spark 2.0 Vector toBreeze 

 1   def toBreeze( _v : Vector ) : BZV[Double] = {
 2     _v match {
 3       case x : org.apache.spark.mllib.linalg.DenseVector => new BDV(_v.toArray)
 4       case x : org.apache.spark.mllib.linalg.SparseVector => {
 5         val sparseVector = _v.toSparse
 6         new BSV(sparseVector.indices, sparseVector.values, sparseVector.size)
 7       }
 8       case _ => {new BDV(Array[Double]())}
 9     }
10   }

 

 1   def fromBreeze(breezeVector: BZV[Double]): Vector = {
 2     breezeVector match {
 3       case v: BDV[Double] =>
 4         if (v.offset == 0 && v.stride == 1 && v.length == v.data.length) {
 5           new org.apache.spark.mlllib.linalg.DenseVector(v.data)
 6         } else {
 7           new org.apache.spark.mlllib.linalg.DenseVector(v.toArray)  // Can't use underlying array directly, so make a new one
 8         }
 9       case v: BSV[Double] =>
10         if (v.index.length == v.used) {
11           new org.apache.spark.mlllib.linalg.SparseVector(v.length, v.index, v.data)
12         } else {
13           new org.apache.spark.mlllib.linalg.SparseVector(v.length, v.index.slice(0, v.used), v.data.slice(0, v.used))
14         }
15       case v: BZV[_] =>
16         sys.error("Unsupported Breeze vector type: " + v.getClass.getName)
17     }
18   }

 

posted @ 2016-11-01 16:15  苏轶然  阅读(958)  评论(0)  编辑  收藏  举报