SPARK的线性代数库BLAS

spark中的BLAS中引入了两个包

import com.github.fommil.netlib.{BLAS => NetlibBLAS, F2jBLAS}
import com.github.fommil.netlib.BLAS.{getInstance => NativeBLAS}

因为private[spark] object BLAS extends Serializable with Logging{ ....}
BLAS是private[spark]修饰，那么我们如何在其他地方之间使用这个类呢？
（1）因此我们在外部使用这个类需要自己创建的类与这个BLAS的包相同；
（2）编译源码。

另外加入依赖

<dependency>     <groupId>com.github.fommil.netlib</groupId>
        <artifactId>all</artifactId>
        <version>1.1.2</version>
        <type>pom</type>
</dependency>

有一个地方不太清楚，希望有人可以解答？

  // For level-3 routines, we use the native BLAS.
  private def nativeBLAS: NetlibBLAS = {
    if (_nativeBLAS == null) {
      _nativeBLAS = NativeBLAS
    }
    _nativeBLAS
  }

routines是如何翻译的?
查阅了资料查到了netlib的BLAS是有三个级别的routines三个级别的routines链接

http://www.netlib.org/blas/#_blas_routines

package org.apache.spark
/**
  * Created by hhy on 2017/12/08.
  */
import org.apache.spark.mllib.linalg.{BLAS, Vectors}
import org.apache.spark.mllib.linalg._
object BLASdemo {
  def main(args: Array[String]):Unit={
    val X=Vectors.dense(1,2,3)
    val Y=Vectors.dense(4,5,6)
    //println(com.github.fommil.netlib.BLAS.getInstance().getClass().getName())
    //println(com.github.fommil.netlib.LAPACK.getInstance().getClass().getName())

    /**
      * 点积 返回值为Double
      * dot(x: DenseVector, y: DenseVector): Double
      * dot(x: SparseVector, y: SparseVector): Double
      *  dot(x: SparseVector, y: DenseVector): Double
      *  dot(x: DenseVector, y: SparseVector): Double
      * */
    val Z=BLAS.dot(X,Y)
    println(Z)   //32


    //x = a * x  scal(a: Double, x: Vector): Unit
    val a=3.0
    BLAS.scal(a,X)    //X是1,2,3
    println("SCAL后的X:"+X)  //SCAL后的X:[3.0,6.0,9.0]

    //y = x  copy(x: Vector, y: Vector): Unit
    val y=Vectors.dense(1,2,3)  //注意两个向量维度要一样否则报错 Exception in thread "main" java.lang.IllegalArgumentException: requirement failed
    //var y=Vectors.dense(1,2,3)  //var  val都修改了y
    BLAS.copy(X,y)        //X是3,6,9
    println("复制X到y得到y:"+y)  //复制X到y得到y:[3.0,6.0,9.0]

    /**
      * y += a * x
      * axpy(a: Double, x: DenseVector, y: DenseVector): Unit
      * axpy(a: Double, x: Vector, y: Vector): Unit
      * axpy(a: Double, x: SparseVector, y: DenseVector): Unit
      * private[spark] def axpy(a: Double, X: DenseMatrix, Y: DenseMatrix): Unit
      * */
    val b=4
    BLAS.axpy(b,X,Y)  //X是3,6,9    Y是4,5,6
    println("Y:"+Y)  //Y:[16.0,29.0,42.0]

    /**矩阵相乘 并且可以设置相乘矩阵的一个系数 同时还可以加上另外一个矩阵
      * C := alpha * A * B + beta * C
      * A:m x k.
      * B:k x n
      * C: m x n.
      *  * */
    def gemm(alpha: Double,A: Matrix,B: DenseMatrix,beta: Double,C: DenseMatrix): Unit
    def gemm(alpha: Double,A: DenseMatrix,B: DenseMatrix,beta: Double,C: DenseMatrix): Unit
    def gemm(alpha: Double,A: SparseMatrix,B: DenseMatrix,beta: Double,C: DenseMatrix): Unit

    /**
      * y := alpha * A * x + beta * y
      * A: m x n
      * x:n x 1
      * y:m x 1
      * */
    def gemv(alpha: Double, A: Matrix, x: Vector, beta: Double, y: DenseVector): Unit={}
    def gemv(alpha: Double, A: DenseMatrix, x: DenseVector, beta: Double, y: DenseVector): Unit={}
    def gemv(alpha: Double, A: DenseMatrix, x: SparseVector, beta: Double, y: DenseVector): Unit={}
    def gemv(alpha: Double, A: SparseMatrix, x: SparseVector, beta: Double, y: DenseVector): Unit={}
    def gemv(alpha: Double, A: SparseMatrix, x: DenseVector, beta: Double, y: DenseVector): Unit={}

    /**
      * 增加alpha * v * v.t 到矩阵  U是以列为主序的矩阵的上三角部分
      * */
    def spr(alpha: Double, v: Vector, U: Array[Double]): Unit={}
    def spr(alpha: Double, v: Vector, U: Array[Double]): Unit={}

    /**
      * A := alpha * x * x^T^ + A
      * x有n个元素  A是 n x n.方阵
      * */
    def syr(alpha: Double, x: Vector, A: DenseMatrix){}
    private def syr(alpha: Double, x: DenseVector, A: DenseMatrix){}
    private def syr(alpha: Double, x: SparseVector, A: DenseMatrix){}
  }
}

SPARK的线性代数库BLAS

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