| # MPMC pretrained pointset |
| # Contains following parameters (refer to QMCPy's MPMC documentation for more details): |
| # 'loss_fn': L2dis |
| # '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 |
| 8.796349763870239258e-01 6.893866509199142456e-02 |
| 2.887657582759857178e-01 6.467466354370117188e-01 |
| 9.147419035434722900e-02 6.615173220634460449e-01 |
| 6.216921806335449219e-01 5.770249366760253906e-01 |
| 2.680578231811523438e-01 7.617455124855041504e-01 |
| 2.006895840167999268e-01 1.489159464836120605e-01 |
| 3.517187833786010742e-01 6.951911449432373047e-01 |
| 5.864950641989707947e-02 8.865255713462829590e-01 |
| 3.163394033908843994e-01 2.280682697892189026e-02 |
| 1.931192427873611450e-01 9.644907116889953613e-01 |
| 2.315186560153961182e-01 4.585404694080352783e-01 |
| 7.459120154380798340e-01 1.888949424028396606e-01 |
| 9.550157189369201660e-01 4.320640265941619873e-01 |
| 9.921810626983642578e-01 9.861670136451721191e-01 |
| 1.554228514432907104e-01 9.885004162788391113e-02 |
| 4.027989208698272705e-01 4.015475139021873474e-02 |
| 3.877033293247222900e-01 5.418397188186645508e-01 |
| 5.066959261894226074e-01 1.792174428701400757e-01 |
| 7.740464806556701660e-01 3.075623810291290283e-01 |
| 1.240408495068550110e-01 1.269999444484710693e-01 |
| 5.503398776054382324e-01 6.834899187088012695e-01 |
| 6.526020169258117676e-01 9.215113520622253418e-01 |
| 7.944347262382507324e-01 9.528233408927917480e-01 |
| 1.452458351850509644e-01 8.281980752944946289e-01 |
| 6.590949296951293945e-01 4.874474927783012390e-02 |
| 8.703733682632446289e-01 8.075695037841796875e-01 |
| 3.946655094623565674e-01 8.705787658691406250e-01 |
| 1.029291823506355286e-01 2.580655217170715332e-01 |
| 2.771404981613159180e-01 7.894251495599746704e-03 |
| 9.806922674179077148e-01 5.599999427795410156e-01 |
| 1.683838963508605957e-01 5.911240577697753906e-01 |
| 7.553394436836242676e-01 6.385993361473083496e-01 |
| 6.842240095138549805e-01 6.001306772232055664e-01 |
| 2.413180768489837646e-01 9.443110823631286621e-01 |
| 6.989389061927795410e-01 2.478267997503280640e-01 |
| 3.009360097348690033e-02 6.039008498191833496e-02 |
| 5.160752534866333008e-01 7.305008172988891602e-01 |
| 8.279656171798706055e-01 5.330767631530761719e-01 |
| 8.919272422790527344e-01 6.195085644721984863e-01 |
| 5.415310859680175781e-01 1.155406981706619263e-01 |
| 1.061331853270530701e-01 9.835984110832214355e-01 |
| 3.009948432445526123e-01 3.661958575248718262e-01 |
| 6.723681688308715820e-01 3.743349015712738037e-01 |
| 1.386188268661499023e-01 4.927818775177001953e-01 |
| 6.856538355350494385e-02 4.391864240169525146e-01 |
| 4.478790462017059326e-01 6.279175281524658203e-01 |
| 3.254104256629943848e-01 6.117476820945739746e-01 |
| 4.876946806907653809e-01 4.247389435768127441e-01 |
| 9.271978139877319336e-01 7.024347186088562012e-01 |
| 7.257731556892395020e-01 1.370855420827865601e-01 |
| 4.376638829708099365e-01 2.370512634515762329e-01 |
| 7.929018139839172363e-02 1.975576579570770264e-01 |
| 5.886805057525634766e-01 2.115035504102706909e-01 |
| 8.152543902397155762e-01 2.748499251902103424e-02 |
| 6.926937103271484375e-01 8.604453206062316895e-01 |
| 4.033129662275314331e-02 7.426058053970336914e-01 |
| 1.843234747648239136e-01 2.889948189258575439e-01 |
| 5.624318718910217285e-01 3.843672871589660645e-01 |
| 4.592042863368988037e-01 4.711390435695648193e-01 |
| 1.751339621841907501e-02 3.994798958301544189e-01 |
| 9.872625470161437988e-01 7.884839773178100586e-01 |
| 7.660555243492126465e-01 7.217109203338623047e-01 |
| 8.365690112113952637e-01 7.535294890403747559e-01 |
| 9.169734716415405273e-01 3.924735188484191895e-01 |
| 8.476507663726806641e-01 5.008125901222229004e-01 |
| 8.584032058715820312e-01 1.586478352546691895e-01 |
| 6.339098811149597168e-01 2.991405725479125977e-01 |
| 4.957431554794311523e-01 8.156892061233520508e-01 |
| 9.028213620185852051e-01 2.674769461154937744e-01 |
| 3.424799442291259766e-01 1.668731123208999634e-01 |
| 3.318751752376556396e-01 9.105492830276489258e-01 |
| 9.450683593750000000e-01 8.782494068145751953e-01 |
| 4.744790792465209961e-01 7.787270843982696533e-02 |
| 2.475827336311340332e-01 3.168912529945373535e-01 |
| 5.266799926757812500e-01 4.121011793613433838e-01 |
| 9.384829998016357422e-01 1.086813136935234070e-01 |
| 5.700878500938415527e-01 8.979501128196716309e-01 |
| 4.222020804882049561e-01 2.746446728706359863e-01 |
| 6.099493503570556641e-01 8.896844089031219482e-02 |
| 2.581269145011901855e-01 5.240633487701416016e-01 |
| 9.087667465209960938e-01 9.925161004066467285e-01 |
| 3.750111460685729980e-01 5.090153813362121582e-01 |
| 5.772465467453002930e-01 5.524856448173522949e-01 |
| 6.008597016334533691e-01 7.737910747528076172e-01 |
| 1.150777339935302734e-01 7.102870345115661621e-01 |
| 4.662457704544067383e-01 9.328215122222900391e-01 |
| 8.023984432220458984e-01 4.472901225090026855e-01 |
| 6.456550955772399902e-01 6.726214885711669922e-01 |
| 1.780443489551544189e-01 7.980761528015136719e-01 |
| 4.143950045108795166e-01 7.845977544784545898e-01 |
| 6.442136131227016449e-03 5.658653974533081055e-01 |
| 8.526362180709838867e-01 9.731512069702148438e-01 |
| 2.119029015302658081e-01 8.499398231506347656e-01 |
| 9.688075184822082520e-01 2.292492836713790894e-01 |
| 7.884668707847595215e-01 3.518037199974060059e-01 |
| 3.634091913700103760e-01 3.311736285686492920e-01 |
| 7.349563241004943848e-01 8.379109501838684082e-01 |
| 5.166999623179435730e-02 3.455037474632263184e-01 |
| 2.200507223606109619e-01 2.229215204715728760e-01 |
| 7.125719785690307617e-01 4.811162352561950684e-01 |
|
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