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Posted to issues@flink.apache.org by "Theodore Vasiloudis (JIRA)" <ji...@apache.org> on 2015/10/16 12:46:05 UTC
[jira] [Updated] (FLINK-2860) The mlr object from the FlinkML
Getting Started code example uses an undefined argument
[ 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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