Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/613#discussion_r29837206
--- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/RegularizationType.scala ---
@@ -0,0 +1,171 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.flink.ml.optimization
+
+import org.apache.flink.api.scala._
+import org.apache.flink.ml.math.{Vector => FlinkVector, BLAS}
+import org.apache.flink.ml.math.Breeze._
+
+import breeze.numerics._
+import breeze.linalg.{norm => BreezeNorm, max => BreezeMax}
+
+
+
+// TODO(tvas): Change name to RegularizationPenalty?
+/** Represents a type of regularization penalty
+ *
+ * Regularization penalties are used to restrict the optimization problem to solutions with
+ * certain desirable characteristics, such as sparsity for the L1 penalty, or penalizing large
+ * weights for the L2 penalty.
+ *
+ * The regularization term, $R(w)$ is added to the objective function, $f(w) = L(w) + \lambda R(w)$
+ * where $\lambda$ is the regularization parameter used to tune the amount of regularization
+ * applied.
+ */
+abstract class RegularizationType extends Serializable {
+
+ /** Updates the weights by taking a step according to the gradient and regularization applied
+ *
+ * @param oldWeights The weights to be updated
+ * @param gradient The gradient according to which we will update the weights
+ * @param effectiveStepSize The effective step size for this iteration
+ * @param regParameter The regularization parameter to be applied in the case of L1
+ * regularization
+ */
+ def takeStep(
+ oldWeights: FlinkVector,
+ gradient: FlinkVector,
+ effectiveStepSize: Double,
+ regParameter: Double) {
+ BLAS.axpy(-effectiveStepSize, gradient, oldWeights)
+ }
+
+ /** Adds regularization to the loss value **/
+ def regLoss(oldLoss: Double, weightVector: FlinkVector, regularizationParameter: Double): Double
+
+}
+
+/** Abstract class for regularization penalties that are differentiable
+ *
+ */
+abstract class DiffRegularizationType extends RegularizationType {
+
+ /** Compute the regularized gradient loss for the given data.
+ * The provided cumGradient is updated in place.
+ *
+ * @param weightVector The current weight vector
+ * @param lossGradient The vector to which the gradient will be added to, in place.
+ * @return The regularized loss. The gradient is updated in place.
+ */
+ def regularizedLossAndGradient(
+ loss: Double,
+ weightVector: FlinkVector,
+ lossGradient: FlinkVector,
+ regularizationParameter: Double) : Double ={
+ val adjustedLoss = regLoss(loss, weightVector, regularizationParameter)
+ regGradient(weightVector, lossGradient, regularizationParameter)
+
+ adjustedLoss
+ }
+
+ /** Adds regularization gradient to the loss gradient. The gradient is updated in place **/
+ def regGradient(
+ weightVector: FlinkVector,
+ lossGradient: FlinkVector,
+ regularizationParameter: Double)
+}
+
+/** Performs no regularization, equivalent to $R(w) = 0$ **/
+class NoRegularization extends RegularizationType {
+ /** Adds regularization to the loss value **/
+ override def regLoss(oldLoss: Double, weightVector: FlinkVector, regularizationParameter: Double):
+ Double = {oldLoss}
+}
+
+/** $L_2$ regularization penalty.
+ *
+ * Penalizes large weights, favoring solutions with more small weights rather than few large ones.
+ *
+ */
+class L2Regularization extends DiffRegularizationType {
+
+ /** Adds regularization to the loss value **/
+ override def regLoss(oldLoss: Double, weightVector: FlinkVector, regParameter: Double)
+ : Double = {
+ val brzVector = weightVector.asBreeze
+ oldLoss + regParameter * (brzVector dot brzVector) / 2
+ }
+
+ /** Adds regularization gradient to the loss gradient. The gradient is updated in place **/
+ override def regGradient(
+ weightVector: FlinkVector,
+ lossGradient: FlinkVector,
+ regParameter: Double): Unit = {
+ BLAS.axpy(regParameter, weightVector, lossGradient)
+ }
+}
+
+/** $L_1$ regularization penalty.
+ *
+ * The $L_1$ penalty can be used to drive a number of the solution coefficients to 0, thereby
+ * producing sparse solutions.
+ *
+ */
+class L1Regularization extends RegularizationType {
+ /** Calculates and applies the regularization amount and the regularization parameter
+ *
+ * Implementation was taken from the Apache Spark Mllib library:
+ * http://git.io/vfZIT
+ * @param oldWeights The old weights
+ * @param effectiveStepSize The effective step size for this iteration
+ * @param regParameter The current regularization parameter
+ * @return A tuple whose first element is the updated weight FlinkVector and the second is the
+ * regularization value
+ */
+ override def takeStep(
+ oldWeights: FlinkVector,
+ gradient: FlinkVector,
+ effectiveStepSize: Double,
+ regParameter: Double) {
+ BLAS.axpy(-effectiveStepSize, gradient, oldWeights)
+ val brzWeights = oldWeights.asBreeze.toDenseVector
--- End diff --
why converting the oldWeights explicitly to a dense vector. It should be dense since the beginning, or at least we should make sure that it is.
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