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From "Hudson (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HAMA-809) Underflow in LogisticRegressionModel
Date Sat, 12 Oct 2013 00:16:42 GMT

    [ https://issues.apache.org/jira/browse/HAMA-809?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13793159#comment-13793159
] 

Hudson commented on HAMA-809:
-----------------------------

SUCCESS: Integrated in Hama-Nightly-for-Hadoop-2.x #61 (See [https://builds.apache.org/job/Hama-Nightly-for-Hadoop-2.x/61/])
HAMA-809 - small refactoring of LRM (tommaso: rev 1531265)
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java
HAMA-809 - applied draft patch to solve LR cost function problems (tommaso: rev 1531245)
* /hama/trunk/examples/src/test/resources/logistic_regression_sample.txt
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/math/DenseDoubleVector.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/CostFunction.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/GradientDescentBSP.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/HypothesisFunction.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LinearRegressionModel.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/LogisticRegressionModel.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/regression/RegressionModel.java
* /hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LinearRegressionModelTest.java
* /hama/trunk/ml/src/test/java/org/apache/hama/ml/regression/LogisticRegressionModelTest.java


> Underflow in LogisticRegressionModel
> ------------------------------------
>
>                 Key: HAMA-809
>                 URL: https://issues.apache.org/jira/browse/HAMA-809
>             Project: Hama
>          Issue Type: Bug
>          Components: machine learning
>    Affects Versions: 0.6.1, 0.6.2
>            Reporter: Tommaso Teofili
>            Assignee: Tommaso Teofili
>             Fix For: 0.7.0
>
>
> There's an underflow bug in _LogisticRegressionModel_ when calculating the hypothesis
output due to _double_ limitations.
> Therefore we should leverage _BigDecimals_ for such operations as they allow for more
precise calculation.
> Also the log algorithm in LRM is wrong and needs to be fixed.



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