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mpmc/dim_2.nsamples_10.nbatch_1.LfL2asd.b_1.txt

@@ -0,0 +1,25 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ags
+# '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
+10 #n, the number of points
+4.718266427516937256e-01 1.424527019262313843e-01
+6.117510795593261719e-02 6.653938293457031250e-01
+2.819448411464691162e-01 2.439030706882476807e-01
+3.516742289066314697e-01 8.250880837440490723e-01
+9.364065527915954590e-01 7.372947335243225098e-01
+1.605054140090942383e-01 3.741312026977539062e-01
+8.563662171363830566e-01 7.126528024673461914e-02
+5.443267226219177246e-01 9.403789639472961426e-01
+7.595964074134826660e-01 5.673643946647644043e-01
+6.419923305511474609e-01 4.420776069164276123e-01

mpmc/dim_2.nsamples_10.nbatch_1.LfL2ctr.b_1.txt

@@ -0,0 +1,25 @@
+# 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
+10 #n, the number of points
+1.531098634004592896e-01 1.531193107366561890e-01
+4.982938766479492188e-01 4.999959468841552734e-01
+1.531102061271667480e-01 8.468897342681884766e-01
+8.468816876411437988e-01 1.531084328889846802e-01
+6.479213833808898926e-01 6.479219794273376465e-01
+5.021970272064208984e-01 4.999971389770507812e-01
+8.468926548957824707e-01 8.468828797340393066e-01
+3.520831763744354248e-01 6.479098200798034668e-01
+3.520871996879577637e-01 3.520870506763458252e-01
+6.479202508926391602e-01 3.520825803279876709e-01

mpmc/dim_2.nsamples_10.nbatch_1.LfL2ext.b_1.txt

@@ -0,0 +1,25 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ext
+# '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
+10 #n, the number of points
+1.875714510679244995e-01 3.208380639553070068e-01
+4.747146666049957275e-01 8.014191985130310059e-01
+3.295053541660308838e-01 5.680010318756103516e-01
+5.272843837738037109e-01 1.971229910850524902e-01
+1.036977581679821014e-02 9.989157915115356445e-01
+1.017735749483108521e-01 8.992429971694946289e-01
+6.712992787361145020e-01 4.313811361789703369e-01
+2.754867891781032085e-04 7.052685618400573730e-01
+9.005260467529296875e-01 9.960620850324630737e-02
+8.137562274932861328e-01 6.781774163246154785e-01

mpmc/dim_2.nsamples_10.nbatch_1.LfL2mix.b_1.txt

@@ -0,0 +1,25 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2mix
+# '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
+10 #n, the number of points
+2.589750885963439941e-01 2.602819502353668213e-01
+9.560486674308776855e-01 7.456503510475158691e-01
+8.391746282577514648e-01 6.297546625137329102e-01
+6.552366018295288086e-01 4.540607929229736328e-01
+4.092887416481971741e-02 1.523647606372833252e-01
+4.207524061203002930e-01 8.384660482406616211e-01
+1.504983901977539062e-01 5.790776610374450684e-01
+3.723665475845336914e-01 5.066074058413505554e-02
+5.462633967399597168e-01 3.451461791992187500e-01
+7.332023382186889648e-01 9.534567594528198242e-01

mpmc/dim_2.nsamples_10.nbatch_1.LfL2per.b_1.txt

@@ -0,0 +1,25 @@
+# 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
+10 #n, the number of points
+8.545594811439514160e-01 3.549260199069976807e-01
+5.319624543190002441e-01 7.706763148307800293e-01
+4.519386291503906250e-01 2.618947923183441162e-01
+3.317783176898956299e-01 5.665844678878784180e-01
+1.333736777305603027e-01 5.329959094524383545e-02
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+7.285389304161071777e-01 9.764190912246704102e-01
+4.813549295067787170e-02 6.534323096275329590e-01
+2.470700740814208984e-01 1.671457141637802124e-01
+9.364526271820068359e-01 8.581103682518005371e-01

mpmc/dim_2.nsamples_10.nbatch_1.LfL2star.b_1.txt

@@ -0,0 +1,25 @@
+# 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
+10 #n, the number of points
+1.617343425750732422e-01 8.548951148986816406e-01
+4.797364473342895508e-01 2.027386426925659180e-01
+9.160805493593215942e-02 9.160773456096649170e-02
+3.537077605724334717e-01 6.441863179206848145e-01
+2.492302656173706055e-01 3.967199325561523438e-01
+5.973119139671325684e-01 5.250751972198486328e-01
+9.346848726272583008e-01 4.651924967765808105e-01
+8.658876419067382812e-01 9.632944464683532715e-01
+6.940287351608276367e-01 7.571041584014892578e-01
+7.860321998596191406e-01 2.799497246742248535e-01

mpmc/dim_2.nsamples_10.nbatch_1.LfL2sym.b_1.txt

@@ -0,0 +1,25 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2sym
+# '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
+10 #n, the number of points
+6.972237229347229004e-01 4.626320151146501303e-04
+1.135149076581001282e-01 6.095793843269348145e-01
+7.316296100616455078e-01 1.374941319227218628e-01
+7.335987091064453125e-01 8.625632524490356445e-01
+2.842253148555755615e-01 1.956112235784530640e-01
+8.853358626365661621e-01 6.161131858825683594e-01
+4.552888572216033936e-01 5.233278274536132812e-01
+2.960996925830841064e-01 7.995240688323974609e-01
+6.030091643333435059e-01 3.792002201080322266e-01
+6.971951723098754883e-01 9.998561143875122070e-01

mpmc/dim_2.nsamples_100.nbatch_1.LfL2asd.b_1.txt

@@ -0,0 +1,115 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ags
+# '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
+3.583003953099250793e-02 1.747141331434249878e-01
+7.545557618141174316e-01 1.337448805570602417e-01
+5.275061726570129395e-01 8.752238750457763672e-01
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mpmc/dim_2.nsamples_100.nbatch_1.LfL2ctr.b_1.txt

@@ -0,0 +1,115 @@
+# 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
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mpmc/dim_2.nsamples_100.nbatch_1.LfL2ext.b_1.txt

@@ -0,0 +1,115 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ext
+# '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
+1.094830259680747986e-01 7.998139858245849609e-01
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mpmc/dim_2.nsamples_100.nbatch_1.LfL2mix.b_1.txt

@@ -0,0 +1,115 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2mix
+# '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
+6.267202552407979965e-03 7.846791148185729980e-01
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mpmc/dim_2.nsamples_100.nbatch_1.LfL2per.b_1.txt

@@ -0,0 +1,115 @@
+# 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
+100 #n, the number of points
+3.400208801031112671e-02 7.122979760169982910e-01
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mpmc/dim_2.nsamples_100.nbatch_1.LfL2star.b_1.txt

@@ -0,0 +1,115 @@
+# 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
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mpmc/dim_2.nsamples_100.nbatch_1.LfL2sym.b_1.txt

@@ -0,0 +1,115 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2sym
+# '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
+2.773974537849426270e-01 3.475862368941307068e-02
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mpmc/dim_2.nsamples_110.nbatch_1.LfL2asd.b_1.txt

@@ -0,0 +1,125 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ags
+# '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
+110 #n, the number of points
+1.629180312156677246e-01 6.391016244888305664e-01
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mpmc/dim_2.nsamples_110.nbatch_1.LfL2ctr.b_1.txt

@@ -0,0 +1,125 @@
+# 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
+110 #n, the number of points
+5.601301789283752441e-01 1.925570070743560791e-01
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mpmc/dim_2.nsamples_110.nbatch_1.LfL2ext.b_1.txt

@@ -0,0 +1,125 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ext
+# '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
+110 #n, the number of points
+4.925502836704254150e-01 2.071973830461502075e-01
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mpmc/dim_2.nsamples_110.nbatch_1.LfL2mix.b_1.txt

@@ -0,0 +1,125 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2mix
+# '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
+110 #n, the number of points
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mpmc/dim_2.nsamples_110.nbatch_1.LfL2per.b_1.txt

@@ -0,0 +1,125 @@
+# 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
+110 #n, the number of points
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mpmc/dim_2.nsamples_110.nbatch_1.LfL2star.b_1.txt

@@ -0,0 +1,125 @@
+# 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
+110 #n, the number of points
+5.051475167274475098e-01 5.414157509803771973e-01
+1.261424422264099121e-01 9.452382922172546387e-01
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mpmc/dim_2.nsamples_110.nbatch_1.LfL2sym.b_1.txt

@@ -0,0 +1,125 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2sym
+# '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
+110 #n, the number of points
+4.031982421875000000e-01 2.268261015415191650e-01
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mpmc/dim_2.nsamples_120.nbatch_1.LfL2asd.b_1.txt

@@ -0,0 +1,135 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ags
+# '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
+120 #n, the number of points
+8.138743638992309570e-01 8.270592689514160156e-01
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mpmc/dim_2.nsamples_120.nbatch_1.LfL2ctr.b_1.txt

@@ -0,0 +1,135 @@
+# 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
+120 #n, the number of points
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mpmc/dim_2.nsamples_120.nbatch_1.LfL2ext.b_1.txt

@@ -0,0 +1,135 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ext
+# '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
+120 #n, the number of points
+9.584276080131530762e-01 2.917540967464447021e-01
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mpmc/dim_2.nsamples_120.nbatch_1.LfL2mix.b_1.txt

@@ -0,0 +1,135 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2mix
+# '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
+120 #n, the number of points
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mpmc/dim_2.nsamples_120.nbatch_1.LfL2per.b_1.txt

@@ -0,0 +1,135 @@
+# 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
+120 #n, the number of points
+3.877795934677124023e-01 8.980672359466552734e-01
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mpmc/dim_2.nsamples_120.nbatch_1.LfL2star.b_1.txt

@@ -0,0 +1,135 @@
+# 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
+120 #n, the number of points
+6.875097751617431641e-01 7.277864813804626465e-01
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mpmc/dim_2.nsamples_120.nbatch_1.LfL2sym.b_1.txt

@@ -0,0 +1,135 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2sym
+# '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
+120 #n, the number of points
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mpmc/dim_2.nsamples_128.nbatch_1.LfL2asd.b_1.txt

@@ -0,0 +1,143 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ags
+# '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
+6.756511330604553223e-01 5.280376076698303223e-01
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mpmc/dim_2.nsamples_128.nbatch_1.LfL2ctr.b_1.txt

@@ -0,0 +1,143 @@
+# 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
+128 #n, the number of points
+4.245600998401641846e-01 2.629759311676025391e-01
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mpmc/dim_2.nsamples_128.nbatch_1.LfL2ext.b_1.txt

@@ -0,0 +1,143 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ext
+# '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
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mpmc/dim_2.nsamples_128.nbatch_1.LfL2mix.b_1.txt

@@ -0,0 +1,143 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2mix
+# '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
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mpmc/dim_2.nsamples_128.nbatch_1.LfL2per.b_1.txt

@@ -0,0 +1,143 @@
+# 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
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mpmc/dim_2.nsamples_128.nbatch_1.LfL2star.b_1.txt

@@ -0,0 +1,143 @@
+# 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
+128 #n, the number of points
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mpmc/dim_2.nsamples_128.nbatch_1.LfL2sym.b_1.txt

@@ -0,0 +1,143 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2sym
+# '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
+1.533618569374084473e-01 5.653718113899230957e-01
+3.647996783256530762e-01 6.506631970405578613e-01
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mpmc/dim_2.nsamples_16.nbatch_1.LfL2asd.b_1.txt

@@ -0,0 +1,31 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ags
+# '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
+16 #n, the number of points
+1.026103645563125610e-01 4.105069339275360107e-01
+5.812466144561767578e-01 1.557531207799911499e-01
+9.053921103477478027e-01 9.165728092193603516e-02
+7.953991293907165527e-01 3.493240177631378174e-01
+2.856402695178985596e-01 4.105979576706886292e-02
+8.448917269706726074e-01 8.468937277793884277e-01
+3.866153955459594727e-02 6.751799583435058594e-01
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+4.012241363525390625e-01 5.373613834381103516e-01
+2.193052917718887329e-01 8.932088017463684082e-01

mpmc/dim_2.nsamples_16.nbatch_1.LfL2ctr.b_1.txt

@@ -0,0 +1,31 @@
+# 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
+16 #n, the number of points
+8.110924959182739258e-01 9.072285890579223633e-02
+3.345458805561065674e-01 4.340894222259521484e-01
+5.429031252861022949e-01 4.571107625961303711e-01
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+2.405019998550415039e-01 8.440246433019638062e-02
+8.964466452598571777e-01 8.964467644691467285e-01
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+2.200445234775543213e-01 6.478926539421081543e-01
+9.255301356315612793e-01 3.392622470855712891e-01

mpmc/dim_2.nsamples_16.nbatch_1.LfL2ext.b_1.txt

@@ -0,0 +1,31 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ext
+# '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
+16 #n, the number of points
+1.240256503224372864e-01 4.454207122325897217e-01
+3.503544139675796032e-04 9.992513060569763184e-01
+3.358014225959777832e-01 8.812022805213928223e-01
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+7.473800182342529297e-01 7.099410295486450195e-01
+3.917143344879150391e-01 3.041903972625732422e-01
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+4.731545448303222656e-01 5.630673170089721680e-01
+2.194630950689315796e-01 6.530299186706542969e-01

mpmc/dim_2.nsamples_16.nbatch_1.LfL2mix.b_1.txt

@@ -0,0 +1,31 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2mix
+# '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
+16 #n, the number of points
+1.010565385222434998e-01 2.825703024864196777e-01
+4.629189372062683105e-01 8.923217654228210449e-01
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+5.868296027183532715e-01 1.068002134561538696e-01
+3.086586110293865204e-02 1.595646291971206665e-01

mpmc/dim_2.nsamples_16.nbatch_1.LfL2per.b_1.txt

@@ -0,0 +1,31 @@
+# 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
+16 #n, the number of points
+4.684073030948638916e-01 1.972783170640468597e-02
+7.217126488685607910e-01 9.318731725215911865e-02
+6.617784500122070312e-01 5.304388999938964844e-01
+2.156377285718917847e-01 5.852209329605102539e-01
+9.192174077033996582e-01 6.486882567405700684e-01
+7.858322858810424805e-01 7.749922871589660645e-01
+9.625631570816040039e-01 2.130611538887023926e-01
+8.469814658164978027e-01 3.995802998542785645e-01
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+1.553743481636047363e-01 2.874630391597747803e-01
+3.455781638622283936e-01 7.191598415374755859e-01

mpmc/dim_2.nsamples_16.nbatch_1.LfL2star.b_1.txt

@@ -0,0 +1,31 @@
+# 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
+16 #n, the number of points
+1.046928018331527710e-01 7.912904024124145508e-01
+5.008146166801452637e-01 2.863610386848449707e-01
+7.189602255821228027e-01 9.968549013137817383e-01
+8.091709613800048828e-01 9.389964938163757324e-01
+6.794946789741516113e-01 1.115590408444404602e-01
+7.651095390319824219e-01 5.231306552886962891e-01
+1.670513004064559937e-01 3.706725537776947021e-01
+6.189951896667480469e-01 6.565732955932617188e-01
+4.149535000324249268e-01 4.644240736961364746e-01
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+2.308866828680038452e-01 8.947597742080688477e-01
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+8.520170450210571289e-01 2.361701875925064087e-01
+5.536813735961914062e-01 7.292049527168273926e-01

mpmc/dim_2.nsamples_16.nbatch_1.LfL2sym.b_1.txt

@@ -0,0 +1,31 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2sym
+# '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
+16 #n, the number of points
+6.990619897842407227e-01 1.761599034070968628e-01
+9.994822740554809570e-01 4.072708487510681152e-01
+6.980910301208496094e-01 7.994100451469421387e-01
+5.084194540977478027e-01 4.257598221302032471e-01
+1.954460293054580688e-01 5.447666645050048828e-01
+5.927724242210388184e-01 6.367537379264831543e-01
+3.202902376651763916e-01 1.164556369185447693e-01
+4.066110551357269287e-01 2.648424208164215088e-01
+4.060102105140686035e-01 8.862603902816772461e-01
+1.703071058727800846e-03 4.059808254241943359e-01
+1.842581778764724731e-01 9.996148347854614258e-01
+3.017129302024841309e-01 7.370691299438476562e-01
+1.832103431224822998e-01 9.704779949970543385e-04
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+8.966570496559143066e-01 3.609837293624877930e-01
+8.042647838592529297e-01 5.385689139366149902e-01

mpmc/dim_2.nsamples_20.nbatch_1.LfL2asd.b_1.txt

@@ -0,0 +1,35 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ags
+# '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
+20 #n, the number of points
+2.132983952760696411e-01 3.357120752334594727e-01
+9.649870395660400391e-01 2.290008068084716797e-01
+5.703530907630920410e-01 5.264854431152343750e-01
+7.274515628814697266e-01 3.312047943472862244e-02
+1.710627824068069458e-01 9.635863304138183594e-01
+3.099051490426063538e-02 4.718796312808990479e-01
+5.280480384826660156e-01 1.248666420578956604e-01
+1.294938027858734131e-01 7.096772640943527222e-02
+9.306486248970031738e-01 7.073882818222045898e-01
+8.688326925039291382e-02 6.892787814140319824e-01
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+6.733024716377258301e-01 9.332812428474426270e-01
+3.787418007850646973e-01 7.627003192901611328e-01
+2.765756249427795410e-01 6.183678507804870605e-01
+4.778832197189331055e-01 8.209783434867858887e-01
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+8.666210770606994629e-01 5.785817503929138184e-01

mpmc/dim_2.nsamples_20.nbatch_1.LfL2ctr.b_1.txt

@@ -0,0 +1,35 @@
+# 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
+20 #n, the number of points
+6.250224262475967407e-02 7.000355124473571777e-01
+9.429550170898437500e-01 6.234894394874572754e-01
+6.402864456176757812e-01 4.729960560798645020e-01
+6.516046077013015747e-02 2.673214375972747803e-01
+7.303112745285034180e-01 9.350391030311584473e-01
+5.794601440429687500e-01 2.715782523155212402e-01
+1.688372790813446045e-01 9.252426624298095703e-01
+3.781355023384094238e-01 1.708168685436248779e-01
+8.249452710151672363e-01 3.568778038024902344e-01
+3.117161095142364502e-01 8.152080178260803223e-01
+4.658922553062438965e-01 4.658963978290557861e-01
+5.341091156005859375e-01 5.341166853904724121e-01
+6.075468659400939941e-01 7.198728919029235840e-01
+3.922614455223083496e-01 6.077395081520080566e-01
+9.145889282226562500e-01 8.541049808263778687e-02
+4.658772349357604980e-01 5.340895056724548340e-01
+1.823318004608154297e-01 7.327946275472640991e-02
+2.804414629936218262e-01 3.914511501789093018e-01
+7.159523367881774902e-01 1.913159936666488647e-01
+8.696924448013305664e-01 8.218357563018798828e-01

mpmc/dim_2.nsamples_20.nbatch_1.LfL2ext.b_1.txt

@@ -0,0 +1,35 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ext
+# '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
+20 #n, the number of points
+4.663817584514617920e-01 3.401584327220916748e-01
+8.950666189193725586e-01 5.980818271636962891e-01
+3.990187644958496094e-01 8.207422494888305664e-01
+1.650935560464859009e-01 2.724073529243469238e-01
+3.103191852569580078e-01 4.525972604751586914e-01
+4.725983738899230957e-01 9.999668598175048828e-01
+9.963135123252868652e-01 1.820981269702315331e-03
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+5.689074993133544922e-01 7.249516248703002930e-02
+2.354491800069808960e-01 6.335247755050659180e-01
+1.344687342643737793e-01 8.819156289100646973e-01
+3.634388148784637451e-01 1.588466167449951172e-01
+6.638805866241455078e-01 9.258922934532165527e-01
+5.255692005157470703e-01 6.903542876243591309e-01

mpmc/dim_2.nsamples_20.nbatch_1.LfL2mix.b_1.txt

@@ -0,0 +1,35 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2mix
+# '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
+20 #n, the number of points
+3.232817649841308594e-01 8.555971086025238037e-02
+5.305804014205932617e-01 3.028832189738750458e-02
+8.240616321563720703e-01 5.755306482315063477e-01
+2.868485450744628906e-01 8.197251558303833008e-01
+9.715815782546997070e-01 6.718769669532775879e-01
+5.731161832809448242e-01 9.248641133308410645e-01
+6.193224787712097168e-01 2.299780100584030151e-01
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+7.812697440385818481e-02 3.169676661491394043e-01

mpmc/dim_2.nsamples_20.nbatch_1.LfL2per.b_1.txt

@@ -0,0 +1,35 @@
+# 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
+20 #n, the number of points
+7.678289711475372314e-02 6.373840570449829102e-01
+4.792043268680572510e-01 9.489140510559082031e-01
+9.808943867683410645e-01 2.931970059871673584e-01
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+7.291942834854125977e-01 4.417760372161865234e-01
+3.788097202777862549e-01 1.356010138988494873e-01

mpmc/dim_2.nsamples_20.nbatch_1.LfL2star.b_1.txt

@@ -0,0 +1,35 @@
+# 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
+20 #n, the number of points
+1.529296636581420898e-01 6.356472969055175781e-01
+5.351250767707824707e-01 9.618563652038574219e-01
+8.833299279212951660e-01 2.921265661716461182e-01
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+5.649859905242919922e-01 3.194783255457878113e-02

mpmc/dim_2.nsamples_20.nbatch_1.LfL2sym.b_1.txt

@@ -0,0 +1,35 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2sym
+# '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
+20 #n, the number of points
+2.820262743625789881e-04 9.009123459691181779e-06
+2.820254594553261995e-04 9.999771118164062500e-01
+2.485832374077290297e-04 5.222017168998718262e-01
+1.753650307655334473e-01 2.791631519794464111e-01
+2.898158729076385498e-01 9.727136045694351196e-02
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+1.969549357891082764e-01 7.101389169692993164e-01

mpmc/dim_2.nsamples_256.nbatch_1.LfL2asd.b_1.txt

@@ -0,0 +1,271 @@
+# MPMC pretrained pointset
+# Contains following parameters (refer to QMCPy's MPMC documentation for more details): 
+# 'loss_fn': L2ags
+# '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
+256 #n, the number of points
+7.525846958160400391e-01 4.244900643825531006e-01
+6.145622134208679199e-01 6.970976591110229492e-01
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