本文整理汇总了Scala中org.apache.spark.mllib.linalg.SingularValueDecomposition类的典型用法代码示例。如果您正苦于以下问题：Scala SingularValueDecomposition类的具体用法？Scala SingularValueDecomposition怎么用？Scala SingularValueDecomposition使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了SingularValueDecomposition类的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Scala代码示例。
示例1: SparkSVDExampleOne
//设置package包名称以及导入依赖的类
package linalg.svd
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.linalg.{Matrix, SingularValueDecomposition, Vector, Vectors}
object SparkSVDExampleOne {
def main(args: Array[String]) {
val denseData = Seq(
Vectors.dense(0.0, 1.0, 2.0, 1.0, 5.0, 3.3, 2.1),
Vectors.dense(3.0, 4.0, 5.0, 3.1, 4.5, 5.1, 3.3),
Vectors.dense(6.0, 7.0, 8.0, 2.1, 6.0, 6.7, 6.8),
Vectors.dense(9.0, 0.0, 1.0, 3.4, 4.3, 1.0, 1.0)
)
val spConfig = (new SparkConf).setMaster("local").setAppName("SparkSVDDemo")
val sc = new SparkContext(spConfig)
val mat: RowMatrix = new RowMatrix(sc.parallelize(denseData, 2))
// Compute the top 20 singular values and corresponding singular vectors.
val svd: SingularValueDecomposition[RowMatrix, Matrix] = mat.computeSVD(7, computeU = true)
val U: RowMatrix = svd.U // The U factor is a RowMatrix.
val s: Vector = svd.s // The singular values are stored in a local dense vector.
val V: Matrix = svd.V // The V factor is a local dense matrix.
println("U:" + U)
println("s:" + s)
println("V:" + V)
sc.stop()
}
} 开发者ID:PacktPublishing，项目名称:Machine-Learning-with-Spark-Second-Edition，代码行数:30，代码来源:SparkSVDExampleOne.scala