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[SPARK-24628][DOC] Typos of the example code in docs/mllib-data-types.md

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## What changes were proposed in this pull request?

The example wants to create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0)), but the list is given as [1, 2, 3, 4, 5, 6]. Now it is changed as [1, 3, 5, 2, 4, 6].

And the example wants to create an RDD of coordinate entries like:
entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)]).
However, it is done with the MatrixEntry class like:
entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)]),
where the third MatrixEntry has a different row index.
Now it is changed as MatrixEntry(2, 1, 3.7).

## How was this patch tested?

This is trivial enough that it should not affect tests.

Author: Weizhe Huang <huangweizhebbdservice.com>

Please review http://spark.apache.org/contributing.html before opening a pull request.

Author: Huangweizhe <huangweizhe@bbdservice.com>

Closes #21612 from huangweizhe123/my_change.
This commit is contained in:
Huangweizhe 2018-07-18 09:45:56 -05:00 committed by Sean Owen
parent 002300dd41
commit ebe9e28488
1 changed files with 2 additions and 2 deletions
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docs/mllib-data-types.md
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@ -317,7 +317,7 @@ Refer to the [`Matrix` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.
from pyspark.mllib.linalg import Matrix, Matrices
# Create a dense matrix ((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
dm2 = Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])
dm2 = Matrices.dense(3, 2, [1, 3, 5, 2, 4, 6])
# Create a sparse matrix ((9.0, 0.0), (0.0, 8.0), (0.0, 6.0))
sm = Matrices.sparse(3, 2, [0, 1, 3], [0, 2, 1], [9, 6, 8])
@ -624,7 +624,7 @@ from pyspark.mllib.linalg.distributed import CoordinateMatrix, MatrixEntry
# Create an RDD of coordinate entries.
# - This can be done explicitly with the MatrixEntry class:
entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(6, 1, 3.7)])
entries = sc.parallelize([MatrixEntry(0, 0, 1.2), MatrixEntry(1, 0, 2.1), MatrixEntry(2, 1, 3.7)])
# - or using (long, long, float) tuples:
entries = sc.parallelize([(0, 0, 1.2), (1, 0, 2.1), (2, 1, 3.7)])