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From tillrohrmann <...@git.apache.org>
Date Thu, 21 May 2015 11:42:07 GMT
```Github user tillrohrmann commented on a diff in the pull request:

---
@@ -76,86 +77,163 @@ class GradientDescent(runParameters: ParameterMap) extends IterativeSolver
{
}

+
+
/** Provides a solution for the given optimization problem
*
* @param data A Dataset of LabeledVector (label, features) pairs
-    * @param initWeights The initial weights that will be optimized
+    * @param initialWeights The initial weights that will be optimized
* @return The weights, optimized for the provided data.
*/
override def optimize(
data: DataSet[LabeledVector],
-    initWeights: Option[DataSet[WeightVector]]): DataSet[WeightVector] = {
-    // TODO: Faster way to do this?
-    val dimensionsDS = data.map(_.vector.size).reduce((a, b) => b)
-
-    val numberOfIterations: Int = parameterMap(Iterations)
+    initialWeights: Option[DataSet[WeightVector]]): DataSet[WeightVector] = {
+    val numberOfIterations: Int = parameters(Iterations)
+    // TODO(tvas): This looks out of place, why don't we get back an Option from
+    // parameters(ConvergenceThreshold)?
+    val convergenceThresholdOption = parameters.get(ConvergenceThreshold)

// Initialize weights
-    val initialWeightsDS: DataSet[WeightVector] = initWeights 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
+    val initialWeightsDS: DataSet[WeightVector] = createInitialWeightsDS(initialWeights,
data)
+
+    // Perform the iterations
+    val optimizedWeights = convergenceThresholdOption match {
+      // No convergence criterion
+      case None =>
+        initialWeightsDS.iterate(numberOfIterations) {
+          weightVectorDS => {
+            SGDStep(data, weightVectorDS)
}
-          WeightVector(denseWeights, wv.intercept)
}
-
+      case Some(convergence) =>
+        /** Calculates the regularized loss, from the data and given weights **/
--- End diff --

Ah I see. But it's a nested function and thus not available to the outside world. Therefore,