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author | Sean Owen <sowen@cloudera.com> | 2014-05-06 20:07:22 -0700 |
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committer | Patrick Wendell <pwendell@gmail.com> | 2014-05-06 20:07:22 -0700 |
commit | 25ad8f93012730115a8a1fac649fe3e842c045b3 (patch) | |
tree | 6bc0dfec7014289e39f4c5c9070ed121e00c4398 /docs/mllib-basics.md | |
parent | a000b5c3b0438c17e9973df4832c320210c29c27 (diff) | |
download | spark-25ad8f93012730115a8a1fac649fe3e842c045b3.tar.gz spark-25ad8f93012730115a8a1fac649fe3e842c045b3.tar.bz2 spark-25ad8f93012730115a8a1fac649fe3e842c045b3.zip |
SPARK-1727. Correct small compile errors, typos, and markdown issues in (primarly) MLlib docs
While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs.
Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown.
Author: Sean Owen <sowen@cloudera.com>
Closes #653 from srowen/SPARK-1727 and squashes the following commits:
6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count
8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output)
99966a9 [Sean Owen] Update issue tracker URL in docs
23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak)
8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs
Diffstat (limited to 'docs/mllib-basics.md')
-rw-r--r-- | docs/mllib-basics.md | 14 |
1 files changed, 9 insertions, 5 deletions
diff --git a/docs/mllib-basics.md b/docs/mllib-basics.md index 710ce1721f..704308802d 100644 --- a/docs/mllib-basics.md +++ b/docs/mllib-basics.md @@ -9,7 +9,7 @@ title: <a href="mllib-guide.html">MLlib</a> - Basics MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices backed by one or more RDDs. In the current implementation, local vectors and matrices are simple data models -to serve public interfaces. The underly linear algebra operations are provided by +to serve public interfaces. The underlying linear algebra operations are provided by [Breeze](http://www.scalanlp.org/) and [jblas](http://jblas.org/). A training example used in supervised learning is called "labeled point" in MLlib. @@ -205,7 +205,7 @@ import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.rdd.RDDimport; -RDD[LabeledPoint] training = MLUtils.loadLibSVMData(sc, "mllib/data/sample_libsvm_data.txt") +RDD<LabeledPoint> training = MLUtils.loadLibSVMData(jsc, "mllib/data/sample_libsvm_data.txt"); {% endhighlight %} </div> </div> @@ -307,6 +307,7 @@ A [`RowMatrix`](api/mllib/index.html#org.apache.spark.mllib.linalg.distributed.R created from a `JavaRDD<Vector>` instance. Then we can compute its column summary statistics. {% highlight java %} +import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.distributed.RowMatrix; @@ -348,10 +349,10 @@ val mat: RowMatrix = ... // a RowMatrix val summary: MultivariateStatisticalSummary = mat.computeColumnSummaryStatistics() println(summary.mean) // a dense vector containing the mean value for each column println(summary.variance) // column-wise variance -println(summary.numNonzers) // number of nonzeros in each column +println(summary.numNonzeros) // number of nonzeros in each column // Compute the covariance matrix. -val Cov: Matrix = mat.computeCovariance() +val cov: Matrix = mat.computeCovariance() {% endhighlight %} </div> </div> @@ -397,11 +398,12 @@ wrapper over `(long, Vector)`. An `IndexedRowMatrix` can be converted to a `Row its row indices. {% highlight java %} +import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.distributed.IndexedRow; import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; import org.apache.spark.mllib.linalg.distributed.RowMatrix; -JavaRDD[IndexedRow] rows = ... // a JavaRDD of indexed rows +JavaRDD<IndexedRow> rows = ... // a JavaRDD of indexed rows // Create an IndexedRowMatrix from a JavaRDD<IndexedRow>. IndexedRowMatrix mat = new IndexedRowMatrix(rows.rdd()); @@ -458,7 +460,9 @@ wrapper over `(long, long, double)`. A `CoordinateMatrix` can be converted to a with sparse rows by calling `toIndexedRowMatrix`. {% highlight scala %} +import org.apache.spark.api.java.JavaRDD; import org.apache.spark.mllib.linalg.distributed.CoordinateMatrix; +import org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix; import org.apache.spark.mllib.linalg.distributed.MatrixEntry; JavaRDD<MatrixEntry> entries = ... // a JavaRDD of matrix entries |