[SPARK-10669] [DOCS] Link to each language's API in codetabs in ML docs: spark.mllib

In the Markdown docs for the spark.mllib Programming Guide, we have code examples with codetabs for each language. We should link to each language's API docs within the corresponding codetab, but we are inconsistent about this. For an example of what we want to do, see the "ChiSqSelector" section in https://github.com/apache/spark/blob/64743870f23bffb8d96dcc8a0181c1452782a151/docs/mllib-feature-extraction.md
This JIRA is just for spark.mllib, not spark.ml.

Please let me know if more work is needed, thanks a lot.

Author: Xin Ren <iamshrek@126.com>

Closes #8977 from keypointt/SPARK-10669.

1 files changed, 47 insertions, 0 deletions

diff --git a/docs/mllib-data-types.md b/docs/mllib-data-types.md
index d8c7bdc63c..3c0c047967 100644
--- a/docs/mllib-data-types.md
+++ b/docs/mllib-data-types.md
@@ -33,6 +33,8 @@ 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.
+
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.{Vector, Vectors}
 
@@ -59,6 +61,8 @@ implementations: [`DenseVector`](api/java/org/apache/spark/mllib/linalg/DenseVec
 using the factory methods implemented in
 [`Vectors`](api/java/org/apache/spark/mllib/linalg/Vectors.html) to create local vectors.
 
+Refer to the [`Vector` Java docs](api/java/org/apache/spark/mllib/linalg/Vector.html) and [`Vectors` Java docs](api/java/org/apache/spark/mllib/linalg/Vectors.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Vector;
 import org.apache.spark.mllib.linalg.Vectors;
@@ -86,6 +90,8 @@ and the following as sparse vectors:
 We recommend using NumPy arrays over lists for efficiency, and using the factory methods implemented
 in [`Vectors`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors) to create sparse vectors.
 
+Refer to the [`Vectors` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors) for more details on the API.
+
 {% highlight python %}
 import numpy as np
 import scipy.sparse as sps
@@ -119,6 +125,8 @@ For multiclass classification, labels should be class indices starting from zero
 A labeled point is represented by the case class
 [`LabeledPoint`](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint).
 
+Refer to the [`LabeledPoint` Scala docs](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
@@ -136,6 +144,8 @@ val neg = LabeledPoint(0.0, Vectors.sparse(3, Array(0, 2), Array(1.0, 3.0)))
 A labeled point is represented by
 [`LabeledPoint`](api/java/org/apache/spark/mllib/regression/LabeledPoint.html).
 
+Refer to the [`LabeledPoint` Java docs](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Vectors;
 import org.apache.spark.mllib.regression.LabeledPoint;
@@ -153,6 +163,8 @@ LabeledPoint neg = new LabeledPoint(0.0, Vectors.sparse(3, new int[] {0, 2}, new
 A labeled point is represented by
 [`LabeledPoint`](api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint).
 
+Refer to the [`LabeledPoint` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.regression.LabeledPoint) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.linalg import SparseVector
 from pyspark.mllib.regression import LabeledPoint
@@ -187,6 +199,8 @@ 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.
+
 {% highlight scala %}
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.MLUtils
@@ -200,6 +214,8 @@ val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_
 [`MLUtils.loadLibSVMFile`](api/java/org/apache/spark/mllib/util/MLUtils.html) reads training
 examples stored in LIBSVM format.
 
+Refer to the [`MLUtils` Java docs](api/java/org/apache/spark/mllib/util/MLUtils.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.util.MLUtils;
@@ -214,6 +230,8 @@ JavaRDD<LabeledPoint> examples =
 [`MLUtils.loadLibSVMFile`](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) reads training
 examples stored in LIBSVM format.
 
+Refer to the [`MLUtils` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.util import MLUtils
 
@@ -246,6 +264,8 @@ 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.
+
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.{Matrix, Matrices}
 
@@ -267,6 +287,8 @@ We recommend using the factory methods implemented
 in [`Matrices`](api/java/org/apache/spark/mllib/linalg/Matrices.html) to create local
 matrices. Remember, local matrices in MLlib are stored in column-major order.
 
+Refer to the [`Matrix` Java docs](api/java/org/apache/spark/mllib/linalg/Matrix.html) and [`Matrices` Java docs](api/java/org/apache/spark/mllib/linalg/Matrices.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.mllib.linalg.Matrix;
 import org.apache.spark.mllib.linalg.Matrices;
@@ -289,6 +311,8 @@ We recommend using the factory methods implemented
 in [`Matrices`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrices) to create local
 matrices. Remember, local matrices in MLlib are stored in column-major order.
 
+Refer to the [`Matrix` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrix) and [`Matrices` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.linalg.Matrices) for more details on the API.
+
 {% highlight python %}
 import org.apache.spark.mllib.linalg.{Matrix, Matrices}
 
@@ -341,6 +365,7 @@ created from an `RDD[Vector]` instance.  Then we can compute its column summary
 [QR decomposition](https://en.wikipedia.org/wiki/QR_decomposition) is of the form A = QR where Q is an orthogonal matrix and R is an upper triangular matrix.
 For [singular value decomposition (SVD)](https://en.wikipedia.org/wiki/Singular_value_decomposition) and [principal component analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis), please refer to [Dimensionality reduction](mllib-dimensionality-reduction.html).
 
+Refer to the [`RowMatrix` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.RowMatrix) for details on the API.
 
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.Vector
@@ -364,6 +389,8 @@ val qrResult = mat.tallSkinnyQR(true)
 A [`RowMatrix`](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html) can be
 created from a `JavaRDD<Vector>` instance.  Then we can compute its column summary statistics.
 
+Refer to the [`RowMatrix` Java docs](api/java/org/apache/spark/mllib/linalg/distributed/RowMatrix.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.mllib.linalg.Vector;
@@ -387,6 +414,8 @@ QRDecomposition<RowMatrix, Matrix> result = mat.tallSkinnyQR(true);
 A [`RowMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.RowMatrix) can be 
 created from an `RDD` of vectors.
 
+Refer to the [`RowMatrix` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.RowMatrix) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.linalg.distributed import RowMatrix
 
@@ -423,6 +452,8 @@ can be created from an `RDD[IndexedRow]` instance, where
 wrapper over `(Long, Vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
 its row indices.
 
+Refer to the [`IndexedRowMatrix` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.distributed.{IndexedRow, IndexedRowMatrix, RowMatrix}
 
@@ -448,6 +479,8 @@ can be created from an `JavaRDD<IndexedRow>` instance, where
 wrapper over `(long, Vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
 its row indices.
 
+Refer to the [`IndexedRowMatrix` Java docs](api/java/org/apache/spark/mllib/linalg/distributed/IndexedRowMatrix.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.mllib.linalg.distributed.IndexedRow;
@@ -475,6 +508,8 @@ can be created from an `RDD` of `IndexedRow`s, where
 wrapper over `(long, vector)`.  An `IndexedRowMatrix` can be converted to a `RowMatrix` by dropping
 its row indices.
 
+Refer to the [`IndexedRowMatrix` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.IndexedRowMatrix) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix
 
@@ -529,6 +564,8 @@ wrapper over `(Long, Long, Double)`.  A `CoordinateMatrix` can be converted to a
 with sparse rows by calling `toIndexedRowMatrix`.  Other computations for 
 `CoordinateMatrix` are not currently supported.
 
+Refer to the [`CoordinateMatrix` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.CoordinateMatrix) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.distributed.{CoordinateMatrix, MatrixEntry}
 
@@ -555,6 +592,8 @@ wrapper over `(long, long, double)`.  A `CoordinateMatrix` can be converted to a
 with sparse rows by calling `toIndexedRowMatrix`. Other computations for 
 `CoordinateMatrix` are not currently supported.
 
+Refer to the [`CoordinateMatrix` Java docs](api/java/org/apache/spark/mllib/linalg/distributed/CoordinateMatrix.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix;
@@ -582,6 +621,8 @@ can be created from an `RDD` of `MatrixEntry` entries, where
 wrapper over `(long, long, float)`.  A `CoordinateMatrix` can be converted to a `RowMatrix` by 
 calling `toRowMatrix`, or to an `IndexedRowMatrix` with sparse rows by calling `toIndexedRowMatrix`.
 
+Refer to the [`CoordinateMatrix` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.CoordinateMatrix) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry
 
@@ -631,6 +672,8 @@ most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling
 `toBlockMatrix` creates blocks of size 1024 x 1024 by default.
 Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`.
 
+Refer to the [`BlockMatrix` Scala docs](api/scala/index.html#org.apache.spark.mllib.linalg.distributed.BlockMatrix) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.distributed.{BlockMatrix, CoordinateMatrix, MatrixEntry}
 
@@ -656,6 +699,8 @@ most easily created from an `IndexedRowMatrix` or `CoordinateMatrix` by calling
 `toBlockMatrix` creates blocks of size 1024 x 1024 by default.
 Users may change the block size by supplying the values through `toBlockMatrix(rowsPerBlock, colsPerBlock)`.
 
+Refer to the [`BlockMatrix` Java docs](api/java/org/apache/spark/mllib/linalg/distributed/BlockMatrix.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.mllib.linalg.distributed.BlockMatrix;
@@ -683,6 +728,8 @@ A [`BlockMatrix`](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed
 can be created from an `RDD` of sub-matrix blocks, where a sub-matrix block is a 
 `((blockRowIndex, blockColIndex), sub-matrix)` tuple.
 
+Refer to the [`BlockMatrix` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.linalg.distributed.BlockMatrix) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.linalg import Matrices
 from pyspark.mllib.linalg.distributed import BlockMatrix