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From "Seth Hendrickson (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-18456) Use matrix abstraction for LogisitRegression coefficients during training
Date Tue, 15 Nov 2016 22:45:58 GMT
Seth Hendrickson created SPARK-18456:
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             Summary: Use matrix abstraction for LogisitRegression coefficients during training
                 Key: SPARK-18456
                 URL: https://issues.apache.org/jira/browse/SPARK-18456
             Project: Spark
          Issue Type: Improvement
          Components: ML
            Reporter: Seth Hendrickson
            Priority: Minor


This is a follow up from [SPARK-18060|https://issues.apache.org/jira/browse/SPARK-18060].
The current code for logistic regression relies on manually indexing flat arrays of column
major coefficients, which can be messy and is hard to maintain. We can use a matrix abstraction
instead of a flat array to simplify things. This will make the code easier to read and maintain.



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