[jira] [Updated] (SPARK-7085) Inconsistent default miniBatchFraction parameters in the train methods of RidgeRegression

2015-04-23 Thread Joseph K. Bradley (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-7085?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Joseph K. Bradley updated SPARK-7085:
-
Assignee: Nobuyuki Kuromatsu

 Inconsistent default miniBatchFraction parameters in the train methods of 
 RidgeRegression
 -

 Key: SPARK-7085
 URL: https://issues.apache.org/jira/browse/SPARK-7085
 Project: Spark
  Issue Type: Bug
  Components: MLlib
Affects Versions: 1.3.1
Reporter: Nobuyuki Kuromatsu
Assignee: Nobuyuki Kuromatsu
Priority: Minor
 Fix For: 1.4.0

   Original Estimate: 168h
  Remaining Estimate: 168h

 The miniBatchFraction parameter in the train method called with 4 arguments 
 is 0.01, that is,
 {code:title=RidgeRegression.scala|borderStyle=solid}
 def train(
   input: RDD[LabeledPoint],
   numIterations: Int,
   stepSize: Double,
   regParam: Double): RidgeRegressionModel = {
 train(input, numIterations, stepSize, regParam, 0.01)
   }
 {code}
 but, the parameter is 1.0 in the other train methods. For example,
 {code:title=RidgeRegression.scala|borderStyle=solid}
   def train(
   input: RDD[LabeledPoint],
   numIterations: Int): RidgeRegressionModel = {
 train(input, numIterations, 1.0, 0.01, 1.0)
   }
 {code}



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[jira] [Updated] (SPARK-7085) Inconsistent default miniBatchFraction parameters in the train methods of RidgeRegression

2015-04-23 Thread Joseph K. Bradley (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-7085?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Joseph K. Bradley updated SPARK-7085:
-
Target Version/s: 1.4.0

 Inconsistent default miniBatchFraction parameters in the train methods of 
 RidgeRegression
 -

 Key: SPARK-7085
 URL: https://issues.apache.org/jira/browse/SPARK-7085
 Project: Spark
  Issue Type: Bug
  Components: MLlib
Affects Versions: 1.3.1
Reporter: Nobuyuki Kuromatsu
Assignee: Nobuyuki Kuromatsu
Priority: Minor
 Fix For: 1.4.0

   Original Estimate: 168h
  Remaining Estimate: 168h

 The miniBatchFraction parameter in the train method called with 4 arguments 
 is 0.01, that is,
 {code:title=RidgeRegression.scala|borderStyle=solid}
 def train(
   input: RDD[LabeledPoint],
   numIterations: Int,
   stepSize: Double,
   regParam: Double): RidgeRegressionModel = {
 train(input, numIterations, stepSize, regParam, 0.01)
   }
 {code}
 but, the parameter is 1.0 in the other train methods. For example,
 {code:title=RidgeRegression.scala|borderStyle=solid}
   def train(
   input: RDD[LabeledPoint],
   numIterations: Int): RidgeRegressionModel = {
 train(input, numIterations, 1.0, 0.01, 1.0)
   }
 {code}



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