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Diffstat (limited to 'docs/mllib-dimensionality-reduction.md')
| -rw-r--r-- | docs/mllib-dimensionality-reduction.md | 7 |
1 files changed, 7 insertions, 0 deletions
diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md index 4e9ecf7c00..ab24663cfe 100644 --- a/docs/mllib-dimensionality-reduction.md +++ b/docs/mllib-dimensionality-reduction.md @@ -44,6 +44,10 @@ say, less than $1000$, but many rows, which we call *tall-and-skinny*. <div class="codetabs"> <div data-lang="scala" markdown="1"> {% highlight scala %} +import org.apache.spark.mllib.linalg.Matrix +import org.apache.spark.mllib.linalg.distributed.RowMatrix +import org.apache.spark.mllib.linalg.SingularValueDecomposition + val mat: RowMatrix = ... // Compute the top 20 singular values and corresponding singular vectors. @@ -74,6 +78,9 @@ and use them to project the vectors into a low-dimensional space. The number of columns should be small, e.g, less than 1000. {% highlight scala %} +import org.apache.spark.mllib.linalg.Matrix +import org.apache.spark.mllib.linalg.distributed.RowMatrix + val mat: RowMatrix = ... // Compute the top 10 principal components. |