Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/9180#discussion_r42923827
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/util/LinearDataGenerator.scala ---
@@ -125,6 +124,58 @@ object LinearDataGenerator {
}
/**
+ * @param intercept Data intercept
+ * @param weights Weights to be applied.
+ * @param xMean the mean of the generated features. Lots of time, if the
features are not properly
+ * standardized, the algorithm with poor implementation
will have difficulty
+ * to converge.
+ * @param xVariance the variance of the generated features.
+ * @param nPoints Number of points in sample.
+ * @param seed Random seed
+ * @param eps Epsilon scaling factor.
+ * @return Seq of LabeledPoint includes sparse vectors..
+ */
+ @Since("1.6.0")
+ def generateLinearSparseInput(
+ intercept: Double,
+ weights: Array[Double],
+ xMean: Array[Double],
+ xVariance: Array[Double],
+ nPoints: Int,
+ seed: Int,
+ eps: Double): Seq[LabeledPoint] = {
+ val rnd = new Random(seed)
+ val x = Array.fill[Array[Double]](nPoints)(
+ Array.fill[Double](weights.length)(rnd.nextDouble()))
+
+ x.foreach { v =>
--- End diff --
You can also add the variance of sparsity such that the num of non zeros
will not be constant.
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