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Li Pu
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very minor comment fix
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  • mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed

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mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala

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@@ -35,7 +35,7 @@ import org.apache.spark.mllib.stat.MultivariateStatisticalSummary
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* [[org.apache.spark.mllib.stat.MultivariateStatisticalSummary]]
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* together with add() and merge() function.
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* A numerically stable algorithm is implemented to compute sample mean and variance:
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*[[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]].
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* [[http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance variance-wiki]].
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* Zero elements (including explicit zero values) are skipped when calling add() and merge(),
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* to have time complexity O(nnz) instead of O(n) for each column.
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*/
@@ -204,7 +204,7 @@ class RowMatrix(
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/**
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* Multiplies the Gramian matrix `A^T A` by a dense vector on the right without computing `A^T A`.
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*
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@param v a dense vector whose length must match the number of columns of this matrix
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* @param v a dense vector whose length must match the number of columns of this matrix
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* @return a dense vector representing the product
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*/
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private[mllib] def multiplyGramianMatrixBy(v: BDV[Double]): BDV[Double] = {

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