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https://issues.apache.org/jira/browse/OPENNLP-155?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13025094#comment-13025094
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Jörn Kottmann commented on OPENNLP-155:
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> BTW, the diff is difficult to read because you changed many white spaces and
> Sorry -- the code was hard to wade through, and reorganizing it helped me
> see what was going on. I also got rid of unnecessary code duplication by
> defining a variable updateValue that is +1 for the correct label and -1 for
> the incorrect labels
When you do refactoring (which influenced the accuracy), reformatting and the
actual change
at once it is hard to understand what happened at all in the end when someone
else looks at it.
> Yes. However, we can certainly fix this so that is is both fast and correct.
> I just coded it to get the right answer, but it is essentially doing double
> work now.
The focus should be on correct, so it is now better than before. How could it
be faster again?
Because now we compute the training stats after every iteration compared to
just once in the previous
implementation.
> unreliable training set accuracy in perceptron
> ----------------------------------------------
>
> Key: OPENNLP-155
> URL: https://issues.apache.org/jira/browse/OPENNLP-155
> Project: OpenNLP
> Issue Type: Improvement
> Components: Maxent
> Affects Versions: maxent-3.0.1-incubating
> Reporter: Jason Baldridge
> Assignee: Jason Baldridge
> Priority: Minor
> Original Estimate: 0h
> Remaining Estimate: 0h
>
> The training accuracies reported during perceptron training were much higher
> than final training accuracy, which turned out to be an artifact of the way
> training examples were ordered.
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