GitHub user jojo19893 opened a pull request:
https://github.com/apache/flink/pull/656
Lossfunctions
We added Logistic Loss Functions and Hinge Loss to the Optimazation
Framework.
See for the implemented Functions:
https://github.com/JohnLangford/vowpal_wabbit/wiki/Loss-functions
Jira Issue:
https://issues.apache.org/jira/browse/FLINK-1979
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/jojo19893/flink master
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/flink/pull/656.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #656
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commit 4431e1d2ed0ebfd230ae997bcbf412c965108034
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-04-21T08:59:34Z
[FLINK-1807] [ml] Adds optimization framework and SGD solver.
Added Stochastic Gradient Descent initial version and some tests.
Added L1, L2 regularization.
Added tests for regularization, fixed parameter setting.
commit e51c63583514d51f727816944df01a0e6e8461eb
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-04-27T14:16:18Z
Added documentation, some minor fixes.
commit 4a2235c4606b03fbee8854dd02dea7faa27ace9b
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-04-28T13:43:17Z
Added license to doc file
commit afb281c0273af944fabdca96d833d95a094e7944
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-04-29T12:36:12Z
Style fixes
commit 9a810b70f55fc62164d873678437f995b44f4d8e
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-05-04T08:50:49Z
Refactored the way regularization is applied.
We are now using pattern matching to determine if a regularization type is
differentiable or not. If it is (L2) we apply the regularization at the
gradient
calculation step, before taking the update step. If it isn't (L1) the
regularization
is applied after the gradient descent step has been taken. This sets us up
nicely
for the L-BFGS algorithm, where we can calculate the regularized loss and
gradient
required if we are using L2.
commit 9c71e1a18f4011fdaec53945308c230ab6a97752
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-05-05T08:50:52Z
Added option to provide UDF for the prediction function, moved SGD
regularization to update step.
Incorporated the rest of Till's comments.
commit 3a0ef8588290c17e83bc0ffa86f1e54d10bf39e0
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-05-05T12:16:50Z
Style hotfix
commit 8314594d547557886630f3076c8f0a72bb478fac
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-05-05T12:35:53Z
Regularization test check fix
commit b8ec680d7833669e19f46c6c69f29b76b82d18f5
Author: Theodore Vasiloudis <[email protected]>
Date: 2015-05-06T14:34:08Z
Added prediction function class to alow non-linear optimization in the
future.
Small refactoring to allow calculation of regularized loss separatly from
regularized gradient.
commit 63115fbeff237642e1be87f143cb5042a4aeeff7
Author: Johannes Günther <jguenth1>
Date: 2015-05-07T09:46:24Z
Implemented Hinge Loss
commit aca48e33b4cb9c90006a77caddc5fa8f8c057217
Author: mguldner <[email protected]>
Date: 2015-05-07T09:49:12Z
Add LogisticLoss Function
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