0981 0.0859 0.0747 0.0646 0.0555 0.0474 0.0402 0.0340 0.0285 0.0237 0.0197 0.0162 0.0132 0.0108 0.0087 0.0070 0.0056 0.0044 0.0035 0.0027 0.0021 0.0016 0.0013 0.0010 0.0007 0.0006 +Gene1 0.0009 0.0012 0.0015 0.0019 0.0024 0.0030 0.0037 0.0046 0.0056 0.0069 0.0083 0.0101 0.0121 0.0145 0.0174 0.0206 0.0243 0.0286 0.0335 0.0389 0.0451 0.0520 0.0597 0.0682 0.0775 0.0876 0.0986 0.1105 0.1231 0.1366 0.1508 0.1657 0.1811 0.1971 0.2134 0.2300 0.2467 0.2633 0.2797 0.2956 0.3110 0.3256 0.3392 0.3517 0.3629 0.3726 0.3808 0.3873 0.3920 0.3948 0.3958 0.3948 0.3920 0.3872 0.3807 0.3726 0.3628 0.3516 0.3391 0.3254 0.3108 0.2955 0.2795 0.2631 0.2465 0.2299 0.2133 0.1969 0.1810 0.1655 0.1507 0.1365 0.1230 0.1103 0.0985 0.0875 0.0774 0.0681 0.0596 0.0520 0.0451 0.0389 0.0334 0.0286 0.0243 0.0206 0.0173 0.0145 0.0121 0.0101 0.0083 0.0068 0.0056 0.0046 0.0037 0.0030 0.0024 0.0019 0.0015 0.0012 0.0009 +Phenotype 0.0010 0.0013 0.0016 0.0021 0.0026 0.0033 0.0042 0.0052 0.0065 0.0080 0.0098 0.0120 0.0146 0.0176 0.0212 0.0253 0.0300 0.0354 0.0416 0.0486 0.0564 0.0651 0.0748 0.0854 0.0970 0.1096 0.1230 0.1374 0.1526 0.1685 0.1850 0.2021 0.2195 0.2370 0.2545 0.2718 0.2887 0.3049 0.3202 0.3344 0.3473 0.3587 0.3684 0.3763 0.3822 0.3860 0.3877 0.3872 0.3846 0.3798 0.3731 0.3644 0.3539 0.3418 0.3283 0.3136 0.2979 0.2813 0.2643 0.2468 0.2293 0.2118 0.1945 0.1777 0.1614 0.1458 0.1310 0.1170 0.1039 0.0918 0.0806 0.0704 0.0612 0.0529 0.0454 0.0388 0.0329 0.0278 0.0234 0.0195 0.0162 0.0134 0.0110 0.0090 0.0073 0.0059 0.0047 0.0038 0.0030 0.0024 0.0019 0.0015 0.0011 0.0009 0.0007 0.0005 0.0004 0.0003 0.0002 0.0002 0.0001 +Gene2 0.0008 0.0010 0.0013 0.0016 0.0020 0.0026 0.0032 0.0041 0.0050 0.0062 0.0077 0.0094 0.0114 0.0138 0.0166 0.0199 0.0237 0.0281 0.0331 0.0388 0.0453 0.0526 0.0607 0.0696 0.0795 0.0903 0.1021 0.1147 0.1282 0.1426 0.1578 0.1736 0.1900 0.2069 0.2241 0.2414 0.2587 0.2758 0.2924 0.3084 0.3236 0.3377 0.3506 0.3621 0.3719 0.3800 0.3863 0.3905 0.3927 0.3929 0.3910 0.3870 0.3811 0.3732 0.3636 0.3524 0.3397 0.3258 0.3108 0.2949 0.2783 0.2613 0.2440 0.2267 0.2095 0.1925 0.1760 0.1601 0.1449 0.1304 0.1167 0.1039 0.0920 0.0811 0.0711 0.0620 0.0537 0.0464 0.0398 0.0340 0.0288 0.0243 0.0205 0.0171 0.0142 0.0117 0.0097 0.0079 0.0064 0.0052 0.0042 0.0034 0.0027 0.0021 0.0017 0.0013 0.0010 0.0008 0.0006 0.0005 0.0004 diff --git a/sourcecodes/data/old/example_sci_bk/Lluthr.txt b/sourcecodes/data/old/example_sci_bk/Lluthr.txt new file mode 100644 index 00000000..2eb3c4fe --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluthr.txt @@ -0,0 +1 @@ +0.5 diff --git a/sourcecodes/data/old/example_sci_bk/Llutier.txt b/sourcecodes/data/old/example_sci_bk/Llutier.txt new file mode 100644 index 00000000..da7084fb --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llutier.txt @@ -0,0 +1 @@ +3,Tier1,1,Genotype,Tier2,3,Gene1,Gene2,Gene3,Tier3,1,Phenotype, \ No newline at end of file diff --git a/sourcecodes/data/old/example_sci_bk/Llutype.txt b/sourcecodes/data/old/example_sci_bk/Llutype.txt new file mode 100644 index 00000000..9ea66ce4 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Llutype.txt @@ -0,0 +1,2 @@ +Genotype Gene3 Gene2 Phenotype Gene1 +2 1 1 1 1 diff --git a/sourcecodes/data/old/example_sci_bk/Lluvar.txt b/sourcecodes/data/old/example_sci_bk/Lluvar.txt new file mode 100644 index 00000000..56a6051c --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluvar.txt @@ -0,0 +1 @@ +1 \ No newline at end of file diff --git a/sourcecodes/data/old/example_sci_bk/Lluvardata.txt b/sourcecodes/data/old/example_sci_bk/Lluvardata.txt new file mode 100644 index 00000000..56a6051c --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluvardata.txt @@ -0,0 +1 @@ +1 \ No newline at end of file diff --git a/sourcecodes/data/old/example_sci_bk/Lluvarname.txt b/sourcecodes/data/old/example_sci_bk/Lluvarname.txt new file mode 100644 index 00000000..3fe283bd --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluvarname.txt @@ -0,0 +1 @@ +Genotype \ No newline at end of file diff --git a/sourcecodes/data/old/example_sci_bk/Lluwhite.txt b/sourcecodes/data/old/example_sci_bk/Lluwhite.txt new file mode 100644 index 00000000..83e81b8b --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/Lluwhite.txt @@ -0,0 +1 @@ +From To diff --git a/sourcecodes/data/old/example_sci_bk/old/Llurun_evidencemodified.sh b/sourcecodes/data/old/example_sci_bk/old/Llurun_evidencemodified.sh new file mode 100644 index 00000000..ee0dde13 --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/old/Llurun_evidencemodified.sh @@ -0,0 +1,38 @@ +#!/bin/sh +# script for execution of deployed applications +# +# Sets up the MCR environment for the current $ARCH and executes +# the specified command. +# +exe_name=$0 +exe_dir=`dirname "$0"` +echo "------------------------------------------" +if [ "x$1" = "x" ]; then + echo Usage: + echo $0 \ args +else + echo Setting up environment variables + MCRROOT="$1" + echo --- + LD_LIBRARY_PATH=.:${MCRROOT}/runtime/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/bin/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/sys/os/glnxa64; + MCRJRE=${MCRROOT}/sys/java/jre/glnxa64/jre/lib/amd64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/native_threads ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/server ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/client ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE} ; + XAPPLRESDIR=${MCRROOT}/X11/app-defaults ; + export LD_LIBRARY_PATH; + export XAPPLRESDIR; + echo LD_LIBRARY_PATH is ${LD_LIBRARY_PATH}; + shift 1 + args= + while [ $# -gt 0 ]; do + token=`echo "$1" | sed 's/ /\\\\ /g'` # Add blackslash before each blank + args="${args} ${token}" + shift + done + "${exe_dir}"/evidencemodified Llu +fi +exit diff --git a/sourcecodes/data/old/example_sci_bk/old/Llurun_initialstructure.sh b/sourcecodes/data/old/example_sci_bk/old/Llurun_initialstructure.sh new file mode 100644 index 00000000..adde14dd --- /dev/null +++ b/sourcecodes/data/old/example_sci_bk/old/Llurun_initialstructure.sh @@ -0,0 +1,38 @@ +#!/bin/sh +# script for execution of deployed applications +# +# Sets up the MCR environment for the current $ARCH and executes +# the specified command. +# +exe_name=$0 +exe_dir=`dirname "$0"` +echo "------------------------------------------" +if [ "x$1" = "x" ]; then + echo Usage: + echo $0 \ args +else + echo Setting up environment variables + MCRROOT="$1" + echo --- + LD_LIBRARY_PATH=.:${MCRROOT}/runtime/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/bin/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/sys/os/glnxa64; + MCRJRE=${MCRROOT}/sys/java/jre/glnxa64/jre/lib/amd64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/native_threads ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/server ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/client ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE} ; + XAPPLRESDIR=${MCRROOT}/X11/app-defaults ; + export LD_LIBRARY_PATH; + export XAPPLRESDIR; + echo LD_LIBRARY_PATH is ${LD_LIBRARY_PATH}; + shift 1 + args= + while [ $# -gt 0 ]; do + token=`echo "$1" | sed 's/ /\\\\ /g'` # Add blackslash before each blank + args="${args} ${token}" + shift + done + "${exe_dir}"/initialstructure Llu +fi +exit diff --git a/sourcecodes/data/old/examplecar/OVIban.txt b/sourcecodes/data/old/examplecar/OVIban.txt new file mode 100644 index 00000000..83e81b8b --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIban.txt @@ -0,0 +1 @@ +From To diff --git a/sourcecodes/data/old/examplecar/OVIcontinuous_input.txt b/sourcecodes/data/old/examplecar/OVIcontinuous_input.txt new file mode 100644 index 00000000..3a2846cf --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIcontinuous_input.txt @@ -0,0 +1,1004 @@ +Starts Dist SpkQual MFuse Alter Starter StMotor BatAge Charging PlugVolt BatVolt Timing Cranks Plugs AirFilter Air Fuel GasTank GasFilter +2 2 2 2 2 2 2 1 2 3 3 2 2 3 2 2 2 2 2 +2 2 2 2 2 2 2 1 2 3 3 2 2 3 2 2 2 2 2 +1 1 1 1 1 1 1 1.09219 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 8.35719 1 3 3 1 2 1 1 1 2 1 1 +2 1 1 1 1 2 1 4.87697 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 3.40886 2 3 3 1 2 1 1 1 2 1 1 +1 1 1 1 1 1 1 0.823531 1 1 1 1 1 1 1 1 2 1 1 +1 1 1 1 1 1 1 1.29151 1 1 1 1 1 1 1 1 1 1 1 +2 1 1 1 1 1 1 3.2245 1 1 1 1 2 1 1 1 2 1 1 +2 2 2 1 1 2 1 3.32396 1 3 1 2 2 2 1 2 1 1 1 +2 1 2 1 1 2 1 4.81455 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 1.8855 2 2 2 2 1 1 2 2 1 1 1 +2 1 2 1 1 2 1 3.16074 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 1 1 1.32025 1 1 1 2 1 1 1 2 1 1 1 +2 1 1 1 1 1 1 0.725881 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 9.15429 2 3 3 1 2 1 1 2 2 1 2 +2 1 2 1 1 1 1 1.29503 2 3 2 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.55555 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 0.899695 2 1 1 1 1 1 2 2 1 1 1 +2 1 2 1 1 2 1 0.972723 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 3.62187 2 3 3 2 2 3 1 1 1 1 1 +1 1 2 1 1 1 1 7.11136 1 1 1 1 1 2 1 1 1 1 1 +1 1 1 1 1 1 1 1.10928 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 2.61643 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 6.2605 1 2 2 1 2 1 1 2 1 1 1 +2 1 2 1 1 2 1 8.55173 1 1 1 1 2 3 1 1 1 1 1 +1 1 1 1 1 1 1 1.86787 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.23631 1 3 1 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 0.728145 1 1 1 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 4.66434 2 3 3 1 2 3 2 2 1 1 1 +2 1 2 1 1 1 1 3.56254 2 3 3 1 1 2 1 1 2 2 1 +2 1 2 1 1 2 1 2.63999 2 3 3 1 2 3 1 2 2 2 1 +2 1 2 1 1 2 1 0.715897 2 3 3 1 2 2 1 1 1 1 1 +2 1 2 1 1 2 1 1.85198 2 3 3 1 2 2 1 1 2 2 1 +2 1 1 1 1 1 1 3.63826 1 1 1 1 1 1 2 2 2 1 1 +2 1 2 1 1 1 1 7.73862 2 3 3 1 1 3 1 1 2 2 1 +2 1 2 1 1 2 1 10.1699 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 2 2 2.97722 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.50117 2 2 2 1 1 3 1 1 1 1 1 +2 1 2 1 1 1 1 0.640869 2 2 2 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.72775 1 3 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 5.46047 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 2.21929 1 2 2 1 2 2 1 1 2 1 1 +1 1 1 1 1 1 1 0.438026 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 0.0684539 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.15012 2 3 3 1 2 2 1 1 1 1 1 +2 1 2 1 1 2 1 0.161533 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 1.11862 2 3 3 1 2 2 1 1 2 1 2 +2 1 1 1 1 1 1 0.410719 1 1 1 1 2 1 1 1 2 1 1 +2 1 2 1 1 2 1 0.0322208 2 3 3 1 2 1 1 1 2 2 1 +1 1 1 1 1 1 1 3.37212 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 2.78532 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.40432 1 1 1 1 1 1 2 1 1 1 1 +2 1 2 1 1 2 1 3.22403 2 3 3 1 1 3 1 1 2 1 1 +2 1 2 1 1 2 1 1.7234 2 3 3 1 2 1 1 2 1 1 1 +2 1 2 1 1 2 1 0.674939 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 6.9812 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.82705 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 0.935587 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.992564 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 3.47892 1 2 2 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 6.52634 1 2 2 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 3.68841 1 1 1 1 1 3 1 1 2 2 1 +1 1 2 1 1 1 1 0.50506 2 2 2 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.33453 1 2 2 1 2 3 1 1 2 1 1 +2 1 2 1 1 1 1 3.39695 2 2 2 2 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.482289 1 2 1 1 2 2 1 1 1 1 1 +2 1 2 1 1 2 1 0.385094 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 3.92753 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 9.63027 2 3 3 1 2 1 1 1 2 2 1 +1 1 1 1 1 1 1 1.77681 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 15.8749 2 2 2 1 1 1 2 2 1 1 1 +1 1 1 1 1 1 1 0.863939 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 3.5007 2 3 2 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.46447 1 1 1 2 1 1 2 2 1 1 1 +2 1 2 1 1 1 1 4.00622 2 2 2 1 1 3 1 1 2 1 1 +1 1 2 1 1 1 1 2.62894 1 1 1 1 1 2 1 1 1 1 1 +1 1 1 1 1 1 1 0.772251 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 0.254104 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 1 1 1.35669 1 1 1 1 1 3 1 1 2 2 1 +1 1 1 1 1 1 1 0.408515 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.73523 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 4.84404 2 3 3 1 2 1 1 1 1 1 1 +2 2 2 1 1 2 1 3.08909 2 3 3 2 2 1 1 1 2 2 1 +2 1 1 1 1 1 1 0.500373 1 1 1 2 1 1 1 2 1 1 1 +1 1 2 1 1 1 1 1.42352 1 1 1 1 1 3 2 1 1 1 1 +2 1 2 1 1 2 1 3.19283 2 2 2 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 2.65803 2 3 3 1 2 1 1 1 2 1 2 +1 1 1 1 1 1 1 4.35117 1 1 1 1 1 1 1 1 1 1 1 +2 1 1 1 1 1 1 0.448913 1 1 1 1 1 1 1 1 2 2 1 +2 1 2 1 1 2 1 4.59592 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.68035 1 1 1 1 1 3 2 2 2 2 1 +2 1 2 1 1 1 1 6.86362 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.88662 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 1 1 6.67913 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 1.72634 1 2 1 1 2 1 1 1 1 1 1 +2 1 1 1 1 1 1 1.01307 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 1.1309 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 2.38128 2 2 2 1 1 1 1 1 2 1 1 +2 1 2 1 1 2 1 2.01195 1 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 4.91973 2 3 3 1 1 2 1 1 2 1 1 +2 1 1 1 1 1 1 3.45795 1 1 1 2 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.66261 1 1 1 2 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 12.2823 1 3 3 1 1 1 1 1 2 1 1 +2 1 2 1 1 2 1 4.98365 2 3 3 1 2 2 1 1 1 1 1 +2 1 2 1 1 1 1 2.28812 1 1 1 1 1 2 1 1 2 2 1 +2 1 2 1 1 1 1 1.98491 2 2 2 2 1 3 1 1 2 1 1 +1 1 1 1 1 1 1 0.441825 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 6.80856 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.14798 1 3 1 2 1 1 2 1 1 1 1 +2 1 2 1 1 2 1 3.47334 2 3 3 1 2 2 1 1 1 1 1 +2 1 2 1 1 2 1 4.29814 1 3 3 1 1 1 1 1 2 1 1 +1 1 2 1 1 1 1 2.14065 1 1 1 1 1 3 1 1 1 1 1 +2 1 1 1 1 1 1 1.96168 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 3.7237 2 3 3 1 2 3 1 2 1 1 1 +1 1 1 1 1 1 1 1.11832 1 1 1 2 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.56047 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.452531 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 4.76471 2 3 3 2 2 1 1 1 1 1 1 +1 1 2 1 1 1 1 3.72712 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 1.60393 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 2.22538 1 1 1 1 1 3 1 1 2 2 1 +1 1 1 1 1 1 1 0.778349 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 1 1 0.136355 2 3 3 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.559837 2 2 2 1 2 1 1 2 1 1 1 +2 1 2 1 1 2 2 1.5521 2 3 3 2 2 1 1 1 2 2 2 +1 1 1 1 1 1 1 1.1683 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 2 1 3.20792 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.0294523 2 2 2 1 1 3 1 1 2 2 1 +2 1 1 1 1 1 1 1.51016 1 1 1 1 2 1 1 1 2 1 1 +2 1 2 1 1 1 1 1.46157 2 2 2 1 1 1 1 1 2 2 1 +2 1 2 1 1 1 1 7.31961 2 3 3 1 1 3 1 1 1 1 1 +2 1 2 1 1 1 1 2.25565 1 2 2 1 1 1 1 1 2 2 1 +2 1 2 1 1 1 1 0.332537 1 3 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 1.16328 2 3 3 1 2 1 2 2 2 2 1 +1 1 2 1 1 1 1 4.18322 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 8.04276 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 4.12436 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.66478 1 1 1 1 1 1 1 1 1 1 1 +1 1 2 1 1 1 1 6.44237 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 3.30168 2 3 3 1 1 2 2 2 1 1 1 +1 1 1 1 1 1 1 2.57221 1 1 1 1 1 1 1 1 1 1 1 +2 1 1 1 1 1 1 1.5302 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 2.23714 2 3 3 1 1 1 1 2 2 2 1 +2 1 2 1 1 2 1 8.72277 2 3 3 1 2 1 1 1 1 1 1 +1 1 2 1 1 1 1 5.18702 1 1 1 1 1 2 1 1 1 1 1 +2 1 2 1 1 2 1 4.1361 1 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 4.03663 2 2 2 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 2.09322 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 1.87756 2 3 3 2 2 1 1 1 2 1 1 +2 1 2 1 1 2 1 14.1937 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 3.41049 1 1 1 1 1 3 1 1 2 2 1 +1 1 1 1 1 1 1 2.93445 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 10.5414 1 2 2 1 1 1 1 2 1 1 1 +2 1 2 1 1 2 1 4.09177 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 2.39508 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 2 1 3.50173 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 6.21675 1 3 2 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.703114 1 2 2 2 2 1 1 1 2 1 1 +2 1 2 1 1 1 1 0.894215 2 3 2 1 1 3 1 2 1 1 1 +2 1 2 1 1 1 1 1.0471 1 2 2 1 1 3 1 1 1 1 1 +1 1 1 1 1 1 1 1.31589 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.68824 1 2 1 1 2 1 1 1 2 2 2 +2 1 2 1 1 2 1 5.48787 2 3 3 1 2 1 1 1 1 1 1 +1 1 2 1 1 1 1 3.34192 1 1 1 2 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 9.5016 2 3 3 1 2 1 1 1 2 1 2 +2 1 2 1 1 2 1 6.07059 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 19.5523 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.31889 2 3 3 2 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.87967 1 1 1 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 2.77823 2 3 3 1 2 1 1 1 1 1 1 +1 1 2 1 1 1 1 3.5956 1 1 1 1 1 3 1 1 1 1 1 +2 1 1 1 1 1 1 1.31325 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.75989 2 2 2 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 0.621366 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 1 1 3.14788 2 2 2 1 1 1 1 1 2 1 1 +2 1 2 1 1 2 1 2.41881 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 4.3647 2 3 3 1 2 3 1 1 2 1 1 +2 1 2 1 1 1 1 0.227306 2 2 2 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 0.0613263 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 13.2932 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.64929 1 1 1 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 7.26046 2 3 3 1 2 1 1 1 2 1 1 +1 1 2 1 1 1 1 0.198224 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 6.24718 2 3 3 1 2 2 1 1 2 1 1 +1 1 1 1 1 1 1 0.818786 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 0.367484 2 3 3 2 2 1 2 2 1 1 1 +2 1 2 1 1 1 1 3.24863 1 3 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 3.67769 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.659442 1 3 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 3.40058 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 1 1 2.95923 1 1 1 2 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.80303 1 1 1 1 2 2 1 1 2 1 1 +1 1 1 1 1 1 1 4.64104 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 6.81146 1 3 3 1 2 2 1 1 1 1 1 +1 1 1 1 1 1 1 1.74035 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 3.33846 2 2 2 1 2 1 1 2 1 1 1 +2 1 2 1 1 1 1 10.5625 1 2 2 1 1 3 1 2 1 1 1 +2 1 2 1 1 1 1 1.75197 2 2 2 2 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 2.57664 1 2 2 1 1 2 2 2 2 1 1 +1 1 2 1 1 1 1 1.31431 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 1 1 0.703583 1 2 1 1 1 1 1 1 2 2 1 +2 1 2 1 1 2 1 4.1339 2 3 3 1 2 3 1 1 2 2 1 +2 1 2 1 1 2 1 0.689182 2 3 3 1 2 1 2 2 1 1 1 +2 1 1 1 1 1 1 1.70247 1 1 1 1 1 1 1 1 2 2 1 +2 1 2 1 1 2 1 3.16831 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 1 1 2.60872 1 1 1 2 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 5.17883 1 1 1 1 1 1 1 1 1 1 1 +1 1 2 1 1 1 1 1.24226 2 2 2 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 16.3393 1 1 1 1 1 1 1 2 1 1 1 +2 1 1 1 1 1 1 0.355235 1 1 1 1 2 1 1 1 2 2 1 +2 1 2 1 1 2 1 10.1607 2 3 3 1 2 2 1 1 2 1 1 +1 1 1 1 1 1 1 0.0379707 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.73676 2 3 3 1 2 1 1 2 1 1 1 +2 1 2 1 1 2 1 0.411721 2 3 3 1 2 1 1 1 2 1 1 +2 1 2 1 1 2 1 5.80853 2 3 3 1 2 1 1 2 1 1 1 +1 1 1 1 1 1 1 2.29781 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 2 1 2.5112 2 3 3 1 2 3 1 2 1 1 1 +1 1 2 1 1 1 1 2.02508 1 1 1 1 1 3 1 1 1 1 1 +2 1 1 1 1 1 1 6.71109 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 9.53679 2 3 3 1 2 1 1 2 1 1 1 +2 1 2 1 1 2 1 0.325964 2 3 3 1 2 2 2 1 1 1 1 +1 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1 1 1 +2 1 2 1 1 1 1 5.10286 2 2 2 1 1 1 1 1 2 2 1 +1 1 1 1 1 1 1 0.918882 1 1 1 1 1 1 1 1 1 1 1 +2 1 1 1 1 1 1 0.0441354 1 1 1 1 1 1 1 1 2 1 1 +1 1 2 1 1 1 1 0.956322 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 1 1 0.501229 1 2 2 1 2 3 1 1 2 2 1 +2 1 2 1 1 2 1 2.03462 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.49454 1 2 2 1 1 3 1 1 1 1 1 +1 1 2 1 1 1 1 3.47399 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 1 1 3.42912 1 1 1 1 1 2 2 1 2 2 1 +1 1 2 1 1 1 1 3.92437 1 1 1 1 1 3 1 1 1 1 1 +2 1 1 1 1 1 1 0.51396 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.12695 2 3 3 1 2 1 1 1 2 1 1 +2 1 2 1 1 2 1 2.44146 2 3 3 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 9.60595 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 2.0039 2 2 2 1 2 1 1 1 2 2 1 +2 1 2 1 1 2 1 4.10378 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 1 1 0.386532 1 1 1 1 1 1 2 2 1 1 1 +2 1 2 1 1 1 1 0.75722 1 1 1 1 1 3 1 1 1 1 1 +1 1 1 1 1 1 1 0.506289 1 1 1 1 1 1 1 2 1 1 1 +1 1 1 1 1 1 1 1.84871 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 6.89466 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.30596 2 2 2 1 1 3 1 1 2 2 1 +2 1 2 1 1 1 1 6.80188 1 2 2 1 1 3 1 1 1 1 1 +2 1 2 1 1 1 1 4.86438 1 3 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 2.90783 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.971687 2 3 2 1 1 1 2 2 1 1 1 +2 1 2 1 1 2 1 1.88183 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.17229 1 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.927847 2 3 3 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.36548 2 2 2 1 1 3 1 1 1 1 1 +2 1 2 1 1 2 1 2.00101 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 1 1 2.28421 1 1 1 1 2 1 2 2 1 1 1 +1 1 1 1 1 1 1 1.86953 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 9.0133 1 2 2 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 0.439457 2 2 2 1 2 3 1 1 1 1 1 +1 1 1 1 1 1 1 4.34038 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.32347 2 2 2 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 6.60777 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 0.806183 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 7.83139 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 2.49472 1 1 1 1 2 2 1 1 1 1 1 +2 1 2 1 1 1 1 0.942534 1 2 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 1.70449 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 3.0436 2 3 3 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 3.93414 1 3 3 1 2 1 1 1 2 2 1 +2 1 2 1 1 2 1 2.13185 2 3 3 2 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 0.262005 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 12.7252 1 2 2 1 2 3 1 1 1 1 1 +1 1 1 1 1 1 1 0.465012 1 1 1 1 1 1 2 2 1 1 1 +1 1 2 1 1 1 1 0.455832 1 1 1 1 1 3 1 1 1 1 1 +2 1 1 1 1 1 1 5.52702 1 1 1 1 2 1 1 1 1 1 1 +1 1 2 1 1 1 1 4.37922 1 1 1 1 1 2 1 1 1 1 1 +2 1 2 1 1 1 1 8.52767 1 2 2 1 1 1 1 1 1 1 1 +1 1 2 1 1 1 1 2.53081 1 2 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.34317 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 2.24552 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 3.39164 2 3 3 1 2 1 1 2 1 1 1 +2 1 1 1 1 1 1 0.635824 1 1 1 1 1 1 1 1 2 2 1 +2 1 2 1 1 2 1 0.810242 2 3 3 2 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 20.3734 1 2 2 1 1 1 1 1 1 1 1 +2 1 1 1 1 1 1 0.314643 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.93743 2 2 2 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 2.55162 2 3 3 1 2 3 1 2 1 1 1 +1 1 2 1 1 1 1 0.729802 2 2 2 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 0.0873788 2 3 3 1 2 2 1 1 1 1 1 +1 1 1 1 1 1 1 2.09206 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 2 1 0.834649 2 3 3 1 2 3 1 1 1 1 1 +1 1 2 1 1 1 1 9.7003 1 1 1 1 1 2 1 1 1 1 1 +2 1 2 1 1 1 1 4.45689 1 1 1 1 1 2 1 1 2 1 1 +2 1 1 1 1 1 1 0.802165 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 5.66099 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.61237 1 1 1 1 1 3 1 1 2 2 1 +1 1 1 1 1 1 1 2.97608 1 1 1 2 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.48756 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 3.87253 2 2 2 2 1 1 1 2 1 1 1 +2 1 2 1 1 2 1 7.42702 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.209732 2 3 3 2 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 2.55715 2 2 2 1 1 2 2 2 1 1 1 +2 1 2 1 1 1 1 0.207224 2 2 2 1 2 1 1 1 1 1 1 +2 1 1 1 1 2 1 7.37554 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.11792 2 3 3 2 2 1 2 2 1 1 1 +2 1 1 1 1 1 1 1.53867 1 1 1 1 1 1 2 2 2 1 2 +2 1 2 1 1 2 1 0.0302816 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 1 1 2.28003 1 1 1 1 2 2 1 1 1 1 1 +2 1 2 1 1 2 1 2.9777 2 3 3 1 2 1 2 1 2 2 1 +1 1 1 1 1 1 1 0.0199808 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 3.29523 1 3 1 1 1 2 1 1 1 1 1 +2 1 2 1 1 2 1 1.64431 2 3 3 1 2 1 1 2 1 1 1 +1 1 1 1 1 1 1 1.65059 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.37525 1 1 1 1 1 1 1 2 1 1 1 +2 1 2 1 1 2 1 1.73187 2 3 3 1 2 1 1 1 2 1 1 +2 1 2 1 1 2 1 1.01163 2 3 3 1 2 1 1 1 2 1 1 +2 1 2 1 1 2 1 1.87413 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 4.35695 2 2 2 1 1 2 1 1 1 1 1 +2 1 2 1 1 1 1 0.273621 2 3 3 1 1 3 1 1 1 1 1 +2 1 1 1 1 2 1 3.43754 1 1 1 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 0.69945 2 2 2 1 1 3 1 1 1 1 1 +1 1 1 1 1 1 1 6.24086 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 0.112064 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 3.88415 1 2 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 1.06286 2 2 2 1 1 1 1 1 2 1 1 +2 1 2 1 1 2 1 2.17353 2 3 3 1 2 1 1 2 1 1 1 +1 1 1 1 1 1 1 1.49488 1 1 1 2 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 8.26156 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 2.29956 2 3 3 1 2 1 1 1 2 1 2 +2 1 2 1 1 2 1 5.02241 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 1.79705 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 2.41045 1 2 2 1 2 2 1 2 1 1 1 +2 1 2 1 1 1 1 3.62587 1 2 2 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 3.03338 2 3 3 1 2 3 1 1 2 2 1 +2 1 1 1 1 1 1 1.32098 1 1 1 1 2 1 1 1 1 1 1 +1 1 2 1 1 1 1 2.90188 1 1 1 1 1 3 1 1 1 1 1 +1 1 2 1 1 1 1 2.5172 2 2 2 1 1 1 1 1 1 1 1 +1 1 2 1 1 1 1 1.59161 1 1 1 1 1 2 1 1 1 1 1 +2 1 2 1 1 1 1 1.51327 1 2 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 3.61682 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 0.582747 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 1 1 1.78714 1 1 1 1 1 1 1 1 2 1 1 +2 1 2 1 1 2 1 3.90871 2 3 3 1 2 1 1 1 2 2 1 +2 1 2 1 1 2 1 2.12194 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 2.85085 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 2.63259 1 1 1 1 1 3 1 1 1 1 1 +2 1 2 1 1 1 1 1.90261 1 1 1 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 1.12473 2 3 3 2 2 2 1 1 1 1 1 +1 1 2 1 1 1 1 0.744409 2 2 2 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 0.553062 2 3 3 1 2 3 2 2 1 1 1 +2 1 2 1 1 2 1 4.13517 2 3 3 2 2 1 1 1 1 1 1 +2 1 2 1 1 1 1 5.34859 1 3 1 1 1 3 1 1 1 1 1 +1 1 2 1 1 1 1 4.14217 2 2 2 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 4.68854 1 1 1 1 1 1 1 1 1 1 1 +2 1 1 1 1 1 1 2.47433 1 1 1 1 2 1 1 1 2 1 1 +2 1 2 1 1 1 1 4.04289 2 3 3 1 2 1 1 1 1 1 1 +1 1 2 1 1 1 1 4.64514 2 2 2 1 1 1 1 2 1 1 1 +2 1 1 1 1 1 1 3.52575 1 1 1 2 1 1 1 1 2 2 1 +2 1 2 1 1 2 1 0.0085396 2 3 3 1 2 1 1 1 2 2 1 +2 1 2 1 1 2 1 3.75628 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.46408 2 3 3 1 2 1 2 2 1 1 1 +1 1 2 1 1 1 1 4.407 1 2 2 1 1 1 2 1 1 1 1 +2 1 2 1 1 2 1 12.9879 2 3 3 1 2 1 1 1 2 1 1 +2 1 2 1 1 2 1 5.42745 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 2.27077 2 3 3 1 2 1 1 1 2 1 1 +1 1 1 1 1 1 1 4.19191 1 1 1 1 1 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.43925 1 1 1 1 1 1 1 2 1 1 1 +1 1 1 1 1 1 1 4.98537 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 1.71189 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 4.91003 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 2 2 1 0.809663 2 3 3 1 2 1 1 1 1 1 1 +2 1 2 1 1 2 1 3.78373 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 1 1 1.64728 1 1 1 1 2 1 1 2 2 1 1 +2 1 2 1 1 2 1 0.977114 2 3 3 1 2 2 1 1 1 1 1 +2 1 2 1 1 2 1 4.78619 2 3 3 1 2 1 1 1 1 1 1 +2 1 1 1 1 2 1 4.88483 1 1 1 2 2 1 1 1 2 1 1 +1 1 1 1 1 1 1 0.0447191 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 2 1 6.22958 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 4.02524 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 5.10729 1 3 2 1 1 1 1 1 2 2 1 +2 1 2 1 1 2 1 8.97099 2 3 3 1 2 3 1 1 1 1 1 +2 1 2 1 1 2 1 5.59486 2 3 3 1 2 1 1 1 1 1 1 +1 1 1 1 1 1 1 1.13786 1 1 1 1 1 1 1 1 1 1 1 +2 1 2 1 1 1 1 11.3795 1 2 2 1 1 1 1 1 1 1 1 + diff --git a/sourcecodes/data/old/examplecar/OVIgraphviz.txt b/sourcecodes/data/old/examplecar/OVIgraphviz.txt new file mode 100644 index 00000000..2aaaecb2 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIgraphviz.txt @@ -0,0 +1,70 @@ +digraph G { +size="10,10"; ratio = fill; +node [shape=square,width=1.5]; +Dist -> BatAge; +Dist -> Timing; +Dist -> Plugs; +Dist -> AirFilter; +Dist -> GasTank; +Dist -> GasFilter; +SpkQual -> Starts; +MFuse -> Starts; +MFuse -> Dist; +MFuse -> SpkQual; +MFuse -> Starter; +MFuse -> BatAge; +MFuse -> Charging; +MFuse -> Timing; +MFuse -> Cranks; +MFuse -> Plugs; +MFuse -> AirFilter; +MFuse -> Air; +MFuse -> Fuel; +MFuse -> GasTank; +MFuse -> GasFilter; +Alter -> Dist; +Alter -> MFuse; +Alter -> BatAge; +Alter -> Charging; +Alter -> Timing; +Alter -> Plugs; +Alter -> AirFilter; +Alter -> Air; +Alter -> GasTank; +Alter -> GasFilter; +Starter -> Cranks; +StMotor -> MFuse; +StMotor -> Alter; +StMotor -> Starter; +StMotor -> BatAge; +StMotor -> Timing; +StMotor -> Plugs; +StMotor -> AirFilter; +StMotor -> Air; +StMotor -> Fuel; +StMotor -> GasTank; +StMotor -> GasFilter; +PlugVolt -> Starts; +PlugVolt -> Dist; +PlugVolt -> SpkQual; +PlugVolt -> MFuse; +PlugVolt -> Alter; +PlugVolt -> Starter; +PlugVolt -> StMotor; +PlugVolt -> Charging; +PlugVolt -> Cranks; +BatVolt -> Starts; +BatVolt -> Dist; +BatVolt -> SpkQual; +BatVolt -> MFuse; +BatVolt -> Alter; +BatVolt -> Starter; +BatVolt -> StMotor; +BatVolt -> Charging; +BatVolt -> PlugVolt; +BatVolt -> Cranks; +Plugs -> SpkQual; +AirFilter -> Air; +GasTank -> Fuel; +GasFilter -> Fuel; +} \ No newline at end of file diff --git a/sourcecodes/data/old/examplecar/OVIk.txt b/sourcecodes/data/old/examplecar/OVIk.txt new file mode 100644 index 00000000..d00491fd --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIk.txt @@ -0,0 +1 @@ +1 diff --git a/sourcecodes/data/old/examplecar/OVImap.txt b/sourcecodes/data/old/examplecar/OVImap.txt new file mode 100644 index 00000000..eda717d2 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVImap.txt @@ -0,0 +1,19 @@ +Starts 3 0.432505 1.751249 +Dist 3 0.094441 1.008991 +SpkQual 2 0.434234 1.748252 +MFuse 2 0.031607 1.000999 +Alter 2 0.054690 1.002997 +Starter 2 0.490666 1.402597 +StMotor 2 0.070534 1.004995 +BatAge 2 2.948418 3.295892 +Charging 2 0.500244 1.497502 +PlugVolt 2 0.908145 2.090909 +BatVolt 2 0.914876 1.999001 +Timing 2 0.293230 1.094905 +Cranks 2 0.500010 1.515485 +Plugs 2 0.819700 1.518482 +AirFilter 3 0.294607 1.095904 +Air 2 0.375671 1.169830 +Fuel 2 0.416982 1.223776 +GasTank 2 0.295974 1.096903 +GasFilter 1 0.164975 1.027972 diff --git a/sourcecodes/data/old/examplecar/OVImapdata.txt b/sourcecodes/data/old/examplecar/OVImapdata.txt new file mode 100644 index 00000000..d1eac895 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVImapdata.txt @@ -0,0 +1 @@ +BatVolt PlugVolt StMotor Alter MFuse Charging Starter Cranks Dist GasFilter GasTank Fuel AirFilter Air Plugs SpkQual Starts Timing BatAge diff --git a/sourcecodes/data/old/examplecar/OVIname.txt b/sourcecodes/data/old/examplecar/OVIname.txt new file mode 100644 index 00000000..e7901e87 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIname.txt @@ -0,0 +1 @@ +Starts Dist SpkQual MFuse Alter Starter StMotor BatAge Charging PlugVolt BatVolt Timing Cranks Plugs AirFilter Air Fuel GasTank GasFilter diff --git a/sourcecodes/data/old/examplecar/OVInet_figure.txt b/sourcecodes/data/old/examplecar/OVInet_figure.txt new file mode 100644 index 00000000..ceed746e --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVInet_figure.txt @@ -0,0 +1,237 @@ +19 +2100 1800 +BatVolt 788 0 +PlugVolt 998 180 +StMotor 788 360 +Alter 998 540 +MFuse 788 720 +Charging 472 900 +Starter 998 900 +Cranks 0 1080 +Dist 1523 900 +GasFilter 263 1080 +GasTank 525 1080 +Fuel 472 1260 +AirFilter 788 1080 +Air 998 1260 +Plugs 1050 1080 +SpkQual 1523 1260 +Starts 788 1440 +Timing 1313 1080 +BatAge 1575 1080 +BatVolt 3 +250 150 +0 +10 2 3 4 5 6 7 8 9 16 17 +1 0.4184 +2 0.1639 +3 0.4177 +PlugVolt 3 +250 150 +1 1 +9 3 4 5 6 7 8 9 16 17 +1 0.3706 +2 0.1678 +3 0.4616 +StMotor 2 +250 150 +2 1 2 +11 4 5 7 10 11 12 13 14 15 18 19 +1 0.9955 +2 0.0045 +Alter 2 +250 150 +3 1 2 3 +10 5 6 9 10 11 13 14 15 18 19 +1 0.9970 +2 0.0030 +MFuse 2 +250 150 +4 1 2 3 4 +14 6 7 8 9 10 11 12 13 14 15 16 17 18 19 +1 0.9990 +2 0.0010 +Charging 2 +250 150 +4 1 2 4 5 +0 +1 0.5024 +2 0.4976 +Starter 2 +250 150 +4 1 2 3 5 +1 8 +1 0.5977 +2 0.4023 +Cranks 2 +250 150 +4 1 2 5 7 +0 +1 0.4848 +2 0.5152 +Dist 2 +250 150 +4 1 2 4 5 +6 10 11 13 15 18 19 +1 0.9910 +2 0.0090 +GasFilter 2 +250 150 +4 3 4 5 9 +1 12 +1 0.9725 +2 0.0275 +GasTank 2 +250 150 +4 3 4 5 9 +1 12 +1 0.9031 +2 0.0969 +Fuel 2 +250 150 +4 3 5 10 11 +0 +1 0.7756 +2 0.2244 +AirFilter 2 +250 150 +4 3 4 5 9 +1 14 +1 0.9040 +2 0.0960 +Air 2 +250 150 +4 3 4 5 13 +0 +1 0.8301 +2 0.1699 +Plugs 3 +250 150 +4 3 4 5 9 +1 16 +1 0.6922 +2 0.0970 +3 0.2109 +SpkQual 2 +250 150 +4 1 2 5 15 +1 17 +1 0.2568 +2 0.7432 +Starts 2 +250 150 +4 1 2 5 16 +0 +1 0.2497 +2 0.7503 +Timing 2 +250 150 +4 3 4 5 9 +0 +1 0.9053 +2 0.0947 +BatAge 1 +250 150 +4 3 4 5 9 +0 +-2.1178 0.0437 +-2.0287 0.0524 +-1.9396 0.0623 +-1.8505 0.0736 +-1.7614 0.0862 +-1.6723 0.1001 +-1.5832 0.1155 +-1.4941 0.1321 +-1.4050 0.1499 +-1.3159 0.1689 +-1.2268 0.1888 +-1.1377 0.2093 +-1.0486 0.2303 +-0.9595 0.2515 +-0.8704 0.2725 +-0.7813 0.2929 +-0.6922 0.3124 +-0.6031 0.3306 +-0.5140 0.3471 +-0.4249 0.3617 +-0.3358 0.3740 +-0.2467 0.3836 +-0.1576 0.3905 +-0.0685 0.3944 +0.0206 0.3952 +0.1097 0.3930 +0.1988 0.3877 +0.2879 0.3795 +0.3770 0.3687 +0.4661 0.3553 +0.5552 0.3398 +0.6443 0.3225 +0.7334 0.3036 +0.8225 0.2837 +0.9116 0.2629 +1.0007 0.2419 +1.0898 0.2207 +1.1789 0.1999 +1.2680 0.1796 +1.3571 0.1601 +1.4462 0.1417 +1.5353 0.1243 +1.6244 0.1083 +1.7135 0.0936 +1.8026 0.0803 +1.8917 0.0683 +1.9808 0.0576 +2.0699 0.0483 +2.1590 0.0401 +2.2481 0.0331 +2.3372 0.0271 +2.4263 0.0220 +2.5154 0.0177 +2.6045 0.0142 +2.6936 0.0112 +2.7827 0.0088 +2.8718 0.0069 +2.9609 0.0054 +3.0500 0.0041 +3.1391 0.0031 +3.2282 0.0024 +3.3173 0.0018 +3.4064 0.0013 +3.4954 0.0010 +3.5845 0.0007 +3.6736 0.0005 +3.7627 0.0004 +3.8518 0.0003 +3.9409 0.0002 +4.0300 0.0001 +4.1191 0.0001 +4.2082 0.0001 +4.2973 0.0000 +4.3864 0.0000 +4.4755 0.0000 +4.5646 0.0000 +4.6537 0.0000 +4.7428 0.0000 +4.8319 0.0000 +4.9210 0.0000 +5.0101 0.0000 +5.0992 0.0000 +5.1883 0.0000 +5.2774 0.0000 +5.3665 0.0000 +5.4556 0.0000 +5.5447 0.0000 +5.6338 0.0000 +5.7229 0.0000 +5.8120 0.0000 +5.9011 0.0000 +5.9902 0.0000 +6.0793 0.0000 +6.1684 0.0000 +6.2575 0.0000 +6.3466 0.0000 +6.4357 0.0000 +6.5248 0.0000 +6.6139 0.0000 +6.7030 0.0000 +6.7921 0.0000 diff --git a/sourcecodes/data/old/examplecar/OVInet_figure_new.txt b/sourcecodes/data/old/examplecar/OVInet_figure_new.txt new file mode 100644 index 00000000..bd2ec975 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVInet_figure_new.txt @@ -0,0 +1,138 @@ +19 +19 +2100 1800 +BatVolt 788 0 +PlugVolt 998 180 +StMotor 788 360 +Alter 998 540 +MFuse 788 720 +Charging 472 900 +Starter 998 900 +Cranks 0 1080 +Dist 1523 900 +GasFilter 263 1080 +GasTank 525 1080 +Fuel 472 1260 +AirFilter 788 1080 +Air 998 1260 +Plugs 1050 1080 +SpkQual 1523 1260 +Starts 788 1440 +Timing 1313 1080 +BatAge 1575 1080 +BatVolt 3 +250 150 +0 +10 2 3 4 5 6 7 8 9 16 17 +1 0.4186 +2 0.1642 +3 0.4172 +PlugVolt 3 +250 150 +1 1 +9 3 4 5 6 7 8 9 16 17 +1 0.3716 +2 0.1684 +3 0.4600 +StMotor 2 +250 150 +2 1 2 +11 4 5 7 10 11 12 13 14 15 18 19 +1 0.9982 +2 0.0018 +Alter 2 +250 150 +3 1 2 3 +10 5 6 9 10 11 13 14 15 18 19 +1 0.9985 +2 0.0015 +MFuse 2 +250 150 +4 1 2 3 4 +14 6 7 8 9 10 11 12 13 14 15 16 17 18 19 +1 1.0000 +2 0.0000 +Charging 2 +250 150 +4 1 2 4 5 +0 +1 0.5027 +2 0.4973 +Starter 2 +250 150 +4 1 2 3 5 +1 8 +1 0.5996 +2 0.4004 +Cranks 2 +250 150 +4 1 2 5 7 +0 +1 0.4861 +2 0.5139 +Dist 2 +250 150 +4 1 2 4 5 +6 10 11 13 15 18 19 +1 0.9948 +2 0.0052 +GasFilter 2 +250 150 +4 3 4 5 9 +1 12 +1 0.9736 +2 0.0264 +GasTank 2 +250 150 +4 3 4 5 9 +1 12 +1 0.9048 +2 0.0952 +Fuel 2 +250 150 +4 3 5 10 11 +0 +1 0.7770 +2 0.2230 +AirFilter 2 +250 150 +4 3 4 5 9 +1 14 +1 0.9045 +2 0.0955 +Air 2 +250 150 +4 3 4 5 13 +0 +1 0.8304 +2 0.1696 +Plugs 3 +250 150 +4 3 4 5 9 +1 16 +1 0.6923 +2 0.0968 +3 0.2108 +SpkQual 2 +250 150 +4 1 2 5 15 +1 17 +1 0.2573 +2 0.7427 +Starts 2 +250 150 +4 1 2 5 16 +0 +1 0.2504 +2 0.7496 +Timing 2 +250 150 +4 3 4 5 9 +0 +1 0.9080 +2 0.0920 +BatAge 1 +250 150 +4 3 4 5 9 +0 +-1.3159 1.0000 diff --git a/sourcecodes/data/old/examplecar/OVInnode.txt b/sourcecodes/data/old/examplecar/OVInnode.txt new file mode 100644 index 00000000..d6b24041 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVInnode.txt @@ -0,0 +1 @@ +19 diff --git a/sourcecodes/data/old/examplecar/OVInrows.txt b/sourcecodes/data/old/examplecar/OVInrows.txt new file mode 100644 index 00000000..7d802a3e --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVInrows.txt @@ -0,0 +1 @@ +1002 diff --git a/sourcecodes/data/old/examplecar/OVIparent.txt b/sourcecodes/data/old/examplecar/OVIparent.txt new file mode 100644 index 00000000..b8626c4c --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIparent.txt @@ -0,0 +1 @@ +4 diff --git a/sourcecodes/data/old/examplecar/OVIstructure_input.txt b/sourcecodes/data/old/examplecar/OVIstructure_input.txt new file mode 100644 index 00000000..cad732f3 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIstructure_input.txt @@ -0,0 +1,20 @@ +Starts Dist SpkQual MFuse Alter Starter StMotor BatAge Charging PlugVolt BatVolt Timing Cranks Plugs AirFilter Air Fuel GasTank GasFilter +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 1 1 +1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +1 1 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 1 1 +0 1 0 1 0 0 0 1 1 0 0 1 0 1 1 1 0 1 1 +0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 +0 0 0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1 1 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +1 1 1 1 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 +1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 diff --git a/sourcecodes/data/old/examplecar/OVIstructure_input_temp.txt b/sourcecodes/data/old/examplecar/OVIstructure_input_temp.txt new file mode 100644 index 00000000..09d40aa3 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIstructure_input_temp.txt @@ -0,0 +1,20 @@ +Starts Dist SpkQual MFuse Alter Starter StMotor BatAge Charging PlugVolt BatVolt Timing Cranks Plugs AirFilter Air Fuel GasTank GasFilter +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 +1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +1.000000 1.000000 1.000000 0.000000 0.000000 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 +0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 1.000000 0.000000 1.000000 1.000000 1.000000 0.000000 1.000000 1.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000 1.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 diff --git a/sourcecodes/data/old/examplecar/OVIstructure_old.txt b/sourcecodes/data/old/examplecar/OVIstructure_old.txt new file mode 100644 index 00000000..22f14ba3 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIstructure_old.txt @@ -0,0 +1,19 @@ +Dist 0.9910 0.0090 +BatAge 0.0437 0.0524 0.0623 0.0736 0.0862 0.1001 0.1155 0.1321 0.1499 0.1689 0.1888 0.2093 0.2303 0.2515 0.2725 0.2929 0.3124 0.3306 0.3471 0.3617 0.3740 0.3836 0.3905 0.3944 0.3952 0.3930 0.3877 0.3795 0.3687 0.3553 0.3398 0.3225 0.3036 0.2837 0.2629 0.2419 0.2207 0.1999 0.1796 0.1601 0.1417 0.1243 0.1083 0.0936 0.0803 0.0683 0.0576 0.0483 0.0401 0.0331 0.0271 0.0220 0.0177 0.0142 0.0112 0.0088 0.0069 0.0054 0.0041 0.0031 0.0024 0.0018 0.0013 0.0010 0.0007 0.0005 0.0004 0.0003 0.0002 0.0001 0.0001 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 +Timing 0.9053 0.0947 +Plugs 0.6922 0.0970 0.2109 +AirFilter 0.9040 0.0960 +GasTank 0.9031 0.0969 +GasFilter 0.9725 0.0275 +SpkQual 0.2568 0.7432 +Starts 0.2497 0.7503 +MFuse 0.9990 0.0010 +Starter 0.5977 0.4023 +Charging 0.5024 0.4976 +Cranks 0.4848 0.5152 +Air 0.8301 0.1699 +Fuel 0.7756 0.2244 +Alter 0.9970 0.0030 +StMotor 0.9955 0.0045 +PlugVolt 0.3706 0.1678 0.4616 +BatVolt 0.4184 0.1639 0.4177 diff --git a/sourcecodes/data/old/examplecar/OVIthr.txt b/sourcecodes/data/old/examplecar/OVIthr.txt new file mode 100644 index 00000000..2eb3c4fe --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIthr.txt @@ -0,0 +1 @@ +0.5 diff --git a/sourcecodes/data/old/examplecar/OVItype.txt b/sourcecodes/data/old/examplecar/OVItype.txt new file mode 100644 index 00000000..0b76081c --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVItype.txt @@ -0,0 +1,2 @@ +Starts Dist SpkQual MFuse Alter Starter StMotor BatAge Charging PlugVolt BatVolt Timing Cranks Plugs AirFilter Air Fuel GasTank GasFilter +2 2 2 2 2 2 2 1 2 3 3 2 2 3 2 2 2 2 2 diff --git a/sourcecodes/data/old/examplecar/OVIvar.txt b/sourcecodes/data/old/examplecar/OVIvar.txt new file mode 100644 index 00000000..dec2bf5d --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIvar.txt @@ -0,0 +1 @@ +19 \ No newline at end of file diff --git a/sourcecodes/data/old/examplecar/OVIvardata.txt b/sourcecodes/data/old/examplecar/OVIvardata.txt new file mode 100644 index 00000000..e595bf94 --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIvardata.txt @@ -0,0 +1 @@ +-1.3159 \ No newline at end of file diff --git a/sourcecodes/data/old/examplecar/OVIvarname.txt b/sourcecodes/data/old/examplecar/OVIvarname.txt new file mode 100644 index 00000000..2453221d --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIvarname.txt @@ -0,0 +1 @@ +BatAge \ No newline at end of file diff --git a/sourcecodes/data/old/examplecar/OVIwhite.txt b/sourcecodes/data/old/examplecar/OVIwhite.txt new file mode 100644 index 00000000..83e81b8b --- /dev/null +++ b/sourcecodes/data/old/examplecar/OVIwhite.txt @@ -0,0 +1 @@ +From To diff --git a/sourcecodes/data/old/examplecar/old/OVIrun_evidencemodified.sh b/sourcecodes/data/old/examplecar/old/OVIrun_evidencemodified.sh new file mode 100644 index 00000000..dab5c39a --- /dev/null +++ b/sourcecodes/data/old/examplecar/old/OVIrun_evidencemodified.sh @@ -0,0 +1,38 @@ +#!/bin/sh +# script for execution of deployed applications +# +# Sets up the MCR environment for the current $ARCH and executes +# the specified command. +# +exe_name=$0 +exe_dir=`dirname "$0"` +echo "------------------------------------------" +if [ "x$1" = "x" ]; then + echo Usage: + echo $0 \ args +else + echo Setting up environment variables + MCRROOT="$1" + echo --- + LD_LIBRARY_PATH=.:${MCRROOT}/runtime/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/bin/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/sys/os/glnxa64; + MCRJRE=${MCRROOT}/sys/java/jre/glnxa64/jre/lib/amd64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/native_threads ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/server ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/client ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE} ; + XAPPLRESDIR=${MCRROOT}/X11/app-defaults ; + export LD_LIBRARY_PATH; + export XAPPLRESDIR; + echo LD_LIBRARY_PATH is ${LD_LIBRARY_PATH}; + shift 1 + args= + while [ $# -gt 0 ]; do + token=`echo "$1" | sed 's/ /\\\\ /g'` # Add blackslash before each blank + args="${args} ${token}" + shift + done + "${exe_dir}"/evidencemodified OVI +fi +exit diff --git a/sourcecodes/data/old/examplecar/old/OVIrun_initialstructure.sh b/sourcecodes/data/old/examplecar/old/OVIrun_initialstructure.sh new file mode 100644 index 00000000..d47ae324 --- /dev/null +++ b/sourcecodes/data/old/examplecar/old/OVIrun_initialstructure.sh @@ -0,0 +1,38 @@ +#!/bin/sh +# script for execution of deployed applications +# +# Sets up the MCR environment for the current $ARCH and executes +# the specified command. +# +exe_name=$0 +exe_dir=`dirname "$0"` +echo "------------------------------------------" +if [ "x$1" = "x" ]; then + echo Usage: + echo $0 \ args +else + echo Setting up environment variables + MCRROOT="$1" + echo --- + LD_LIBRARY_PATH=.:${MCRROOT}/runtime/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/bin/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/sys/os/glnxa64; + MCRJRE=${MCRROOT}/sys/java/jre/glnxa64/jre/lib/amd64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/native_threads ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/server ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/client ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE} ; + XAPPLRESDIR=${MCRROOT}/X11/app-defaults ; + export LD_LIBRARY_PATH; + export XAPPLRESDIR; + echo LD_LIBRARY_PATH is ${LD_LIBRARY_PATH}; + shift 1 + args= + while [ $# -gt 0 ]; do + token=`echo "$1" | sed 's/ /\\\\ /g'` # Add blackslash before each blank + args="${args} ${token}" + shift + done + "${exe_dir}"/initialstructure OVI +fi +exit diff --git a/sourcecodes/data/old/examplezoo/fSfban.txt b/sourcecodes/data/old/examplezoo/fSfban.txt new file mode 100644 index 00000000..9197eeec --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfban.txt @@ -0,0 +1,261 @@ +From To +aquatic airborne +aquatic venomous +aquatic predator +aquatic domestic +airborne aquatic +airborne venomous +airborne predator +airborne domestic +venomous aquatic +venomous airborne +venomous predator +venomous domestic +predator aquatic +predator airborne +predator venomous +predator domestic +domestic aquatic +domestic airborne +domestic venomous +domestic predator +eggs aquatic +eggs airborne +eggs venomous +eggs predator +eggs domestic +milk aquatic +milk airborne +milk venomous +milk predator +milk domestic +backbone aquatic +backbone airborne +backbone venomous +backbone predator +backbone domestic +breathes aquatic +breathes airborne +breathes venomous +breathes predator +breathes domestic +catsize aquatic +catsize airborne +catsize venomous +catsize predator +catsize domestic +tail aquatic +tail airborne +tail venomous +tail predator +tail domestic +toothed aquatic +toothed airborne +toothed venomous +toothed predator +toothed domestic +hair aquatic +hair airborne +hair venomous +hair predator +hair domestic +feathers aquatic +feathers airborne +feathers venomous +feathers predator +feathers domestic +fins aquatic +fins airborne +fins venomous +fins predator +fins domestic +legs aquatic +legs airborne +legs venomous +legs predator +legs domestic +type aquatic +type airborne +type venomous +type predator +type domestic +eggs aquatic +eggs airborne +eggs venomous +eggs predator +eggs domestic +milk aquatic +milk airborne +milk venomous +milk predator +milk domestic +backbone aquatic +backbone airborne +backbone venomous +backbone predator +backbone domestic +breathes aquatic +breathes airborne +breathes venomous +breathes predator +breathes domestic +catsize eggs +catsize milk +catsize backbone +catsize breathes +tail eggs +tail milk +tail backbone +tail breathes +toothed eggs +toothed milk +toothed backbone +toothed breathes +hair eggs +hair milk +hair backbone +hair breathes +feathers eggs +feathers milk +feathers backbone +feathers breathes +fins eggs +fins milk +fins backbone +fins breathes +legs eggs +legs milk +legs backbone +legs breathes +type eggs +type milk +type backbone +type breathes +catsize tail +catsize toothed +catsize hair +catsize feathers +catsize fins +catsize legs +tail catsize +tail toothed +tail hair +tail feathers +tail fins +tail legs +toothed catsize +toothed tail +toothed hair +toothed feathers +toothed fins +toothed legs +hair catsize +hair tail +hair toothed +hair feathers +hair fins +hair legs +feathers catsize +feathers tail +feathers toothed +feathers hair +feathers fins +feathers legs +fins catsize +fins tail +fins toothed +fins hair +fins feathers +fins legs +legs catsize +legs tail +legs toothed +legs hair +legs feathers +legs fins +catsize aquatic +catsize airborne +catsize venomous +catsize predator +catsize domestic +tail aquatic +tail airborne +tail venomous +tail predator +tail domestic +toothed aquatic +toothed airborne +toothed venomous +toothed predator +toothed domestic +hair aquatic +hair airborne +hair venomous +hair predator +hair domestic +feathers aquatic +feathers airborne +feathers venomous +feathers predator +feathers domestic +fins aquatic +fins airborne +fins venomous +fins predator +fins domestic +legs aquatic +legs airborne +legs venomous +legs predator +legs domestic +catsize eggs +catsize milk +catsize backbone +catsize breathes +tail eggs +tail milk +tail backbone +tail breathes +toothed eggs +toothed milk +toothed backbone +toothed breathes +hair eggs +hair milk +hair backbone +hair breathes +feathers eggs +feathers milk +feathers backbone +feathers breathes +fins eggs +fins milk +fins backbone +fins breathes +legs eggs +legs milk +legs backbone +legs breathes +type catsize +type tail +type toothed +type hair +type feathers +type fins +type legs +type aquatic +type airborne +type venomous +type predator +type domestic +type eggs +type milk +type backbone +type breathes +type catsize +type tail +type toothed +type hair +type feathers +type fins +type legs diff --git a/sourcecodes/data/old/examplezoo/fSfcontinuous_input.txt b/sourcecodes/data/old/examplezoo/fSfcontinuous_input.txt new file mode 100644 index 00000000..340ad4f6 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfcontinuous_input.txt @@ -0,0 +1,104 @@ +hair feathers eggs milk airborne aquatic predator toothed backbone breathes venomous fins legs tail domestic catsize type +2 2 2 2 2 2 2 2 2 2 2 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2 1 1 2 2 2 1 1 2 2 1 1 1 +2 1 1 2 1 1 2 2 2 2 1 1 2 1 2 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 2 1 1 2 1 +1 2 2 1 2 2 2 1 2 2 1 1 2 2 1 1 2 +1 2 2 1 2 1 2 1 2 2 1 1 2 2 1 1 2 +1 2 2 1 1 1 2 1 2 2 1 1 2 2 1 1 2 +1 2 2 1 2 1 1 1 2 2 1 1 2 2 1 1 2 +1 2 2 1 1 1 1 1 2 2 1 1 2 2 1 2 2 +1 2 2 1 2 1 1 1 2 2 1 1 2 2 2 1 2 +1 2 2 1 1 2 2 1 2 2 1 1 2 2 1 2 2 +1 2 2 1 2 1 1 1 2 2 1 1 2 2 1 1 2 +1 2 2 1 1 1 2 1 2 2 1 1 2 2 1 2 2 +2 1 1 2 1 2 2 2 2 2 1 2 2 2 1 2 1 +1 2 2 1 2 2 2 1 2 2 1 1 2 2 1 1 2 +1 2 2 1 2 2 2 1 2 2 1 1 2 2 1 1 2 +1 2 2 1 2 1 1 1 2 2 1 1 2 2 1 1 2 +2 1 1 2 1 1 1 2 2 2 1 1 2 2 1 1 1 +1 2 2 1 2 2 1 1 2 2 1 1 2 2 1 2 2 +2 1 1 2 2 1 1 2 2 2 1 1 2 2 1 1 1 +1 2 2 1 2 1 2 1 2 2 1 1 2 2 1 2 2 +2 1 1 2 1 1 1 2 2 2 1 1 2 2 1 2 1 +1 2 2 1 2 1 1 1 2 2 1 1 2 2 1 1 2 +2 1 1 2 1 1 2 2 2 2 1 1 3 1 1 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 1 1 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 2 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 1 2 1 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +1 1 2 1 1 2 2 1 1 1 1 1 3 1 1 1 7 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 1 2 1 +1 1 2 1 1 2 2 2 2 2 1 1 3 1 1 1 5 +1 1 2 1 1 2 2 2 2 2 2 1 3 1 1 1 5 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 2 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 2 1 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 1 1 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 2 2 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 1 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +1 1 2 1 1 2 2 2 2 2 1 1 3 2 1 1 5 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 1 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 1 2 1 +2 1 2 2 1 2 2 1 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 2 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 2 2 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 2 2 1 +1 1 2 1 1 2 1 2 2 2 1 1 3 1 1 1 5 +1 1 2 1 1 1 1 1 2 2 1 1 3 2 1 2 3 +1 1 2 1 1 1 2 2 2 2 1 1 3 2 1 1 3 +2 1 1 2 1 1 1 2 2 2 1 1 3 2 1 1 1 +2 1 1 2 1 1 2 2 2 2 1 1 3 2 1 2 1 +1 1 2 1 1 2 2 1 1 1 1 1 4 1 1 1 7 +1 1 2 1 1 2 2 1 1 1 1 1 5 1 1 1 7 +1 1 2 1 1 1 1 1 1 2 1 1 5 1 1 1 6 +1 1 2 1 2 1 1 1 1 2 1 1 5 1 1 1 6 +2 1 2 1 2 1 1 1 1 2 2 1 5 1 2 1 6 +2 1 2 1 2 1 1 1 1 2 1 1 5 1 1 1 6 +1 1 2 1 2 1 2 1 1 2 1 1 5 1 1 1 6 +1 1 2 1 1 2 2 1 1 1 1 1 5 1 1 1 7 +2 1 2 1 2 1 1 1 1 2 1 1 5 1 1 1 6 +1 1 2 1 1 1 1 1 1 2 1 1 5 1 1 1 6 +2 1 2 1 2 1 1 1 1 2 2 1 5 1 1 1 6 +1 1 2 1 1 2 2 1 1 1 1 1 6 1 1 2 7 +1 1 1 1 1 1 2 1 1 2 2 1 6 2 1 1 7 + diff --git a/sourcecodes/data/old/examplezoo/fSfgraphviz.txt b/sourcecodes/data/old/examplezoo/fSfgraphviz.txt new file mode 100644 index 00000000..701801c6 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfgraphviz.txt @@ -0,0 +1,61 @@ +digraph G { +size="10,10"; ratio = fill; +node [shape=square,width=1.5]; +feathers -> type; +eggs -> hair; +eggs -> feathers; +eggs -> toothed; +eggs -> backbone; +eggs -> breathes; +eggs -> legs; +eggs -> tail; +eggs -> catsize; +milk -> hair; +milk -> feathers; +milk -> toothed; +milk -> backbone; +milk -> breathes; +milk -> legs; +milk -> tail; +milk -> catsize; +milk -> type; +airborne -> eggs; +airborne -> milk; +airborne -> backbone; +airborne -> breathes; +airborne -> catsize; +aquatic -> eggs; +aquatic -> milk; +aquatic -> breathes; +aquatic -> fins; +backbone -> hair; +backbone -> feathers; +backbone -> toothed; +backbone -> fins; +backbone -> legs; +backbone -> tail; +backbone -> type; +breathes -> hair; +breathes -> toothed; +breathes -> backbone; +breathes -> fins; +breathes -> legs; +breathes -> catsize; +breathes -> type; +venomous -> hair; +venomous -> feathers; +venomous -> eggs; +venomous -> milk; +venomous -> toothed; +venomous -> backbone; +venomous -> breathes; +venomous -> fins; +venomous -> legs; +venomous -> tail; +venomous -> catsize; +legs -> type; +domestic -> eggs; +domestic -> milk; +domestic -> fins; +domestic -> tail; +} \ No newline at end of file diff --git a/sourcecodes/data/old/examplezoo/fSfk.txt b/sourcecodes/data/old/examplezoo/fSfk.txt new file mode 100644 index 00000000..f599e28b --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfk.txt @@ -0,0 +1 @@ +10 diff --git a/sourcecodes/data/old/examplezoo/fSfmap.txt b/sourcecodes/data/old/examplezoo/fSfmap.txt new file mode 100644 index 00000000..4a123441 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfmap.txt @@ -0,0 +1,17 @@ +hair 2 0.496921 1.425743 +feathers 2 0.400495 1.198020 +eggs 2 0.495325 1.584158 +milk 2 0.493522 1.405941 +airborne 2 0.427750 1.237624 +aquatic 2 0.481335 1.356436 +predator 2 0.499505 1.554455 +toothed 2 0.491512 1.603960 +backbone 2 0.384605 1.821782 +breathes 2 0.407844 1.792079 +venomous 2 0.271410 1.079208 +fins 6 0.376013 1.168317 +legs 2 1.253194 2.544554 +tail 2 0.439397 1.742574 +domestic 2 0.336552 1.128713 +catsize 7 0.498314 1.435644 +type 2 2.102709 2.831683 diff --git a/sourcecodes/data/old/examplezoo/fSfmapdata.txt b/sourcecodes/data/old/examplezoo/fSfmapdata.txt new file mode 100644 index 00000000..19d91997 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfmapdata.txt @@ -0,0 +1 @@ +domestic venomous predator aquatic airborne milk eggs breathes catsize backbone tail legs fins toothed feathers type hair diff --git a/sourcecodes/data/old/examplezoo/fSfname.txt b/sourcecodes/data/old/examplezoo/fSfname.txt new file mode 100644 index 00000000..c68267c4 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfname.txt @@ -0,0 +1 @@ +hair feathers eggs milk airborne aquatic predator toothed backbone breathes venomous fins legs tail domestic catsize type diff --git a/sourcecodes/data/old/examplezoo/fSfnet_figure.txt b/sourcecodes/data/old/examplezoo/fSfnet_figure.txt new file mode 100644 index 00000000..0c8f9428 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfnet_figure.txt @@ -0,0 +1,130 @@ +17 +1800 1200 +domestic 43 0 +venomous 343 0 +predator 643 0 +aquatic 943 0 +airborne 1243 0 +milk 523 171 +eggs 1123 171 +breathes 643 343 +catsize 523 514 +backbone 1123 514 +tail 0 686 +legs 257 686 +fins 514 686 +toothed 771 686 +feathers 1029 686 +type 823 857 +hair 1286 686 +domestic 2 +250 150 +0 +4 6 7 11 13 +1 0.8672 +2 0.1328 +venomous 2 +250 150 +0 +11 6 7 8 9 10 11 12 13 14 15 17 +1 0.8860 +2 0.1140 +predator 2 +250 150 +0 +0 +1 0.4455 +2 0.5545 +aquatic 2 +250 150 +0 +4 6 7 8 13 +1 0.6726 +2 0.3274 +airborne 2 +250 150 +0 +5 6 7 8 9 10 +1 0.8149 +2 0.1851 +milk 2 +250 150 +4 1 2 4 5 +9 8 9 10 11 12 14 15 16 17 +1 0.4703 +2 0.5297 +eggs 2 +250 150 +4 1 2 4 5 +8 8 9 10 11 12 14 15 17 +1 0.4962 +2 0.5038 +breathes 2 +250 150 +5 2 4 5 6 7 +7 9 10 12 13 14 16 17 +1 0.1666 +2 0.8334 +catsize 2 +250 150 +5 2 5 6 7 8 +0 +1 0.4789 +2 0.5211 +backbone 2 +250 150 +5 2 5 6 7 8 +7 11 12 13 14 15 16 17 +1 0.1743 +2 0.8257 +tail 2 +250 150 +5 1 2 6 7 10 +0 +1 0.2426 +2 0.7574 +legs 6 +250 150 +5 2 6 7 8 10 +1 16 +1 0.1827 +2 0.2108 +3 0.4747 +4 0.0111 +5 0.0799 +6 0.0409 +fins 2 +250 150 +5 1 2 4 8 10 +0 +1 0.8751 +2 0.1249 +toothed 2 +250 150 +5 2 6 7 8 10 +0 +1 0.3799 +2 0.6201 +feathers 2 +250 150 +4 2 6 7 10 +1 16 +1 0.8693 +2 0.1307 +type 7 +250 150 +5 6 8 10 12 15 +0 +1 0.5297 +2 0.1307 +3 0.0476 +4 0.0854 +5 0.0323 +6 0.0578 +7 0.1165 +hair 2 +250 150 +5 2 6 7 8 10 +0 +1 0.4597 +2 0.5403 diff --git a/sourcecodes/data/old/examplezoo/fSfnnode.txt b/sourcecodes/data/old/examplezoo/fSfnnode.txt new file mode 100644 index 00000000..98d9bcb7 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfnnode.txt @@ -0,0 +1 @@ +17 diff --git a/sourcecodes/data/old/examplezoo/fSfnrows.txt b/sourcecodes/data/old/examplezoo/fSfnrows.txt new file mode 100644 index 00000000..257e5632 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfnrows.txt @@ -0,0 +1 @@ +102 diff --git a/sourcecodes/data/old/examplezoo/fSfparent.txt b/sourcecodes/data/old/examplezoo/fSfparent.txt new file mode 100644 index 00000000..7ed6ff82 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfparent.txt @@ -0,0 +1 @@ +5 diff --git a/sourcecodes/data/old/examplezoo/fSfstructure_input.txt b/sourcecodes/data/old/examplezoo/fSfstructure_input.txt new file mode 100644 index 00000000..c49d600b --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfstructure_input.txt @@ -0,0 +1,18 @@ +hair feathers eggs milk airborne aquatic predator toothed backbone breathes venomous fins legs tail domestic catsize type +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 +1 1 0 0 0 0 0 1 1 1 0 0 1 1 0 1 0 +1 1 0 0 0 0 0 1 1 1 0 0 1 1 0 1 1 +0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 1 0 +0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +1 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 1 +1 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 1 +1 1 1 1 0 0 0 1 1 1 0 1 1 1 0 1 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 +0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 diff --git a/sourcecodes/data/old/examplezoo/fSfstructure_input_temp.txt b/sourcecodes/data/old/examplezoo/fSfstructure_input_temp.txt new file mode 100644 index 00000000..8910aaee --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfstructure_input_temp.txt @@ -0,0 +1,18 @@ +hair feathers eggs milk airborne aquatic predator toothed backbone breathes venomous fins legs tail domestic catsize type +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 +1.000000 0.800011 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 0.000000 1.000000 0.000000 +1.000000 1.000000 0.500003 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 0.000000 1.000000 1.000000 +0.000000 0.400004 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 +0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 0.000000 0.000000 1.000000 +0.800018 0.799985 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 0.000000 0.000000 1.000000 1.000000 +1.000000 1.000000 1.000000 1.000000 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 0.000000 1.000000 1.000000 1.000000 0.000000 1.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.199982 0.000000 0.900015 0.900010 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 diff --git a/sourcecodes/data/old/examplezoo/fSfstructure_old.txt b/sourcecodes/data/old/examplezoo/fSfstructure_old.txt new file mode 100644 index 00000000..bf00f5d5 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfstructure_old.txt @@ -0,0 +1,16 @@ +feathers 0.8693 0.1307 +type 0.5297 0.1307 0.0476 0.0854 0.0323 0.0578 0.1165 +eggs 0.4962 0.5038 +hair 0.4597 0.5403 +toothed 0.3799 0.6201 +backbone 0.1743 0.8257 +breathes 0.1666 0.8334 +legs 0.1827 0.2108 0.4747 0.0111 0.0799 0.0409 +tail 0.2426 0.7574 +catsize 0.4789 0.5211 +milk 0.4703 0.5297 +airborne 0.8149 0.1851 +aquatic 0.6726 0.3274 +fins 0.8751 0.1249 +venomous 0.8860 0.1140 +domestic 0.8672 0.1328 diff --git a/sourcecodes/data/old/examplezoo/fSfthr.txt b/sourcecodes/data/old/examplezoo/fSfthr.txt new file mode 100644 index 00000000..aec258df --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfthr.txt @@ -0,0 +1 @@ +0.8 diff --git a/sourcecodes/data/old/examplezoo/fSftier.txt b/sourcecodes/data/old/examplezoo/fSftier.txt new file mode 100644 index 00000000..ca417b31 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSftier.txt @@ -0,0 +1 @@ +4,Tier1,5,aquatic,airborne,venomous,predator,domestic,Tier2,4,eggs,milk,backbone,breathes,Tier3,7,catsize,tail,toothed,hair,feathers,fins,legs,Tier4,1,type, \ No newline at end of file diff --git a/sourcecodes/data/old/examplezoo/fSftype.txt b/sourcecodes/data/old/examplezoo/fSftype.txt new file mode 100644 index 00000000..41c0f73e --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSftype.txt @@ -0,0 +1,2 @@ +hair feathers eggs milk airborne aquatic predator toothed backbone breathes venomous fins legs tail domestic catsize type +2 2 2 2 2 2 2 2 2 2 2 2 6 2 2 2 7 diff --git a/sourcecodes/data/old/examplezoo/fSfwhite.txt b/sourcecodes/data/old/examplezoo/fSfwhite.txt new file mode 100644 index 00000000..83e81b8b --- /dev/null +++ b/sourcecodes/data/old/examplezoo/fSfwhite.txt @@ -0,0 +1 @@ +From To diff --git a/sourcecodes/data/old/examplezoo/old/fSfrun_initialstructure.sh b/sourcecodes/data/old/examplezoo/old/fSfrun_initialstructure.sh new file mode 100644 index 00000000..f20a9a29 --- /dev/null +++ b/sourcecodes/data/old/examplezoo/old/fSfrun_initialstructure.sh @@ -0,0 +1,38 @@ +#!/bin/sh +# script for execution of deployed applications +# +# Sets up the MCR environment for the current $ARCH and executes +# the specified command. +# +exe_name=$0 +exe_dir=`dirname "$0"` +echo "------------------------------------------" +if [ "x$1" = "x" ]; then + echo Usage: + echo $0 \ args +else + echo Setting up environment variables + MCRROOT="$1" + echo --- + LD_LIBRARY_PATH=.:${MCRROOT}/runtime/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/bin/glnxa64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRROOT}/sys/os/glnxa64; + MCRJRE=${MCRROOT}/sys/java/jre/glnxa64/jre/lib/amd64 ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/native_threads ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/server ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE}/client ; + LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:${MCRJRE} ; + XAPPLRESDIR=${MCRROOT}/X11/app-defaults ; + export LD_LIBRARY_PATH; + export XAPPLRESDIR; + echo LD_LIBRARY_PATH is ${LD_LIBRARY_PATH}; + shift 1 + args= + while [ $# -gt 0 ]; do + token=`echo "$1" | sed 's/ /\\\\ /g'` # Add blackslash before each blank + args="${args} ${token}" + shift + done + "${exe_dir}"/initialstructure fSf +fi +exit diff --git a/sourcecodes/data/old/examplezoo/standardized_data.txt b/sourcecodes/data/old/examplezoo/standardized_data.txt new file mode 100644 index 00000000..0975061e --- /dev/null +++ b/sourcecodes/data/old/examplezoo/standardized_data.txt @@ -0,0 +1,102 @@ +domestic venomous predator aquatic airborne milk eggs breathes catsize backbone tail legs fins toothed feathers type hair +1 1 2 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +2 1 1 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +1 1 2 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +1 1 2 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 7 1 +1 1 2 2 1 1 2 1 2 2 2 1 2 2 1 4 1 +1 1 2 2 1 2 1 2 2 2 2 1 2 2 1 1 1 +1 1 1 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +1 1 2 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +1 1 2 2 1 1 2 1 2 2 2 1 2 2 1 4 1 +1 1 2 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +1 2 2 1 1 1 2 2 1 2 2 1 1 2 1 3 1 +1 1 2 2 1 2 1 2 2 2 2 1 2 2 1 1 1 +1 1 1 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +1 1 2 2 1 2 1 2 2 2 1 1 2 2 1 1 2 +1 2 2 2 1 1 1 1 1 2 2 1 1 2 1 3 1 +1 2 2 2 1 1 2 1 1 1 1 1 1 1 1 7 1 +1 1 2 1 1 1 2 2 1 2 2 1 1 2 1 3 1 +1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 7 1 +1 1 1 2 1 1 2 1 1 2 2 1 2 2 1 4 1 +1 2 2 2 1 1 2 1 2 2 2 1 2 2 1 4 1 +1 1 2 2 1 1 2 1 2 2 2 1 2 2 1 4 1 +1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 7 1 +2 1 1 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 2 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +2 1 1 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 1 2 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 1 1 2 1 2 2 2 2 2 2 1 1 2 2 1 +1 1 1 1 2 2 1 2 1 2 2 2 1 2 1 1 2 +2 1 2 1 1 2 1 2 2 2 1 2 1 2 1 1 2 +1 1 1 1 1 2 1 2 2 2 1 2 1 2 1 1 2 +1 1 2 2 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 2 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 2 1 1 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 1 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 1 1 1 1 2 2 2 2 2 2 1 1 2 2 1 +2 1 1 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 2 2 1 1 2 2 2 2 2 2 1 1 2 2 1 +1 1 1 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 2 1 1 1 2 2 2 2 2 2 1 1 2 2 1 +1 1 2 2 1 2 1 2 2 2 2 2 2 2 1 1 2 +1 1 2 2 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 2 2 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 1 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 1 1 1 2 1 2 1 2 2 2 1 2 1 1 2 +1 1 1 2 2 1 2 2 2 2 2 2 1 1 2 2 1 +1 1 1 1 2 2 1 2 1 2 2 2 1 2 1 1 2 +1 1 2 1 2 1 2 2 2 2 2 2 1 1 2 2 1 +1 1 1 1 1 2 1 2 2 2 2 2 1 2 1 1 2 +1 1 1 1 2 1 2 2 1 2 2 2 1 1 2 2 1 +1 1 2 1 1 2 1 2 2 2 1 3 1 2 1 1 2 +1 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 1 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +2 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +2 1 1 1 1 2 1 2 1 2 1 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 2 1 1 2 1 1 1 1 3 1 1 1 7 1 +1 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 2 1 1 2 2 1 2 1 3 1 2 1 5 1 +1 2 2 2 1 1 2 2 1 2 1 3 1 2 1 5 1 +1 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +2 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +2 1 1 1 1 2 1 2 1 2 2 3 1 2 1 1 2 +1 1 1 1 1 2 1 2 1 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 2 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 1 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 2 1 1 2 2 1 2 2 3 1 2 1 5 1 +1 1 2 1 1 2 1 2 1 2 2 3 1 2 1 1 2 +1 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 2 1 2 2 2 2 2 2 3 1 1 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +2 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +2 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +2 1 1 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 1 2 1 1 2 2 1 2 1 3 1 2 1 5 1 +1 1 1 1 1 1 2 2 2 2 2 3 1 1 1 3 1 +1 1 2 1 1 1 2 2 1 2 2 3 1 2 1 3 1 +1 1 1 1 1 2 1 2 1 2 2 3 1 2 1 1 2 +1 1 2 1 1 2 1 2 2 2 2 3 1 2 1 1 2 +1 1 2 2 1 1 2 1 1 1 1 4 1 1 1 7 1 +1 1 2 2 1 1 2 1 1 1 1 5 1 1 1 7 1 +1 1 1 1 1 1 2 2 1 1 1 5 1 1 1 6 1 +1 1 1 1 2 1 2 2 1 1 1 5 1 1 1 6 1 +2 2 1 1 2 1 2 2 1 1 1 5 1 1 1 6 2 +1 1 1 1 2 1 2 2 1 1 1 5 1 1 1 6 2 +1 1 2 1 2 1 2 2 1 1 1 5 1 1 1 6 1 +1 1 2 2 1 1 2 1 1 1 1 5 1 1 1 7 1 +1 1 1 1 2 1 2 2 1 1 1 5 1 1 1 6 2 +1 1 1 1 1 1 2 2 1 1 1 5 1 1 1 6 1 +1 2 1 1 2 1 2 2 1 1 1 5 1 1 1 6 2 +1 1 2 2 1 1 2 1 2 1 1 6 1 1 1 7 1 +1 2 2 1 1 1 1 2 1 1 2 6 1 1 1 7 1 diff --git a/sourcecodes/execute_bn_gom.php b/sourcecodes/execute_bn_gom.php index d9f2bc93..f91d4bd7 100644 --- a/sourcecodes/execute_bn_gom.php +++ b/sourcecodes/execute_bn_gom.php @@ -79,15 +79,6 @@ for($i=1;$i<=$n;$i++) fwrite($fout,"$endstring"); fclose($fout); -//$file1="./data/".$keyval."run_initialstructure.sh"; -//$initiallines=file_get_contents("./data/temp_shell_file_initial_structure"); -//$all_lines="$initiallines"."$keyval\nfi\nexit"; -//$fp = fopen($file1,"w"); -//fwrite($fp, "$all_lines\n"); -//fclose($fp); -//prepare and execute shell script for matlab with a write lock -//$cmd="./runmat.sh $keyval"; -//system($cmd); shell_exec('./run_octave '.$keyval); diff --git a/sourcecodes/faq.php b/sourcecodes/faq.php index 65926f33..e9f48b13 100644 --- a/sourcecodes/faq.php +++ b/sourcecodes/faq.php @@ -110,14 +110,14 @@ While it does not fully support dynamic Bayesian network modeling, BNW can be us
    -

    After structure learning, it is possible that all variables will not be connected within a single network. Some variables may not be found to be associated with any other variables in the input dataset and may be left out of any network model, or there may be two or more distinct networks. In these cases, the highest scoring network model did not have all variables in a single network given the data and our scoring metric. To our knowledge, parameter learning and predictions with these unconnected models should still work correctly, but this has not been tested extensively.
    +

    After structure learning, it is possible that all variables will not be connected within a single network. Some variables may not be found to be associated with any other variables in the input dataset and may be left out of any network model, or there may be two or more distinct networks. In these cases, the highest scoring network model did not have all variables in a single network given the data and our scoring metric.

    -

    7. Can I restore a previous session containing a model in BNW?

     +

    7. Can I restore a previous session containing a model in BNW?



    diff --git a/sourcecodes/help.php b/sourcecodes/help.php index a6f6d336..683eeebe 100644 --- a/sourcecodes/help.php +++ b/sourcecodes/help.php @@ -33,6 +33,7 @@ include("header_new.inc");
  • Details of structure learning methods
  • Formatting files for BNW
  • Downloadable structure learning package +
  • Recent updates to BNW @@ -128,33 +129,37 @@ Two prediction modes are available in BNW: evidence and intervention. In the evi

    Data file format

    • @@ -133,7 +133,7 @@ While it does not fully support dynamic Bayesian network modeling, BNW can be us
    -

    We are aware that the structural constraint interface does not display or operate correctly using Internet Explorer. This seems to be due to IE not supporting HTML5 drag-and-drop functions, and we have not noticed any problems with this interface using other web browsers. The majority of testing of BNW has been performed using recent versions of Google Chrome and Mozilla Firefox web browsers on computers with Windows operating systems. We have also done used several browers on Linux and Apple computers and have not noticed problems.
    +

    We are aware that the structural constraint interface does not display or operate correctly using some older versions of Internet Explorer that did not support HTML5 drag-and-drop functions. We have not noticed any problems with this interface using recent versions of Internet Explorer, Microsoft Edge, or other web browsers. The majority of testing of BNW has been performed using recent versions of Google Chrome and Mozilla Firefox web browsers on computers with Windows operating systems. We have also done used several browers on Linux and Apple computers and have not noticed problems.
    -

    Data files uploaded to the Bayesian Network Webserver should be tab-delimited text files with the names of the variables in the first row of the file and the values of the variables for each sample or individual in the remaining rows.

    BNW automatically determines whether variables contain continuous or discrete data. To help ensure that BNW correctly parses data files, users should follow these formatting guidelines:
    - 1. Variable names should start with a letter, not a number, and should not contain any whitespace characters.
    - 2. Discrete variables should be listed before continuous variables, that is, discrete variables should be the leftmost columns of the file.
    -3. The values of the levels of discrete variables should be integers starting with 1.
    - 4. The data values for each continuous variables should include a period (.) followed by a number in at least one of the samples.

    - An example input data file for a file with 5 variables is given below. The network contains 2 discrete (Disc1 and Disc2) variables, which are given in the first two columns of the file, and 3 continuous variables (Cont1, Cont2, and Cont3). Disc1 is a discrete variable with two states (1 and 2), while Disc2 has three states (1, 2, and 3). Although the samples of Cont2 are integral values, we wish to deal with this variable as continuous, not discrete. Therefore, the value of Cont2 for the first sample is given as '3.0' instead of '3' so that one of the values of Cont2 contains a '.' followed by a number and Cont2 is interpreted as a continuous variable. +

    Data files uploaded to the Bayesian Network Webserver should be tab-delimited text files with the names of the variables in the first row of the file and the values of the variables for each sample or individual in the remaining rows.

    Variable names should not contain any whitespace characters.

    BNW automatically determines whether each variable contains continuous or discrete data. BNW applies the following rules, in order, to determine if variables should be considered discrete or continuous:

    + 1. If a variable contains 3 or fewer different values, the variable is considered to be discrete.

    + 2. If a variable contains more than 20 different values, the variable is considered to be continuous.

    + 3. If the ratio of the number of different values for a variable compared to the number of cases in the data set is large, the variable is considered to be continuous. Specifically, if this ratio is 1/3 or larger, the variable is considered to be continuous.

    + 4. If none of the first three rules apply, the data set is inspected to determine if any of the values for the variable contain a period (.). If at least one value contains a period, the variable is considered to be continous; otherwise, the variable is considered to be discrete.

    + Users can examine whether or not BNW has correctly loaded input data files and classified variables by clicking on "View uploaded variables and data" on the left-hand menu after uploading a dataset. We believe that BNW should correctly classify variables in most cases, but users may occasionally need to add or remove a period to the data of some variables.

    + An example input data file for a file with 5 variables is given below. The network contains 2 discrete (Disc1 and Disc2) variables, which are given in the first two columns of the file, and 3 continuous variables (Cont1, Cont2, and Cont3). Disc1 is a discrete variable with two states (1 and 2), while Disc2 has two states (A and B). Although the samples of Cont2 are integral values, we wish to deal with this variable as continuous, not discrete. Therefore, the value of Cont2 for the first sample is given as '3.0' instead of '3' so that one of the values of Cont2 contains a '.', helping to ensure that Cont2 is interpreted as a continuous variable.


    - + - + - + - + - + - + + + +
    Disc1 Disc2 Cont1 Cont2 Cont3
    2 1 3.25 3.0 0.972 A 3.25 3.0 0.97
    2 3 2.46 2 0.932 B 2.46 2 0.93
    1 2 4.21 33 0.431 A 4.21 33 0.43
    2 3 3.76 8 0.882 B 3.76 8 0.88
    2 1 3.69 4 0.912 A 3.69 4 0.91
    1 1 4.27 13 0.381 B 4.27 13 0.38
    1 A 4.12 9 0.45

    @@ -194,6 +199,25 @@ Two prediction modes are available in BNW: evidence and intervention. In the evi
    +
    +
    + + +

    8. Recent updates to BNW

     + +
    +
    +

    BNW has recently been updated to add features and improve the user experience. These updates have included improving the network model visualizations and allowing users to more quickly load large data sets. Additionally, in a change that is invisible to users, BNW now uses Octave to perform parameter learning with the Bayes Net Toolbox.

    +Major changes and new features that have been added to BNW include:
    +
    1) Increased flexibility in formatting of uploaded data files. One major change is that BNW now allows for users to upload data files in which discrete variables have alphabetic values. For example, a file containing a node for the genotype of BXD mice can now have values of B and D; the values do not have to be recoded as integers. A full description of the proper format for input files in BNW considering these changes can be found here.
    +
    2) Added "View uploaded variables and data" button to allow users to ensure that uploaded data sets are loaded and parsed correctly. After users upload a data set, clicking this button provides the ability to view either the uploaded data file directly or view a variable description file that shows how BNW has parsed the data. The variable description file lists the number of variables and cases (e.g., individuals or samples) in the data file. It also indicates whether each variable is discrete or continuous and the criteria that was used to make this determination. For discrete variables, the possible states of the variable are provided. For continuous variables, the mean and standard deviation of the variable is provided.
    +
    3) Added "View parameters" button to allow users to quantify network parameters. Users can now view the parameters of the network models considering the original data set that was uploaded by the user or the predicted parameters considering the evidence or intervention that has been entered by the user. This feature allows users to quantify predictions using the Bayesian network model.
    +
    After performing structure learning and viewing their network model, users now have the ability to click a "View parameters" button on menu to the left of the model structure. This button provides a link to a file containing the original parameters of the model. For discrete nodes, the fraction of cases for each possible state in the variable is provided. For continuous nodes, the mean and standard deviation of the Gaussian distribution that best fits the data in the variable are provided.

    +If users have made predictions using either the evidence or intervention modes, a link to a file containing the parameters of the network considering the entered evidence or intervention is provided. The file lists the predicted fraction of states for discrete nodes and predicted mean and standard deviation of the Gaussian distribution for continuous nodes. Nodes for which evidence or intervention has been entered are also noted in the file. +
    +

    + +


    diff --git a/sourcecodes/home.php b/sourcecodes/home.php index 571124c8..06d8337a 100644 --- a/sourcecodes/home.php +++ b/sourcecodes/home.php @@ -23,10 +23,11 @@ include("header_new.inc");

    BNW: from data to predictions



    +
    +

    BNW has recently been updated. Read more about these updates here. +

    The Bayesian Network Web Server (BNW) is a comprehensive web server for Bayesian network modeling of biological data sets. It is designed so that users can quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data, and use the model to understand and make predictions about relationships between the variables in the model. Many real world data sets, including those used to create genetic network models, contain both discrete (e.g., genotypes) and continuous (e.g., gene expression traits) variables, and BNW allows for modeling of these hybrid data sets. -

    -*A note about browser usage: The BNW interface for adding structural constraints uses HTML5 features that are may not be supported by Internet Explorer. We have not observed any problems with the interface using recent versions of any other web browser, and, therefore, we suggest against using Internet Explorer when accessing BNW.

    @@ -35,6 +36,9 @@ How to cite the BNW:
    1. Ziebarth JD, Bhattacharya A, Cui Y (2013) Bayesian Network Webserver: a comprehensive tool for biological network modeling. Bioinformatics. 29(21): 2801-2803.
    +
    + 2. Ziebarth JD, Cui Y (2017) Precise network modeling of system genetics data using the Bayesian Network Webserver. In: Schughart K, Williams R (eds) System Genetics. Methods in Molecular Biology, vol 1488. Humana Press, New York, NY. +

    Developed and maintained by: Yan Cui's Lab at University of Tennessee Health Science Center
    diff --git a/sourcecodes/k-best/index.html b/sourcecodes/k-best/index.html index 43d547ad..59912a77 100644 --- a/sourcecodes/k-best/index.html +++ b/sourcecodes/k-best/index.html @@ -1,6 +1,6 @@ Bayesian Network Web Server - + diff --git a/sourcecodes/network_layout_evd.php b/sourcecodes/network_layout_evd.php index 09554624..27e4fb06 100644 --- a/sourcecodes/network_layout_evd.php +++ b/sourcecodes/network_layout_evd.php @@ -317,7 +317,7 @@ for($i=0;$i<$nnode;$i++) var cnode=""; var keyv=""; - var chart = new google.visualization.BarChart(document.getElementById('')); + var chart = new google.visualization.ColumnChart(document.getElementById('')); function selectHandler() { var selectedItem = chart.getSelection()[0]; if (selectedItem) { @@ -326,17 +326,13 @@ for($i=0;$i<$nnode;$i++) var s = window.prompt('Selected evidence ' + topping + ' for ' + cnode + '. Enter new evidence ', topping ); window.location.href = "add_evd.php?name=" + cnode + "&evidence=" + s + "&My_key=" + keyv; - - - - //alert('The user selected ' + topping + topname); } } chart.draw(data, {title:"", titleTextStyle: {fontSize: }, width:, height:, - vAxis: {textStyle: {fontSize:}}, - hAxis: {minValue: 0, maxValue: 1, gridlines: {count: 3}}, legend: {position: 'none'}, + vAxis: {textStyle: {fontSize:9},minValue: 0, maxValue: 1}, + hAxis: {textStyle: {fontSize:11}}, legend: {position: 'none'}, backgroundColor: {stroke: 'black', strokeWidth: 5}} ); google.visualization.events.addListener(chart, 'select', selectHandler); @@ -356,7 +352,6 @@ for($i=0;$i<$nnode;$i++) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - // $val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); @@ -370,7 +365,6 @@ for($i=0;$i<$nnode;$i++) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - // $val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); @@ -402,7 +396,7 @@ for($i=0;$i<$nnode;$i++) title:"", titleTextStyle: {fontSize: }, legend: {position: 'none'}, width:, height:, - vAxis: {minValue: 0, maxValue: 1, viewWindow: {min:0}, gridlines: {count: 5}, textStyle: {fontSize: 9}}, + vAxis: {minValue: 0, maxValue: 0.5, viewWindow: {min:0}, gridlines: {count: 5}, textStyle: {fontSize: 9}}, hAxis: {gridlines: {count: 4}, textStyle: {fontSize: 9},viewWindowMode: 'maximized'}, chartArea:{left:30,top:25,right:8,bottom:25}, backgroundColor: {stroke: 'black', strokeWidth: 5}} diff --git a/sourcecodes/network_layout_evd_2.php b/sourcecodes/network_layout_evd_2.php index 0daf9df9..df400828 100644 --- a/sourcecodes/network_layout_evd_2.php +++ b/sourcecodes/network_layout_evd_2.php @@ -1,5 +1,5 @@ - var chart = new google.visualization.BarChart(document.getElementById('')); + var chart = new google.visualization.ColumnChart(document.getElementById('')); function selectHandler() { var selectedItem = chart.getSelection()[0]; if (selectedItem) { @@ -559,8 +557,8 @@ else chart.draw(data, {title:"", titleTextStyle: {fontSize: }, width:, height:, - vAxis: {textStyle: {fontSize:}}, - hAxis: {minValue: 0, maxValue: 1, gridlines: {count: 3}}, legend: {position: 'none'}, + vAxis: {textStyle: {fontSize:9},minValue:0,maxValue:1}, + hAxis: {textStyle: {fontSize:11}}, legend: {position: 'none'}, backgroundColor: {stroke: '', strokeWidth: 5}} ); google.visualization.events.addListener(chart, 'select', selectHandler); @@ -594,7 +592,6 @@ if($evd==-9999) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - // $val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -611,7 +608,6 @@ if($evd==-9999) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - // $val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); @@ -628,7 +624,6 @@ if($evd==-9999) } else { - // $evd=map($name,$evd,$keyval); $evduse=$evd; ?> var data = google.visualization.arrayToDataTable([ @@ -640,7 +635,6 @@ else for($j=0;$j<100;$j++) { $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - // $val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); @@ -663,7 +657,6 @@ else if($j==100) { $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - // $val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); $c_i_old++; @@ -712,7 +705,7 @@ else legend: {position: 'none'}, width:, height:, hAxis: {gridlines: {count: 4}, textStyle: {fontSize: 9},viewWindowMode: 'maximized'}, - vAxis: {maxValue: 1, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, + vAxis: {maxValue: 0.5, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, chartArea:{left:30,top:25,right:8,bottom:25}, backgroundColor: {stroke: '', strokeWidth: 5}} ); diff --git a/sourcecodes/network_layout_evd_2_example.php b/sourcecodes/network_layout_evd_2_example.php index 030d2c3a..d474b533 100644 --- a/sourcecodes/network_layout_evd_2_example.php +++ b/sourcecodes/network_layout_evd_2_example.php @@ -1,5 +1,5 @@ - var chart = new google.visualization.BarChart(document.getElementById('')); + var chart = new google.visualization.ColumnChart(document.getElementById('')); function selectHandler() { var selectedItem = chart.getSelection()[0]; if (selectedItem) { @@ -610,16 +608,13 @@ else window.location.href = "add_evd_example.php?name=" + cnode + "&evidence=" + s + "&My_key=" + keyv; - - - //alert('The user selected ' + topping + topname); } } chart.draw(data, {title:"", titleTextStyle: {fontSize: }, width:, height:, - vAxis: {textStyle: {fontSize:}}, - hAxis: {minValue: 0, maxValue: 1, gridlines: {count: 3}}, legend: {position: 'none'}, + vAxis: {textStyle: {fontSize:9},minValue:0,maxValue:1}, + hAxis: {textStyle: {fontSize:11}}, legend: {position: 'none'}, backgroundColor: {stroke: '', strokeWidth: 5}} ); google.visualization.events.addListener(chart, 'select', selectHandler); @@ -653,7 +648,6 @@ if($evd==-9999) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -670,8 +664,6 @@ if($evd==-9999) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); - $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -687,7 +679,6 @@ if($evd==-9999) } else { - //$evd=map($name,$evd,$keyval); $evduse=$evd; ?> var data = google.visualization.arrayToDataTable([ @@ -700,7 +691,6 @@ else for($j=0;$j<100;$j++) { $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); @@ -723,7 +713,6 @@ else if($j==100) { $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); $c_i_old++; @@ -772,7 +761,7 @@ else legend: {position: 'none'}, width:, height:, hAxis: {gridlines: {count: 4}, textStyle: {fontSize: 9}, viewWindowMode: 'maximized'}, - vAxis: {maxValue: 1, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, + vAxis: {maxValue: 0.5, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, chartArea:{left:30,top:25,right:8,bottom:25}, backgroundColor: {stroke: '', strokeWidth: 5}} ); diff --git a/sourcecodes/network_layout_evd_example.php b/sourcecodes/network_layout_evd_example.php index 8cc4bd00..3f22064d 100644 --- a/sourcecodes/network_layout_evd_example.php +++ b/sourcecodes/network_layout_evd_example.php @@ -1,5 +1,5 @@ "; var keyv=""; - var chart = new google.visualization.BarChart(document.getElementById('')); + var chart = new google.visualization.ColumnChart(document.getElementById('')); function selectHandler() { var selectedItem = chart.getSelection()[0]; if (selectedItem) { @@ -370,8 +370,8 @@ for($i=0;$i<$nnode;$i++) chart.draw(data, {title:"", titleTextStyle: {fontSize: }, width:, height:, - vAxis: {textStyle: {fontSize:}}, - hAxis: {minValue: 0, maxValue: 1, gridlines: {count: 3}}, legend: {position: 'none'}, + vAxis: {textStyle: {fontSize:9},minValue: 0, maxValue: 1}, + hAxis: {textStyle: {fontSize:11}}, legend: {position: 'none'}, backgroundColor: {stroke: 'black', strokeWidth: 5}} ); google.visualization.events.addListener(chart, 'select', selectHandler); @@ -391,8 +391,6 @@ for($i=0;$i<$nnode;$i++) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); - $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -405,8 +403,6 @@ for($i=0;$i<$nnode;$i++) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); - $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -437,7 +433,7 @@ for($i=0;$i<$nnode;$i++) title:"", titleTextStyle: {fontSize: }, legend: {position: 'none'}, width:, height:, - vAxis: {minValue: 0, maxValue: 1, viewWindow: {min:0}, gridlines: {count: 5}, textStyle: {fontSize: 9}}, + vAxis: {minValue: 0, maxValue: 0.5, viewWindow: {min:0}, gridlines: {count: 5}, textStyle: {fontSize: 9}}, hAxis: {gridlines: {count: 4}, textStyle: {fontSize: 9},viewWindowMode: 'maximized'}, chartArea:{left:30,top:25,right:8,bottom:25}, backgroundColor: {stroke: 'black', strokeWidth: 5}} diff --git a/sourcecodes/network_layout_inv.php b/sourcecodes/network_layout_inv.php index df5f1cd9..b9375e67 100644 --- a/sourcecodes/network_layout_inv.php +++ b/sourcecodes/network_layout_inv.php @@ -1,5 +1,5 @@ "; var keyv=""; - var chart = new google.visualization.BarChart(document.getElementById('')); + var chart = new google.visualization.ColumnChart(document.getElementById('')); function selectHandler() { var selectedItem = chart.getSelection()[0]; if (selectedItem) { @@ -317,17 +317,13 @@ for($i=0;$i<$nnode;$i++) var s = window.prompt('Selected evidence ' + topping + ' for ' + cnode + '. Enter new evidence ', topping ); window.location.href = "add_inv.php?name=" + cnode + "&evidence=" + s + "&My_key=" + keyv; - - - - //alert('The user selected ' + topping + topname); - } + } } chart.draw(data, {title:"", titleTextStyle: {fontSize: }, width:, height:, - vAxis: {textStyle: {fontSize:}}, - hAxis: {minValue: 0, maxValue: 1, gridlines: {count: 3}}, legend: {position: 'none'}, + hAxis: {textStyle: {fontSize:11}}, + vAxis: {minValue: 0, maxValue: 1, textStyle: {fontSize: 9}}, legend: {position: 'none'}, backgroundColor: {stroke: 'black', strokeWidth: 5}} ); google.visualization.events.addListener(chart, 'select', selectHandler); @@ -347,8 +343,6 @@ for($i=0;$i<$nnode;$i++) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - // $val1=map($name,$val1,$keyval); - $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -361,8 +355,6 @@ for($i=0;$i<$nnode;$i++) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - // $val1=map($name,$val1,$keyval); - $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -395,7 +387,7 @@ for($i=0;$i<$nnode;$i++) legend: {position: 'none'}, width:, height:, hAxis: {gridlines: {count: 4}, textStyle: {fontSize: 9}, viewWindowMode: 'maximized'}, - vAxis: {maxValue: 1, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, + vAxis: {maxValue: 0.5, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, chartArea:{left:30,top:25,right:8,bottom:25}, backgroundColor: {stroke: 'black', strokeWidth: 5}} ); diff --git a/sourcecodes/network_layout_inv_2.php b/sourcecodes/network_layout_inv_2.php index 858de277..550fdf4a 100644 --- a/sourcecodes/network_layout_inv_2.php +++ b/sourcecodes/network_layout_inv_2.php @@ -1,5 +1,5 @@ - var chart = new google.visualization.BarChart(document.getElementById('')); + var chart = new google.visualization.ColumnChart(document.getElementById('')); function selectHandler() { var selectedItem = chart.getSelection()[0]; if (selectedItem) { @@ -772,8 +764,8 @@ else chart.draw(data, {title:"", titleTextStyle: {fontSize: }, width:, height:, - vAxis: {textStyle: {fontSize:}}, - hAxis: {minValue: 0, maxValue: 1, gridlines: {count: 3}}, legend: {position: 'none'}, + hAxis: {textStyle: {fontSize:11}}, + vAxis: {minValue: 0, maxValue: 1, textStyle: {fontSize: 9}}, legend: {position: 'none'}, backgroundColor: {stroke: '', strokeWidth: 5}} ); google.visualization.events.addListener(chart, 'select', selectHandler); @@ -813,14 +805,11 @@ if($evd==-9999) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - // $val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); - $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); $c_i_old++; @@ -832,15 +821,11 @@ if($evd==-9999) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); - $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); - $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); $c_i_old++; @@ -863,7 +848,6 @@ if($evd==-9999) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -880,8 +864,6 @@ if($evd==-9999) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); - $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -898,7 +880,6 @@ if($evd==-9999) } else { - //$evd=map($name,$evd,$keyval); $evduse=$evd; ?> var data = google.visualization.arrayToDataTable([ @@ -910,8 +891,6 @@ else for($j=0;$j<100;$j++) { $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); - $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); $c_i_old++; @@ -933,7 +912,6 @@ else if($j==100) { $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); $c_i_old++; @@ -980,7 +958,7 @@ else legend: {position: 'none'}, width:, height:, hAxis: {gridlines: {count: 4}, textStyle: {fontSize: 9}, viewWindowMode: 'maximized'}, - vAxis: {maxValue: 1, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, + vAxis: {maxValue: 0.5, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, chartArea:{left:30,top:25,right:8,bottom:25}, backgroundColor: {stroke: '', strokeWidth: 5}} ); diff --git a/sourcecodes/network_layout_inv_2_example.php b/sourcecodes/network_layout_inv_2_example.php index 7835ec0d..ab7b2546 100644 --- a/sourcecodes/network_layout_inv_2_example.php +++ b/sourcecodes/network_layout_inv_2_example.php @@ -1,5 +1,5 @@ - var chart = new google.visualization.BarChart(document.getElementById('')); + var chart = new google.visualization.ColumnChart(document.getElementById('')); function selectHandler() { var selectedItem = chart.getSelection()[0]; if (selectedItem) { @@ -766,8 +762,8 @@ else chart.draw(data, {title:"", titleTextStyle: {fontSize: }, width:, height:, - vAxis: {textStyle: {fontSize:}}, - hAxis: {minValue: 0, maxValue: 1, gridlines: {count: 3}}, legend: {position: 'none'}, + hAxis: {textStyle: {fontSize:11}}, + vAxis: {minValue: 0, maxValue: 1, textStyle: {fontSize: 9}}, legend: {position: 'none'}, backgroundColor: {stroke: '', strokeWidth: 5}} ); google.visualization.events.addListener(chart, 'select', selectHandler); @@ -807,13 +803,11 @@ if($evd==-9999) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); @@ -826,14 +820,12 @@ if($evd==-9999) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); @@ -857,7 +849,6 @@ if($evd==-9999) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); $c_i++; @@ -874,7 +865,6 @@ if($evd==-9999) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); @@ -892,7 +882,6 @@ if($evd==-9999) } else { - //$evd=map($name,$evd,$keyval); $evduse=$evd; ?> var data = google.visualization.arrayToDataTable([ @@ -904,7 +893,6 @@ else for($j=0;$j<100;$j++) { $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); @@ -927,7 +915,6 @@ else if($j==100) { $val1_old=trim($data_read_old[$s_i_old][$c_i_old]); - //$val1_old=map($name,$val1_old,$keyval); $c_i_old++; $val2_old=trim($data_read_old[$s_i_old][$c_i_old]); $c_i_old++; @@ -974,7 +961,7 @@ else legend: {position: 'none'}, width:, height:, hAxis: {gridlines: {count: 4}, textStyle: {fontSize: 9}, viewWindowMode: 'maximized'}, - vAxis: {maxValue: 1, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, + vAxis: {maxValue: 0.5, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, chartArea:{left:30,top:25,right:8,bottom:25}, backgroundColor: {stroke: '', strokeWidth: 5}} ); diff --git a/sourcecodes/network_layout_inv_example.php b/sourcecodes/network_layout_inv_example.php index 04543523..24801cf0 100644 --- a/sourcecodes/network_layout_inv_example.php +++ b/sourcecodes/network_layout_inv_example.php @@ -1,5 +1,5 @@ "; var keyv=""; - var chart = new google.visualization.BarChart(document.getElementById('')); + var chart = new google.visualization.ColumnChart(document.getElementById('')); function selectHandler() { var selectedItem = chart.getSelection()[0]; if (selectedItem) { @@ -393,17 +393,13 @@ for($i=0;$i<$nnode;$i++) var s = window.prompt('Selected evidence ' + topping + ' for ' + cnode + '. Enter new evidence ', topping ); window.location.href = "add_inv_example.php?name=" + cnode + "&evidence=" + s + "&My_key=" + keyv; - - - - //alert('The user selected ' + topping + topname); - } + } } chart.draw(data, {title:"", titleTextStyle: {fontSize: }, width:, height:, - vAxis: {textStyle: {fontSize:}}, - hAxis: {minValue: 0, maxValue: 1, gridlines: {count: 3}}, legend: {position: 'none'}, + hAxis: {textStyle: {fontSize:11}}, + vAxis: {minValue: 0, maxValue: 1, textStyle: {fontSize: 9}}, legend: {position: 'none'}, backgroundColor: {stroke: 'black', strokeWidth: 5}} ); google.visualization.events.addListener(chart, 'select', selectHandler); @@ -423,7 +419,6 @@ for($i=0;$i<$nnode;$i++) for($j=0;$j<100;$j++) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); @@ -437,7 +432,6 @@ for($i=0;$i<$nnode;$i++) if($j==100) { $val1=trim($data_read[$s_i][$c_i]); - //$val1=map($name,$val1,$keyval); $c_i++; $val2=trim($data_read[$s_i][$c_i]); @@ -471,7 +465,7 @@ for($i=0;$i<$nnode;$i++) legend: {position: 'none'}, width:, height:, hAxis: {gridlines: {count: 4}, textStyle: {fontSize: 9}, viewWindowMode: 'maximized'}, - vAxis: {maxValue: 1, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, + vAxis: {maxValue: 0.5, minValue: 0,viewWindow: {min:0}, gridlines: {count:5}, textStyle: {fontSize: 9}}, chartArea:{left:30,top:25,right:8,bottom:25}, backgroundColor: {stroke: 'black', strokeWidth: 5}} ); diff --git a/sourcecodes/parameter_learning/Predictmultiple.m b/sourcecodes/parameter_learning/Predictmultiple.m index f03568ef..a7236e35 100644 --- a/sourcecodes/parameter_learning/Predictmultiple.m +++ b/sourcecodes/parameter_learning/Predictmultiple.m @@ -1,4 +1,18 @@ function Predictmultiple(pre) +% Predictmultiple is used when predicting the impact of entering +% evidence on the network. The 'multiple' part refers to +% it working when evidence for multiple nodes is entered. +% +% The input is 'pre'-- the prefix for the network and data +% in BNW. It reads information from several files from BNW. +% +% The output is ???net_figure_new.txt. It also calls +% writeParameters_ev to write the parameter file. +% +% It is called by the run_octave_evd file in the 'sourcecodes' directory. + + + dfile=strcat(pre,'structure_input.txt'); sfile=dfile; dfile=strcat(pre,'continuous_input.txt'); @@ -28,14 +42,14 @@ mapfile = strcat(pre,'map.txt'); fmap = fopen(mapfile,'r'); for i=1:nnodes buffer = fgetl(mapfile); - temp = cell(1,4); - for j=1:4 + 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{4}); - stdevs_orig{i} = str2num(temp{3}); + means_orig{i} = str2num(temp{3}); + stdevs_orig{i} = str2num(temp{2}); end fclose(fmap); diff --git a/sourcecodes/parameter_learning/Predictmultipleintervention.m b/sourcecodes/parameter_learning/Predictmultipleintervention.m new file mode 100644 index 00000000..7e6140a9 --- /dev/null +++ b/sourcecodes/parameter_learning/Predictmultipleintervention.m @@ -0,0 +1,107 @@ +function Predictmultipleintervention(pre) +% Predictmultipleintervention is used when predicting the impact of +% intervention on the network. The 'multiple' part refers to +% it working when intervention for multiple nodes is entered. +% +% The input is 'pre'-- the prefix for the network and data +% in BNW. It reads information from several files from BNW. +% +% The output is ???net_figure_new.txt. It also calls +% writeParameters_int to write the parameter file. +% +% It is called by the run_octave_inv file in the 'sourcecodes' directory. + +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 diff --git a/sourcecodes/parameter_learning/checkDiscreteNodes.m b/sourcecodes/parameter_learning/checkDiscreteNodes.m index c9d0692c..8cbce9d0 100644 --- a/sourcecodes/parameter_learning/checkDiscreteNodes.m +++ b/sourcecodes/parameter_learning/checkDiscreteNodes.m @@ -7,7 +7,9 @@ function [ ] = checkDiscreteNodes( bnet, cases) % bnet: BNT bnet % cases: cell array of data % +% checkDiscreteNodes is called by readInput.m % + node_sizes = bnet.node_sizes; dnodes = bnet.dnodes; ndisc = size(dnodes,2); diff --git a/sourcecodes/parameter_learning/checkStructure.m b/sourcecodes/parameter_learning/checkStructure.m index 5931c187..be3befda 100644 --- a/sourcecodes/parameter_learning/checkStructure.m +++ b/sourcecodes/parameter_learning/checkStructure.m @@ -1,7 +1,7 @@ function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, cases, dag, node_sizes) - %checkStructure Check to see if nodes are sorted correctly. They must be + %checkStructure Check to see if nodes are sorted correctly. Nodes must be % in topological order (i.e., parents before children) before parameter - % learning can take place. + % learning can take place. This function performs this sorting. % %Input and output have the same meaning. The output has just been %topologically ordered. @@ -9,6 +9,10 @@ function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, c % cases = cell array with the data. % dag = matrix with the strucutre of the network. % node_sizes = vector with the size of each node. +% +% checkStructure is called by readInput.m + + %make connections array %count how big you need the connections array to be diff --git a/sourcecodes/parameter_learning/code_backup/Predictmultiple.m b/sourcecodes/parameter_learning/code_backup/Predictmultiple.m new file mode 100644 index 00000000..9d107628 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/Predictmultiple.m @@ -0,0 +1,72 @@ +function Predictmultiple(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'); + +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'); + +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_ev(pre,bnet,nnodes,labels,cases,stdevs,means,select_var_new,select_var_data_new); + +end diff --git a/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m b/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m new file mode 100644 index 00000000..e9f741f2 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/Predictmultipleintrvention.m @@ -0,0 +1,95 @@ +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 diff --git a/sourcecodes/parameter_learning/code_backup/checkDiscreteNodes.m b/sourcecodes/parameter_learning/code_backup/checkDiscreteNodes.m new file mode 100644 index 00000000..c9d0692c --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/checkDiscreteNodes.m @@ -0,0 +1,37 @@ +function [ ] = checkDiscreteNodes( bnet, cases) + %checkDiscreteNodes Checks if states of discrete nodes are be integers from 1 to M + % where M is the number of states of the node. (M should be the same as + % node_sizes in the bnet). + % + %Input: + % bnet: BNT bnet + % cases: cell array of data + % +% +node_sizes = bnet.node_sizes; +dnodes = bnet.dnodes; +ndisc = size(dnodes,2); +ncases = size(cases,2); + +%check to see that all data for discrete nodes are integers +for i = 1:ndisc + inode = dnodes(i); + data = cases(inode,:); + isize = node_sizes(inode); + states = zeros(1,isize); + for j = 1:isize + states(j) = j; + end + for j = 1:ncases + k = int64(data{j}); + if ~any(k==states) + error(['Discrete nodes must be integers from 1 to the number of states']); + end + end +end + + +end + + + diff --git a/sourcecodes/parameter_learning/code_backup/checkStructure.m b/sourcecodes/parameter_learning/code_backup/checkStructure.m new file mode 100644 index 00000000..b4de9403 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/checkStructure.m @@ -0,0 +1,78 @@ +function [ labels, cases, dag, node_sizes, ord_flag ] = checkStructure(labels, cases, dag, node_sizes) + %checkStructure Check to see if nodes are sorted correctly. Nodes must be + % in topological order (i.e., parents before children) before parameter + % learning can take place. This function performs this sorting. + % + %Input and output have the same meaning. The output has just been + %topologically ordered. + % labels = cell array with the names of the nodes. + % cases = cell array with the data. + % dag = matrix with the strucutre of the network. + % node_sizes = vector with the size of each node. + +%make connections array +%count how big you need the connections array to be +nnodes = size(dag,1); +narcs = 0; +for i = 1:nnodes + for j = 1:nnodes + if dag(i,j) == 1 + narcs = narcs + 1; + end + end +end +%fill connections array with label names +connections = cell(narcs,2); +ncount = 0; +for i = 1:nnodes + for j = 1:nnodes + if dag(i,j) == 1 + ncount = ncount + 1; + connections{ncount,1} = labels{i}; + connections{ncount,2} = labels{j}; + end + end +end + +%get topologically sorted dag and labels +[new_dag, new_labels] = mk_adj_mat(connections, labels, 1); + +%check to see if order changed +ord_flag = 0; +for i = 1:nnodes + if ~strcmp(new_labels{i},labels{i}) + ord_flag = 1; + end +end + +if ord_flag + %get new ordering of nodes + order = cell(1,nnodes); + for i = 1:nnodes + for j = 1:nnodes + if strcmp(new_labels{j},labels{i}) + order{i} = j; + end + end + end + + %reorder cases and node_sizes + new_cases = cell(size(cases)); + for i = 1:nnodes + new_cases(order{i},:) = cases(i,:); + end + new_node_sizes = zeros(1,nnodes); + for i = 1:nnodes + new_node_sizes(order{i}) = node_sizes(i); + end + + + dag = new_dag; + cases = new_cases; + node_sizes = new_node_sizes; + labels = new_labels; +end + +end +%end checkStructure.m + diff --git a/sourcecodes/parameter_learning/code_backup/drawFigure.m b/sourcecodes/parameter_learning/code_backup/drawFigure.m new file mode 100644 index 00000000..f84bffa3 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/drawFigure.m @@ -0,0 +1,390 @@ +function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) +%drawFigure writes the parameters and data that are needed to draw the +%structure of a Bayesian network for BNW. +% This is the first function that + + + +if nargin < 8, + drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means); +else + drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata); +end; + +end + + + +function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) +%Function to use if there is no entered evidence. +% +% +%Before each printed line, I will have a line that starts with %%% +% that describes what will be on that line + +%Create an empty evidence cell array. + +%val=cases; +%for i = 1:nnodes +% val(i,1)=val(i,2); + +%end + +A=cell2mat(cases'); +Amax=max(A); +Amin=min(A); + + +evidence = cell(1,nnodes); +engine = jtree_inf_engine(bnet); + +evidence{selectvar}=selectdata; + +[engine,loglik]=enter_evidence(engine,evidence); + +%Open the file, and write the nodes to a file. +fileID = fopen(filename,'w'); + +%%%%Evidence node +fprintf(fileID,'%i\n',selectvar); +%%% The number of nodes +fprintf(fileID,'%i\n',nnodes); +%Get canvas size +labels_temp = cellstr(labels); +[x,y] = make_layout(bnet.dag); + +x = x - min(x); +y = 1 - y; +y = y - min(y); + +[x_dim,y_dim] = canvasSize(nnodes,x,y); + +%%% The dimensions of the canvas for the javascript code +fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim) + +x = x*x_dim; +y = y*y_dim; +for i = 1:nnodes, +%%% The name and X- and Y-positions of each node + fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i))); +end + +%Get the number of parents and children for each node. +num_par = zeros(1,nnodes); +%For parents, sum down columns +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + num_par(i) = num_par(i) + 1; + end + end +end +num_child = zeros(1,nnodes); +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + num_child(i) = num_child(i) + 1; + end + end +end + + +for i = 1:nnodes, + %%% The name and type of each node (1=continuous, the number of states + %%% if it is discrete + fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i)); + %%% The size of the node, I am going to keep them + %%% 250(width) by 150(height) for now + %Could modify this to change the width based on the length of the node + %name + fprintf(fileID,'%i\t%i\n',250,150); + %%% The number of parents of the node, and the parents + if num_par(i) == 0; + %%% If no parents: + fprintf(fileID,'%i\n',num_par(i)); + else + parents = zeros(1,num_par(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + parents(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_par(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_par(i),parents(1,:)); + end + + + %%% The number of children of the node, and the children + if num_child(i) == 0; + %%% If no children: + fprintf(fileID,'%i\n',num_child(i)); + else + children = zeros(1,num_child(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + children(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_child(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_child(i),children(1,:)); + end + + predict = marginal_nodes(engine,i); + if isempty(evidence{i}) + if bnet.node_sizes(i) ~= 1, + for j = 1:bnet.node_sizes(i), + %%%For discrete nodes, the state and the percent of that state + fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j)); + end; + else + + [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i)); + %%%For continuous nodes, print x and the pdf of a normal curve. + for j = 1:101, + %%Undo standardization + xvals(j,1) = xvals(j,1)*stdevs{i}+means{i} + fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1)); + end; + end; + else + fprintf(fileID,'%6.4f\t%6.4f\n',selectdata,1); + end + +end +%fprintf(fileID,'%s\t %\n',labels_temp{:}); + + +fclose(fileID); + +end + + + + + + +function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means) +%Function to use if there is no entered evidence. +% +% +%Before each printed line, I will have a line that starts with %%% +% that describes what will be on that line +A=cell2mat(cases'); +Amax=max(A); +Amin=min(A); + +%Create an empty evidence cell array. +evidence = cell(1,nnodes); +engine = jtree_inf_engine(bnet); +[engine,loglik] = enter_evidence(engine,evidence); + +%Open the file, and write the nodes to a file. +fileID = fopen(filename,'w'); +%%% The number of nodes +fprintf(fileID,'%i\n',nnodes); + +%Get canvas size + +labels_temp = cellstr(labels); +[x,y] = make_layout(bnet.dag); +%[x,y] = layout_dag(bnet.dag); + + +x = x - min(x); +y = 1 - y; +y = y - min(y); + +[x_dim,y_dim] = canvasSize(nnodes,x,y); + +%%% The dimensions of the canvas for the javascript code +fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim) + +x = x*x_dim; +y = y*y_dim; +for i = 1:nnodes, +%%% The name and X- and Y-positions of each node + fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i))); +end + +%Get the number of parents and children for each node. +num_par = zeros(1,nnodes); +%For parents, sum down columns +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + num_par(i) = num_par(i) + 1; + end + end +end +num_child = zeros(1,nnodes); +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + num_child(i) = num_child(i) + 1; + end + end +end + + +for i = 1:nnodes, + %%% The name and type of each node (1=continuous, the number of states + %%% if it is discrete + fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i)); + %%% The size of the node, I am going to keep them + %%% 250(width) by 150(height) for now + %Could modify this to change the width based on the length of the node + %name + fprintf(fileID,'%i\t%i\n',250,150); + %%% The number of parents of the node, and the parents + if num_par(i) == 0; + %%% If no parents: + fprintf(fileID,'%i\n',num_par(i)); + else + parents = zeros(1,num_par(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + parents(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_par(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_par(i),parents(1,:)); + end + + + %%% The number of children of the node, and the children + if num_child(i) == 0; + %%% If no children: + fprintf(fileID,'%i\n',num_child(i)); + else + children = zeros(1,num_child(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + children(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_child(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_child(i),children(1,:)); + end + + predict = marginal_nodes(engine,i); + if bnet.node_sizes(i) ~= 1, + for j = 1:bnet.node_sizes(i), + %%%For discrete nodes, the state and the percent of that state + fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j)); + end; + else + %cases(i) + % MAX(cases(i)) + % MIN(cases(i)) + [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i)); + %%%For continuous nodes, print x and the pdf of a normal curve. + for j = 1:101, + %%Undo standardization + x_vals(j,1) = x_vals(j,1)*stdevs{i}+means{i}; + fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1)); + end; + end; +end +%fprintf(fileID,'%s\t %\n',labels_temp{:}); + + +fclose(fileID); + +end + + +function [x_dim, y_dim] = canvasSize(nnodes,x,y) +%canvasSize Function to calculate the size of the canvas to +% build the network structure + + +%I am going to assume that the node size will be +% height = 150, width = 250 +% so there will be a node spacing of +% 200 (in y-dim) and 300 (in x-dim). +y_space = 200; +x_space = 300; + +%Set default minimum x and y dimensions +x_dim = 1200; +y_dim = 1200; + +%get unique y values +y_unique = unique(y); +size_y = size(y_unique,2); +y_dim_temp = size_y*y_space; + +%get the maximum nodes in any layer +size_x = zeros(1,size_y); +for i = 1:size_y, + for j = 1:nnodes, + if y_unique(i) == y(j), + size_x(1,i) = size_x(1,i) + 1; + end; + end; +end; +size_x = max(size_x); +x_dim_temp = size_x*x_space; + +if x_dim_temp > x_dim, + x_dim = x_dim_temp; +end; + +if y_dim_temp > y_dim, + y_dim = y_dim_temp; +end; +end + +function [x_vals,y_vals] = calcGaussian(mu,Sigma,maxval,minval) +%Function to calculate 101 points of Gaussian function to use in plotting +% Gets the probability density of the mean value and 50 evenly spaced +% points up to 3Sigma below the mean and 50 evenly space points up to +% 3Sigma above the mean. +%maxval +%minval +x_vals = zeros(101,1); +y_vals = zeros(101,1); + +%x_vals(1,1) = mu - 3*Sigma; +x_vals(1,1) = minval - 1; +gap=((maxval+1)-(minval - 1))/100; +%x_vals(1,1) = 0;%mu - 3*Sigma; +for i = 1:100, + % x_vals(i+1,1) = x_vals(1,1) + i*6*Sigma/100; + x_vals(i+1,1) = x_vals(i,1) + gap; + %x_vals(i+1,1) = x_vals(i,1) + 1/100; +end + +for i = 1:101, + y_vals(i,1) = normpdf(x_vals(i,1),mu,Sigma); +end + +end diff --git a/sourcecodes/parameter_learning/code_backup/drawFigure.m~ b/sourcecodes/parameter_learning/code_backup/drawFigure.m~ new file mode 100644 index 00000000..404a65f7 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/drawFigure.m~ @@ -0,0 +1,388 @@ +function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) +%drawFigure writes the parameters and data that are needed to draw the +%structure of a Bayesian network. + + +if nargin < 8, + drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means); +else + drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata); +end; + +end + + + +function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) +%Function to use if there is no entered evidence. +% +% +%Before each printed line, I will have a line that starts with %%% +% that describes what will be on that line + +%Create an empty evidence cell array. + +%val=cases; +%for i = 1:nnodes +% val(i,1)=val(i,2); + +%end + +A=cell2mat(cases'); +Amax=max(A); +Amin=min(A); + + +evidence = cell(1,nnodes); +engine = jtree_inf_engine(bnet); + +evidence{selectvar}=selectdata; + +[engine,loglik]=enter_evidence(engine,evidence); + +%Open the file, and write the nodes to a file. +fileID = fopen(filename,'w'); + +%%%%Evidence node +fprintf(fileID,'%i\n',selectvar); +%%% The number of nodes +fprintf(fileID,'%i\n',nnodes); +%Get canvas size +labels_temp = cellstr(labels); +[x,y] = make_layout(bnet.dag); + +x = x - min(x); +y = 1 - y; +y = y - min(y); + +[x_dim,y_dim] = canvasSize(nnodes,x,y); + +%%% The dimensions of the canvas for the javascript code +fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim) + +x = x*x_dim; +y = y*y_dim; +for i = 1:nnodes, +%%% The name and X- and Y-positions of each node + fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i))); +end + +%Get the number of parents and children for each node. +num_par = zeros(1,nnodes); +%For parents, sum down columns +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + num_par(i) = num_par(i) + 1; + end + end +end +num_child = zeros(1,nnodes); +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + num_child(i) = num_child(i) + 1; + end + end +end + + +for i = 1:nnodes, + %%% The name and type of each node (1=continuous, the number of states + %%% if it is discrete + fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i)); + %%% The size of the node, I am going to keep them + %%% 250(width) by 150(height) for now + %Could modify this to change the width based on the length of the node + %name + fprintf(fileID,'%i\t%i\n',250,150); + %%% The number of parents of the node, and the parents + if num_par(i) == 0; + %%% If no parents: + fprintf(fileID,'%i\n',num_par(i)); + else + parents = zeros(1,num_par(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + parents(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_par(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_par(i),parents(1,:)); + end + + + %%% The number of children of the node, and the children + if num_child(i) == 0; + %%% If no children: + fprintf(fileID,'%i\n',num_child(i)); + else + children = zeros(1,num_child(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + children(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_child(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_child(i),children(1,:)); + end + + predict = marginal_nodes(engine,i); + if isempty(evidence{i}) + if bnet.node_sizes(i) ~= 1, + for j = 1:bnet.node_sizes(i), + %%%For discrete nodes, the state and the percent of that state + fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j)); + end; + else + + [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i)); + %%%For continuous nodes, print x and the pdf of a normal curve. + for j = 1:101, + %%Undo standardization + xvals(j,1) = xvals(j,1)*stdevs{i}+means{i} + fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1)); + end; + end; + else + fprintf(fileID,'%6.4f\t%6.4f\n',selectdata,1); + end + +end +%fprintf(fileID,'%s\t %\n',labels_temp{:}); + + +fclose(fileID); + +end + + + + + + +function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means) +%Function to use if there is no entered evidence. +% +% +%Before each printed line, I will have a line that starts with %%% +% that describes what will be on that line +A=cell2mat(cases'); +Amax=max(A); +Amin=min(A); + +%Create an empty evidence cell array. +evidence = cell(1,nnodes); +engine = jtree_inf_engine(bnet); +[engine,loglik] = enter_evidence(engine,evidence); + +%Open the file, and write the nodes to a file. +fileID = fopen(filename,'w'); +%%% The number of nodes +fprintf(fileID,'%i\n',nnodes); + +%Get canvas size + +labels_temp = cellstr(labels); +[x,y] = make_layout(bnet.dag); +%[x,y] = layout_dag(bnet.dag); + + +x = x - min(x); +y = 1 - y; +y = y - min(y); + +[x_dim,y_dim] = canvasSize(nnodes,x,y); + +%%% The dimensions of the canvas for the javascript code +fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim) + +x = x*x_dim; +y = y*y_dim; +for i = 1:nnodes, +%%% The name and X- and Y-positions of each node + fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i))); +end + +%Get the number of parents and children for each node. +num_par = zeros(1,nnodes); +%For parents, sum down columns +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + num_par(i) = num_par(i) + 1; + end + end +end +num_child = zeros(1,nnodes); +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + num_child(i) = num_child(i) + 1; + end + end +end + + +for i = 1:nnodes, + %%% The name and type of each node (1=continuous, the number of states + %%% if it is discrete + fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i)); + %%% The size of the node, I am going to keep them + %%% 250(width) by 150(height) for now + %Could modify this to change the width based on the length of the node + %name + fprintf(fileID,'%i\t%i\n',250,150); + %%% The number of parents of the node, and the parents + if num_par(i) == 0; + %%% If no parents: + fprintf(fileID,'%i\n',num_par(i)); + else + parents = zeros(1,num_par(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + parents(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_par(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_par(i),parents(1,:)); + end + + + %%% The number of children of the node, and the children + if num_child(i) == 0; + %%% If no children: + fprintf(fileID,'%i\n',num_child(i)); + else + children = zeros(1,num_child(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + children(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_child(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_child(i),children(1,:)); + end + + predict = marginal_nodes(engine,i); + if bnet.node_sizes(i) ~= 1, + for j = 1:bnet.node_sizes(i), + %%%For discrete nodes, the state and the percent of that state + fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j)); + end; + else + %cases(i) + % MAX(cases(i)) + % MIN(cases(i)) + [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i)); + %%%For continuous nodes, print x and the pdf of a normal curve. + for j = 1:101, + %%Undo standardization + x_vals(j,1) = x_vals(j,1)*stdevs{i}+means{i}; + fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1)); + end; + end; +end +%fprintf(fileID,'%s\t %\n',labels_temp{:}); + + +fclose(fileID); + +end + + +function [x_dim, y_dim] = canvasSize(nnodes,x,y) +%canvasSize Function to calculate the size of the canvas to +% build the network structure + + +%I am going to assume that the node size will be +% height = 150, width = 250 +% so there will be a node spacing of +% 200 (in y-dim) and 300 (in x-dim). +y_space = 200; +x_space = 300; + +%Set default minimum x and y dimensions +x_dim = 1200; +y_dim = 1200; + +%get unique y values +y_unique = unique(y); +size_y = size(y_unique,2); +y_dim_temp = size_y*y_space; + +%get the maximum nodes in any layer +size_x = zeros(1,size_y); +for i = 1:size_y, + for j = 1:nnodes, + if y_unique(i) == y(j), + size_x(1,i) = size_x(1,i) + 1; + end; + end; +end; +size_x = max(size_x); +x_dim_temp = size_x*x_space; + +if x_dim_temp > x_dim, + x_dim = x_dim_temp; +end; + +if y_dim_temp > y_dim, + y_dim = y_dim_temp; +end; +end + +function [x_vals,y_vals] = calcGaussian(mu,Sigma,maxval,minval) +%Function to calculate 101 points of Gaussian function to use in plotting +% Gets the probability density of the mean value and 50 evenly spaced +% points up to 3Sigma below the mean and 50 evenly space points up to +% 3Sigma above the mean. +%maxval +%minval +x_vals = zeros(101,1); +y_vals = zeros(101,1); + +%x_vals(1,1) = mu - 3*Sigma; +x_vals(1,1) = minval - 1; +gap=((maxval+1)-(minval - 1))/100; +%x_vals(1,1) = 0;%mu - 3*Sigma; +for i = 1:100, + % x_vals(i+1,1) = x_vals(1,1) + i*6*Sigma/100; + x_vals(i+1,1) = x_vals(i,1) + gap; + %x_vals(i+1,1) = x_vals(i,1) + 1/100; +end + +for i = 1:101, + y_vals(i,1) = normpdf(x_vals(i,1),mu,Sigma); +end + +end diff --git a/sourcecodes/parameter_learning/code_backup/drawFigureM.m b/sourcecodes/parameter_learning/code_backup/drawFigureM.m new file mode 100644 index 00000000..91b8698f --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/drawFigureM.m @@ -0,0 +1,230 @@ +function [] = drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) +%drawFigureM writes the parameters and data that are needed to draw the +%structure of a Bayesian network after added evidence or intervention + +fileID = fopen(filename,'w'); + + +A=cell2mat(cases'); +Amax=max(A); +Amin=min(A); + + +evidence = cell(1,nnodes); +engine = jtree_inf_engine(bnet); + +m = size(selectvar,1); + +ev_dat = zeros(1,nnodes); +for i = 1:m, + di=selectvar(i,1); + ev_dat(di)=selectdata(i,1); +%Need to standardized 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); + fprintf(fileID,'%i\t',di); +end + +fprintf(fileID,'\n'); + +[engine,loglik]=enter_evidence(engine,evidence); + +%Open the file, and write the nodes to a file. +%%% The number of nodes +fprintf(fileID,'%i\n',nnodes); +%Get canvas size +labels_temp = cellstr(labels); +[x,y] = make_layout(bnet.dag); +x = x - min(x); +y = 1 - y; +y = y - min(y); +[x_dim,y_dim] = canvasSize(nnodes,x,y); + +%%% The dimensions of the canvas for the javascript code +fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim); +x = x*x_dim; +y = y*y_dim; +for i = 1:nnodes, +%%% The name and X- and Y-positions of each node + fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i))); +end + +%Get the number of parents and children for each node. +num_par = zeros(1,nnodes); +%For parents, sum down columns +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + num_par(i) = num_par(i) + 1; + end + end +end +num_child = zeros(1,nnodes); +for i = 1:nnodes, + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + num_child(i) = num_child(i) + 1; + end + end +end + +for i = 1:nnodes, + %%% The name and type of each node (1=continuous, the number of states + %%% if it is discrete + fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i)); + %%% The size of the node, I am going to keep them + %%% 250(width) by 150(height) for now + %Could modify this to change the width based on the length of the node + %name + fprintf(fileID,'%i\t%i\n',250,150); + %%% The number of parents of the node, and the parents + if num_par(i) == 0; + %%% If no parents: + fprintf(fileID,'%i\n',num_par(i)); + else + parents = zeros(1,num_par(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(j,i) == 1, + parents(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_par(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_par(i),parents(1,:)); + end + + + %%% The number of children of the node, and the children + if num_child(i) == 0; + %%% If no children: + fprintf(fileID,'%i\n',num_child(i)); + else + children = zeros(1,num_child(i)); + k = 1; + for j = 1:nnodes, + if bnet.dag(i,j) == 1, + children(1,k) = j; + k = k + 1; + end + end + format = '%i\t'; + for j = 1:num_child(i)-1, + format = strcat(format,'%i\t'); + end + format = strcat(format,'%i\n'); + %%%If there are parents: + fprintf(fileID,format,num_child(i),children(1,:)); + end + + predict = marginal_nodes(engine,i); + if isempty(evidence{i}) + if bnet.node_sizes(i) ~= 1, + for j = 1:bnet.node_sizes(i), + %%%For discrete nodes, the state and the percent of that state + fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j)); + end; + else + [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i)); + %%%For continuous nodes, print x and the pdf of a normal curve. + for j = 1:101, + %%Undo standardization + x_vals(j,1) = x_vals(j,1)*stdevs{i}+means{i}; + fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1)); + end; + end; + else + if bnet.node_sizes(i) == 1, + fprintf(fileID,'%6.4f\t%6.4f\n',ev_dat(i)*stdevs{i}+means{i},1); + else + fprintf(fileID,'%6.4f\t%6.4f\n',ev_dat(i),1); + endif + end + +end + +fclose(fileID); +end + + + + + + + + + +function [x_dim, y_dim] = canvasSize(nnodes,x,y) +%canvasSize Function to calculate the size of the canvas to +% build the network structure + + +%I am going to assume that the node size will be +% height = 150, width = 250 +% so there will be a node spacing of +% 200 (in y-dim) and 300 (in x-dim). +y_space = 200; +x_space = 300; + +%Set default minimum x and y dimensions +x_dim = 1200; +y_dim = 1200; + +%get unique y values +y_unique = unique(y); +size_y = size(y_unique,2); +y_dim_temp = size_y*y_space; + +%get the maximum nodes in any layer +size_x = zeros(1,size_y); +for i = 1:size_y, + for j = 1:nnodes, + if y_unique(i) == y(j), + size_x(1,i) = size_x(1,i) + 1; + end; + end; +end; +size_x = max(size_x); +x_dim_temp = size_x*x_space; + +if x_dim_temp > x_dim, + x_dim = x_dim_temp; +end; + +if y_dim_temp > y_dim, + y_dim = y_dim_temp; +end; +end + +function [x_vals,y_vals] = calcGaussian(mu,Sigma,maxval,minval) +%Function to calculate 101 points of Gaussian function to use in plotting +% Gets the probability density of the mean value and 50 evenly spaced +% points up to 3Sigma below the mean and 50 evenly space points up to +% 3Sigma above the mean. +%maxval +%minval +x_vals = zeros(101,1); +y_vals = zeros(101,1); + +%x_vals(1,1) = mu - 3*Sigma; +x_vals(1,1) = minval - 1; +gap=((maxval+1)-(minval - 1))/100; +%x_vals(1,1) = 0;%mu - 3*Sigma; +for i = 1:100, + % x_vals(i+1,1) = x_vals(1,1) + i*6*Sigma/100; + x_vals(i+1,1) = x_vals(i,1) + gap; + %x_vals(i+1,1) = x_vals(i,1) + 1/100; +end + +for i = 1:101, + y_vals(i,1) = normpdf(x_vals(i,1),mu,Sigma); +end + +end diff --git a/sourcecodes/parameter_learning/code_backup/getParams.m b/sourcecodes/parameter_learning/code_backup/getParams.m new file mode 100644 index 00000000..31f84ffb --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/getParams.m @@ -0,0 +1,22 @@ +function [ bnet ] = getParams( bnet, cases ) +%getParams Code to initialize CPT and do parameter learning. +%This will be very basic for now. I can add more options later. + +dnodes = bnet.dnodes; +cnodes = bnet.cnodes; +nnodes = size(dnodes,2)+size(cnodes,2); + +%make dnodes tabular_CPT +for i = 1:size(dnodes,2) + bnet.CPD{dnodes(i)} = tabular_CPD(bnet,dnodes(i)); +end + +for i = 1:size(cnodes,2) + bnet.CPD{cnodes(i)} = gaussian_CPD(bnet,cnodes(i)); +end + +bnet = learn_params(bnet,cases); + + +end + diff --git a/sourcecodes/parameter_learning/code_backup/parameterLearning.m b/sourcecodes/parameter_learning/code_backup/parameterLearning.m new file mode 100644 index 00000000..872e94b1 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/parameterLearning.m @@ -0,0 +1,17 @@ +function [ bnet ] = parameterLearning( bnet,cases,engine_name ) +%parameterLearning Do parameter learning and inference + +%engine is an optional argument +if nargin < 3 + engine_name = 'jtree_inf_engine'; +end + + +%First do parameter learning with all the data +[bnet] = getParams(bnet,cases); + + + + +end + diff --git a/sourcecodes/parameter_learning/code_backup/prepareInput.m b/sourcecodes/parameter_learning/code_backup/prepareInput.m new file mode 100644 index 00000000..838dcd2c --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/prepareInput.m @@ -0,0 +1,294 @@ +function [ ] = prepareInput( pre ) + % + % This function takes files that are uploaded to BNW and creates output + % files that can be used for structure and parameter learning. + % It replaces php code that was previously in bn_file_load_gom.php. + % There are several improvements in performance and ease of use: + % 1) Loading files is significantly (~5x) faster for large input files. + % 2) The allowed values for discrete variables are more flexible. + % (e.g., A genotype variable be 'B' and 'D' instead of having + % to replace to make them '1' and '2'.) + % 3) Continuous variables may be identified as continuous in some cases + % even if there is not a period. + % 4) The states of discrete variables should be correctly ordered in + % almost all cases. + % 5) An additional output file is written that will let users check if + % the input file has been uploaded and parsed correctly. + % 6) Future updates to this code should be easier than updating the php. + % + % + % Input: ???continuous_input_orig.txt + % This is the input file that is uploaded to BNW. + % It is directly written out by the BNW php code with no modification. + % The file format is a header line containing the variable names + % followed by the data, with each case in a row. + % + % Output: There are many output files. + % 1) The main output file is ???continuous_input.txt that can be + % used by the structure learning code and parameter learning codes. + % The first line is variable names, the second line is the node type + % (continuous nodes should have 1, discrete nodes have the number + % of states), and the rest is the data. + % 2) A new output file is ???input_desc.txt, a file that describes the + % data so users can check that it has been parsed correctly. + % 3) ???nlevels.txt: The states of discrete variables. + % 4) ???name.txt: The names of the variables as uploaded. + % 5) ???type.txt: The number of states for each variables + % (1 indicates a continuous variable.) + % 6/7) ???nnode.txt and ???nrows.txt: number of nodes and cases + % 8-12) ???ban.txt, ???white.txt, ???k.txt, ???thr.txt, and + % ???parent.txt: Files with default values for structure learning. + % + +% open file for input, include error handling +dfile=strcat(pre,'continuous_input_orig.txt'); + +fin = fopen(dfile,'r'); +if fin < 0 + error(['Could not open ',dfile,' for input']); +end + +% Get the number of cases (the number of rows in the file excluding the header) +ncases = fskipl(fin,Inf) - 1; + +frewind(fin); + +% Read in first line to get the number of nodes and the node labels. +buffer = fgetl(fin); %get header line as a string +nnodes = numel(strfind(buffer,"\t")) + 1; +labels = cell(1,nnodes); +for j=1:nnodes + [next,buffer] = strtok(buffer); + labels{j} = next; +end + +% Read in the data +data = cell(ncases,nnodes); +for i = 1:ncases + buffer = fgetl(fin); + for j = 1:nnodes + [next,buffer] = strtok(buffer); + data{i,j} = next; + end +end + +% Determine whether or not the nodes are continuous or discrete. +% First, treat them as all discrete and get the states and number of stats(levels). +levels = cell(1,nnodes); +states = []; +for j = 1:nnodes + states{end+1} = unique(data(:,j)); + levels{j} = size(states{j},1); +end + +reason = cell(1,nnodes); +%Now do some checks to see if nodes are discrete or continuous +for j = 1:nnodes + % If there are 3 or less unique values, I will assume that the node is discrete. + if levels{j} < 4; + reason{j} = "It was determined to be discrete because there are a small number (<4) of possible values."; + continue + % If there are as many unique values as a third of the number of cases, + % I will assume that the node is continuous. + elseif levels{j} > ncases/3; + levels{j} = 1; + reason{j} = "It was determined to be continuous because there are a large number of possible values compared to the number of cases."; + continue + % If there are more than twenty unique values, + % I will assume that the node is continuous. + elseif levels{j} > 20; + levels{j} = 1; + reason{j} = "It was determined to be continuous because there are many (>20) possible values."; + continue + % Otherwise, I will scan through the individual values. + % If any of the values contain a '.', I will assume it is continuous. + else + reason{j} = "It was determined to be discrete by default."; + period_test = 0; + column = data(:,j); + k = 1; + while period_test == 0 + period_test = sum(cell2mat(strfind(column(k),"."))); + if period_test != 0; + reason{j} = "This variable was determined to be continuous because there were several possible values and at least one value contained a period(.)."; + levels{j} = 1; + end + k++; + if k > ncases + break + end + end + end +end + +%I need to check if any discrete nodes are listed after continuous nodes. +%If so, I need to rearrange the columns. +max_disc = 0; +min_cont = nnodes + 1; +for i = 1:nnodes + if levels{i} > 1 + max_disc = i; + elseif min_cont == nnodes+1 + min_cont = i; + end +end +%If max_disc > min_cont, you need to rearrange the nodes +% to put the discrete nodes first. +if max_disc > min_cont + levels_old = levels; + labels_old = labels; + data_old = data; + states_old = states; + reason_old = reason; + new_order = {}; + for i=1:nnodes + if levels_old{i} > 1 + new_order{end+1} = i; + end + end + for i=1:nnodes + if levels_old{i} == 1 + new_order{end+1} = i; + end + end + labels = {}; + levels = {}; + states = {}; + reason = {}; + for i =1:nnodes + labels{i} = labels_old{new_order{i}}; + levels{i} = levels_old{new_order{i}}; + states{i} = states_old{new_order{i}}; + reason{i} = reason_old{new_order{i}}; + for j=1:ncases + data{j,i} = data_old{j,new_order{i}}; + end + end + +endif + + +%Write other files that are used by BNW for this key. +%The first group of files establish default settings for structure learning. +outfile = strcat(pre,'white.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'From\tTo\n'); +fclose(fout); + +outfile = strcat(pre,'ban.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'From\tTo\n'); +fclose(fout); + +outfile = strcat(pre,'k.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'1\n'); +fclose(fout); + +outfile = strcat(pre,'parent.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'4\n'); +fclose(fout); + +outfile = strcat(pre,'thr.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'0.5\n'); +fclose(fout); + + +%The next group of files have information about the uploaded file. +outfile = strcat(pre,'name.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%s\t',labels{1:end-1}); +fprintf(fout,'%s\n',labels{end}); +fclose(fout); + +outfile = strcat(pre,'nnode.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%i\n',nnodes); +fclose(fout); + +outfile = strcat(pre,'nrows.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%i\n',ncases); +fclose(fout); + +outfile = strcat(pre,'type.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%s\t',labels{1:end-1}); +fprintf(fout,'%s\n',labels{end}); +fprintf(fout,'%i\t',levels{1:end-1}); +fprintf(fout,'%i\n',levels{end}); +fclose(fout); + +%This output file contains the states for discrete nodes. +% The unique matlab function already sorts the states. +outfile = strcat(pre,'nlevels.txt'); +fout = fopen(outfile,'w'); +for i = 1:nnodes + if levels{i} > 1 + fprintf(fout,'%s\t',labels{i},states{i}{1:end-1}); + fprintf(fout,'%s\n',states{i}{end}); + end +end +fclose(fout); + + +%Print a file with a short description of the input. +descfile = strcat(pre,'input_desc.txt'); +dout = fopen(descfile,'w'); +fprintf(dout,['As loaded, the input file had the following properties:\n\n']); +dout = fopen(descfile,'a'); +fprintf(dout,'There are %i variables and %i cases(rows).\n',size(labels,2),ncases); +fprintf(dout,'The variable names are:\n'); +fprintf(dout,'%s\t',labels{1:end-1}); +fprintf(dout,'%s\n\n',labels{end}); +for i=1:nnodes + if levels{i} == 1 + fprintf(dout,'%s is a continuous variable.\n',labels{i}); + fprintf(dout,'%s\n',reason{i}); + column = str2double(data(:,i)); + colmean = mean(column); + colstd = std(column); + fprintf(dout,'It has a mean of %6.3f and a standard deviation of %6.3f\n\n',mean(column),std(column)) + else + fprintf(dout,'%s is a discrete variable with %i states.\n',labels{i},levels{i}); + fprintf(dout,'%s\n',reason{i}); + fprintf(dout,'The states are: '); + fprintf(dout,'%s ',states{i}{1:end-1}); + fprintf(dout,'%s\n\n',states{i}{end}); + end +end +fclose(fout); + +outfile = strcat(pre,'continuous_input.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%s\t',labels{1:end-1}); +fprintf(fout,'%s\n',labels{end}); +fprintf(fout,'%i\t',levels{1:end-1}); +fprintf(fout,'%i\n',levels{end}); +%Need to replace states in discrete variables with integers for BNT +for i = 1:nnodes + if levels{i} > 1 + for j = 1:ncases + for k=1:size(states{i},1) + if data{j,i} == states{i}{k} + data{j,i} = sprintf('%i',num2cell(k){1});; + break + end + end + end + end +end +for i = 1:ncases + fprintf(fout,'%s\t',data{i,1:end-1}); + fprintf(fout,'%s\n',data{i,end}); +end +fclose(fout); + + + + + +end +% end of prepareInput.m \ No newline at end of file diff --git a/sourcecodes/parameter_learning/code_backup/prepareInput.m~ b/sourcecodes/parameter_learning/code_backup/prepareInput.m~ new file mode 100644 index 00000000..9fc0f97f --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/prepareInput.m~ @@ -0,0 +1,294 @@ +function [ ] = prepareInput( pre ) + % + % This function takes files that are uploaded to BNW and creates output + % files that can be used for structure and parameter learning. + % It replaces php code that was previously in bn_file_load_gom.php. + % There are several improvements in performance and ease of use: + % 1) Loading files is significantly (~5x) faster for large input files. + % 2) The allowed values for discrete variables are more flexible. + % (e.g., A genotype variable be 'B' and 'D' instead of having + % to replace to make them '1' and '2'.) + % 3) Continuous variables may be identified as continuous in some cases + % even if there is not a period. + % 4) The states of discrete variables should be correctly ordered in + % almost all cases. + % 5) An additional output file is written that will let users check if + % the input file has been uploaded and parsed correctly. + % 6) Future updates to this code should be easier than updating the php. + % + % + % Input: ???continuous_input_orig.txt + % This is the input file that is uploaded to BNW. + % It is directly written out by the BNW php code with no modification. + % The file format is a header line containing the variable names + % followed by the data, with each case in a row. + % + % Output: There are many output files. + % 1) The main output file is ???continuous_input.txt that can be + % used by the structure learning code and parameter learning codes. + % The first line is variable names, the second line is the node type + % (continuous nodes should have 1, discrete nodes have the number + % of states), and the rest is the data. + % 2) A new output file is ???input_desc.txt, a file that describes the + % data so users can check that it has been parsed correctly. + % 3) ???nlevels.txt: The states of discrete variables. + % 4) ???name.txt: The names of the variables as uploaded. + % 5) ???type.txt: The number of states for each variables + % (1 indicates a continuous variable.) + % 6/7) ???nnode.txt and ???nrows.txt: number of nodes and cases + % 8-12) ???ban.txt, ???white.txt, ???k.txt, ???thr.txt, and + % ???parent.txt: Files with default values for structure learning. + % + +% open file for input, include error handling +dfile=strcat(pre,'continuous_input_orig.txt'); + +fin = fopen(dfile,'r'); +if fin < 0 + error(['Could not open ',dfile,' for input']); +end + +% Get the number of cases (the number of rows in the file excluding the header) +ncases = fskipl(fin,Inf) - 1; + +frewind(fin); + +% Read in first line to get the number of nodes and the node labels. +buffer = fgetl(fin); %get header line as a string +nnodes = numel(strfind(buffer,"\t")) + 1; +labels = cell(1,nnodes); +for j=1:nnodes + [next,buffer] = strtok(buffer); + labels{j} = next; +end + +% Read in the data +data = cell(ncases,nnodes); +for i = 1:ncases + buffer = fgetl(fin); + for j = 1:nnodes + [next,buffer] = strtok(buffer); + data{i,j} = next; + end +end + +% Determine whether or not the nodes are continuous or discrete. +% First, treat them as all discrete and get the states and number of stats(levels). +levels = cell(1,nnodes); +states = []; +for j = 1:nnodes + states{end+1} = unique(data(:,j)); + levels{j} = size(states{j},1); +end + +reason = cell(1,nnodes); +%Now do some checks to see if nodes are discrete or continuous +for j = 1:nnodes + % If there are 3 or less unique values, I will assume that the node is discrete. + if levels{j} < 4; + reason{j} = "This was determined to be discrete because there are few (<4) different values."; + continue + % If there are as many unique values as a third of the number of cases, + % I will assume that the node is continuous. + elseif levels{j} > ncases/3; + levels{j} = 1; + reason{j} = "This was determined to be continuous because there are a large number of different values compared to the number of cases."; + continue + % If there are more than twenty unique values, + % I will assume that the node is continuous. + elseif levels{j} > 20; + levels{j} = 1; + reason{j} = "This was determined to be continuous because there are many (>20) possible values."; + continue + % Otherwise, I will scan through the individual values. + % If any of the values contain a '.', I will assume it is continuous. + else + reason{j} = "This variable was determined to be discrete."; + period_test = 0; + column = data(:,j); + k = 1; + while period_test == 0 + period_test = sum(cell2mat(strfind(column(k),"."))); + if period_test != 0; + reason{j} = "This variable was determined to be continuous because there were several possible values and at least one value contained a period (".")."; + levels{j} = 1; + end + k++; + if k > ncases + break + end + end + end +end + +%I need to check if any discrete nodes are listed after continuous nodes. +%If so, I need to rearrange the columns. +max_disc = 0; +min_cont = nnodes + 1; +for i = 1:nnodes + if levels{i} > 1 + max_disc = i; + elseif min_cont == nnodes+1 + min_cont = i; + end +end +%If max_disc > min_cont, you need to rearrange the nodes +% to put the discrete nodes first. +if max_disc > min_cont + levels_old = levels; + labels_old = labels; + data_old = data; + states_old = states; + reason_old = reason; + new_order = {}; + for i=1:nnodes + if levels_old{i} > 1 + new_order{end+1} = i; + end + end + for i=1:nnodes + if levels_old{i} == 1 + new_order{end+1} = i; + end + end + labels = {}; + levels = {}; + states = {}; + reason = {}; + for i =1:nnodes + labels{i} = labels_old{new_order{i}}; + levels{i} = levels_old{new_order{i}}; + states{i} = states_old{new_order{i}}; + reason{i} = reason_old{new_order{i}}; + for j=1:ncases + data{j,i} = data_old{j,new_order{i}}; + end + end + +endif + + +%Write other files that are used by BNW for this key. +%The first group of files establish default settings for structure learning. +outfile = strcat(pre,'white.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'From\tTo\n'); +fclose(fout); + +outfile = strcat(pre,'ban.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'From\tTo\n'); +fclose(fout); + +outfile = strcat(pre,'k.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'1\n'); +fclose(fout); + +outfile = strcat(pre,'parent.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'4\n'); +fclose(fout); + +outfile = strcat(pre,'thr.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'0.5\n'); +fclose(fout); + + +%The next group of files have information about the uploaded file. +outfile = strcat(pre,'name.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%s\t',labels{1:end-1}); +fprintf(fout,'%s\n',labels{end}); +fclose(fout); + +outfile = strcat(pre,'nnode.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%i\n',nnodes); +fclose(fout); + +outfile = strcat(pre,'nrows.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%i\n',ncases); +fclose(fout); + +outfile = strcat(pre,'type.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%s\t',labels{1:end-1}); +fprintf(fout,'%s\n',labels{end}); +fprintf(fout,'%i\t',levels{1:end-1}); +fprintf(fout,'%i\n',levels{end}); +fclose(fout); + +%This output file contains the states for discrete nodes. +% The unique matlab function already sorts the states. +outfile = strcat(pre,'nlevels.txt'); +fout = fopen(outfile,'w'); +for i = 1:nnodes + if levels{i} > 1 + fprintf(fout,'%s\t',labels{i},states{i}{1:end-1}); + fprintf(fout,'%s\n',states{i}{end}); + end +end +fclose(fout); + + +%Print a file with a short description of the input. +descfile = strcat(pre,'input_desc.txt'); +dout = fopen(descfile,'w'); +fprintf(dout,['As loaded, the input file had the following properties:\n\n']); +dout = fopen(descfile,'a'); +fprintf(dout,'There are %i variables and %i cases(rows)\n',size(labels,2),ncases); +fprintf(dout,'The variable names are:\n'); +fprintf(dout,'%s\t',labels{1:end-1}); +fprintf(dout,'%s\n\n',labels{end}); +for i=1:nnodes + if levels{i} == 1 + fprintf(dout,'%s is a continuous variable\n',labels{i}); + fprintf(dout,'%s\n',reason{i}); + column = str2double(data(:,i)); + colmean = mean(column); + colstd = std(column); + fprintf(dout,'It has a mean of %6.3f and a standard deviation of %6.3f\n\n',mean(column),std(column)) + else + fprintf(dout,'%s is a discrete variable with %i states\n',labels{i},levels{i}); + fprintf(dout,'%s\n',reason{i}); + fprintf(dout,'The states are: '); + fprintf(dout,'%s ',states{i}{1:end-1}); + fprintf(dout,'%s\n\n',states{i}{end}); + end +end +fclose(fout); + +outfile = strcat(pre,'continuous_input.txt'); +fout = fopen(outfile,'w'); +fprintf(fout,'%s\t',labels{1:end-1}); +fprintf(fout,'%s\n',labels{end}); +fprintf(fout,'%i\t',levels{1:end-1}); +fprintf(fout,'%i\n',levels{end}); +%Need to replace states in discrete variables with integers for BNT +for i = 1:nnodes + if levels{i} > 1 + for j = 1:ncases + for k=1:size(states{i},1) + if data{j,i} == states{i}{k} + data{j,i} = sprintf('%i',num2cell(k){1});; + break + end + end + end + end +end +for i = 1:ncases + fprintf(fout,'%s\t',data{i,1:end-1}); + fprintf(fout,'%s\n',data{i,end}); +end +fclose(fout); + + + + + +end +% end of prepareInput.m \ No newline at end of file diff --git a/sourcecodes/parameter_learning/code_backup/readInput.m b/sourcecodes/parameter_learning/code_backup/readInput.m new file mode 100644 index 00000000..891d7f36 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/readInput.m @@ -0,0 +1,63 @@ +function [ labels, cases, bnet, node_sizes, data,labelsold] = readInput( dfile, sfile, nnodes, std_flag ) + %readInput is to be used when reading in a network with a known structure + % + %Input: + % dfile = name of the file containing the data (required) + % sfile = name of the file containing the structure (required) + % nnodes = number of nodes in the network (required) + % std_flag = flag for whether or not to standardize the data. + % (optional-- Default is FALSE) + % + % See readInputData.m and readInputStructure.m for description of the + % format of the dfile and sfile, respectively. + % + %Output: + % labels = cell array with the names of the nodes. + % cases = cell array with the data. + % bnet = BNT bayesian network with the input structure. + +if nargin < 4 + std_flag = false(1); +end + + +% read in the file with the data +[labelsold,node_sizes,cases, data] = readInputData(dfile,nnodes); + + +% read in the file with the structure +[dag] = readInputStructure(sfile,labelsold); + + +% check the ordering of the nodes and reorder if necessary +[labels,cases,dag,node_sizes,ord_flag] = checkStructure(labelsold,cases,dag,node_sizes); + +dcount = 0; +for i = 1:nnodes + if node_sizes(i) ~= 1 + dcount = dcount + 1; + end +end +discrete = zeros(1,dcount); +dcount = 0; +for i = 1:nnodes + if node_sizes(i) ~= 1 + dcount = dcount + 1; + discrete(dcount) = i; + end +end + +bnet = mk_bnet(dag,node_sizes,'discrete',discrete,'names',labels); + +%bnet.dag + +checkDiscreteNodes(bnet,cases); + +% standardize continuous data to have a mean = 0 and std = 1 +if (std_flag) + [cases] = standardizeData(labels,node_sizes,cases); +end + + +end +% end of readInput.m diff --git a/sourcecodes/parameter_learning/code_backup/readInputData.m b/sourcecodes/parameter_learning/code_backup/readInputData.m new file mode 100644 index 00000000..706e2751 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/readInputData.m @@ -0,0 +1,75 @@ +function [ labels , node_sizes, cases, data] = readInputData( dfile , nnodes ) + % readColData reads data from a file containing data in columns + % that have text titles, and possibly other header text + % + % Input: + % dfile = name of the file containing the data.(required) + % nnodes = number of columns in the data file. (required) + % + % Function assumes the following format for the input file: + % 1) First line has labels for each of the nodes. There cannot + % be spaces in any node label. + % 2) The next line is the "node_sizes" of the nodes. If the + % nodes are discrete, this number will be equal to the number + % of states. If the nodes are continuous, they should be + % equal to 1. The function assumes that any nodes with + % node_size = 1 is continuous. + % 3) The rest of the file is numeric data. The data in the input + % data has the number of columns equal to the number of + % nodes in the network and the number of rows equal to + % the number of samples. + % + % + % Output: + % labels = cell array with node (column) labels. + % node_sizes = vector with the size of each node + % cases = cell array with the data. The cases array is transposed + % in comparison with the input data to agree with the format of + % cell data used in BNT. + +% open file for input, include error handling +fin = fopen(dfile,'r'); +if fin < 0 + error(['Could not open ',dfile,' for input']); +end + +% Read in first line to get the node labels. +labels = cell(1,nnodes); +buffer = fgetl(fin); %get header line as a string +for j=1:nnodes + [next,buffer] = strtok(buffer); + labels{j} = next; +end + +% Read in the data. Use the vetorized fscanf function to load all +% numerical values into one vector. Then reshape this vector into a +% matrix. + +data = fscanf(fin,'%f'); % Load the numerical values into one long vector + + + + +nd = length(data); % total number of data points +nr = nd/nnodes; % number of rows; check (next statement) to make sure +if nr ~= round(nd/nnodes) + fprintf(1,'\ndata: nrow = %f\tncol = %d\n',nr,nnodes); + fprintf(1,'number of data points = %d does not equal nrow*ncol\n',nd); + error('data is not rectangular') +end + +data = reshape(data,nnodes,nr)'; % have to transpose the reshaped array + + +node_sizes = zeros(1,nnodes); +for j = 1:nnodes + node_sizes(j) = data(1,j); +end + +nr = nr - 1; +data(1,:) = []; +cases = cell(nnodes,nr); +cases(:,:) = num2cell(data'); + +end +% end of readInputData.m \ No newline at end of file diff --git a/sourcecodes/parameter_learning/code_backup/readInputStructure.m b/sourcecodes/parameter_learning/code_backup/readInputStructure.m new file mode 100644 index 00000000..6b3cbece --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/readInputStructure.m @@ -0,0 +1,72 @@ +function [ dag ] = readInputStructure( sfile, labels ) +%readInputStructure Read in file with structure information + % + %Input: + % sfile = name of the file containing the data (required) + % labels = cell array with node labels. (required) + % nnodes = number of columns in the data file. (required) + % + % Function assumes the following format for the structure input file: + % 1) The first line has node labels. These must be the same as + % in the input data file. They cannot contain spaces. + % 2) The remainder of the file contains the structure of the dag. + % The structure of a graph is a N-by-N matrix, where N is the + % number of nodes. There are 1's in the matrix representing + % parent-child relationships. For each 1, the row indicates + % the parent and the column indicates the child. For + % example, a 1 in the (2,3) position of the matrix indicates + % that there is an arc pointing from node 2 to node 3. + % + % + % Output: + % dag = matrix with the structure. +% +% Read in first line of the structure file +% open file for input, include error handling +fin = fopen(sfile,'r'); +if fin < 0 + error(['Could not open ',sfile,' for input']); +end + +nnodes = size(labels,2); +% Read in first line to get the node labels. +labels_test = cell(1,nnodes); +buffer = fgetl(fin); %get header line as a string +for j=1:nnodes + [next,buffer] = strtok(buffer); + labels_test{j} = next; +end + +for j=1:nnodes + if labels_test{j} ~= labels{j} + fprintf(['Label of node ',j,' is not consistent in input and structure files']) + end +end + +data = fscanf(fin,'%f'); + +nd = length(data); % total number of data points +nr = nd/nnodes; % number of rows; check (next statement) to make sure +if nr ~= round(nd/nnodes) + fprintf(1,'\ndata: nrow = %f\tncol = %d\n',nr,nnodes); + fprintf(1,'number of data points = %d does not equal nrow*ncol\n',nd); + error('Structure file does not have the correct dimensions (1)') +end +% check to make sure that structure is square +if nr ~= nnodes + error('Structure file does not have the correct dimensions (2)') +end + +data = reshape(data,nnodes,nr)'; % have to transpose the reshaped array + + +dag = zeros(nnodes,nnodes); +for i = 1:size(data,1) + for j = 1:size(data,2) + dag(i,j) = data(i,j); + end +end + + +end +% end of readInputStructure.m diff --git a/sourcecodes/parameter_learning/code_backup/runBN_initial.m b/sourcecodes/parameter_learning/code_backup/runBN_initial.m new file mode 100644 index 00000000..0deff1b5 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/runBN_initial.m @@ -0,0 +1,57 @@ +function runBN_initial(pre) +sfile=strcat(pre,'structure_input.txt'); +dfile=strcat(pre,'continuous_input.txt'); + +nnodefile=strcat(pre,'nnode.txt'); +fnnode = fopen(nnodefile,'r'); +nnodes = fscanf(fnnode,'%d'); + + +mapfilename=strcat(pre,'mapdata.txt'); +mapvalfilename=strcat(pre,'map.txt'); + +mapfile = fopen(mapfilename,'w'); + +mapval = fopen(mapvalfilename,'w'); + + +Std_flag=true; +[labels,cases,bnet,node_sizes,data,labelsold]=readInput(dfile,sfile,nnodes,Std_flag); +s=std(data,0,1); +m=mean(data); + +for i=1:nnodes + fprintf(mapval,'%s\t%f\t%f\n',labelsold{i},s(i),m(i)); +end + +fprintf(mapfile,'%s',labels{1}); +for i=2:nnodes + fprintf(mapfile,'\t%s',labels{i}); +end +fprintf(mapfile,'\n'); +fclose(mapval); +fclose(mapfile); + +%Need to rearrange the means and stdevs to match the new labeling. +means = cell(1,nnodes); +stdevs = cell(1,nnodes); +for i = 1:nnodes + for j = 1:nnodes + if strcmp(labels{i},labelsold{j}) + means{i} = m(j); + stdevs{i} = s(j); + break + end + end +end + + +[bnet]=parameterLearning(bnet,cases); + +filename=strcat(pre,'net_figure.txt'); + +drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means); + +writeParameters(pre,nnodes,bnet,labels,cases,labelsold,s,m); + +end diff --git a/sourcecodes/parameter_learning/code_backup/standardizeData.m b/sourcecodes/parameter_learning/code_backup/standardizeData.m new file mode 100644 index 00000000..db5e04c7 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/standardizeData.m @@ -0,0 +1,25 @@ +function [ cases ] = standardizeData( labels, node_sizes, cases ) +%standardizeData standardizes continuous nodes so they have a mean = 0 +% and standard deviation = 1 + + +nnodes = size(labels,2); + +%fprintf(['Standardizing data for continuous nodes\n']) +for i = 1:nnodes + if node_sizes(i) == 1 + temp = cell2num(cases(i,:)); + [temp] = standardize(temp); + cases(i,:) = num2cell(temp); + end +end + +%write standardized data to file +%fprintf(['Standardized data is written to file standardized_data.txt\n']) +%fout = 'standardized_data.txt'; +%txt = sprintf([repmat('%s\t',1,size(labels,2))],labels{:}); +%dlmwrite(fout,txt,''); +%dlmwrite(fout,cell2num(cases'),'-append','delimiter','\t'); + +end + diff --git a/sourcecodes/parameter_learning/code_backup/writeParameters.m b/sourcecodes/parameter_learning/code_backup/writeParameters.m new file mode 100644 index 00000000..0790a8e2 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/writeParameters.m @@ -0,0 +1,106 @@ +function [] = writeParameters(pre,nnodes,bnet,labels,cases,labelsold,s,m) +%Writes a file that contains the parameters of the network with no evidence. + + +%%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); + %%%Print the name of the node + fprintf(fileID,'%s\n',labels{nodeid}); + %%%Print the type of node + if bnet.node_sizes(nodeid) == 1; + line = 'Continuous node\n'; + fprintf(fileID,line); + %%% 'i' in the line below is correct: m and s are had original node labeling + adj_mu = predict.mu*s(i)+m(i); + adj_sigma = s(i)*predict.Sigma; + fprintf(fileID,'%6.4f\t%6.4f\n\n',adj_mu,adj_sigma); + 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 + 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 + diff --git a/sourcecodes/parameter_learning/code_backup/writeParameters_ev.m b/sourcecodes/parameter_learning/code_backup/writeParameters_ev.m new file mode 100644 index 00000000..fc24e2e5 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/writeParameters_ev.m @@ -0,0 +1,151 @@ +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. + +%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 + diff --git a/sourcecodes/parameter_learning/code_backup/writeParameters_int.m b/sourcecodes/parameter_learning/code_backup/writeParameters_int.m new file mode 100644 index 00000000..ed92d593 --- /dev/null +++ b/sourcecodes/parameter_learning/code_backup/writeParameters_int.m @@ -0,0 +1,186 @@ +function [] = writeParameters_int(pre,bnet,nnodes,labels,cases,stdevs,means,selectvar,selectdata) +%Writes a file that contains the parameters of the network after intervention. + + +%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 + diff --git a/sourcecodes/parameter_learning/drawFigure.m b/sourcecodes/parameter_learning/drawFigure.m index 404a65f7..7da07a90 100644 --- a/sourcecodes/parameter_learning/drawFigure.m +++ b/sourcecodes/parameter_learning/drawFigure.m @@ -1,186 +1,17 @@ -function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) +function [] = drawFigure(nnodes,bnet,labels,filename,cases,stdevs,means) %drawFigure writes the parameters and data that are needed to draw the -%structure of a Bayesian network. - - -if nargin < 8, - drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means); -else - drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata); -end; - -end - - - -function [] = drawFigureEv(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) -%Function to use if there is no entered evidence. -% +%structure of a Bayesian network for BNW. +% This is the function that is called to create the initial +% net_figure file for the network (before evidence or intervention). % -%Before each printed line, I will have a line that starts with %%% -% that describes what will be on that line - -%Create an empty evidence cell array. - -%val=cases; -%for i = 1:nnodes -% val(i,1)=val(i,2); - -%end - -A=cell2mat(cases'); -Amax=max(A); -Amin=min(A); - - -evidence = cell(1,nnodes); -engine = jtree_inf_engine(bnet); - -evidence{selectvar}=selectdata; - -[engine,loglik]=enter_evidence(engine,evidence); - -%Open the file, and write the nodes to a file. -fileID = fopen(filename,'w'); - -%%%%Evidence node -fprintf(fileID,'%i\n',selectvar); -%%% The number of nodes -fprintf(fileID,'%i\n',nnodes); -%Get canvas size -labels_temp = cellstr(labels); -[x,y] = make_layout(bnet.dag); - -x = x - min(x); -y = 1 - y; -y = y - min(y); - -[x_dim,y_dim] = canvasSize(nnodes,x,y); - -%%% The dimensions of the canvas for the javascript code -fprintf(fileID,'%i\t%i\t\n',x_dim,y_dim) - -x = x*x_dim; -y = y*y_dim; -for i = 1:nnodes, -%%% The name and X- and Y-positions of each node - fprintf(fileID,'%s\t%i\t%i\n',labels{i},round(x(i)),round(y(i))); -end - -%Get the number of parents and children for each node. -num_par = zeros(1,nnodes); -%For parents, sum down columns -for i = 1:nnodes, - for j = 1:nnodes, - if bnet.dag(j,i) == 1, - num_par(i) = num_par(i) + 1; - end - end -end -num_child = zeros(1,nnodes); -for i = 1:nnodes, - for j = 1:nnodes, - if bnet.dag(i,j) == 1, - num_child(i) = num_child(i) + 1; - end - end -end - - -for i = 1:nnodes, - %%% The name and type of each node (1=continuous, the number of states - %%% if it is discrete - fprintf(fileID,'%s\t%i\n',labels{i},bnet.node_sizes(i)); - %%% The size of the node, I am going to keep them - %%% 250(width) by 150(height) for now - %Could modify this to change the width based on the length of the node - %name - fprintf(fileID,'%i\t%i\n',250,150); - %%% The number of parents of the node, and the parents - if num_par(i) == 0; - %%% If no parents: - fprintf(fileID,'%i\n',num_par(i)); - else - parents = zeros(1,num_par(i)); - k = 1; - for j = 1:nnodes, - if bnet.dag(j,i) == 1, - parents(1,k) = j; - k = k + 1; - end - end - format = '%i\t'; - for j = 1:num_par(i)-1, - format = strcat(format,'%i\t'); - end - format = strcat(format,'%i\n'); - %%%If there are parents: - fprintf(fileID,format,num_par(i),parents(1,:)); - end - - - %%% The number of children of the node, and the children - if num_child(i) == 0; - %%% If no children: - fprintf(fileID,'%i\n',num_child(i)); - else - children = zeros(1,num_child(i)); - k = 1; - for j = 1:nnodes, - if bnet.dag(i,j) == 1, - children(1,k) = j; - k = k + 1; - end - end - format = '%i\t'; - for j = 1:num_child(i)-1, - format = strcat(format,'%i\t'); - end - format = strcat(format,'%i\n'); - %%%If there are parents: - fprintf(fileID,format,num_child(i),children(1,:)); - end - - predict = marginal_nodes(engine,i); - if isempty(evidence{i}) - if bnet.node_sizes(i) ~= 1, - for j = 1:bnet.node_sizes(i), - %%%For discrete nodes, the state and the percent of that state - fprintf(fileID,'%i\t%6.4f\n',j,predict.T(j)); - end; - else - - [x_vals,y_vals] = calcGaussian(predict.mu,predict.Sigma,Amax(i),Amin(i)); - %%%For continuous nodes, print x and the pdf of a normal curve. - for j = 1:101, - %%Undo standardization - xvals(j,1) = xvals(j,1)*stdevs{i}+means{i} - fprintf(fileID,'%6.4f\t%6.4f\n',x_vals(j,1),y_vals(j,1)); - end; - end; - else - fprintf(fileID,'%6.4f\t%6.4f\n',selectdata,1); - end - -end -%fprintf(fileID,'%s\t %\n',labels_temp{:}); - - -fclose(fileID); - -end - - - - - - -function [] = drawFigureNoEv(nnodes,bnet,labels,filename,cases,stdevs,means) -%Function to use if there is no entered evidence. -% % -%Before each printed line, I will have a line that starts with %%% -% that describes what will be on that line +% The output is the file specified by 'filename'. +% For BNW, this file is called: ???net_figure.txt +% where ??? is the prefix. +% +% drawFigure is called by runBN_intial.m +% + A=cell2mat(cases'); Amax=max(A); Amin=min(A); diff --git a/sourcecodes/parameter_learning/drawFigureM.m b/sourcecodes/parameter_learning/drawFigureM.m index 91b8698f..820f06dd 100644 --- a/sourcecodes/parameter_learning/drawFigureM.m +++ b/sourcecodes/parameter_learning/drawFigureM.m @@ -1,6 +1,15 @@ function [] = drawFigureM(nnodes,bnet,labels,filename,cases,stdevs,means,selectvar,selectdata) %drawFigureM writes the parameters and data that are needed to draw the -%structure of a Bayesian network after added evidence or intervention +%structure of a Bayesian network after adding evidence or intervention +%It creates the net_figure_new file after evidence/intervetion. +% +% The output file is specified by 'filename'. +% For BNW, the file is named ???net_figure_new.txt +% where ??? is the prefix. +% +% drawFigureM is called by Predictmultiple.m and Predictmultipleintervention.m + + fileID = fopen(filename,'w'); diff --git a/sourcecodes/parameter_learning/parameterLearning.m b/sourcecodes/parameter_learning/parameterLearning.m index 872e94b1..3ef6c0b3 100644 --- a/sourcecodes/parameter_learning/parameterLearning.m +++ b/sourcecodes/parameter_learning/parameterLearning.m @@ -1,5 +1,14 @@ function [ bnet ] = parameterLearning( bnet,cases,engine_name ) %parameterLearning Do parameter learning and inference +% It returns the bnet with parameters learned from the data in cases. +% +% This is very basic now. It could be modified to use different engine +% types in the future. Now, I always use the 'jtree_inf_engine'. +% +% +% parameterLearning is called by runBN_initial.m, +% Predictmultiple.m, and Predictmultipleintervention.m + %engine is an optional argument if nargin < 3 @@ -15,3 +24,25 @@ end end +function [ bnet ] = getParams( bnet, cases ) +%getParams Code to initialize CPT and do parameter learning. +%This will be very basic for now. I can add more options later. +% + +dnodes = bnet.dnodes; +cnodes = bnet.cnodes; +nnodes = size(dnodes,2)+size(cnodes,2); + +%make dnodes tabular_CPT +for i = 1:size(dnodes,2) + bnet.CPD{dnodes(i)} = tabular_CPD(bnet,dnodes(i)); +end + +for i = 1:size(cnodes,2) + bnet.CPD{cnodes(i)} = gaussian_CPD(bnet,cnodes(i)); +end + +bnet = learn_params(bnet,cases); + + +end diff --git a/sourcecodes/parameter_learning/prepareInput.m b/sourcecodes/parameter_learning/prepareInput.m index 28a06c15..84d2ae17 100644 --- a/sourcecodes/parameter_learning/prepareInput.m +++ b/sourcecodes/parameter_learning/prepareInput.m @@ -39,6 +39,8 @@ function [ ] = prepareInput( pre ) % 8-12) ???ban.txt, ???white.txt, ???k.txt, ???thr.txt, and % ???parent.txt: Files with default values for structure learning. % + % It is called by the run_prep_input script in the 'sourcecodes' directory. + % open file for input, include error handling dfile=strcat(pre,'continuous_input_orig.txt'); @@ -81,28 +83,36 @@ for j = 1:nnodes levels{j} = size(states{j},1); end +reason = cell(1,nnodes); %Now do some checks to see if nodes are discrete or continuous for j = 1:nnodes % If there are 3 or less unique values, I will assume that the node is discrete. if levels{j} < 4; + reason{j} = "It was determined to be discrete because there are a small number (<4) of possible values."; continue % If there are as many unique values as a third of the number of cases, % I will assume that the node is continuous. elseif levels{j} > ncases/3; levels{j} = 1; + reason{j} = "It was determined to be continuous because there are a large number of possible values compared to the number of cases."; + continue % If there are more than twenty unique values, % I will assume that the node is continuous. elseif levels{j} > 20; levels{j} = 1; + reason{j} = "It was determined to be continuous because there are many (>20) possible values."; + continue % Otherwise, I will scan through the individual values. % If any of the values contain a '.', I will assume it is continuous. else + reason{j} = "It was determined to be discrete by default."; period_test = 0; column = data(:,j); k = 1; while period_test == 0 period_test = sum(cell2mat(strfind(column(k),"."))); if period_test != 0; + reason{j} = "This variable was determined to be continuous because there were several possible values and at least one value contained a period(.)."; levels{j} = 1; end k++; @@ -131,6 +141,7 @@ if max_disc > min_cont labels_old = labels; data_old = data; states_old = states; + reason_old = reason; new_order = {}; for i=1:nnodes if levels_old{i} > 1 @@ -145,10 +156,12 @@ if max_disc > min_cont labels = {}; levels = {}; states = {}; + reason = {}; for i =1:nnodes labels{i} = labels_old{new_order{i}}; levels{i} = levels_old{new_order{i}}; states{i} = states_old{new_order{i}}; + reason{i} = reason_old{new_order{i}}; for j=1:ncases data{j,i} = data_old{j,new_order{i}}; end @@ -228,19 +241,21 @@ descfile = strcat(pre,'input_desc.txt'); dout = fopen(descfile,'w'); fprintf(dout,['As loaded, the input file had the following properties:\n\n']); dout = fopen(descfile,'a'); -fprintf(dout,'There are %i variables and %i cases(rows)\n',size(labels,2),ncases); +fprintf(dout,'There are %i variables and %i cases(rows).\n',size(labels,2),ncases); fprintf(dout,'The variable names are:\n'); fprintf(dout,'%s\t',labels{1:end-1}); fprintf(dout,'%s\n\n',labels{end}); for i=1:nnodes if levels{i} == 1 - fprintf(dout,'%s is a continuous variable\n',labels{i}); + fprintf(dout,'%s is a continuous variable.\n',labels{i}); + fprintf(dout,'%s\n',reason{i}); column = str2double(data(:,i)); colmean = mean(column); colstd = std(column); fprintf(dout,'It has a mean of %6.3f and a standard deviation of %6.3f\n\n',mean(column),std(column)) else - fprintf(dout,'%s is a discrete variable with %i states\n',labels{i},levels{i}); + fprintf(dout,'%s is a discrete variable with %i states.\n',labels{i},levels{i}); + fprintf(dout,'%s\n',reason{i}); fprintf(dout,'The states are: '); fprintf(dout,'%s ',states{i}{1:end-1}); fprintf(dout,'%s\n\n',states{i}{end}); diff --git a/sourcecodes/parameter_learning/readInput.m b/sourcecodes/parameter_learning/readInput.m index 891d7f36..87612c76 100644 --- a/sourcecodes/parameter_learning/readInput.m +++ b/sourcecodes/parameter_learning/readInput.m @@ -15,6 +15,9 @@ function [ labels, cases, bnet, node_sizes, data,labelsold] = readInput( dfile, % labels = cell array with the names of the nodes. % cases = cell array with the data. % bnet = BNT bayesian network with the input structure. + % + % readInput is called by runBN_initial.m + if nargin < 4 std_flag = false(1); diff --git a/sourcecodes/parameter_learning/readInputData.m b/sourcecodes/parameter_learning/readInputData.m index 706e2751..f06aeaa5 100644 --- a/sourcecodes/parameter_learning/readInputData.m +++ b/sourcecodes/parameter_learning/readInputData.m @@ -26,6 +26,10 @@ function [ labels , node_sizes, cases, data] = readInputData( dfile , nnodes ) % cases = cell array with the data. The cases array is transposed % in comparison with the input data to agree with the format of % cell data used in BNT. + % + % readInputData is called by readInput.m + + % open file for input, include error handling fin = fopen(dfile,'r'); diff --git a/sourcecodes/parameter_learning/readInputStructure.m b/sourcecodes/parameter_learning/readInputStructure.m index 6b3cbece..a91c34df 100644 --- a/sourcecodes/parameter_learning/readInputStructure.m +++ b/sourcecodes/parameter_learning/readInputStructure.m @@ -21,6 +21,9 @@ function [ dag ] = readInputStructure( sfile, labels ) % Output: % dag = matrix with the structure. % +% readInputStructure is called by runBN_initial.m + + % Read in first line of the structure file % open file for input, include error handling fin = fopen(sfile,'r'); diff --git a/sourcecodes/parameter_learning/runBN_initial.m b/sourcecodes/parameter_learning/runBN_initial.m index c2dec164..16ce06dc 100644 --- a/sourcecodes/parameter_learning/runBN_initial.m +++ b/sourcecodes/parameter_learning/runBN_initial.m @@ -1,4 +1,17 @@ function runBN_initial(pre) +% runBN_initial is used to create the net_figure file +% for a network without entered evidence or intervention. +% +% The input is 'pre'-- the prefix for the network and data +% in BNW. It uses this identifier to read several files from +% BNW. +% +% The output is ???net_figure.txt. It also calls writeParameters +% to write the parameter file. +% +% runBN_initial is called by run_octave in the 'sourcecodes' directory. +% + sfile=strcat(pre,'structure_input.txt'); dfile=strcat(pre,'continuous_input.txt'); @@ -21,7 +34,7 @@ s=std(data,0,1); m=mean(data); for i=1:nnodes - fprintf(mapval,'%s\t%d\t%f\t%f\n',labelsold{i},node_sizes(i),s(i),m(i)); + fprintf(mapval,'%s\t%f\t%f\n',labelsold{i},s(i),m(i)); end fprintf(mapfile,'%s',labels{1}); diff --git a/sourcecodes/parameter_learning/standardizeData.m b/sourcecodes/parameter_learning/standardizeData.m index db5e04c7..9f36fc72 100644 --- a/sourcecodes/parameter_learning/standardizeData.m +++ b/sourcecodes/parameter_learning/standardizeData.m @@ -1,7 +1,8 @@ function [ cases ] = standardizeData( labels, node_sizes, cases ) %standardizeData standardizes continuous nodes so they have a mean = 0 % and standard deviation = 1 - +% +% standardizeData is called by readInput.m nnodes = size(labels,2); @@ -14,12 +15,6 @@ for i = 1:nnodes end end -%write standardized data to file -%fprintf(['Standardized data is written to file standardized_data.txt\n']) -%fout = 'standardized_data.txt'; -%txt = sprintf([repmat('%s\t',1,size(labels,2))],labels{:}); -%dlmwrite(fout,txt,''); -%dlmwrite(fout,cell2num(cases'),'-append','delimiter','\t'); end diff --git a/sourcecodes/parameter_learning/writeParameters.m b/sourcecodes/parameter_learning/writeParameters.m index 0790a8e2..42b2a4ef 100644 --- a/sourcecodes/parameter_learning/writeParameters.m +++ b/sourcecodes/parameter_learning/writeParameters.m @@ -1,5 +1,10 @@ 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 %%Get the types of the nodes. diff --git a/sourcecodes/parameter_learning/writeParameters_ev.m b/sourcecodes/parameter_learning/writeParameters_ev.m index fc24e2e5..1f07c745 100644 --- a/sourcecodes/parameter_learning/writeParameters_ev.m +++ b/sourcecodes/parameter_learning/writeParameters_ev.m @@ -1,5 +1,11 @@ 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'); diff --git a/sourcecodes/parameter_learning/writeParameters_int.m b/sourcecodes/parameter_learning/writeParameters_int.m index ed92d593..69dcbb93 100644 --- a/sourcecodes/parameter_learning/writeParameters_int.m +++ b/sourcecodes/parameter_learning/writeParameters_int.m @@ -1,6 +1,10 @@ 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'); diff --git a/sourcecodes/run_octave_inv b/sourcecodes/run_octave_inv index 85e4ad10..f9e568e3 100644 --- a/sourcecodes/run_octave_inv +++ b/sourcecodes/run_octave_inv @@ -4,4 +4,4 @@ arg_list = argv(); addpath("../bnt-master"); addpath(genpathKPM("../bnt-master")); addpath("../parameter_learning"); -Predictmultipleintrvention(arg_list{1}); +Predictmultipleintervention(arg_list{1}); diff --git a/sourcecodes/upload_structure_file.php b/sourcecodes/upload_structure_file.php index b876d639..cb59f560 100644 --- a/sourcecodes/upload_structure_file.php +++ b/sourcecodes/upload_structure_file.php @@ -1,13 +1,17 @@ + + +

    + - - - - - - - - -

    Upload data file

    @@ -117,199 +103,41 @@ else -     +     >
    -
    1) - { - fprintf($flevel,"%s",$level_arr[$j][0]); - $m=$data_type[$j]; - for($jj=1;$jj<=$m;$jj++) - { - fprintf($flevel,"\t%s",$level_arr[$j][$jj]); - } - - fprintf($flevel,"\n"); - } -} - -fwrite($fptype,"$name_t\n"); -for($j=0;$j<$lcc;$j++) -{ - fprintf($fptype,"$data_type[$j]\t"); -} -fwrite($fptype,"\n"); -//////////////////////////////////// - - -fwrite($fpmain, "$l_type\n"); -$l_c=0; -//perform discrete level replacement -for($m=0;$m<$lcc;$m++) //column +if($searchID!="") { - if($data_type[$m]>1) + if ($UploadValue=="NO") { - $r=$data_type[$m]; - for($j=1;$j<=$r;$j++) - { - $search=$level_arr[$m][$j]; - $replace=$j."#"; - for($n=0;$n<$lc;$n++) //row - { - $val=$cont_arr_tmp[$n][$m]; - if($val==$search) - $cont_arr_tmp[$n][$m]=$replace; //alter levels - } - } - for($n=0;$n<$lc;$n++) //row - $cont_arr_tmp[$n][$m]=str_replace('#','',$cont_arr_tmp[$n][$m]); - } -} - -//write data to output file -for($j=0;$j<$lc;$j++) -{ - fprintf($fpmain,"%s",$cont_arr_tmp[$j][0]); - for($jj=1;$jj<$lcc;$jj++) - fprintf($fpmain,"\t%s",$cont_arr_tmp[$j][$jj]); - fprintf($fpmain, "\n"); + $fpdata = fopen($dir.$keyval."continuous_input_orig.txt","w"); + fwrite($fpdata,$searchID); + } + shell_exec('./run_prep_input '.$keyval); +?> + + + + + + -- cgit 1.4.1