SMLG (Statistical Machine Learning Group) Discussion Forum

I used Efrain's code and got the scores for different parents sets in a giving order and a giving variable. I used sprinkler dataset. The order I used is 0, 1, 2, 3, 4, 5, 6, 7. Each number means the 1st column (or the 1st variable), the 2nd column (or the 2nd variable), the 3rd column, ..., in the dataset file.


The scores are following:

-72.300931187921023
-64.815017844821099 -67.029426584943309
-65.606357051560266 -68.393477876804468 -24.320555846929555 -23.892865578321899
-57.917316536364019 -60.540943639885214 -46.58076400925836 -37.788282219748901 -51.405358171741561 -42.459561676983576 -38.863855727721955 -44.944531753759954
-70.344589690814118 -58.721905743888342 -63.319411750342404 -62.660497129252576 -69.11451536101869 -45.950214325379903 -48.482104959028796 -59.111014851999265 -66.098867830318355 -67.724441782785078 -64.761134684402705 -50.815164770190783 -53.802246108024583 -52.536866320157813 -70.316167120559967 -56.378507494074391
-64.815017844821099 -53.591114899407856 -52.494463855331965 -51.905248832682943 -62.818884063841161 -44.982253089975273 -35.449828206659255 -38.262282181464421 -52.711947273618186 -45.588524783700905 -52.469656730517265 -55.56716205142812 -41.537454210686235 -54.487608342450933 -41.405029625036903 -47.93765576088505 -37.454611302963862 -38.49396741892383 -40.46444908340424 -41.379303313687267 -43.25526541484372 -51.505376747228929 -55.661111679013302 -43.550665188113648 -47.834157666451588 -44.639602579934767 -40.781179936687906 -45.145371739791706 -45.987827386467423 -47.450653202909805 -47.62786578280916 -49.816264008524151
-64.815017844821099 -66.463504688309897 -67.243339068876423 -67.139756194062329 -67.165947384528508 -56.151585299512817 -67.494051643246095 -71.56127108966642 -71.255398806017467 -71.863971765157103 -60.126374729380053 -70.120424623184718 -71.362113618800521 -72.497883307066772 -58.089443166561402 -71.012628835405806 -72.148473673131548 -58.187701209399442 -70.893453734296415 -57.777341178490943 -72.15206377056117 -48.8452917753986 -78.006310735257387 -82.115157133262542 -63.467670975858461 -75.098250126935625 -79.877721818299605 -64.814840584478361 -75.102203780161332 -66.075338347295656 -78.886062054395524 -54.150757587274946 -78.787579139861194 -61.513480782077714 -77.021717099200742 -62.49482122971132 -79.833892667722537 -53.087986840237704 -62.443706711786206 -77.747171128928898 -53.171151623503746 -53.477856266132108 -89.503257834815557 -68.710802556556814 -83.609524091831773 -73.233969792284626 -88.856748458989259 -59.815317696826767 -73.367769449969941 -86.265774665409907 -60.662971584812411 -63.224502441945305 -66.656131387577659 -86.484684113454918 -57.161186394675248 -59.752963775099133 -58.40564745098056 -79.008477614756927 -97.757609732048621 -64.716876357678913 -70.259333164286929 -69.652523086748516 -63.36104377030771 -75.476317024145629
-70.344589690814118 -73.170319997975852 -71.766767309108715 -70.410865633518284 -71.6487230291104 -72.299234622654964 -73.055763228837762 -63.781513701139048 -76.804725172612052 -75.672987067308213 -77.614327201540419 -77.376552462620282 -78.401409793046923 -67.5321171791213 -73.197462633588103 -76.85771035638767 -74.66380019477765 -74.85495052768789 -66.984599916355279 -75.560411301156691 -70.641002783847597 -72.869229025736701 -62.690720694159609 -72.878715790222046 -76.650757608988997 -64.893216941803345 -77.399243719026217 -64.893462440710124 -69.540379176916545 -80.210882660493326 -88.039088362154203 -80.467747359173359 -82.927852542305757 -74.831430863582028 -85.308565259159437 -76.960139750784876 -81.153082246285464 -70.410143456456012 -82.546245789017291 -86.800148371007452 -71.999067012944877 -83.687641307533312 -72.444549322000526 -77.218977421702022 -80.181900341658633 -75.796956437694789 -77.133068059441555 -67.35433836123434 -78.572236847192173 -83.645402854552842 -74.037753283703466 -82.295647082552392 -70.222453844115989 -73.748076576685293 -75.362486451179635 -80.140495515867343 -68.314031597959072 -77.557426388777131 -65.72668753806434 -67.790452158078011 -80.544884960791833 -67.337316904786704 -72.403382519968801 -71.381534481087499 -92.489054435813443 -83.582793572674262 -88.313241332353883 -77.872087509928505 -90.290592696150412 -98.14251709667802 -88.42917472158706 -88.915710416290324 -79.349368171466821 -82.902020458241751 -86.905369064242834 -94.17556217358846 -82.150520970996766 -84.571435484347546 -75.216721036188559 -76.737967720076512 -91.751281854943628 -80.381395552916743 -84.411684074383047 -80.441700948598594 -81.931636699563825 -86.373581656045047 -74.417796666823733 -84.802103288131278 -71.992994762866871 -74.785616165030987 -88.96167342809801 -76.09241016662223 -83.710494637773351 -78.911670438306786 -84.832900987903201 -72.722456041266312 -76.191096021974445 -72.940487006305517 -75.854596682641926 -95.724665075352945 -104.07820037062886 -91.82405146164254 -93.629725853419785 -83.109928915488283 -87.1812568145059 -101.78526099604426 -92.458008370857002 -100.01867629756509 -88.972947338702767 -97.450003723864526 -88.713317708278879 -91.634480721116049 -83.344870658156864 -90.676580588375984 -93.527589786974588 -80.508419153218156 -84.653666562299804 -81.668056505322824 -87.30057295913322 -82.723800444960773 -108.69132008269976 -98.817307132182719 -104.23794968841941 -93.926851634831834 -104.96646775746277 -100.07479414933029 -92.917963280609442 -112.77741636858212


Next I am going to add label for each score.

The following is the scores of parents sets for every variable in giving order of 0, 1, 2, 3, 4, 5, 6, 7. These numbers mean the column number of raw input text file (sprinkler dataset). The "#" means the parent sets is empty or this variable does not have parent.

The symbol in front of ":" is the parents set. The symbol behind of ":" is the score.

For example:

For variable 2:
#, : -65.6064; 0, : -68.3935; 0,1, : -23.8929; 1, : -24.3206;

That means: for variable 2, if the parent is only 0, the score is -68.3935; if the parents is 0 and 1, the score is -23.8929.


Total score:
-389.9586893

For variable 0:
#, : -72.3009;

For variable 1:
#, : -64.815; 0, : -67.0294;

For variable 2:
#, : -65.6064; 0, : -68.3935; 0,1, : -23.8929; 1, : -24.3206;

For variable 3:
#, : -57.9173; 0, : -60.5409; 0,1, : -51.4054; 0,1,2, : -44.9445; 0,2, : -42.4596; 1, : -46.5808; 1,2, : -38.8639; 2, : -37.7883;

For variable 4:
#, : -70.3446; 0, : -58.7219; 0,1, : -45.9502; 0,1,2, : -50.8152; 0,1,2,3, : -56.3785; 0,1,3, : -53.8022; 0,2, : -48.4821; 0,2,3, : -52.5369; 0,3, : -59.111; 1, : -63.3194; 1,2, : -66.0989; 1,2,3, : -70.3162; 1,3, : -67.7244; 2, : -62.6605; 2,3, : -64.7611; 3, : -69.1145;

For variable 5:
#, : -64.815; 0, : -53.5911; 0,1, : -35.4498; 0,1,2, : -37.4546; 0,1,2,3, : -40.7812; 0,1,2,3,4, : -49.8163; 0,1,2,4, : -45.1454; 0,1,3, : -38.494; 0,1,3,4, : -45.9878; 0,1,4, : -40.4644; 0,2, : -38.2623; 0,2,3, : -41.3793; 0,2,3,4, : -47.4507; 0,2,4, : -43.2553; 0,3, : -52.7119; 0,3,4, : -51.5054; 0,4, : -45.5885; 1, : -52.4945; 1,2, : -52.4697; 1,2,3, : -55.6611; 1,2,3,4, : -47.6279; 1,2,4, : -43.5507; 1,3, : -55.5672; 1,3,4, : -47.8342; 1,4, : -41.5375; 2, : -51.9052; 2,3, : -54.4876; 2,3,4, : -44.6396; 2,4, : -41.405; 3, : -62.8189; 3,4, : -47.9377; 4, : -44.9823;

For variable 6:
#, : -64.815; 0, : -66.4635; 0,1, : -71.5613; 0,1,2, : -78.0063; 0,1,2,3, : -89.5033; 0,1,2,3,4, : -79.0085; 0,1,2,3,4,5, : -75.4763; 0,1,2,3,5, : -97.7576; 0,1,2,4, : -68.7108; 0,1,2,4,5, : -64.7169; 0,1,2,5, : -83.6095; 0,1,3, : -82.1152; 0,1,3,4, : -73.234; 0,1,3,4,5, : -70.2593; 0,1,3,5, : -88.8567; 0,1,4, : -63.4677; 0,1,4,5, : -59.8153; 0,1,5, : -75.0983; 0,2, : -71.2554; 0,2,3, : -79.8777; 0,2,3,4, : -73.3678; 0,2,3,4,5, : -69.6525; 0,2,3,5, : -86.2658; 0,2,4, : -64.8148; 0,2,4,5, : -60.663; 0,2,5, : -75.1022; 0,3, : -71.864; 0,3,4, : -66.0753; 0,3,4,5, : -63.2245; 0,3,5, : -78.8861; 0,4, : -60.1264; 0,4,5, : -54.1508; 0,5, : -70.1204; 1, : -67.2433; 1,2, : -71.3621; 1,2,3, : -78.7876; 1,2,3,4, : -66.6561; 1,2,3,4,5, : -63.361; 1,2,3,5, : -86.4847; 1,2,4, : -61.5135; 1,2,4,5, : -57.1612; 1,2,5, : -77.0217; 1,3, : -72.4979; 1,3,4, : -62.4948; 1,3,4,5, : -59.753; 1,3,5, : -79.8339; 1,4, : -58.0894; 1,4,5, : -53.088; 1,5, : -71.0126; 2, : -67.1398; 2,3, : -72.1485; 2,3,4, : -62.4437; 2,3,4,5, : -58.4056; 2,3,5, : -77.7472; 2,4, : -58.1877; 2,4,5, : -53.1712; 2,5, : -70.8935; 3, : -67.1659; 3,4, : -57.7773; 3,4,5, : -53.4779; 3,5, : -72.1521; 4, : -56.1516; 4,5, : -48.8453; 5, : -67.4941;

For variable 7:
#, : -70.3446; 0, : -73.1703; 0,1, : -76.8047; 0,1,2, : -80.2109; 0,1,2,3, : -92.4891; 0,1,2,3,4, : -95.7247; 0,1,2,3,4,5, : -108.691; 0,1,2,3,4,5,6, : -112.777; 0,1,2,3,4,6, : -98.8173; 0,1,2,3,5, : -104.078; 0,1,2,3,5,6, : -104.238; 0,1,2,3,6, : -91.8241; 0,1,2,4, : -83.5828; 0,1,2,4,5, : -93.6297; 0,1,2,4,5,6, : -93.9269; 0,1,2,4,6, : -83.1099; 0,1,2,5, : -88.3132; 0,1,2,5,6, : -87.1813; 0,1,2,6, : -77.8721; 0,1,3, : -88.0391; 0,1,3,4, : -90.2906; 0,1,3,4,5, : -101.785; 0,1,3,4,5,6, : -104.966; 0,1,3,4,6, : -92.458; 0,1,3,5, : -98.1425; 0,1,3,5,6, : -100.019; 0,1,3,6, : -88.4292; 0,1,4, : -80.4677; 0,1,4,5, : -88.9157; 0,1,4,5,6, : -88.9729; 0,1,4,6, : -79.3494; 0,1,5, : -82.9279; 0,1,5,6, : -82.902; 0,1,6, : -74.8314; 0,2, : -75.673; 0,2,3, : -85.3086; 0,2,3,4, : -86.9054; 0,2,3,4,5, : -97.45; 0,2,3,4,5,6, : -100.075; 0,2,3,4,6, : -88.7133; 0,2,3,5, : -94.1756; 0,2,3,5,6, : -91.6345; 0,2,3,6, : -82.1505; 0,2,4, : -76.9601; 0,2,4,5, : -84.5714; 0,2,4,5,6, : -83.3449; 0,2,4,6, : -75.2167; 0,2,5, : -81.1531; 0,2,5,6, : -76.738; 0,2,6, : -70.4101; 0,3, : -77.6143; 0,3,4, : -82.5462; 0,3,4,5, : -91.7513; 0,3,4,5,6, : -90.6766; 0,3,4,6, : -80.3814; 0,3,5, : -86.8001; 0,3,5,6, : -84.4117; 0,3,6, : -71.9991; 0,4, : -77.3766; 0,4,5, : -83.6876; 0,4,5,6, : -80.4417; 0,4,6, : -72.4445; 0,5, : -78.4014; 0,5,6, : -77.219; 0,6, : -67.5321; 1, : -71.7668; 1,2, : -73.1975; 1,2,3, : -80.1819; 1,2,3,4, : -81.9316; 1,2,3,4,5, : -93.5276; 1,2,3,4,5,6, : -92.918; 1,2,3,4,6, : -80.5084; 1,2,3,5, : -86.3736; 1,2,3,5,6, : -84.6537; 1,2,3,6, : -74.4178; 1,2,4, : -75.797; 1,2,4,5, : -84.8021; 1,2,4,5,6, : -81.6681; 1,2,4,6, : -71.993; 1,2,5, : -77.1331; 1,2,5,6, : -74.7856; 1,2,6, : -67.3543; 1,3, : -76.8577; 1,3,4, : -78.5722; 1,3,4,5, : -88.9617; 1,3,4,5,6, : -87.3006; 1,3,4,6, : -76.0924; 1,3,5, : -83.6454; 1,3,5,6, : -83.7105; 1,3,6, : -74.0378; 1,4, : -74.6638; 1,4,5, : -82.2956; 1,4,5,6, : -78.9117; 1,4,6, : -70.2225; 1,5, : -74.855; 1,5,6, : -73.7481; 1,6, : -66.9846; 2, : -70.4109; 2,3, : -75.5604; 2,3,4, : -75.3625; 2,3,4,5, : -84.8329; 2,3,4,5,6, : -82.7238; 2,3,4,6, : -72.7225; 2,3,5, : -80.1405; 2,3,5,6, : -76.1911; 2,3,6, : -68.314; 2,4, : -70.641; 2,4,5, : -77.5574; 2,4,5,6, : -72.9405; 2,4,6, : -65.7267; 2,5, : -72.8692; 2,5,6, : -67.7905; 2,6, : -62.6907; 3, : -71.6487; 3,4, : -72.8787; 3,4,5, : -80.5449; 3,4,5,6, : -75.8546; 3,4,6, : -67.3373; 3,5, : -76.6508; 3,5,6, : -72.4034; 3,6, : -64.8932; 4, : -72.2992; 4,5, : -77.3992; 4,5,6, : -71.3815; 4,6, : -64.8935; 5, : -73.0558; 5,6, : -69.5404; 6, : -63.7815;

The following scores are based on the order of 4 7 5 3 2 0 1 6.

Total score:
-400.734583398


For variable 0:
#, : -70.3446;

For variable 1:
#, : -70.3446; 4, : -72.2992;

For variable 2:
#, : -64.815; 4, : -44.9823; 4,7, : -50.0823; 7, : -67.5262;

For variable 3:
#, : -57.9173; 4, : -56.6872; 4,5, : -59.6426; 4,7, : -57.2667; 4,7,5, : -62.7883; 5, : -55.9212; 7, : -59.2214; 7,5, : -59.5162;

For variable 4:
#, : -65.6064; 3, : -45.4773; 4, : -57.9223; 4,3, : -41.1239; 4,5, : -54.345; 4,5,3, : -37.8259; 4,7, : -56.264; 4,7,3, : -43.6077; 4,7,5, : -54.5032; 4,7,5,3, : -42.1139; 5, : -52.6966; 5,3, : -37.146; 7, : -65.6726; 7,3, : -49.389; 7,5, : -52.5101; 7,5,3, : -40.6358;

For variable 5:
#, : -72.3009; 2, : -75.0881; 3, : -74.9246; 3,2, : -79.7593; 4, : -60.6782; 4,2, : -60.9097; 4,3, : -64.9211; 4,3,2, : -67.5351; 4,5, : -61.2845; 4,5,2, : -62.7599; 4,5,3, : -68.4888; 4,5,3,2, : -70.3461; 4,7, : -65.7556; 4,7,2, : -67.2288; 4,7,3, : -74.5886; 4,7,3,2, : -79.0779; 4,7,5, : -67.5729; 4,7,5,2, : -69.7739; 4,7,5,3, : -79.6952; 4,7,5,3,2, : -82.9632; 5, : -61.077; 5,2, : -61.4451; 5,3, : -64.8176; 5,3,2, : -66.651; 7, : -75.1267; 7,2, : -80.3502; 7,3, : -80.8902; 7,3,2, : -89.5075; 7,5, : -66.4227; 7,5,2, : -69.7289; 7,5,3, : -74.967; 7,5,3,2, : -80.6861;

For variable 6:
#, : -64.815; 0, : -67.0294; 2, : -23.5292; 2,0, : -22.5288; 3, : -53.4785; 3,0, : -57.8938; 3,2, : -24.6048; 3,2,0, : -25.0138; 4, : -57.7898; 4,0, : -54.2577; 4,2, : -26.9676; 4,2,0, : -24.8619; 4,3, : -52.0884; 4,3,0, : -52.5851; 4,3,2, : -30.1598; 4,3,2,0, : -28.8554; 4,5, : -54.345; 4,5,0, : -49.1337; 4,5,2, : -29.1132; 4,5,2,0, : -26.752; 4,5,3, : -51.9849; 4,5,3,0, : -47.0675; 4,5,3,2, : -33.1481; 4,5,3,2,0, : -31.221; 4,7, : -60.1544; 4,7,0, : -57.3489; 4,7,2, : -32.1235; 4,7,2,0, : -31.4845; 4,7,3, : -57.7819; 4,7,3,0, : -60.3294; 4,7,3,2, : -36.729; 4,7,3,2,0, : -37.6747; 4,7,5, : -59.2414; 4,7,5,0, : -54.3617; 4,7,5,2, : -36.3579; 4,7,5,2,0, : -35.8103; 4,7,5,3, : -60.4017; 4,7,5,3,0, : -57.1015; 4,7,5,3,2, : -41.8428; 4,7,5,3,2,0, : -42.4624; 5, : -52.4945; 5,0, : -48.8881; 5,2, : -24.0936; 5,2,0, : -21.7211; 5,3, : -46.2267; 5,3,0, : -43.6759; 5,3,2, : -25.7783; 5,3,2,0, : -24.4157; 7, : -66.2372; 7,0, : -70.6638; 7,2, : -26.3158; 7,2,0, : -27.0667; 7,3, : -58.6875; 7,3,0, : -68.3186; 7,3,2, : -29.2263; 7,3,2,0, : -32.1943; 7,5, : -54.2937; 7,5,0, : -53.4146; 7,5,2, : -28.3575; 7,5,2,0, : -28.8813; 7,5,3, : -53.2214; 7,5,3,0, : -55.0182; 7,5,3,2, : -32.0114; 7,5,3,2,0, : -34.3183;

For variable 7:
#, : -64.815; 0, : -66.4635; 0,1, : -71.5613; 1, : -67.2433; 2, : -67.1398; 2,0, : -71.2554; 2,0,1, : -78.0063; 2,1, : -71.3621; 3, : -67.1659; 3,0, : -71.864; 3,0,1, : -82.1152; 3,1, : -72.4979; 3,2, : -72.1485; 3,2,0, : -79.8777; 3,2,0,1, : -89.5033; 3,2,1, : -78.7876; 4, : -56.1516; 4,0, : -60.1264; 4,0,1, : -63.4677; 4,1, : -58.0894; 4,2, : -58.1877; 4,2,0, : -64.8148; 4,2,0,1, : -68.7108; 4,2,1, : -61.5135; 4,3, : -57.7773; 4,3,0, : -66.0753; 4,3,0,1, : -73.234; 4,3,1, : -62.4948; 4,3,2, : -62.4437; 4,3,2,0, : -73.3678; 4,3,2,0,1, : -79.0085; 4,3,2,1, : -66.6561; 4,5, : -48.8453; 4,5,0, : -54.1508; 4,5,0,1, : -59.8153; 4,5,1, : -53.088; 4,5,2, : -53.1712; 4,5,2,0, : -60.663; 4,5,2,0,1, : -64.7169; 4,5,2,1, : -57.1612; 4,5,3, : -53.4779; 4,5,3,0, : -63.2245; 4,5,3,0,1, : -70.2593; 4,5,3,1, : -59.753; 4,5,3,2, : -58.4056; 4,5,3,2,0, : -69.6525; 4,5,3,2,0,1, : -75.4763; 4,5,3,2,1, : -63.361; 4,7, : -48.7458; 4,7,0, : -55.1944; 4,7,0,1, : -62.3493; 4,7,1, : -53.6481; 4,7,2, : -53.2734; 4,7,2,0, : -63.0714; 4,7,2,0,1, : -68.2379; 4,7,2,1, : -57.7095; 4,7,3, : -52.2359; 4,7,3,0, : -63.9105; 4,7,3,0,1, : -75.4014; 4,7,3,1, : -60.015; 4,7,3,2, : -59.8037; 4,7,3,2,0, : -75.1757; 4,7,3,2,0,1, : -82.1011; 4,7,3,2,1, : -65.2329; 4,7,5, : -42.8276; 4,7,5,0, : -50.9048; 4,7,5,0,1, : -59.8726; 4,7,5,1, : -49.704; 4,7,5,2, : -48.5542; 4,7,5,2,0, : -59.4364; 4,7,5,2,0,1, : -65.014; 4,7,5,2,1, : -54.0271; 4,7,5,3, : -48.7876; 4,7,5,3,0, : -62.1498; 4,7,5,3,0,1, : -73.4405; 4,7,5,3,1, : -58.0919; 4,7,5,3,2, : -56.2965; 4,7,5,3,2,0, : -72.2773; 4,7,5,3,2,0,1, : -79.5624; 4,7,5,3,2,1, : -62.7514; 5, : -67.4941; 5,0, : -70.1204; 5,0,1, : -75.0983; 5,1, : -71.0126; 5,2, : -70.8935; 5,2,0, : -75.1022; 5,2,0,1, : -83.6095; 5,2,1, : -77.0217; 5,3, : -72.1521; 5,3,0, : -78.8861; 5,3,0,1, : -88.8567; 5,3,1, : -79.8339; 5,3,2, : -77.7472; 5,3,2,0, : -86.2658; 5,3,2,0,1, : -97.7576; 5,3,2,1, : -86.4847; 7, : -58.2519; 7,0, : -60.8253; 7,0,1, : -69.588; 7,1, : -62.4612; 7,2, : -59.4196; 7,2,0, : -65.9926; 7,2,0,1, : -75.6675; 7,2,1, : -65.519; 7,3, : -60.4104; 7,3,0, : -66.2487; 7,3,0,1, : -82.5052; 7,3,1, : -69.6779; 7,3,2, : -64.9021; 7,3,2,0, : -76.7197; 7,3,2,0,1, : -88.8383; 7,3,2,1, : -73.0235; 7,5, : -63.9787; 7,5,0, : -68.938; 7,5,0,1, : -75.0724; 7,5,1, : -69.9058; 7,5,2, : -65.8147; 7,5,2,0, : -70.6871; 7,5,2,0,1, : -82.4775; 7,5,2,1, : -74.6743; 7,5,3, : -67.9047; 7,5,3,0, : -76.4976; 7,5,3,0,1, : -90.7329; 7,5,3,1, : -79.899; 7,5,3,2, : -73.7978; 7,5,3,2,0, : -83.7247; 7,5,3,2,0,1, : -97.9174; 7,5,3,2,1, : -84.7648;

The following is the sorted scores for each variable.

For variable 0:
#,|0 : -72.3009;

For variable 1:
#,|1 : -64.815; 0,|1 : -67.0294;

For variable 2:
0,1,|2 : -23.8929; 1,|2 : -24.3206; #,|2 : -65.6064; 0,|2 : -68.3935;

For variable 3:
2,|3 : -37.7883; 1,2,|3 : -38.8639; 0,2,|3 : -42.4596; 0,1,2,|3 : -44.9445; 1,|3 : -46.5808; 0,1,|3 : -51.4054; #,|3 : -57.9173; 0,|3 : -60.5409;

For variable 4:
0,1,|4 : -45.9502; 0,2,|4 : -48.4821; 0,1,2,|4 : -50.8152; 0,2,3,|4 : -52.5369; 0,1,3,|4 : -53.8022; 0,1,2,3,|4 : -56.3785; 0,|4 : -58.7219; 0,3,|4 : -59.111; 2,|4 : -62.6605; 1,|4 : -63.3194; 2,3,|4 : -64.7611; 1,2,|4 : -66.0989; 1,3,|4 : -67.7244; 3,|4 : -69.1145; 1,2,3,|4 : -70.3162; #,|4 : -70.3446;

For variable 5:
0,1,|5 : -35.4498; 0,1,2,|5 : -37.4546; 0,2,|5 : -38.2623; 0,1,3,|5 : -38.494; 0,1,4,|5 : -40.4644; 0,1,2,3,|5 : -40.7812; 0,2,3,|5 : -41.3793; 2,4,|5 : -41.405; 1,4,|5 : -41.5375; 0,2,4,|5 : -43.2553; 1,2,4,|5 : -43.5507; 2,3,4,|5 : -44.6396; 4,|5 : -44.9823; 0,1,2,4,|5 : -45.1454; 0,4,|5 : -45.5885; 0,1,3,4,|5 : -45.9878; 0,2,3,4,|5 : -47.4507; 1,2,3,4,|5 : -47.6279; 1,3,4,|5 : -47.8342; 3,4,|5 : -47.9377; 0,1,2,3,4,|5 : -49.8163; 0,3,4,|5 : -51.5054; 2,|5 : -51.9052; 1,2,|5 : -52.4697; 1,|5 : -52.4945; 0,3,|5 : -52.7119; 0,|5 : -53.5911; 2,3,|5 : -54.4876; 1,3,|5 : -55.5672; 1,2,3,|5 : -55.6611; 3,|5 : -62.8189; #,|5 : -64.815;

For variable 6:
4,5,|6 : -48.8453; 1,4,5,|6 : -53.088; 2,4,5,|6 : -53.1712; 3,4,5,|6 : -53.4779; 0,4,5,|6 : -54.1508; 4,|6 : -56.1516; 1,2,4,5,|6 : -57.1612; 3,4,|6 : -57.7773; 1,4,|6 : -58.0894; 2,4,|6 : -58.1877; 2,3,4,5,|6 : -58.4056; 1,3,4,5,|6 : -59.753; 0,1,4,5,|6 : -59.8153; 0,4,|6 : -60.1264; 0,2,4,5,|6 : -60.663; 1,2,4,|6 : -61.5135; 2,3,4,|6 : -62.4437; 1,3,4,|6 : -62.4948; 0,3,4,5,|6 : -63.2245; 1,2,3,4,5,|6 : -63.361; 0,1,4,|6 : -63.4677; 0,1,2,4,5,|6 : -64.7169; 0,2,4,|6 : -64.8148; #,|6 : -64.815; 0,3,4,|6 : -66.0753; 0,|6 : -66.4635; 1,2,3,4,|6 : -66.6561; 2,|6 : -67.1398; 3,|6 : -67.1659; 1,|6 : -67.2433; 5,|6 : -67.4941; 0,1,2,4,|6 : -68.7108; 0,2,3,4,5,|6 : -69.6525; 0,5,|6 : -70.1204; 0,1,3,4,5,|6 : -70.2593; 2,5,|6 : -70.8935; 1,5,|6 : -71.0126; 0,2,|6 : -71.2554; 1,2,|6 : -71.3621; 0,1,|6 : -71.5613; 0,3,|6 : -71.864; 2,3,|6 : -72.1485; 3,5,|6 : -72.1521; 1,3,|6 : -72.4979; 0,1,3,4,|6 : -73.234; 0,2,3,4,|6 : -73.3678; 0,1,5,|6 : -75.0983; 0,2,5,|6 : -75.1022; 0,1,2,3,4,5,|6 : -75.4763; 1,2,5,|6 : -77.0217; 2,3,5,|6 : -77.7472; 0,1,2,|6 : -78.0063; 1,2,3,|6 : -78.7876; 0,3,5,|6 : -78.8861; 0,1,2,3,4,|6 : -79.0085; 1,3,5,|6 : -79.8339; 0,2,3,|6 : -79.8777; 0,1,3,|6 : -82.1152; 0,1,2,5,|6 : -83.6095; 0,2,3,5,|6 : -86.2658; 1,2,3,5,|6 : -86.4847; 0,1,3,5,|6 : -88.8567; 0,1,2,3,|6 : -89.5033; 0,1,2,3,5,|6 : -97.7576;

For variable 7:
2,6,|7 : -62.6907; 6,|7 : -63.7815; 3,6,|7 : -64.8932; 4,6,|7 : -64.8935; 2,4,6,|7 : -65.7267; 1,6,|7 : -66.9846; 3,4,6,|7 : -67.3373; 1,2,6,|7 : -67.3543; 0,6,|7 : -67.5321; 2,5,6,|7 : -67.7905; 2,3,6,|7 : -68.314; 5,6,|7 : -69.5404; 1,4,6,|7 : -70.2225; #,|7 : -70.3446; 0,2,6,|7 : -70.4101; 2,|7 : -70.4109; 2,4,|7 : -70.641; 4,5,6,|7 : -71.3815; 3,|7 : -71.6487; 1,|7 : -71.7668; 1,2,4,6,|7 : -71.993; 0,3,6,|7 : -71.9991; 4,|7 : -72.2992; 3,5,6,|7 : -72.4034; 0,4,6,|7 : -72.4445; 2,3,4,6,|7 : -72.7225; 2,5,|7 : -72.8692; 3,4,|7 : -72.8787; 2,4,5,6,|7 : -72.9405; 5,|7 : -73.0558; 0,|7 : -73.1703; 1,2,|7 : -73.1975; 1,5,6,|7 : -73.7481; 1,3,6,|7 : -74.0378; 1,2,3,6,|7 : -74.4178; 1,4,|7 : -74.6638; 1,2,5,6,|7 : -74.7856; 0,1,6,|7 : -74.8314; 1,5,|7 : -74.855; 0,2,4,6,|7 : -75.2167; 2,3,4,|7 : -75.3625; 2,3,|7 : -75.5604; 0,2,|7 : -75.673; 1,2,4,|7 : -75.797; 3,4,5,6,|7 : -75.8546; 1,3,4,6,|7 : -76.0924; 2,3,5,6,|7 : -76.1911; 3,5,|7 : -76.6508; 0,2,5,6,|7 : -76.738; 0,1,|7 : -76.8047; 1,3,|7 : -76.8577; 0,2,4,|7 : -76.9601; 1,2,5,|7 : -77.1331; 0,5,6,|7 : -77.219; 0,4,|7 : -77.3766; 4,5,|7 : -77.3992; 2,4,5,|7 : -77.5574; 0,3,|7 : -77.6143; 0,1,2,6,|7 : -77.8721; 0,5,|7 : -78.4014; 1,3,4,|7 : -78.5722; 1,4,5,6,|7 : -78.9117; 0,1,4,6,|7 : -79.3494; 2,3,5,|7 : -80.1405; 1,2,3,|7 : -80.1819; 0,1,2,|7 : -80.2109; 0,3,4,6,|7 : -80.3814; 0,4,5,6,|7 : -80.4417; 0,1,4,|7 : -80.4677; 1,2,3,4,6,|7 : -80.5084; 3,4,5,|7 : -80.5449; 0,2,5,|7 : -81.1531; 1,2,4,5,6,|7 : -81.6681; 1,2,3,4,|7 : -81.9316; 0,2,3,6,|7 : -82.1505; 1,4,5,|7 : -82.2956; 0,3,4,|7 : -82.5462; 2,3,4,5,6,|7 : -82.7238; 0,1,5,6,|7 : -82.902; 0,1,5,|7 : -82.9279; 0,1,2,4,6,|7 : -83.1099; 0,2,4,5,6,|7 : -83.3449; 0,1,2,4,|7 : -83.5828; 1,3,5,|7 : -83.6454; 0,4,5,|7 : -83.6876; 1,3,5,6,|7 : -83.7105; 0,3,5,6,|7 : -84.4117; 0,2,4,5,|7 : -84.5714; 1,2,3,5,6,|7 : -84.6537; 1,2,4,5,|7 : -84.8021; 2,3,4,5,|7 : -84.8329; 0,2,3,|7 : -85.3086; 1,2,3,5,|7 : -86.3736; 0,3,5,|7 : -86.8001; 0,2,3,4,|7 : -86.9054; 0,1,2,5,6,|7 : -87.1813; 1,3,4,5,6,|7 : -87.3006; 0,1,3,|7 : -88.0391; 0,1,2,5,|7 : -88.3132; 0,1,3,6,|7 : -88.4292; 0,2,3,4,6,|7 : -88.7133; 0,1,4,5,|7 : -88.9157; 1,3,4,5,|7 : -88.9617; 0,1,4,5,6,|7 : -88.9729; 0,1,3,4,|7 : -90.2906; 0,3,4,5,6,|7 : -90.6766; 0,2,3,5,6,|7 : -91.6345; 0,3,4,5,|7 : -91.7513; 0,1,2,3,6,|7 : -91.8241; 0,1,3,4,6,|7 : -92.458; 0,1,2,3,|7 : -92.4891; 1,2,3,4,5,6,|7 : -92.918; 1,2,3,4,5,|7 : -93.5276; 0,1,2,4,5,|7 : -93.6297; 0,1,2,4,5,6,|7 : -93.9269; 0,2,3,5,|7 : -94.1756; 0,1,2,3,4,|7 : -95.7247; 0,2,3,4,5,|7 : -97.45; 0,1,3,5,|7 : -98.1425; 0,1,2,3,4,6,|7 : -98.8173; 0,1,3,5,6,|7 : -100.019; 0,2,3,4,5,6,|7 : -100.075; 0,1,3,4,5,|7 : -101.785; 0,1,2,3,5,|7 : -104.078; 0,1,2,3,5,6,|7 : -104.238; 0,1,3,4,5,6,|7 : -104.966; 0,1,2,3,4,5,|7 : -108.691; 0,1,2,3,4,5,6,|7 : -112.777;

From each variable, if we take the first highest scores, we get the structure with the highest score. The following is the structure with the highest score.

-391.733
#,|0; #,|1; 0,1,|2; 2,|3; 0,1,|4; 0,1,|5; 4,5,|6; 2,6,|7;

The following are structures with best scores.


-391.733 #,|0; #,|1; 0,1,|2; 2,|3; 0,1,|4; 0,1,|5; 4,5,|6; 2,6,|7;

-392.161 #,|0; #,|1; 1,|2; 2,|3; 0,1,|4; 0,1,|5; 4,5,|6; 2,6,|7;

-392.809 #,|0; #,|1; 0,1,|2; 1,2,|3; 0,1,|4; 0,1,|5; 4,5,|6; 2,6,|7;

-392.824 #,|0; #,|1; 0,1,|2; 2,|3; 0,1,|4; 0,1,|5; 4,5,|6; 6,|7;

-393.738 #,|0; #,|1; 0,1,|2; 2,|3; 0,1,|4; 0,1,2,|5; 4,5,|6; 2,6,|7;

-393.948 #,|0; 0,|1; 0,1,|2; 2,|3; 0,1,|4; 0,1,|5; 4,5,|6; 2,6,|7;

-394.265 #,|0; #,|1; 0,1,|2; 2,|3; 0,2,|4; 0,1,|5; 4,5,|6; 2,6,|7;

-395.976 #,|0; #,|1; 0,1,|2; 2,|3; 0,1,|4; 0,1,|5; 1,4,5,|6; 2,6,|7;

Here I was trying to get a structure sets which is 90% or 99% highest score in the whole list. I did a test.

When I added the second highest one, the percentage is 65.656%. Suppose the order is A, B, C, D, E, F, G
-391.74 = log( P(A) + P(B))
0.65656

When I added the third highest one, the percentage is 74.725%.
-391.449 = log( P(A) + P(B) + P(C))
0.74725

When I added the 4th highest one, the percentage is 90.7962%.
-391.352 = log( P(A) + P(B) + P(C) + P(D))
0.907962

When I added the 5th highest one, the percentage is 93.0604%.
-391.28 = log( P(A) + P(B) + P(C) + P(D) + P(E))
0.930604

When I added the 6th highest one, the percentage is 95.1892%.
-391.231 = log( P(A) + P(B) + P(C) + P(D) + P(E) + P(F))
0.951892

When I added the third highest one, the percentage is 99.138%.
-391.222 = log( P(A) + P(B) + P(C) + P(D) + P(E) + P(F) +P(G))
0.99138

If I want to achieve 99.99%, I need add more structures to the list.

The following are the best several structure:

#,|0 #,|0 #,|0 #,|0 #,|0 #,|0
#,|1 #,|1 #,|1 #,|1 #,|1 #,|1
0,1,|2 1,|2 0,1,|2 0,1,|2 0,1,|2 0,1,|2
2,|3 2,|3 1,2,|3 2,|3 2,|3 2,|3
0,1,|4 0,1,|4 0,1,|4 0,1,|4 0,1,|4 0,1,|4
0,1,|5 0,1,|5 0,1,|5 0,1,|5 0,1,2,|5 0,1,|5
4,5,|6 4,5,|6 4,5,|6 4,5,|6 4,5,|6 4,5,|6
2,6,|7 2,6,|7 2,6,|7 6,|7 2,6,|7 3,6,|7
-391.7331 -392.1608 -392.8087 -392.8239 -393.7379 -392.8239

I updated the best structures with good format and the percentage.

The following is the best structures based on that user specified 99.9%.

Best several structures are:
-391.733 : [0] [1] [2|0:1] [3|2] [4|0:1] [5|0:1] [6|4:5] [7|2:6]
-392.161 : [0][1][2|1][3|2][4|0:1][5|0:1][6|4:5][7|2:6]
-392.809 : [0][1][2|0:1][3|1:2][4|0:1][5|0:1][6|4:5][7|2:6]
-392.824 : [0][1][2|0:1][3|2][4|0:1][5|0:1][6|4:5][7|6]
-393.738 : [0][1][2|0:1][3|2][4|0:1][5|0:1:2][6|4:5][7|2:6]
-393.936 : [0][1][2|0:1][3|2][4|0:1][5|0:1][6|4:5][7|3:6]
-393.936 : [0][1][2|0:1][3|2][4|0:1][5|0:1][6|4:5][7|4:6]
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