Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/6768#discussion_r32290348
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala ---
@@ -270,12 +270,28 @@ object MLUtils {
* Returns a new vector with `1.0` (bias) appended to the input vector.
*/
def appendBias(vector: Vector): Vector = {
- val vector1 = vector.toBreeze match {
- case dv: BDV[Double] => BDV.vertcat(dv, new BDV[Double](Array(1.0)))
- case sv: BSV[Double] => BSV.vertcat(sv, new BSV[Double](Array(0),
Array(1.0), 1))
- case v: Any => throw new IllegalArgumentException("Do not support
vector type " + v.getClass)
+ vector match {
+ case dv: DenseVector =>
+ val inputValues = dv.values
+ val inputLength = inputValues.length
+ val outputValues = Array.ofDim[Double](inputLength + 1)
+ System.arraycopy(inputValues, 0, outputValues, 0, inputLength)
+ outputValues(inputLength) = 1.0
+ Vectors.dense(outputValues)
+ case sv: SparseVector =>
+ val inputValues = sv.values
+ val inputIndices = sv.indices
+ val inputValuesLength = inputValues.length
+ val dim = sv.size
+ val outputValues = Array.ofDim[Double](inputValuesLength + 1)
+ val outputIndices = Array.ofDim[Int](inputValuesLength + 1)
+ System.arraycopy(inputValues, 0, outputValues, 0,
inputValuesLength)
+ System.arraycopy(inputIndices, 0, outputIndices, 0,
inputValuesLength)
+ outputValues(inputValuesLength) = 1.0
+ outputIndices(inputValuesLength) = dim
+ Vectors.sparse(dim + 1, outputIndices, outputValues)
+ case _ => throw new IllegalArgumentException("Do not support vector
type " + vector.getClass)
--- End diff --
Please use
```scala
case _ => throw new IllegalArgumentException(s"Do not support vector type
${vector.getClass}")
```
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