Github user MechCoder commented on a diff in the pull request:
https://github.com/apache/spark/pull/4459#discussion_r24310707
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixture.scala ---
@@ -215,20 +217,29 @@ private object ExpectationSum {
def add(
weights: Array[Double],
dists: Array[MultivariateGaussian])
- (sums: ExpectationSum, x: BreezeVector[Double]): ExpectationSum = {
+ (sums: ExpectationSum, x: BV[Double]): ExpectationSum = {
val p = weights.zip(dists).map {
case (weight, dist) => MLUtils.EPSILON + weight * dist.pdf(x)
}
val pSum = p.sum
sums.logLikelihood += math.log(pSum)
- val xxt = x * new Transpose(x)
var i = 0
+ val isSparse = x match {
+ case _: BSV[Double] => true
+ case _: BDV[Double] => false
+ }
while (i < sums.k) {
p(i) /= pSum
sums.weights(i) += p(i)
sums.means(i) += x * p(i)
- BLAS.syr(p(i), Vectors.fromBreeze(x).asInstanceOf[DenseVector],
- Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix])
+ if (isSparse){
+ BLAS.syr(p(i), Vectors.fromBreeze(x).asInstanceOf[SparseVector],
+ Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix])
+ }
+ else {
+ BLAS.syr(p(i), Vectors.fromBreeze(x).asInstanceOf[DenseVector],
+ Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix])
+ }
--- End diff --
If I do that, I get a type check error, saying value values is not a member
of org.apache.spark.mllib.linalg.Vector which might explain why there are
separate dot methods for Sparse and Dense Vectors.
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