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https://issues.apache.org/jira/browse/SPARK-16445?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15394654#comment-15394654
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Xin Ren edited comment on SPARK-16445 at 7/26/16 10:17 PM:
-----------------------------------------------------------

I'm still working on it, hopefully by end of this weekend I can submit PR :)

I just have a quick question that which parameters should be passed from R 
command?

For fit() of wrapper class, there are many parameters 
https://github.com/apache/spark/compare/master...keypointt:SPARK-16445?expand=1#diff-ccb8590441998a896d1b74ca605b56efR62
{code}
  def fit(
      formula: String,
      data: DataFrame,
      blockSize: Int,
      layers: Array[Int],
      initialWeights: Vector,
      solver: String,
      seed: Long,
      maxIter: Int,
      tol: Double,
      stepSize: Double
     ): MultilayerPerceptronClassifierWrapper = {
{code}


And for R part, should I pass all the parameters from R command? 
https://github.com/apache/spark/compare/master...keypointt:SPARK-16445?expand=1#diff-7ede1519b4a56647801b51af33c2dd18R461

I find in the example 
(http://spark.apache.org/docs/latest/ml-classification-regression.html#multilayer-perceptron-classifier),
 only below parameters are being set, the rest are just usign default values

{code}
val trainer = new MultilayerPerceptronClassifier()
  .setLayers(layers)
  .setBlockSize(128)
  .setSeed(1234L)
  .setMaxIter(100)
{code}


was (Author: iamshrek):
I'm still working on it, hopefully by end of this weekend I can submit PR :)

I just have a quick question that which parameters should be passed from R 
command?

For fit() of wrapper class, there are many parameters 
https://github.com/apache/spark/compare/master...keypointt:SPARK-16445?expand=1#diff-ccb8590441998a896d1b74ca605b56efR62
{code}
  def fit(
      formula: String,
      data: DataFrame,
      blockSize: Int,
      layers: Array[Int],
      initialWeights: Vector,
      solver: String,
      seed: Long,
      maxIter: Int,
      tol: Double,
      stepSize: Double
     ): MultilayerPerceptronClassifierWrapper = {
{code}


And for R part, should I pass all the parameters from R command? 
https://github.com/apache/spark/compare/master...keypointt:SPARK-16445?expand=1#diff-7ede1519b4a56647801b51af33c2dd18R461
I find in the example 
(http://spark.apache.org/docs/latest/ml-classification-regression.html#multilayer-perceptron-classifier),
 only below parameters are being set, the rest are just usign default values

{code}
val trainer = new MultilayerPerceptronClassifier()
  .setLayers(layers)
  .setBlockSize(128)
  .setSeed(1234L)
  .setMaxIter(100)
{code}

> Multilayer Perceptron Classifier wrapper in SparkR
> --------------------------------------------------
>
>                 Key: SPARK-16445
>                 URL: https://issues.apache.org/jira/browse/SPARK-16445
>             Project: Spark
>          Issue Type: Sub-task
>          Components: MLlib, SparkR
>            Reporter: Xiangrui Meng
>            Assignee: Xin Ren
>
> Follow instructions in SPARK-16442 and implement multilayer perceptron 
> classifier wrapper in SparkR.



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