Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/5767#discussion_r29325700
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala ---
@@ -110,59 +110,70 @@ class LinearRegression extends Regressor[Vector,
LinearRegression, LinearRegress
val yMean = statCounter.mean
val yStd = math.sqrt(statCounter.variance)
- val featuresMean = summarizer.mean.toArray
- val featuresStd = summarizer.variance.toArray.map(math.sqrt)
-
- // Since we implicitly do the feature scaling when we compute the cost
function
- // to improve the convergence, the effective regParam will be changed.
- val effectiveRegParam = paramMap(regParam) / yStd
- val effectiveL1RegParam = paramMap(elasticNetParam) * effectiveRegParam
- val effectiveL2RegParam = (1.0 - paramMap(elasticNetParam)) *
effectiveRegParam
-
- val costFun = new LeastSquaresCostFun(instances, yStd, yMean,
- featuresStd, featuresMean, effectiveL2RegParam)
-
- val optimizer = if (paramMap(elasticNetParam) == 0.0 ||
effectiveRegParam == 0.0) {
- new BreezeLBFGS[BDV[Double]](paramMap(maxIter), 10, paramMap(tol))
- } else {
- new BreezeOWLQN[Int, BDV[Double]](paramMap(maxIter), 10,
effectiveL1RegParam, paramMap(tol))
- }
+ // If the yStd is zero, then the intercept is yStd with zero weights.
--- End diff --
...the intercept is yMean...
---
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