=== POWER ===
dims=6
--- train ---
type(d)=<class 'numpy.ndarray'>
d.shape=(1659917, 6)
d.min()=-5.4458876
d.max()=14.193776
d.std()=0.99975556
d.mean()=-0.0001789214
--- validate ---
type(d)=<class 'numpy.ndarray'>
d.shape=(184435, 6)
d.min()=-4.8555665
d.max()=13.883265
d.std()=1.002201
d.mean()=0.0016102941
shift=array([0.973, 5.458, 0.275, 0.321, 0.836, 1.744], dtype=float32)
scale=array([0.093, 0.102, 0.068, 0.07 , 0.258, 0.282], dtype=float32)
0.0007940014 0.9838768
0.0007940014 0.9799193
=== HEPMASS ===
dims=21
--- train ---
type(d)=<class 'numpy.ndarray'>
d.shape=(315123, 21)
d.min()=-8.052965
d.max()=9.956657
d.std()=0.9999904
d.mean()=4.19782e-05
--- validate ---
type(d)=<class 'numpy.ndarray'>
d.shape=(35013, 21)
d.min()=-6.862822
d.max()=8.979903
d.std()=1.000074
d.mean()=-0.00037781012
shift=array([2.277, 2.544, 1.746, 8.065, 1.743, 5.195, 3.061, 1.742, 4.245,
       2.749, 1.743, 3.165, 2.534, 1.744, 2.012, 2.314, 1.744, 3.083,
       3.427, 5.94 , 2.533], dtype=float32)
scale=array([0.173, 0.193, 0.282, 0.09 , 0.282, 0.099, 0.16 , 0.282, 0.111,
       0.178, 0.282, 0.125, 0.193, 0.282, 0.155, 0.212, 0.282, 0.109,
       0.09 , 0.061, 0.148], dtype=float32)
0.0007377353 0.9804877
0.0012944827 0.98048466
=== MINIBOONE ===
dims=43
--- train ---
type(d)=<class 'numpy.ndarray'>
d.shape=(29556, 43)
d.min()=-23.49716
d.max()=31.425434
d.std()=1.0007212
d.mean()=-0.0003430785
--- validate ---
type(d)=<class 'numpy.ndarray'>
d.shape=(3284, 43)
d.min()=-12.925126
d.max()=29.044012
d.std()=0.9934807
d.mean()=0.0030877085
shift=array([ 2.46 ,  1.198,  0.488,  3.988,  1.495,  3.301,  6.534,  4.47 ,
        3.211,  6.294,  1.356,  3.102,  1.938,  1.469,  1.433,  0.607,
        4.04 ,  6.414, 11.067,  6.697,  2.279, 17.099, 23.509,  2.894,
        3.577,  5.511,  2.481,  7.021,  1.432,  3.617,  3.518,  2.473,
        5.524,  3.694,  3.256,  3.203, 11.397,  1.907,  1.689, 21.022,
        1.855,  7.054,  5.308], dtype=float32)
scale=array([0.076, 0.104, 0.067, 0.071, 0.078, 0.108, 0.109, 0.084, 0.089,
       0.059, 0.216, 0.244, 0.087, 0.139, 0.146, 0.03 , 0.083, 0.076,
       0.041, 0.077, 0.064, 0.054, 0.035, 0.083, 0.067, 0.069, 0.084,
       0.082, 0.091, 0.194, 0.112, 0.096, 0.105, 0.06 , 0.067, 0.124,
       0.056, 0.052, 0.049, 0.039, 0.087, 0.037, 0.089], dtype=float32)
0.00034953654 0.9869311
0.0007554137 0.98066604
=== BSDS300 ===
dims=63
--- train ---
type(d)=<class 'numpy.ndarray'>
d.shape=(1000000, 63)
d.min()=-0.9036107
d.max()=0.89652115
d.std()=0.088673905
d.mean()=1.4027781e-05
--- validate ---
type(d)=<class 'numpy.ndarray'>
d.shape=(50000, 63)
d.min()=-0.80331016
d.max()=0.9197678
d.std()=0.08211054
d.mean()=2.6888047e-05
shift=array([0.871, 0.854, 0.833, 0.829, 0.839, 0.812, 0.874, 0.916, 0.884,
       0.849, 0.765, 0.773, 0.807, 0.802, 0.797, 0.853, 0.822, 0.724,
       0.798, 0.887, 0.77 , 0.807, 0.795, 0.849, 0.779, 0.755, 0.764,
       0.791, 0.795, 0.725, 0.851, 0.816, 0.817, 0.792, 0.773, 0.882,
       0.848, 0.782, 0.779, 0.842, 0.773, 0.8  , 0.824, 0.849, 0.778,
       0.755, 0.768, 0.89 , 0.832, 0.795, 0.744, 0.701, 0.81 , 0.832,
       0.828, 0.846, 0.852, 0.847, 0.899, 0.802, 0.871, 0.876, 0.848],
      dtype=float32)
scale=array([0.581, 0.59 , 0.605, 0.589, 0.601, 0.616, 0.585, 0.557, 0.564,
       0.57 , 0.644, 0.642, 0.603, 0.634, 0.605, 0.59 , 0.573, 0.608,
       0.594, 0.579, 0.626, 0.628, 0.618, 0.595, 0.604, 0.616, 0.648,
       0.641, 0.624, 0.65 , 0.603, 0.602, 0.597, 0.608, 0.638, 0.585,
       0.58 , 0.633, 0.625, 0.565, 0.606, 0.613, 0.616, 0.61 , 0.636,
       0.644, 0.631, 0.572, 0.582, 0.602, 0.601, 0.671, 0.623, 0.604,
       0.586, 0.593, 0.555, 0.569, 0.556, 0.6  , 0.574, 0.566, 0.576],
      dtype=float32)
0.0065308795 0.9845685
0.0069483765 0.98418546
benchmark_datasets:
  bsds300:
    scale:
    - 0.5809999704360962
    - 0.5899999737739563
    - 0.6050000190734863
    - 0.5889999866485596
    - 0.6010000109672546
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    - 0.5950000286102295
    - 0.6039999723434448
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    - 0.6480000019073486
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    - 0.6240000128746033
    - 0.6499999761581421
    - 0.6029999852180481
    - 0.6019999980926514
    - 0.597000002861023
    - 0.6079999804496765
    - 0.6380000114440918
    - 0.5849999785423279
    - 0.5799999833106995
    - 0.6330000162124634
    - 0.625
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    - 0.6060000061988831
    - 0.6129999756813049
    - 0.6159999966621399
    - 0.6100000143051147
    - 0.6359999775886536
    - 0.6439999938011169
    - 0.6309999823570251
    - 0.5720000267028809
    - 0.5820000171661377
    - 0.6019999980926514
    - 0.6010000109672546
    - 0.6710000038146973
    - 0.6230000257492065
    - 0.6039999723434448
    - 0.5860000252723694
    - 0.5929999947547913
    - 0.5550000071525574
    - 0.5690000057220459
    - 0.5559999942779541
    - 0.6000000238418579
    - 0.5740000009536743
    - 0.5659999847412109
    - 0.5759999752044678
    shift:
    - 0.8709999918937683
    - 0.8539999723434448
    - 0.8330000042915344
    - 0.8289999961853027
    - 0.8389999866485596
    - 0.8119999766349792
    - 0.8740000128746033
    - 0.9160000085830688
    - 0.8840000033378601
    - 0.8489999771118164
    - 0.7649999856948853
    - 0.7730000019073486
    - 0.8069999814033508
    - 0.8019999861717224
    - 0.796999990940094
    - 0.8529999852180481
    - 0.8220000267028809
    - 0.7239999771118164
    - 0.7979999780654907
    - 0.8870000243186951
    - 0.7699999809265137
    - 0.8069999814033508
    - 0.7950000166893005
    - 0.8489999771118164
    - 0.7789999842643738
    - 0.7549999952316284
    - 0.7639999985694885
    - 0.7910000085830688
    - 0.7950000166893005
    - 0.7250000238418579
    - 0.8510000109672546
    - 0.8159999847412109
    - 0.8169999718666077
    - 0.7919999957084656
    - 0.7730000019073486
    - 0.8820000290870667
    - 0.8479999899864197
    - 0.7820000052452087
    - 0.7789999842643738
    - 0.8420000076293945
    - 0.7730000019073486
    - 0.800000011920929
    - 0.8240000009536743
    - 0.8489999771118164
    - 0.777999997138977
    - 0.7549999952316284
    - 0.7680000066757202
    - 0.8899999856948853
    - 0.8320000171661377
    - 0.7950000166893005
    - 0.7440000176429749
    - 0.7009999752044678
    - 0.8100000023841858
    - 0.8320000171661377
    - 0.828000009059906
    - 0.8460000157356262
    - 0.8519999980926514
    - 0.847000002861023
    - 0.8989999890327454
    - 0.8019999861717224
    - 0.8709999918937683
    - 0.8759999871253967
    - 0.8479999899864197
  hepmass:
    scale:
    - 0.17299999296665192
    - 0.19300000369548798
    - 0.28200000524520874
    - 0.09000000357627869
    - 0.28200000524520874
    - 0.0989999994635582
    - 0.1599999964237213
    - 0.28200000524520874
    - 0.11100000143051147
    - 0.17800000309944153
    - 0.28200000524520874
    - 0.125
    - 0.19300000369548798
    - 0.28200000524520874
    - 0.1550000011920929
    - 0.21199999749660492
    - 0.28200000524520874
    - 0.10899999737739563
    - 0.09000000357627869
    - 0.061000000685453415
    - 0.14800000190734863
    shift:
    - 2.2769999504089355
    - 2.5439999103546143
    - 1.746000051498413
    - 8.0649995803833
    - 1.7430000305175781
    - 5.195000171661377
    - 3.061000108718872
    - 1.7419999837875366
    - 4.244999885559082
    - 2.749000072479248
    - 1.7430000305175781
    - 3.1649999618530273
    - 2.5339999198913574
    - 1.74399995803833
    - 2.01200008392334
    - 2.313999891281128
    - 1.74399995803833
    - 3.0829999446868896
    - 3.427000045776367
    - 5.940000057220459
    - 2.5329999923706055
  miniboone:
    scale:
    - 0.07599999755620956
    - 0.10400000214576721
    - 0.06700000166893005
    - 0.07100000232458115
    - 0.07800000160932541
    - 0.1080000028014183
    - 0.10899999737739563
    - 0.08399999886751175
    - 0.08900000154972076
    - 0.05900000035762787
    - 0.2160000056028366
    - 0.24400000274181366
    - 0.08699999749660492
    - 0.13899999856948853
    - 0.1459999978542328
    - 0.029999999329447746
    - 0.08299999684095383
    - 0.07599999755620956
    - 0.04100000113248825
    - 0.07699999958276749
    - 0.06400000303983688
    - 0.05400000140070915
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    - 0.08299999684095383
    - 0.06700000166893005
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    - 0.08399999886751175
    - 0.0820000022649765
    - 0.09099999815225601
    - 0.1940000057220459
    - 0.1120000034570694
    - 0.09600000083446503
    - 0.10499999672174454
    - 0.05999999865889549
    - 0.06700000166893005
    - 0.12399999797344208
    - 0.0560000017285347
    - 0.052000001072883606
    - 0.04899999871850014
    - 0.039000000804662704
    - 0.08699999749660492
    - 0.03700000047683716
    - 0.08900000154972076
    shift:
    - 2.4600000381469727
    - 1.1979999542236328
    - 0.4880000054836273
    - 3.98799991607666
    - 1.4950000047683716
    - 3.3010001182556152
    - 6.533999919891357
    - 4.46999979019165
    - 3.2109999656677246
    - 6.294000148773193
    - 1.3559999465942383
    - 3.1019999980926514
    - 1.937999963760376
    - 1.468999981880188
    - 1.4329999685287476
    - 0.6069999933242798
    - 4.039999961853027
    - 6.414000034332275
    - 11.067000389099121
    - 6.697000026702881
    - 2.2790000438690186
    - 17.099000930786133
    - 23.509000778198242
    - 2.8940000534057617
    - 3.5769999027252197
    - 5.511000156402588
    - 2.4809999465942383
    - 7.020999908447266
    - 1.4320000410079956
    - 3.617000102996826
    - 3.5179998874664307
    - 2.4730000495910645
    - 5.52400016784668
    - 3.694000005722046
    - 3.25600004196167
    - 3.203000068664551
    - 11.397000312805176
    - 1.906999945640564
    - 1.6890000104904175
    - 21.02199935913086
    - 1.8550000190734863
    - 7.053999900817871
    - 5.308000087738037
  power:
    scale:
    - 0.09300000220537186
    - 0.10199999809265137
    - 0.06800000369548798
    - 0.07000000029802322
    - 0.257999986410141
    - 0.28200000524520874
    shift:
    - 0.9729999899864197
    - 5.458000183105469
    - 0.2750000059604645
    - 0.32100000977516174
    - 0.8360000252723694
    - 1.74399995803833
