Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/692#discussion_r30779835
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/GradientDescent.scala
---
@@ -36,19 +36,20 @@ import org.apache.flink.ml.optimization.Solver._
* At the moment, the whole partition is used for SGD, making it
effectively a batch gradient
* descent. Once a sampling operator has been introduced, the algorithm
can be optimized
*
- * @param runParameters The parameters to tune the algorithm. Currently
these include:
- * [[Solver.LossFunction]] for the loss function to
be used,
- * [[Solver.RegularizationType]] for the type of
regularization,
- * [[Solver.RegularizationParameter]] for the
regularization parameter,
+ * The parameters to tune the algorithm are:
+ * [[Solver.LossFunctionParameter]] for the loss
function to be used,
+ * [[Solver.RegularizationTypeParameter]] for the
type of regularization,
+ * [[Solver.RegularizationValueParameter]] for the
regularization parameter,
* [[IterativeSolver.Iterations]] for the maximum
number of iteration,
* [[IterativeSolver.Stepsize]] for the learning
rate used.
+ * [[IterativeSolver.ConvergenceThreshold]] when
provided the algorithm will
+ * stop the iterations if the change in the value of
the objective
+ * function between successive iterations is is
smaller than this value.
*/
-class GradientDescent(runParameters: ParameterMap) extends IterativeSolver
{
+class GradientDescent() extends IterativeSolver() {
--- End diff --
Do we need the parenthesis?
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