Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/16002#discussion_r89854110
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/optim/NormalEquationSolver.scala ---
@@ -33,11 +33,16 @@ import
org.apache.spark.mllib.linalg.CholeskyDecomposition
* format. None when an optimization program is used to solve
the normal equations.
* @param objectiveHistory Option containing the objective history when an
optimization program is
* used to solve the normal equations. None when
an analytic solver is used.
+ * @param status Status of the solution. If the solution was produced by
[[CholeskySolver]], status
+ * value greater than 0 indicates singular matrix error;
status value less than 0
+ * indicates illegal input; status == 0 indicates success.
If the solution was
+ * produced by [[QuasiNewtonSolver]], status always return 0
which means success.
*/
private[ml] class NormalEquationSolution(
val coefficients: Array[Double],
val aaInv: Option[Array[Double]],
- val objectiveHistory: Option[Array[Double]])
+ val objectiveHistory: Option[Array[Double]],
--- End diff --
We have a bunch of fields that mean something in one case, and nothing in
another (hence the options). What would you think about the following
abstraction (not sure about the names though)?:
````scala
sealed trait NormalEquationSolution {
def coefficients: Array[Double]
}
case class DirectNormalEquationSolution(
coefficients: Array[Double],
aaInv: Array[Double],
status: Int) extends NormalEquationSolution
case class OptimizerNormalEquationSolution(
coefficients: Array[Double],
objectiveHistory: Array[Double]) extends NormalEquationSolution
````
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