本地向量
一個本地向量有intereage, 0-based indices及double類型,貯存在單一機器上。MLlib 支持兩種類型的本地向量: dense及sparse。
dense向量:透過輸入double陣列回傳。
sparse向量:透過兩個平行陣列:indices及values回傳
舉個例子,向量(1.0, 0.0, 3.0)可以被表示成:
dense格式-[1.0, 0.0, 3.0]
sparse格式-(3, [0, 2], [1.0, 3.0]),3表示此vector的大小。
本地向量的基類是Vector,而且提供DenseVector及SparseVector兩個實作。以下介紹使用工廠方法在Vectors中去創建本地向量。
import org.apache.spark.mllib.linalg.{Vector, Vectors}
// Create a dense vector (1.0, 0.0, 3.0).
val dv: Vector = Vectors.dense(1.0, 0.0, 3.0)
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its indices and values corresponding to nonzero entries.
val sv1: Vector = Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0))
// Create a sparse vector (1.0, 0.0, 3.0) by specifying its nonzero entries.
val sv2: Vector = Vectors.sparse(3, Seq((0, 1.0), (2, 3.0)))
Note: Scala會預設匯入scala.collection.immutable.Vector,所以你必須自行匯入org.apache.spark.mllib.linalg.Vector,才能使用MLlib的Vector
DenseVector 與 SparseVector源碼
@SQLUserDefinedType(udt = classOf[VectorUDT])
class DenseVector(val values: Array[Double]) extends Vector {
override def size: Int = values.length
override def toString: String = values.mkString("[", ",", "]")
override def toArray: Array[Double] = values
private[mllib] override def toBreeze: BV[Double] = new BDV[Double](values)
override def apply(i: Int) = values(i)
override def copy: DenseVector = {
new DenseVector(values.clone())
}
private[spark] override def foreachActive(f: (Int, Double) => Unit) = {
var i = 0
val localValuesSize = values.size
val localValues = values
while (i < localValuesSize) {
f(i, localValues(i))
i += 1
}
}
}
/**
* A dense vector represented by a value array.
*/
@SQLUserDefinedType(udt = classOf[VectorUDT])
class DenseVector(val values: Array[Double]) extends Vector {
override def size: Int = values.length
override def toString: String = values.mkString("[", ",", "]")
override def toArray: Array[Double] = values
private[mllib] override def toBreeze: BV[Double] = new BDV[Double](values)
override def apply(i: Int) = values(i)
override def copy: DenseVector = {
new DenseVector(values.clone())
}
private[spark] override def foreachActive(f: (Int, Double) => Unit) = {
var i = 0
val localValuesSize = values.size
val localValues = values
while (i < localValuesSize) {
f(i, localValues(i))
i += 1
}
}
}