def toBreeze( _v : Vector ) : BZV[Double] = {

     _v match {

       case x : org.apache.spark.mllib.linalg.DenseVector => new BDV(_v.toArray)

       case x : org.apache.spark.mllib.linalg.SparseVector => {

         val sparseVector = _v.toSparse

         new BSV(sparseVector.indices, sparseVector.values, sparseVector.size)

       }

       case _ => {new BDV(Array[Double]())}

     }

   }

   def fromBreeze(breezeVector: BZV[Double]): Vector = {

     breezeVector match {

       case v: BDV[Double] =>

         if (v.offset == 0 && v.stride == 1 && v.length == v.data.length) {

           new org.apache.spark.mlllib.linalg.DenseVector(v.data)

         } else {

           new org.apache.spark.mlllib.linalg.DenseVector(v.toArray)  // Can't use underlying array directly, so make a new one

         }

       case v: BSV[Double] =>

         if (v.index.length == v.used) {

           new org.apache.spark.mlllib.linalg.SparseVector(v.length, v.index, v.data)

         } else {

           new org.apache.spark.mlllib.linalg.SparseVector(v.length, v.index.slice(0, v.used), v.data.slice(0, v.used))

         }

       case v: BZV[_] =>

         sys.error("Unsupported Breeze vector type: " + v.getClass.getName)

     }

   }