Github user srowen commented on a diff in the pull request:
https://github.com/apache/spark/pull/13729#discussion_r67480171
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -937,50 +937,47 @@ class BinaryLogisticRegressionSummary
private[classification] (
* Two LogisticAggregator can be merged together to have a summary of loss
and gradient of
* the corresponding joint dataset.
*
- * @param coefficients The coefficients corresponding to the features.
* @param numClasses the number of possible outcomes for k classes
classification problem in
* Multinomial Logistic Regression.
* @param fitIntercept Whether to fit an intercept term.
- * @param featuresStd The standard deviation values of the features.
- * @param featuresMean The mean values of the features.
*/
private class LogisticAggregator(
- coefficients: Vector,
+ numFeatures: Int,
numClasses: Int,
- fitIntercept: Boolean,
- featuresStd: Array[Double],
- featuresMean: Array[Double]) extends Serializable {
+ fitIntercept: Boolean) extends Serializable {
private var weightSum = 0.0
private var lossSum = 0.0
- private val coefficientsArray = coefficients match {
- case dv: DenseVector => dv.values
- case _ =>
- throw new IllegalArgumentException(
- s"coefficients only supports dense vector but got type
${coefficients.getClass}.")
- }
-
- private val dim = if (fitIntercept) coefficientsArray.length - 1 else
coefficientsArray.length
-
- private val gradientSumArray =
Array.ofDim[Double](coefficientsArray.length)
+ private val dim = numFeatures
--- End diff --
Do you need dim here, or can just reference numFeatures later in the class?
I had to look twice at the line below to make sure the logic wasn't
reversed from before but I see why it works out.
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