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From "Theodore Vasiloudis (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (FLINK-2860) The mlr object from the FlinkML Getting Started code example uses an undefined argument
Date Fri, 16 Oct 2015 10:46:05 GMT

     [ https://issues.apache.org/jira/browse/FLINK-2860?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Theodore Vasiloudis updated FLINK-2860:
---------------------------------------
    Description: 
The [getting started guide code example|https://ci.apache.org/projects/flink/flink-docs-master/libs/ml/#getting-started]
uses the following code:
{code}
val trainingData: DataSet[LabeledVector] = ...
val testingData: DataSet[Vector] = ...

val mlr = MultipleLinearRegression()
  .setStepsize(1.0)
  .setIterations(100)
  .setConvergenceThreshold(0.001)

mlr.fit(trainingData, parameters)
{code}

The call to {{mlr.fit()}} uses a {{parameters}} argument that is unnecessary, we should remove
that.

  was:
The getting started guide code example uses the following code:
{code}
val trainingData: DataSet[LabeledVector] = ...
val testingData: DataSet[Vector] = ...

val mlr = MultipleLinearRegression()
  .setStepsize(1.0)
  .setIterations(100)
  .setConvergenceThreshold(0.001)

mlr.fit(trainingData, parameters)
{code}

The call to {{mlr.fit()}} uses a {{parameters}} argument that is unnecessary, we should remove
that.


> The mlr object from the FlinkML Getting Started code example uses an undefined argument
> ---------------------------------------------------------------------------------------
>
>                 Key: FLINK-2860
>                 URL: https://issues.apache.org/jira/browse/FLINK-2860
>             Project: Flink
>          Issue Type: Improvement
>          Components: Machine Learning Library
>    Affects Versions: 0.9.1
>            Reporter: Theodore Vasiloudis
>            Priority: Trivial
>              Labels: ML
>
> The [getting started guide code example|https://ci.apache.org/projects/flink/flink-docs-master/libs/ml/#getting-started]
uses the following code:
> {code}
> val trainingData: DataSet[LabeledVector] = ...
> val testingData: DataSet[Vector] = ...
> val mlr = MultipleLinearRegression()
>   .setStepsize(1.0)
>   .setIterations(100)
>   .setConvergenceThreshold(0.001)
> mlr.fit(trainingData, parameters)
> {code}
> The call to {{mlr.fit()}} uses a {{parameters}} argument that is unnecessary, we should
remove that.



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