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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: Streaming mllib [SPARK-2438][MLLIB]
Date Sat, 02 Aug 2014 00:41:06 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/1361#discussion_r15725538
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionWithSGD.scala
---
    @@ -0,0 +1,86 @@
    +/*
    + * 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.spark.mllib.regression
    +
    +import org.apache.spark.annotation.Experimental
    +import org.apache.spark.mllib.linalg.{Vector, Vectors}
    +
    +/**
    + * Train or predict a linear regression model on streaming data. Training uses
    + * Stochastic Gradient Descent to update the model based on each new batch of
    + * incoming data from a DStream (see `LinearRegressionWithSGD` for model equation)
    + *
    + * Each batch of data is assumed to be an RDD of LabeledPoints.
    + * The number of data points per batch can vary, but the number
    + * of features must be constant. An initial weight
    + * vector must be provided.
    + *
    + * Use a builder pattern to construct a streaming linear regression
    + * analysis in an application, like:
    + *
    + *  val model = new StreamingLinearRegressionWithSGD()
    + *    .setStepSize(0.5)
    + *    .setNumIterations(10)
    + *    .setInitialWeights(Vectors.dense(...))
    + *    .trainOn(DStream)
    + *
    + */
    +@Experimental
    +class StreamingLinearRegressionWithSGD (
    +    private var stepSize: Double,
    +    private var numIterations: Int,
    +    private var miniBatchFraction: Double,
    +    private var initialWeights: Vector)
    +  extends StreamingLinearAlgorithm[
    +    LinearRegressionModel, LinearRegressionWithSGD] with Serializable {
    +
    +  /**
    +   * Construct a StreamingLinearRegression object with default parameters:
    +   * {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, initialWeights: [0.0,
0.0]}.
    +   */
    +  def this() = this(0.1, 50, 1.0, Vectors.dense(0.0, 0.0))
    --- End diff --
    
    Yes.


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