Github user dbtsai commented on a diff in the pull request:

    https://github.com/apache/spark/pull/10788#discussion_r49962448
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/mllib/classification/LogisticRegression.scala
 ---
    @@ -374,4 +383,82 @@ class LogisticRegressionWithLBFGS
           new LogisticRegressionModel(weights, intercept, numFeatures, 
numOfLinearPredictor + 1)
         }
       }
    +
    +  /**
    +   * Run the algorithm with the configured parameters on an input RDD
    +   * of LabeledPoint entries starting from the initial weights provided.
    +   * If a known updater is used calls the ml implementation, to avoid
    +   * applying a regularization penalty to the intercept, otherwise
    +   * defaults to the mllib implementation. If more than two classes
    +   * or feature scaling is disabled, always uses mllib implementation.
    +   * If using ml implementation, uses ml code to generate initial weights.
    +   */
    +  override def run(input: RDD[LabeledPoint]): LogisticRegressionModel = {
    +    run(input, generateInitialWeights(input), false)
    +  }
    +
    +  /**
    +   * Run the algorithm with the configured parameters on an input RDD
    +   * of LabeledPoint entries starting from the initial weights provided.
    +   * If a known updater is used calls the ml implementation, to avoid
    +   * applying a regularization penalty to the intercept, otherwise
    +   * defaults to the mllib implementation. If more than two classes
    +   * or feature scaling is disabled, always uses mllib implementation.
    +   * Uses user provided weights.
    +   */
    +  override def run(input: RDD[LabeledPoint], initialWeights: Vector): 
LogisticRegressionModel = {
    +    run(input, initialWeights, true)
    --- End diff --
    
    `run(input, generateInitialWeights(input), userSuppliedWeights = true)`


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