Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/15394#discussion_r83006722
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/WeightedLeastSquares.scala ---
@@ -85,73 +101,193 @@ private[ml] class WeightedLeastSquares(
val triK = summary.triK
val wSum = summary.wSum
val bBar = summary.bBar
- val bStd = summary.bStd
+ val bbBar = summary.bbBar
val aBar = summary.aBar
- val aVar = summary.aVar
+ val aStd = summary.aStd
val abBar = summary.abBar
val aaBar = summary.aaBar
- val aaValues = aaBar.values
-
- if (bStd == 0) {
- if (fitIntercept) {
- logWarning(s"The standard deviation of the label is zero, so the
coefficients will be " +
- s"zeros and the intercept will be the mean of the label; as a
result, " +
- s"training is not needed.")
- val coefficients = new DenseVector(Array.ofDim(k-1))
+ val aaBarValues = aaBar.values
+ val numFeatures = abBar.size
+ val rawBStd = summary.bStd
+ // if b is constant (rawBStd is zero), then b cannot be scaled. In
this case
+ // setting bStd=abs(bBar) ensures that b is not scaled anymore in
l-bfgs algorithm.
+ val bStd = if (rawBStd == 0.0) math.abs(bBar) else rawBStd
+
+ if (rawBStd == 0) {
+ if (fitIntercept || bBar == 0.0) {
+ if (bBar == 0.0) {
+ logWarning(s"Mean and standard deviation of the label are zero,
so the coefficients " +
+ s"and the intercept will all be zero; as a result, training is
not needed.")
+ } else {
+ logWarning(s"The standard deviation of the label is zero, so the
coefficients will be " +
+ s"zeros and the intercept will be the mean of the label; as a
result, " +
+ s"training is not needed.")
+ }
+ val coefficients = new DenseVector(Array.ofDim(numFeatures))
val intercept = bBar
val diagInvAtWA = new DenseVector(Array(0D))
- return new WeightedLeastSquaresModel(coefficients, intercept,
diagInvAtWA)
+ return new WeightedLeastSquaresModel(coefficients, intercept,
diagInvAtWA, Array(0D))
+ } else {
+ require(!(regParam > 0.0 && standardizeLabel), "The standard
deviation of the label is " +
+ "zero. Model cannot be regularized with standardization=true")
+ logWarning(s"The standard deviation of the label is zero. Consider
setting " +
+ s"fitIntercept=true.")
+ }
+ }
+
+ val aBarStd = new Array[Double](numFeatures)
+ var j = 0
+ while (j < numFeatures) {
+ if (aStd(j) == 0.0) {
+ aBarStd(j) = 0.0
} else {
- require(!(regParam > 0.0 && standardizeLabel),
- "The standard deviation of the label is zero. " +
- "Model cannot be regularized with standardization=true")
- logWarning(s"The standard deviation of the label is zero. " +
- "Consider setting fitIntercept=true.")
+ aBarStd(j) = aBar(j) / aStd(j)
+ }
+ j += 1
+ }
+
+ val abBarStd = new Array[Double](numFeatures)
+ j = 0
+ while (j < numFeatures) {
+ if (aStd(j) == 0.0) {
+ abBarStd(j) = 0.0
+ } else {
+ abBarStd(j) = abBar(j) / (aStd(j) * bStd)
+ }
+ j += 1
+ }
+
+ val aaBarStd = new Array[Double](triK)
+ j = 0
+ var kk = 0
--- End diff --
I'd like to use ```p``` or ```q``` rather than ```kk```.
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