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https://issues.apache.org/jira/browse/FLINK-2013?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16189249#comment-16189249
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ASF GitHub Bot commented on FLINK-2013:
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Github user mtunique commented on a diff in the pull request:
https://github.com/apache/flink/pull/3756#discussion_r142308699
--- Diff:
flink-libraries/flink-ml/src/test/scala/org/apache/flink/ml/regression/MultipleLinearRegressionITSuite.scala
---
@@ -75,15 +75,16 @@ class MultipleLinearRegressionITSuite
val parameters = ParameterMap()
- parameters.add(MultipleLinearRegression.Stepsize, 2.0)
- parameters.add(MultipleLinearRegression.Iterations, 10)
- parameters.add(MultipleLinearRegression.ConvergenceThreshold, 0.001)
+ parameters.add(WithIterativeSolver.Stepsize, 2.0)
+ parameters.add(WithIterativeSolver.Iterations, 10)
+ parameters.add(WithIterativeSolver.ConvergenceThreshold, 0.001)
mlr.fit(sparseInputDS, parameters)
val weightList = mlr.weightsOption.get.collect()
val WeightVector(weights, intercept) = weightList.head
+ println(weightList)
--- End diff --
Ok I will delete it.
> Create generalized linear model framework
> -----------------------------------------
>
> Key: FLINK-2013
> URL: https://issues.apache.org/jira/browse/FLINK-2013
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Theodore Vasiloudis
> Assignee: Theodore Vasiloudis
> Labels: ML
>
> [Generalized linear
> models|http://en.wikipedia.org/wiki/Generalized_linear_model] (GLMs) provide
> an abstraction for many learning models that can be used for regression and
> classification tasks.
> Some example GLMs are linear regression, logistic regression, LASSO and ridge
> regression.
> The goal for this JIRA is to provide interfaces for the set of common
> properties and functions these models share.
> In order to achieve that, a design pattern similar to the one that
> [sklearn|http://scikit-learn.org/stable/modules/linear_model.html] and
> [MLlib|http://spark.apache.org/docs/1.3.0/mllib-linear-methods.html] employ
> will be used.
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