| # MPMC pretrained pointset |
| # Contains following parameters (refer to QMCPy's MPMC documentation for more details): |
| # 'loss_fn': L2ctr |
| # 'lr': 0.001 |
| # 'nlayers': 3 |
| # 'weight_decay': 1e-06 |
| # 'nhid': 32 |
| # 'nbatch': 1 |
| # 'epochs': 200000 |
| # 'start_reduce': 100000 |
| # 'radius': 0.35 |
| # 'weights': None |
| # 'n_projections': 15 |
| 2 #d, the number of dimensions |
| 100 #n, the number of points |
| 1.997410356998443604e-01 6.868484616279602051e-01 |
| 8.200907111167907715e-01 6.528407931327819824e-01 |
| 1.586754322052001953e-01 4.451254308223724365e-01 |
| 4.913831353187561035e-01 3.934878408908843994e-01 |
| 3.333078920841217041e-01 4.888990521430969238e-01 |
| 7.691671252250671387e-01 4.511461555957794189e-01 |
| 4.444168508052825928e-01 8.835573196411132812e-01 |
| 7.596493363380432129e-01 5.404554009437561035e-01 |
| 5.501159280538558960e-02 4.116271734237670898e-01 |
| 4.331797063350677490e-01 9.634071588516235352e-02 |
| 1.403832286596298218e-01 3.219910338521003723e-02 |
| 5.911989212036132812e-01 9.230418205261230469e-01 |
| 5.496469140052795410e-01 5.262841582298278809e-01 |
| 3.491946458816528320e-01 7.058427333831787109e-01 |
| 4.216218292713165283e-01 6.418108940124511719e-01 |
| 8.120504021644592285e-01 2.363380640745162964e-01 |
| 4.033814370632171631e-01 7.989755272865295410e-01 |
| 7.220111489295959473e-01 9.046420454978942871e-01 |
| 1.151855513453483582e-01 5.436145067214965820e-01 |
| 3.337040543556213379e-02 6.011053323745727539e-01 |
| 9.660548567771911621e-01 7.776319384574890137e-01 |
| 4.688100814819335938e-01 1.967978030443191528e-01 |
| 3.251728713512420654e-01 7.555320262908935547e-01 |
| 1.179676130414009094e-01 2.766693234443664551e-01 |
| 8.250819444656372070e-01 4.780340194702148438e-01 |
| 3.754751384258270264e-01 9.200546145439147949e-01 |
| 7.458836585283279419e-02 6.602256894111633301e-01 |
| 6.273087263107299805e-01 8.009387254714965820e-01 |
| 8.809520006179809570e-01 4.250488579273223877e-01 |
| 2.395828366279602051e-01 5.744001865386962891e-01 |
| 5.109449028968811035e-01 3.336417973041534424e-01 |
| 2.027825266122817993e-01 2.348484992980957031e-01 |
| 7.462704777717590332e-01 7.545252442359924316e-01 |
| 3.016121685504913330e-01 9.376106858253479004e-01 |
| 7.533065676689147949e-01 1.708392351865768433e-01 |
| 8.376750946044921875e-01 1.456952244043350220e-01 |
| 9.223718047142028809e-01 8.154445886611938477e-02 |
| 5.333227515220642090e-01 1.547121107578277588e-01 |
| 3.579103946685791016e-01 5.232701301574707031e-01 |
| 6.191568970680236816e-01 1.927265077829360962e-01 |
| 4.501451551914215088e-01 4.265729486942291260e-01 |
| 1.289017796516418457e-01 8.645351529121398926e-01 |
| 5.607796907424926758e-01 8.616803288459777832e-01 |
| 6.662546396255493164e-01 3.892906010150909424e-01 |
| 6.482068896293640137e-01 5.851760879158973694e-02 |
| 4.593833386898040771e-01 5.603795051574707031e-01 |
| 1.384409237653017044e-02 7.767558097839355469e-01 |
| 9.322063624858856201e-02 1.628842949867248535e-01 |
| 8.670450448989868164e-01 1.172483861446380615e-01 |
| 2.857287228107452393e-01 2.989452779293060303e-01 |
| 3.918319940567016602e-01 1.822730600833892822e-01 |
| 4.770246446132659912e-01 7.444489598274230957e-01 |
| 3.422658741474151611e-01 1.166732683777809143e-01 |
| 9.869869947433471680e-01 7.060934901237487793e-01 |
| 1.780993640422821045e-01 1.353930979967117310e-01 |
| 8.395400047302246094e-01 8.842847347259521484e-01 |
| 8.993853926658630371e-01 2.787670493125915527e-01 |
| 3.075179755687713623e-01 1.253193244338035583e-02 |
| 5.142847895622253418e-01 6.654452681541442871e-01 |
| 7.855967283248901367e-01 2.090975940227508545e-01 |
| 9.813403487205505371e-01 3.667915984988212585e-02 |
| 7.326636910438537598e-01 3.429445028305053711e-01 |
| 6.955520510673522949e-01 6.309449672698974609e-01 |
| 6.084619164466857910e-01 5.877916216850280762e-01 |
| 5.595140457153320312e-01 2.999433279037475586e-01 |
| 6.518495082855224609e-01 7.327796816825866699e-01 |
| 4.105070829391479492e-01 3.640411198139190674e-01 |
| 6.838261485099792480e-01 2.595296800136566162e-01 |
| 1.510430127382278442e-01 7.288141846656799316e-01 |
| 4.121527075767517090e-02 8.368884772062301636e-02 |
| 5.000053048133850098e-01 4.841435849666595459e-01 |
| 7.730858922004699707e-01 6.814561486244201660e-01 |
| 7.068417072296142578e-01 5.117027163505554199e-01 |
| 9.430137276649475098e-01 9.434631466865539551e-01 |
| 5.358713865280151367e-01 7.201654314994812012e-01 |
| 5.468813776969909668e-01 4.919578731060028076e-01 |
| 7.127141952514648438e-01 1.270160637795925140e-02 |
| 6.010757684707641602e-01 4.074910879135131836e-01 |
| 7.961089611053466797e-01 9.857886433601379395e-01 |
| 2.802005112171173096e-01 6.255009770393371582e-01 |
| 4.888768494129180908e-02 9.594683647155761719e-01 |
| 2.627798914909362793e-01 3.231499791145324707e-01 |
| 2.224589437246322632e-01 9.861271381378173828e-01 |
| 8.812554478645324707e-01 5.652391314506530762e-01 |
| 2.203525602817535400e-01 3.834988474845886230e-01 |
| 5.819894075393676758e-01 9.825672954320907593e-02 |
| 9.632019996643066406e-01 3.154447972774505615e-01 |
| 1.773819774389266968e-01 8.147498965263366699e-01 |
| 7.271870225667953491e-02 3.462336361408233643e-01 |
| 2.599354684352874756e-01 8.402431607246398926e-01 |
| 2.416360229253768921e-01 6.021430343389511108e-02 |
| 9.209991693496704102e-01 6.063022017478942871e-01 |
| 3.209580481052398682e-01 4.655430912971496582e-01 |
| 9.467343986034393311e-02 9.000492095947265625e-01 |
| 6.763351559638977051e-01 9.637168645858764648e-01 |
| 9.422768950462341309e-01 3.712683618068695068e-01 |
| 1.416723057627677917e-02 2.086518257856369019e-01 |
| 8.682407736778259277e-01 8.506595492362976074e-01 |
| 9.014168977737426758e-01 8.204362392425537109e-01 |
| 3.664174973964691162e-01 2.544662356376647949e-01 |
|
|
