Github user nakul02 commented on a diff in the pull request:
https://github.com/apache/spark/pull/9756#discussion_r46596717
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala ---
@@ -131,35 +131,30 @@ object LinearDataGenerator {
eps: Double,
sparsity: Double): Seq[LabeledPoint] = {
require(0.0 <= sparsity && sparsity <= 1.0)
- val rnd = new Random(seed)
- val x = Array.fill[Array[Double]](nPoints)(
- Array.fill[Double](weights.length)(rnd.nextDouble()))
-
- val sparseRnd = new Random(seed)
- x.foreach { v =>
- var i = 0
- val len = v.length
- while (i < len) {
- if (sparseRnd.nextDouble() < sparsity) {
- v(i) = 0.0
- } else {
- v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i)
- }
- i += 1
- }
- }
- val y = x.map { xi =>
- blas.ddot(weights.length, xi, 1, weights, 1) + intercept + eps *
rnd.nextGaussian()
- }
-
- y.zip(x).map { p =>
- if (sparsity == 0.0) {
+ val rnd = new Random(seed)
+ if (sparsity == 0.0) {
+ (0 until nPoints).map { _ =>
+ val features = Vectors.dense(weights.indices.map { i =>
+ (rnd.nextDouble() - 0.5) * math.sqrt(12.0 * xVariance(i)) +
xMean(i)
--- End diff --
The way the array is built for dense and sparse features is different. I
suspect it might be more clunky to try and find something common between them
to refactor something out into a local def.
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