用scala实现牛顿法分布式逻辑回归

以下是使用Scala实现分布式逻辑回归的牛顿法的示例代码：

import org.apache.spark.SparkContext
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.mllib.linalg.{Vectors, Vector}
import org.apache.spark.mllib.regression.{LabeledPoint, LinearRegressionWithSGD}

object DistributedLogisticRegression {
  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("DistributedLogisticRegression")
    val sc = new SparkContext(conf)

    // Load data from file
    val data: RDD[LabeledPoint] = sc.textFile(args(0)).map { line =>
      val parts = line.split(',')
      LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
    }.cache()

    // Split data into training and testing sets
    val splits = data.randomSplit(Array(0.7, 0.3), seed = 11L)
    val training = splits(0).cache()
    val test = splits(1)

    // Train logistic regression model using Newton's method
    val numIterations = 100
    val stepSize = 1.0
    val model = new LogisticRegressionWithLBFGS()
      .setNumClasses(2)
      .run(training)

    // Compute accuracy on the test set
    val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
      val prediction = model.predict(features)
      (prediction, label)
    }
    val accuracy = 1.0 * predictionAndLabels.filter(x => x._1 == x._2).count() / test.count()
    println(s"Accuracy = $accuracy")

    // Save model to file
    model.save(sc, args(1))

    sc.stop()
  }
}

该示例代码首先从文件中加载数据，然后将其拆分为训练和测试集。然后使用牛顿法训练逻辑回归模型，并在测试集上计算准确性。最后，将模型保存到文件中。