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Diffstat (limited to 'docs/mllib-data-types.md')
| -rw-r--r-- | docs/mllib-data-types.md | 16 |
1 files changed, 6 insertions, 10 deletions
diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md index 2ffe0f1c2b..ef56aebbc3 100644 --- a/docs/mllib-data-types.md +++ b/docs/mllib-data-types.md @@ -33,7 +33,7 @@ implementations: [`DenseVector`](api/scala/index.html#org.apache.spark.mllib.lin using the factory methods implemented in [`Vectors`](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) to create local vectors. -Refer to the [`Vector` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) and [`Vectors` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors) for details on the API. +Refer to the [`Vector` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.Vector) and [`Vectors` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$) for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.{Vector, Vectors} @@ -199,7 +199,7 @@ After loading, the feature indices are converted to zero-based. [`MLUtils.loadLibSVMFile`](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) reads training examples stored in LIBSVM format. -Refer to the [`MLUtils` Scala docs](api/scala/index.html#org.apache.spark.mllib.util.MLUtils) for details on the API. +Refer to the [`MLUtils` Scala docs](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) for details on the API. {% highlight scala %} import org.apache.spark.mllib.regression.LabeledPoint @@ -264,7 +264,7 @@ We recommend using the factory methods implemented in [`Matrices`](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$) to create local matrices. Remember, local matrices in MLlib are stored in column-major order. -Refer to the [`Matrix` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix) and [`Matrices` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices) for details on the API. +Refer to the [`Matrix` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.Matrix) and [`Matrices` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$) for details on the API. {% highlight scala %} import org.apache.spark.mllib.linalg.{Matrix, Matrices} @@ -331,7 +331,7 @@ sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8]) A distributed matrix has long-typed row and column indices and double-typed values, stored distributively in one or more RDDs. It is very important to choose the right format to store large and distributed matrices. Converting a distributed matrix to a different format may require a -global shuffle, which is quite expensive. Three types of distributed matrices have been implemented +global shuffle, which is quite expensive. Four types of distributed matrices have been implemented so far. The basic type is called `RowMatrix`. A `RowMatrix` is a row-oriented distributed @@ -344,6 +344,8 @@ An `IndexedRowMatrix` is similar to a `RowMatrix` but with row indices, which can be used for identifying rows and executing joins. A `CoordinateMatrix` is a distributed matrix stored in [coordinate list (COO)](https://en.wikipedia.org/wiki/Sparse_matrix#Coordinate_list_.28COO.29) format, backed by an RDD of its entries. +A `BlockMatrix` is a distributed matrix backed by an RDD of `MatrixBlock` +which is a tuple of `(Int, Int, Matrix)`. ***Note*** @@ -535,12 +537,6 @@ rowsRDD = mat.rows # Convert to a RowMatrix by dropping the row indices. rowMat = mat.toRowMatrix() - -# Convert to a CoordinateMatrix. -coordinateMat = mat.toCoordinateMatrix() - -# Convert to a BlockMatrix. -blockMat = mat.toBlockMatrix() {% endhighlight %} </div> |