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README.md
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test: 10000 samples
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```python
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# test/avg/val.pt:
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[-0.11362826824188232, -0.7059057950973511, 0.4819808006286621, 2.2327630519866943]
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# test/avg/sq.pt:
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[52.59075927734375, 30.115631103515625, 44.977020263671875, 30.228885650634766]
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# train/avg/val.pt:
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[-0.1536690890789032, -0.7142514586448669, 0.4706766605377197, 2.24863600730896]
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# train/avg/sq.pt:
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[51.99677276611328, 30.184646606445312, 44.909732818603516, 30.234216690063477]
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```
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test: 10000 samples
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```python
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# test/avg/val.pt (mean):
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[-0.11362826824188232, -0.7059057950973511, 0.4819808006286621, 2.2327630519866943]
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# test/avg/sq.pt:
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[52.59075927734375, 30.115631103515625, 44.977020263671875, 30.228885650634766]
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# std
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# (sq - val**2)**.5
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[7.251058578491211, 5.442180633544922, 6.689148902893066, 5.024306297302246]
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# 1/std
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[0.1379109025001526, 0.18374986946582794, 0.14949584007263184, 0.19903245568275452]
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# train/avg/val.pt (mean):
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[-0.1536690890789032, -0.7142514586448669, 0.4706766605377197, 2.24863600730896]
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# train/avg/sq.pt:
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[51.99677276611328, 30.184646606445312, 44.909732818603516, 30.234216690063477]
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# std
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# (sq - val**2)**.5
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[7.2092413902282715, 5.447429656982422, 6.68492317199707, 5.017753601074219]
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# 1/std
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[0.1387108564376831, 0.18357281386852264, 0.14959034323692322, 0.1992923617362976]
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```
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