《Spark MLlib 机器学习》第三章代码

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sunbow0

最新推荐文章于 2025-02-02 01:20:07 发布

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分类专栏： Spark Spark MLlib 文章标签： spark mllib 机器学习

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本文提供《Spark MLlib 机器学习》第三章的代码讲解，涵盖Breeze介绍及MLlib分布式矩阵的详细内容，帮助读者深入理解Spark上的机器学习实践。

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《Spark MLlib 机器学习》第三章代码

3.1 Breeze 介绍

package book_code

import org.apache.log4j.{ Level, Logger }
import org.apache.spark.{ SparkConf, SparkContext }
import breeze.linalg._
import breeze.numerics._
import org.apache.spark.mllib.linalg.Vectors

object breeze_test01 {

  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("breeze_test01")
    val sc = new SparkContext(conf)
    Logger.getRootLogger.setLevel(Level.WARN)

    // 3.1.1 Breeze 创建函数
    val m1 = DenseMatrix.zeros[Double](2, 3)
    val v1 = DenseVector.zeros[Double](3)
    val v2 = DenseVector.ones[Double](3)
    val v3 = DenseVector.fill(3) { 5.0 }
    val v4 = DenseVector.range(1, 10, 2)
    val m2 = DenseMatrix.eye[Double](3)
    val v6 = diag(DenseVector(1.0, 2.0, 3.0))
    val m3 = DenseMatrix((1.0, 2.0), (3.0, 4.0))
    val v8 = DenseVector(1, 2, 3, 4)
    val v9 = DenseVector(1, 2, 3, 4).t
    val v10 = DenseVector.tabulate(3) { i => 2 * i }
    val m4 = DenseMatrix.tabulate(3, 2) { case (i, j) => i + j }
    val v11 = new DenseVector(Array(1, 2, 3, 4))
    val m5 = new DenseMatrix(2, 3, Array(11, 12, 13, 21, 22, 23))
    val v12 = DenseVector.rand(4)
    val m6 = DenseMatrix.rand(2, 3)

    // 3.1.2 Breeze 元素访问及操作函数
    // 元素访问
    val a = DenseVector(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
    a(0)
    a(1 to 4)
    a(5 to 0 by -1)
    a(1 to -1)
    a(-1)
    val m = DenseMatrix((1.0, 2.0, 3.0), (3.0, 4.0, 5.0))
    m(0, 1)
    m(::, 1)

    // 元素操作
    val m_1 = DenseMatrix((1.0, 2.0, 3.0), (3.0, 4.0, 5.0))
    m_1.reshape(3, 2)
    m_1.toDenseVector

    val m_3 = DenseMatrix((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0))
    lowerTriangular(m_3)
    upperTriangular(m_3)
    m_3.copy
    diag(m_3)
    m_3(::, 2) := 5.0
    m_3
    m_3(1 to 2, 1 to 2) := 5.0
    m_3

    val a_1 = DenseVector(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
    a_1(1 to 4) := 5
    a_1(1 to 4) := DenseVector(1, 2, 3, 4)
    a_1
    val a1 = DenseMatrix((1.0, 2.0, 3.0), (4.0, 5.0, 6.0))
    val a2 = DenseMatrix((1.0, 1.0, 1.0), (2.0, 2.0, 2.0))
    DenseMatrix.vertcat(a1, a2)
    DenseMatrix.horzcat(a1, a2)
    val b1 = DenseVector(1, 2, 3, 4)
    val b2 = DenseVector(1, 1, 1, 1)
    DenseVector.vertcat(b1, b2)

    // 3.1.3 Breeze 数值计算函数
    val a_3 = DenseMatrix((1.0, 2.0, 3.0), (4.0, 5.0, 6.0))
    val b_3 = DenseMatrix((1.0, 1.0, 1.0), (2.0, 2.0, 2.0))
    a_3 + b_3
    a_3 :* b_3
    a_3 :/ b_3
    a_3 :< b_3
    a_3 :== b_3
    a_3 :+= 1.0
    a_3 :*= 2.0
    max(a_3)
    argmax(a_3)
    DenseVector(1, 2, 3, 4) dot DenseVector(1, 1, 1, 1)

    // 3.1.4 Breeze 求和函数
    val a_4 = DenseMatrix((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0))
    sum(a_4)
    sum(a_4, Axis._0)
    sum(a_4, Axis._1)
    trace(a_4)
    accumulate(DenseVector(1, 2, 3, 4))

    // 3.1.5 Breeze 布尔函数
    val a_5 = DenseVector(true, false, true)
    val b_5 = DenseVector(false, true, true)
    a_5 :& b_5
    a_5 :| b_5
    !a_5
    val a_5_2 = DenseVector(1.0, 0.0, -2.0)
    any(a_5_2)
    all(a_5_2)

    // 3.1.6 Breeze 线性代数函数
    val a_6 = DenseMatrix((1.0, 2.0, 3.0), (4.0, 5.0, 6.0), (7.0, 8.0, 9.0))
    val b_6 = DenseMatrix((1.0, 1.0, 1.0), (1.0, 1.0, 1.0), (1.0, 1.0, 1.0))
    a_6 \ b_6
    a_6.t
    det(a_6)
    inv(a_6)
    val svd.SVD(u, s, v) = svd(a_6)
    a_6.rows
    a_6.cols

    // 3.1.7 Breeze 取整函数
    val a_7 = DenseVector(1.2, 0.6, -2.3)
    round(a_7)
    ceil(a_7)
    floor(a_7)
    signum(a_7)
    abs(a_7)

  }
}

3.6 MLlib 分布式矩阵

package book_code

import org.apache.log4j.{ Level, Logger }
import org.apache.spark.{ SparkConf, SparkContext }
import breeze.linalg._
import breeze.numerics._
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.distributed.RowMatrix

object rowmatri_test01 {
  
    def main(args: Array[String]) {
    val conf = new SparkConf().setAppName("rowmatri_test01")
    val sc = new SparkContext(conf)
    Logger.getRootLogger.setLevel(Level.WARN)

    // 3.6 分布式矩阵
    // 3.6.2 行矩阵（RowMatrix）
    val rdd1 = sc.parallelize(Array(Array(1.0, 2.0, 3.0, 4.0), Array(2.0, 3.0, 4.0, 5.0), Array(3.0, 4.0, 5.0, 6.0))).map(f => Vectors.dense(f))
    val RM = new RowMatrix(rdd1)
    val simic1 = RM.columnSimilarities(0.5)
    val simic2 = RM.columnSimilarities()
    val simic3 = RM.computeColumnSummaryStatistics()
    simic3.max
    simic3.min
    simic3.mean
    val cc1 = RM.computeCovariance
    val cc2 = RM.computeGramianMatrix
    val pc1 = RM.computePrincipalComponents(3)
    val svd = RM.computeSVD(4, true)
    val U = svd.U
    U.rows.foreach(println)
    val s = svd.s
    val V = svd.V
    }

}

代码和数据地址网盘：

http://pan.baidu.com/s/1c1J8ZN6