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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 commented on SPARK-16445:
---------------------------------
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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