spark LocalLR 源码
spark LocalLR 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/LocalLR.scala
/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples
import java.util.Random
import breeze.linalg.{DenseVector, Vector}
/**
* Logistic regression based classification.
*
* This is an example implementation for learning how to use Spark. For more conventional use,
* please refer to org.apache.spark.ml.classification.LogisticRegression.
*/
object LocalLR {
val N = 10000 // Number of data points
val D = 10 // Number of dimensions
val R = 0.7 // Scaling factor
val ITERATIONS = 5
val rand = new Random(42)
case class DataPoint(x: Vector[Double], y: Double)
def generateData: Array[DataPoint] = {
def generatePoint(i: Int): DataPoint = {
val y = if (i % 2 == 0) -1 else 1
val x = DenseVector.fill(D) {rand.nextGaussian + y * R}
DataPoint(x, y)
}
Array.tabulate(N)(generatePoint)
}
def showWarning(): Unit = {
System.err.println(
"""WARN: This is a naive implementation of Logistic Regression and is given as an example!
|Please use org.apache.spark.ml.classification.LogisticRegression
|for more conventional use.
""".stripMargin)
}
def main(args: Array[String]): Unit = {
showWarning()
val data = generateData
// Initialize w to a random value
val w = DenseVector.fill(D) {2 * rand.nextDouble - 1}
println(s"Initial w: $w")
for (i <- 1 to ITERATIONS) {
println(s"On iteration $i")
val gradient = DenseVector.zeros[Double](D)
for (p <- data) {
val scale = (1 / (1 + math.exp(-p.y * (w.dot(p.x)))) - 1) * p.y
gradient += p.x * scale
}
w -= gradient
}
println(s"Final w: $w")
}
}
// scalastyle:on println
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