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From: thvasilo <git@git.apache.org>
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Subject: [GitHub] flink pull request: [FLINK-1844] [ml] Add Normaliser to ML
 library
Content-Type: text/plain
Message-Id: <20150608133941.40CBDDFF72@git1-us-west.apache.org>
Date: Mon,  8 Jun 2015 13:39:41 +0000 (UTC)

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

    https://github.com/apache/flink/pull/798#discussion_r31914466
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/preprocessing/MinMaxScaler.scala ---
    @@ -0,0 +1,254 @@
    +/*
    + * 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.preprocessing
    +
    +import breeze.linalg
    +import breeze.linalg.{max, min}
    +import org.apache.flink.api.common.typeinfo.TypeInformation
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml._
    +import org.apache.flink.ml.common.{LabeledVector, Parameter, ParameterMap}
    +import org.apache.flink.ml.math.Breeze._
    +import org.apache.flink.ml.math.{BreezeVectorConverter, Vector}
    +import org.apache.flink.ml.pipeline.{FitOperation, TransformOperation, Transformer}
    +import org.apache.flink.ml.preprocessing.MinMaxScaler.{Max, Min}
    +
    +import scala.reflect.ClassTag
    +
    +/** Scales observations, so that all features are in a user-specified range.
    +  * By default for [[MinMaxScaler]] transformer range = [0,1].
    +  *
    +  * This transformer takes a subtype of  [[Vector]] of values and maps it to a
    +  * scaled subtype of [[Vector]] such that each feature lies between a user-specified range.
    +  *
    +  * This transformer can be prepended to all [[Transformer]] and
    +  * [[org.apache.flink.ml.pipeline.Predictor]] implementations which expect as input a subtype
    +  * of [[Vector]].
    +  *
    +  * @example
    +  * {{{
    +  *               val trainingDS: DataSet[Vector] = env.fromCollection(data)
    +  *               val transformer = MinMaxScaler().setMin(-1.0)
    +  *
    +  *               transformer.fit(trainingDS)
    +  *               val transformedDS = transformer.transform(trainingDS)
    +  * }}}
    +  *
    +  * =Parameters=
    +  *
    +  * - [[Min]]: The minimum value of the range of the transformed data set; by default equal to 0
    +  * - [[Max]]: The maximum value of the range of the transformed data set; by default
    +  * equal to 1
    +  */
    +class MinMaxScaler extends Transformer[MinMaxScaler] {
    +
    +  var metricsOption: Option[DataSet[(linalg.Vector[Double], linalg.Vector[Double])]] = None
    --- End diff --
    
    Not right now, so these can remain. I was mostly concerned that this parameter was user-facing, meaning the user had to provide Breeze vectors as parameters, but that is not the case.


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