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From: tillrohrmann <git@git.apache.org>
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Subject: [GitHub] flink pull request: [WIP] - [FLINK-1807/1889] - Optimization
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Message-Id: <20150507093116.8B946E4415@git1-us-west.apache.org>
Date: Thu,  7 May 2015 09:31:16 +0000 (UTC)

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/613#discussion_r29836930
  
    --- 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 **/
    --- End diff --
    
    Maybe add docs for the parameters and the return value


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