Github user dbtsai commented on a diff in the pull request: https://github.com/apache/spark/pull/13796#discussion_r74678564 --- Diff: mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala --- @@ -945,13 +955,139 @@ class BinaryLogisticRegressionSummary private[classification] ( private class LogisticAggregator( private val numFeatures: Int, numClasses: Int, - fitIntercept: Boolean) extends Serializable { + fitIntercept: Boolean, + multinomial: Boolean, + standardize: Boolean) extends Serializable { private var weightSum = 0.0 private var lossSum = 0.0 - private val gradientSumArray = - Array.ofDim[Double](if (fitIntercept) numFeatures + 1 else numFeatures) + private val totalCoefficientLength = { + val cols = if (fitIntercept) numFeatures + 1 else numFeatures + val rows = if (multinomial) numClasses else 1 + rows * cols + } + + private val gradientSumArray = Array.ofDim[Double](totalCoefficientLength) + + /** Update gradient and loss using binary loss function. */ + private def binaryUpdateInPlace( + features: Vector, + weight: Double, + label: Double, + coefficients: Array[Double], + gradient: Array[Double], + featuresStd: Array[Double], + numFeaturesPlusIntercept: Int, + standardize: Boolean): Unit = { + val margin = - { + var sum = 0.0 + features.foreachActive { (index, value) => + if (featuresStd(index) != 0.0 && value != 0.0) { + val x = if (standardize) value / featuresStd(index) else value --- End diff -- Based on your documentation on `Feature scaling`, you said that you're caching the `scaled features` to improve the performance, then why do you need to divide by `featuresStd(index)` if `standardize == true`?
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