Add VecMVM

[tczhp]

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[witgo]

L1 的参数大于0时,会导致精度下降. 可以试试这里的算法, 在我的测试中效果还不错.

[witgo]

似乎有一些bug ,我跑的结果不太对:

Training loss : 3.7500901095175676
Training loss : 3.6481977670037447
Training loss : 3.0154451631968673
Training loss : 2.301574750976185
Training loss : 1.64470397565321
(Iteration 5/1000) Test RMSE:                     1.233504
Training loss : 1.2284820306510036
Training loss : 1.4732805597950736
Training loss : 1.1205931927870145
Training loss : 0.9359770573525823
Training loss : 0.9216058314346698
(Iteration 10/1000) Test RMSE:                     0.925369
Training loss : 0.913410135170783
Training loss : 1.1816797199323703
Training loss : 1.651559537221694
Training loss : 1.1079543313491855
Training loss : 0.9330512755211859
(Iteration 15/1000) Test RMSE:                     0.933812
Training loss : 0.91655996522135
Training loss : 1.3159227421256716
Training loss : 1.4240307849058782
Training loss : 1.105259133711867
Training loss : 0.9165354277926252
(Iteration 20/1000) Test RMSE:                     0.919473
Training loss : 0.9028139663645766
Training loss : 1.3253980953053717
Training loss : 1.350138999697169
Training loss : 0.9381364146316845
Training loss : 0.902270442685908
(Iteration 25/1000) Test RMSE:                     0.910946
Training loss : 0.8880398655525505
Training loss : 1.2689394561424192
Training loss : 1.3283529096666535
Training loss : 1.0666820486691853
Training loss : 0.8905154300146964
(Iteration 30/1000) Test RMSE:                     0.901018
Training loss : 0.8713604279066419
Training loss : 1.3965301563797694
Training loss : 1.2681387865382572
Training loss : 0.8955762717430986
Training loss : 0.8638836408180803
(Iteration 35/1000) Test RMSE:                     0.892930
Training loss : 0.8515089350824213
Training loss : 1.236073300499966
Training loss : 1.3715636757285579
Training loss : 1.0443572333235276
Training loss : 0.860208250298429
(Iteration 40/1000) Test RMSE:                     0.893269
Training loss : 0.8388994489050412
Training loss : 1.3173294754531584
Training loss : 1.3666662175047999
Training loss : 0.8870536556003747
Training loss : 0.8364603386191529
(Iteration 45/1000) Test RMSE:                     0.889765
Training loss : 0.8201107027514734
Training loss : 1.2157862845113319
Training loss : 1.3496795985855927
Training loss : 1.002764761280667
Training loss : 0.831910395941351
(Iteration 50/1000) Test RMSE:                     0.894383

[MetaFlowRepo]

Haipeng, would you please take a look?

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On Sat, Aug 13, 2016 at 10:07 PM -0700, "Guoqiang Li" notifications@github.com wrote:

似乎有一些bug ,我跑的结果不太对:

Training loss : 3.7500901095175676
Training loss : 3.6481977670037447
Training loss : 3.0154451631968673
Training loss : 2.301574750976185
Training loss : 1.64470397565321
(Iteration 5/1000) Test RMSE:                     1.233504
Training loss : 1.2284820306510036
Training loss : 1.4732805597950736
Training loss : 1.1205931927870145
Training loss : 0.9359770573525823
Training loss : 0.9216058314346698
(Iteration 10/1000) Test RMSE:                     0.925369
Training loss : 0.913410135170783
Training loss : 1.1816797199323703
Training loss : 1.651559537221694
Training loss : 1.1079543313491855
Training loss : 0.9330512755211859
(Iteration 15/1000) Test RMSE:                     0.933812
Training loss : 0.91655996522135
Training loss : 1.3159227421256716
Training loss : 1.4240307849058782
Training loss : 1.105259133711867
Training loss : 0.9165354277926252
(Iteration 20/1000) Test RMSE:                     0.919473
Training loss : 0.9028139663645766
Training loss : 1.3253980953053717
Training loss : 1.350138999697169
Training loss : 0.9381364146316845
Training loss : 0.902270442685908
(Iteration 25/1000) Test RMSE:                     0.910946
Training loss : 0.8880398655525505
Training loss : 1.2689394561424192
Training loss : 1.3283529096666535
Training loss : 1.0666820486691853
Training loss : 0.8905154300146964
(Iteration 30/1000) Test RMSE:                     0.901018
Training loss : 0.8713604279066419
Training loss : 1.3965301563797694
Training loss : 1.2681387865382572
Training loss : 0.8955762717430986
Training loss : 0.8638836408180803
(Iteration 35/1000) Test RMSE:                     0.892930
Training loss : 0.8515089350824213
Training loss : 1.236073300499966
Training loss : 1.3715636757285579
Training loss : 1.0443572333235276
Training loss : 0.860208250298429
(Iteration 40/1000) Test RMSE:                     0.893269
Training loss : 0.8388994489050412
Training loss : 1.3173294754531584
Training loss : 1.3666662175047999
Training loss : 0.8870536556003747
Training loss : 0.8364603386191529
(Iteration 45/1000) Test RMSE:                     0.889765
Training loss : 0.8201107027514734
Training loss : 1.2157862845113319
Training loss : 1.3496795985855927
Training loss : 1.002764761280667
Training loss : 0.831910395941351
(Iteration 50/1000) Test RMSE:                     0.894383

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#61 (comment)

[witgo]

可以移到外面.放到类里面像这样:

@transient private lazy val his = Array.fill(featureSize + views.length, rank)(0D)

[witgo]

可以看下我测试的分支:
https://github.com/witgo/zen/tree/vecMVM