| # MPMC pretrained pointset |
| # Contains following parameters (refer to QMCPy's MPMC documentation for more details): |
| # 'loss_fn': L2per |
| # '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 |
| 128 #n, the number of points |
| 2.316357940435409546e-01 5.515685677528381348e-01 |
| 8.800894021987915039e-01 8.017933964729309082e-01 |
| 8.973165750503540039e-01 8.410831093788146973e-01 |
| 5.995514392852783203e-01 3.568570017814636230e-01 |
| 9.891525506973266602e-01 6.001392006874084473e-01 |
| 8.870135545730590820e-01 1.945458799600601196e-01 |
| 5.290864706039428711e-01 6.607088446617126465e-01 |
| 3.642175495624542236e-01 9.723697900772094727e-01 |
| 3.899813592433929443e-01 1.226555183529853821e-01 |
| 5.229892581701278687e-02 2.543488740921020508e-01 |
| 2.464114576578140259e-01 2.006237804889678955e-01 |
| 8.485790491104125977e-01 9.064659476280212402e-02 |
| 1.695401519536972046e-01 6.227246522903442383e-01 |
| 7.945187091827392578e-01 7.165040969848632812e-01 |
| 3.414402306079864502e-01 8.254581689834594727e-01 |
| 1.008736044168472290e-01 3.332736790180206299e-01 |
| 6.235533356666564941e-01 8.338714838027954102e-01 |
| 1.923584938049316406e-01 5.280063748359680176e-01 |
| 8.262329101562500000e-01 2.947613000869750977e-01 |
| 1.543763875961303711e-01 4.348405897617340088e-01 |
| 3.017728626728057861e-01 4.666140377521514893e-01 |
| 7.081124186515808105e-01 5.603711605072021484e-01 |
| 3.793696872889995575e-03 4.100925624370574951e-01 |
| 5.597998499870300293e-01 7.545315027236938477e-01 |
| 3.261591792106628418e-01 4.197646081447601318e-01 |
| 4.977132976055145264e-01 7.314004898071289062e-01 |
| 8.338493704795837402e-01 5.366783142089843750e-01 |
| 9.039502739906311035e-01 2.402762174606323242e-01 |
| 1.773390322923660278e-01 1.531233191490173340e-01 |
| 2.710923254489898682e-01 7.784598469734191895e-01 |
| 4.426358640193939209e-01 7.644881010055541992e-01 |
| 2.904956415295600891e-02 5.129167437553405762e-01 |
| 5.859966576099395752e-02 7.471298575401306152e-01 |
| 8.645890951156616211e-01 9.979990124702453613e-01 |
| 8.584675192832946777e-01 5.840531587600708008e-01 |
| 8.318310976028442383e-02 3.093278110027313232e-01 |
| 4.513745009899139404e-02 5.739519596099853516e-01 |
| 8.725436925888061523e-01 3.800387084484100342e-01 |
| 4.111013114452362061e-01 3.727061748504638672e-01 |
| 8.095107674598693848e-01 4.264693260192871094e-01 |
| 7.639487981796264648e-01 4.971483647823333740e-01 |
| 8.415237069129943848e-01 7.396258115768432617e-01 |
| 8.029682040214538574e-01 4.452893510460853577e-02 |
| 9.104813635349273682e-02 6.538797020912170410e-01 |
| 2.938947677612304688e-01 2.705332636833190918e-01 |
| 1.219250857830047607e-01 4.888678789138793945e-01 |
| 9.273853898048400879e-01 1.292646378278732300e-01 |
| 1.145860254764556885e-01 6.586857885122299194e-02 |
| 4.748211205005645752e-01 2.234970331192016602e-01 |
| 4.587510526180267334e-01 4.572740793228149414e-01 |
| 6.931642293930053711e-01 7.862321734428405762e-01 |
| 9.738488197326660156e-01 2.794101238250732422e-01 |
| 1.359991542994976044e-02 3.634479641914367676e-01 |
| 3.574630916118621826e-01 5.678802132606506348e-01 |
| 3.951920866966247559e-01 5.055088400840759277e-01 |
| 7.863898873329162598e-01 1.390642970800399780e-01 |
| 2.795759439468383789e-01 6.064125299453735352e-01 |
| 5.912493467330932617e-01 1.621536463499069214e-01 |
| 4.033855795860290527e-01 8.563319444656372070e-01 |
| 6.783333420753479004e-01 4.424439966678619385e-01 |
| 2.871141433715820312e-01 5.146084353327751160e-02 |
| 8.182913064956665039e-01 8.880385160446166992e-01 |
| 2.079875320196151733e-01 3.881345391273498535e-01 |
| 9.112416505813598633e-01 4.518709480762481689e-01 |
| 7.536925375461578369e-02 8.171645998954772949e-01 |
| 2.240184694528579712e-01 9.506453871726989746e-01 |
| 9.820030927658081055e-01 9.669535160064697266e-01 |
| 7.008238434791564941e-01 1.849739104509353638e-01 |
| 2.624852359294891357e-01 3.488000631332397461e-01 |
| 1.063456088304519653e-01 8.492926359176635742e-01 |
| 5.839436054229736328e-01 8.098969459533691406e-01 |
| 5.532097220420837402e-01 2.864435315132141113e-01 |
| 7.175542712211608887e-01 3.176290690898895264e-01 |
| 9.991204142570495605e-01 8.968817591667175293e-01 |
| 6.309328675270080566e-01 2.626286745071411133e-01 |
| 2.562647461891174316e-01 4.536637570708990097e-03 |
| 9.428262710571289062e-01 4.749355614185333252e-01 |
| 6.379683017730712891e-01 6.141114830970764160e-01 |
| 3.180646300315856934e-01 6.761422753334045410e-01 |
| 2.404704093933105469e-01 7.237383127212524414e-01 |
| 2.165288478136062622e-01 1.769229173660278320e-01 |
| 9.592993855476379395e-01 1.691065132617950439e-01 |
| 1.313284039497375488e-01 6.855204105377197266e-01 |
| 3.098238706588745117e-01 9.261863827705383301e-01 |
| 6.545681953430175781e-01 3.949353992938995361e-01 |
| 5.051087737083435059e-01 4.046150147914886475e-01 |
| 7.708067893981933594e-01 8.639107346534729004e-01 |
| 3.796844184398651123e-01 7.075992822647094727e-01 |
| 3.737412393093109131e-02 2.977235056459903717e-02 |
| 7.316185235977172852e-01 6.296862363815307617e-01 |
| 6.143651604652404785e-01 5.200912356376647949e-01 |
| 4.814641177654266357e-01 5.906550884246826172e-01 |
| 6.686624884605407715e-01 6.929631233215332031e-01 |
| 5.436853766441345215e-01 4.815443158149719238e-01 |
| 9.349905252456665039e-01 9.423011541366577148e-01 |
| 4.266014397144317627e-01 6.380690336227416992e-01 |
| 4.510398507118225098e-01 1.451464891433715820e-01 |
| 4.659118056297302246e-01 8.733844757080078125e-01 |
| 5.374283194541931152e-01 3.620713204145431519e-02 |
| 6.061927676200866699e-01 9.352312088012695312e-01 |
| 4.900945425033569336e-01 2.002010680735111237e-02 |
| 3.730597496032714844e-01 2.079811543226242065e-01 |
| 1.458102911710739136e-01 8.797854185104370117e-01 |
| 6.796260178089141846e-02 1.142689287662506104e-01 |
| 2.015385329723358154e-01 7.940998673439025879e-01 |
| 6.848899126052856445e-01 1.074080765247344971e-01 |
| 5.202162265777587891e-01 9.029989242553710938e-01 |
| 9.194230437278747559e-01 6.454538106918334961e-01 |
| 7.552447319030761719e-01 6.700142621994018555e-01 |
| 3.330220282077789307e-01 9.879111498594284058e-02 |
| 4.352888464927673340e-01 3.254075348377227783e-01 |
| 4.202853739261627197e-01 9.897046685218811035e-01 |
| 5.681597590446472168e-01 8.332640677690505981e-02 |
| 6.615210175514221191e-01 9.583629965782165527e-01 |
| 9.511158466339111328e-01 7.704635262489318848e-01 |
| 9.672421813011169434e-01 7.009934782981872559e-01 |
| 1.849705278873443604e-01 9.116487503051757812e-01 |
| 7.234510779380798340e-01 9.829214215278625488e-01 |
| 5.751807689666748047e-01 5.448196530342102051e-01 |
| 3.487299978733062744e-01 3.014068007469177246e-01 |
| 5.143592953681945801e-01 2.465207725763320923e-01 |
| 7.399773597717285156e-01 9.191077351570129395e-01 |
| 6.459580659866333008e-01 6.047710403800010681e-02 |
| 1.379073113203048706e-01 2.313235998153686523e-01 |
| 7.482486367225646973e-01 2.166854441165924072e-01 |
| 1.611520797014236450e-01 7.698188722133636475e-02 |
| 2.054861746728420258e-02 1.279736682772636414e-02 |
| 7.786564826965332031e-01 3.418277800083160400e-01 |
|
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