Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/15893#discussion_r88767607
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
---
@@ -586,15 +577,24 @@ class LogisticRegression @Since("1.2.0") (
Note that the intercept in scaled space and original space is
the same;
as a result, no scaling is needed.
*/
- val rawCoefficients = state.x.toArray.clone()
- val coefficientArray = Array.tabulate(numCoefficientSets *
numFeatures) { i =>
- val colMajorIndex = (i % numFeatures) * numCoefficientSets + i /
numFeatures
- val featureIndex = i % numFeatures
- if (featuresStd(featureIndex) != 0.0) {
- rawCoefficients(colMajorIndex) / featuresStd(featureIndex)
- } else {
- 0.0
+ val allCoefficients = state.x.toArray.clone()
+ val allCoefMatrix = new DenseMatrix(numCoefficientSets,
numFeaturesPlusIntercept,
+ allCoefficients)
+ val denseCoefficientMatrix = new DenseMatrix(numCoefficientSets,
numFeatures,
+ new Array[Double](numCoefficientSets * numFeatures),
isTransposed = true)
+ val interceptVec = if ($(fitIntercept) || !isMultinomial) {
--- End diff --
Should we consistently use `interceptVector`?
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