Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/2451#discussion_r17807758
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala ---
@@ -157,3 +157,221 @@ class HingeGradient extends Gradient {
}
}
}
+
+/**
+ * :: DeveloperApi ::
+ * Class used to compute the gradient for a loss function, given a series
of data points.
+ */
+@DeveloperApi
+abstract class MultiModelGradient extends Serializable {
+ /**
+ * Compute the gradient and loss given the features of all data points.
+ *
+ * @param data features for one data point
+ * @param label label for this data point
+ * @param weights weights/coefficients corresponding to features
+ *
+ * @return (gradient: DenseMatrix, loss: Double)
+ */
+ def compute(data: Matrix, label: DenseMatrix,
+ weights: DenseMatrix): (DenseMatrix, Matrix)
+
+ /**
+ * Compute the gradient and loss given the features of a series of data
point,
+ * add the gradient to a provided matrix to avoid creating new objects,
and return loss.
+ *
+ * @param data features for the data points
+ * @param label label for the data points
+ * @param weights weights/coefficients corresponding to features
+ * @param cumGradient the computed gradient will be added to this matrix
+ *
+ * @return loss
+ */
+ def compute(data: Matrix, label: DenseMatrix,
+ weights: DenseMatrix, cumGradient: DenseMatrix):
Matrix
+}
+
+/**
+ * :: DeveloperApi ::
+ * Compute gradient and loss for a logistic loss function, as used in
binary classification.
+ * See also the documentation for the precise formulation.
+ */
+@DeveloperApi
+class MultiModelLogisticGradient extends MultiModelGradient {
+
+ private def sigmoid(p: DenseMatrix): DenseMatrix = {
+ def takeSigmoid(p: Double): Double = {
+ 1.0 / (math.exp(-p) + 1.0)
+ }
+ p.map(takeSigmoid)
+ }
+
+ override def compute(data: Matrix, label: DenseMatrix,
--- End diff --
Can this be implemented using the below compute method to avoid code
duplication?
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