用ｓｃａｌａ语言求解NR算法实现分布式逻辑回归，并求解逻辑回归的参数

抱歉，我是AI语言模型，无法提供完整的代码实现。以下是一个基于Scala的分布式逻辑回归的示例代码，供参考：

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.rdd.RDD
import scala.math.exp

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

    // Load data
    val data = sc.textFile("data.txt")
      .map(line => {
        val parts = line.split(',')
        (parts.last.toDouble, parts.dropRight(1).map(_.toDouble))
      })

    // Initialize the weights
    val weights = Array.fill(data.first()._2.length)(0.0)

    // Run NR algorithm for 10 iterations
    val numIterations = 10
    (1 to numIterations).foldLeft(weights)((w, i) => {
      val hessian = data.map(row => {
        val x = row._2
        val y = row._1
        val p = 1.0 / (1.0 + exp(-w.zip(x).map(xw => xw._1 * xw._2).sum))
        val diag = p * (1 - p)
        (diag, x.map(xi => xi * diag))
      }).reduce((a, b) => (a._1 + b._1, a._2.zip(b._2).map(x => x._1 + x._2)))

      val gradient = data.map(row => {
        val x = row._2
        val y = row._1
        val p = 1.0 / (1.0 + exp(-w.zip(x).map(xw => xw._1 * xw._2).sum))
        x.map(xi => (y - p) * xi)
      }).reduce((a, b) => a.zip(b).map(x => x._1 + x._2))

      val step = hessian._2.zip(gradient).map(x => x._1 / x._2)
      w.zip(step).map(x => x._1 + x._2)
    })
  }
}

该代码使用了牛顿-拉夫逊算法（Newton-Raphson algorithm，简称 NR 算法）来求解逻辑回归的参数。具体实现中，首先加载数据并初始化权重向量，然后迭代进行牛顿-拉夫逊算法，计算 Hessian 矩阵和梯度，并根据它们更新权重向量。最终得到的权重向量即为逻辑回归的参数。

注意，该示例代码仅供参考，实际使用时需要根据具体数据和需求进行修改和优化。