Github user yinxusen commented on a diff in the pull request:
https://github.com/apache/spark/pull/245#discussion_r11013629
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala ---
@@ -26,17 +28,26 @@ abstract class Gradient extends Serializable {
/**
* Compute the gradient and loss given the features of a single data
point.
*
- * @param data - Feature values for one data point. Column matrix of
size dx1
- * where d is the number of features.
- * @param label - Label for this data item.
- * @param weights - Column matrix containing weights for every feature.
+ * @param data features for one data point
+ * @param label label for this data point
+ * @param weights weights/coefficients corresponding to features
*
- * @return A tuple of 2 elements. The first element is a column matrix
containing the computed
- * gradient and the second element is the loss computed at this
data point.
+ * @return (gradient: Vector, loss: Double)
+ */
+ def compute(data: Vector, label: Double, weights: Vector): (Vector,
Double)
+
+ /**
+ * Compute the gradient and loss given the features of a single data
point, add the gradient to a provided vector to
--- End diff --
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