diff options
| -rw-r--r-- | docs/mllib-data-types.md | 41 |
1 files changed, 15 insertions, 26 deletions
diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md index 24d22b9bcd..fe6c1bf7bf 100644 --- a/docs/mllib-data-types.md +++ b/docs/mllib-data-types.md @@ -298,23 +298,22 @@ In general the use of non-deterministic RDDs can lead to errors. ### BlockMatrix -A `BlockMatrix` is a distributed matrix backed by an RDD of `MatrixBlock`s, where `MatrixBlock` is +A `BlockMatrix` is a distributed matrix backed by an RDD of `MatrixBlock`s, where a `MatrixBlock` is a tuple of `((Int, Int), Matrix)`, where the `(Int, Int)` is the index of the block, and `Matrix` is the sub-matrix at the given index with size `rowsPerBlock` x `colsPerBlock`. -`BlockMatrix` supports methods such as `.add` and `.multiply` with another `BlockMatrix`. -`BlockMatrix` also has a helper function `.validate` which can be used to debug whether the +`BlockMatrix` supports methods such as `add` and `multiply` with another `BlockMatrix`. +`BlockMatrix` also has a helper function `validate` which can be used to check whether the `BlockMatrix` is set up properly. <div class="codetabs"> <div data-lang="scala" markdown="1"> A [`BlockMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.BlockMatrix) can be -most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` using `.toBlockMatrix()`. -`.toBlockMatrix()` will create blocks of size 1024 x 1024. Users may change the sizes of their blocks -by supplying the values through `.toBlockMatrix(rowsPerBlock, colsPerBlock)`. +most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. +`toBlockMatrix` creates blocks of size 1024 x 1024 by default. +Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. {% highlight scala %} -import org.apache.spark.mllib.linalg.SingularValueDecomposition import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry} val entries: RDD[MatrixEntry] = ... // an RDD of (i, j, v) matrix entries @@ -323,29 +322,24 @@ val coordMat: CoordinateMatrix = new CoordinateMatrix(entries) // Transform the CoordinateMatrix to a BlockMatrix val matA: BlockMatrix = coordMat.toBlockMatrix().cache() -// validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. +// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. // Nothing happens if it is valid. -matA.validate +matA.validate() // Calculate A^T A. -val AtransposeA = matA.transpose.multiply(matA) - -// get SVD of 2 * A -val A2 = matA.add(matA) -val svd = A2.toIndexedRowMatrix().computeSVD(20, false, 1e-9) +val ata = matA.transpose.multiply(matA) {% endhighlight %} </div> <div data-lang="java" markdown="1"> -A [`BlockMatrix`](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.BlockMatrix) can be -most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` using `.toBlockMatrix()`. -`.toBlockMatrix()` will create blocks of size 1024 x 1024. Users may change the sizes of their blocks -by supplying the values through `.toBlockMatrix(rowsPerBlock, colsPerBlock)`. +A [`BlockMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/BlockMatrix.html) can be +most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling `toBlockMatrix`. +`toBlockMatrix` creates blocks of size 1024 x 1024 by default. +Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`. {% highlight java %} import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.mllib.linalg.SingularValueDecomposition; import org.apache.spark.mllib.linalg.distributed.BlockMatrix; import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; @@ -356,17 +350,12 @@ CoordinateMatrix coordMat = new CoordinateMatrix(entries.rdd()); // Transform the CoordinateMatrix to a BlockMatrix BlockMatrix matA = coordMat.toBlockMatrix().cache(); -// validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. +// Validate whether the BlockMatrix is set up properly. Throws an Exception when it is not valid. // Nothing happens if it is valid. matA.validate(); // Calculate A^T A. -BlockMatrix AtransposeA = matA.transpose().multiply(matA); - -// get SVD of 2 * A -BlockMatrix A2 = matA.add(matA); -SingularValueDecomposition<IndexedRowMatrix, Matrix> svd = - A2.toIndexedRowMatrix().computeSVD(20, false, 1e-9); +BlockMatrix ata = matA.transpose().multiply(matA); {% endhighlight %} </div> </div> |