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ASF GitHub Bot commented on FLINK-1992: --------------------------------------- Github user thvasilo commented on a diff in the pull request: https://github.com/apache/flink/pull/692#discussion_r30785863 --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/Solver.scala --- @@ -40,6 +44,32 @@ abstract class Solver extends Serializable with WithParameters { data: DataSet[LabeledVector], initialWeights: Option[DataSet[WeightVector]]): DataSet[WeightVector] + /** Creates initial weights vector, creating a DataSet with a WeightVector element + * + * @param initialWeights An Option that may contain an initial set of weights + * @param data The data for which we optimize the weights + * @return A DataSet containing a single WeightVector element + */ + def createInitialWeightsDS(initialWeights: Option[DataSet[WeightVector]], + data: DataSet[LabeledVector]): DataSet[WeightVector] = { + // TODO: Faster way to do this? + val dimensionsDS = data.map(_.vector.size).reduce((a, b) => b) + + initialWeights match { + // Ensure provided weight vector is a DenseVector + case Some(wvDS) => + wvDS.map { wv => { + val denseWeights = wv.weights match { + case dv: DenseVector => dv + case sv: SparseVector => sv.toDenseVector + } + WeightVector(denseWeights, wv.intercept) --- End diff -- Not sure what you mean here, is it `WeightVector(denseWeights, wv.intercept)` that is off? > Add convergence criterion to SGD optimizer > ------------------------------------------ > > Key: FLINK-1992 > URL: https://issues.apache.org/jira/browse/FLINK-1992 > Project: Flink > Issue Type: Improvement > Components: Machine Learning Library > Reporter: Till Rohrmann > Assignee: Theodore Vasiloudis > Priority: Minor > Labels: ML > Fix For: 0.9 > > > Currently, Flink's SGD optimizer runs for a fixed number of iterations. It > would be good to support a dynamic convergence criterion, too. -- This message was sent by Atlassian JIRA (v6.3.4#6332)