diff options
| -rw-r--r-- | docs/mllib-data-types.md (renamed from docs/mllib-basics.md) | 4 | ||||
| -rw-r--r-- | docs/mllib-dimensionality-reduction.md | 4 | ||||
| -rw-r--r-- | docs/mllib-guide.md | 9 | ||||
| -rw-r--r-- | docs/mllib-statistics.md (renamed from docs/mllib-stats.md) | 156 |
4 files changed, 87 insertions, 86 deletions
diff --git a/docs/mllib-basics.md b/docs/mllib-data-types.md index 8752df4129..101dc2f869 100644 --- a/docs/mllib-basics.md +++ b/docs/mllib-data-types.md @@ -1,7 +1,7 @@ --- layout: global -title: Basics - MLlib -displayTitle: <a href="mllib-guide.html">MLlib</a> - Basics +title: Data Types - MLlib +displayTitle: <a href="mllib-guide.html">MLlib</a> - Data Types --- * Table of contents diff --git a/docs/mllib-dimensionality-reduction.md b/docs/mllib-dimensionality-reduction.md index 9f2cf6d48e..21cb35b427 100644 --- a/docs/mllib-dimensionality-reduction.md +++ b/docs/mllib-dimensionality-reduction.md @@ -11,7 +11,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Dimensionality Reduction of reducing the number of variables under consideration. It can be used to extract latent features from raw and noisy features or compress data while maintaining the structure. -MLlib provides support for dimensionality reduction on the <a href="mllib-basics.html#rowmatrix">RowMatrix</a> class. +MLlib provides support for dimensionality reduction on the <a href="mllib-data-types.html#rowmatrix">RowMatrix</a> class. ## Singular value decomposition (SVD) @@ -58,7 +58,7 @@ passes, $O(n)$ storage on each executor, and $O(n k)$ storage on the driver. ### SVD Example MLlib provides SVD functionality to row-oriented matrices, provided in the -<a href="mllib-basics.html#rowmatrix">RowMatrix</a> class. +<a href="mllib-data-types.html#rowmatrix">RowMatrix</a> class. <div class="codetabs"> <div data-lang="scala" markdown="1"> diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 4d4198b9e0..d3a510b3c1 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -7,12 +7,13 @@ MLlib is Spark's scalable machine learning library consisting of common learning including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below: -* [Data types](mllib-basics.html) -* [Basic statistics](mllib-stats.html) - * random data generation - * stratified sampling +* [Data types](mllib-data-types.html) +* [Basic statistics](mllib-statistics.html) * summary statistics + * correlations + * stratified sampling * hypothesis testing + * random data generation * [Classification and regression](mllib-classification-regression.html) * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html) * [decision trees](mllib-decision-tree.html) diff --git a/docs/mllib-stats.md b/docs/mllib-statistics.md index 511a9fbf71..c463241399 100644 --- a/docs/mllib-stats.md +++ b/docs/mllib-statistics.md @@ -1,7 +1,7 @@ --- layout: global -title: Statistics Functionality - MLlib -displayTitle: <a href="mllib-guide.html">MLlib</a> - Statistics Functionality +title: Basic Statistics - MLlib +displayTitle: <a href="mllib-guide.html">MLlib</a> - Basic Statistics --- * Table of contents @@ -25,7 +25,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Statistics Functionality \newcommand{\zero}{\mathbf{0}} \]` -## Summary Statistics +## Summary statistics We provide column summary statistics for `RDD[Vector]` through the function `colStats` available in `Statistics`. @@ -104,81 +104,7 @@ print summary.numNonzeros() </div> -## Random data generation - -Random data generation is useful for randomized algorithms, prototyping, and performance testing. -MLlib supports generating random RDDs with i.i.d. values drawn from a given distribution: -uniform, standard normal, or Poisson. - -<div class="codetabs"> -<div data-lang="scala" markdown="1"> -[`RandomRDDs`](api/scala/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory -methods to generate random double RDDs or vector RDDs. -The following example generates a random double RDD, whose values follows the standard normal -distribution `N(0, 1)`, and then map it to `N(1, 4)`. - -{% highlight scala %} -import org.apache.spark.SparkContext -import org.apache.spark.mllib.random.RandomRDDs._ - -val sc: SparkContext = ... - -// Generate a random double RDD that contains 1 million i.i.d. values drawn from the -// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions. -val u = normalRDD(sc, 1000000L, 10) -// Apply a transform to get a random double RDD following `N(1, 4)`. -val v = u.map(x => 1.0 + 2.0 * x) -{% endhighlight %} -</div> - -<div data-lang="java" markdown="1"> -[`RandomRDDs`](api/java/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory -methods to generate random double RDDs or vector RDDs. -The following example generates a random double RDD, whose values follows the standard normal -distribution `N(0, 1)`, and then map it to `N(1, 4)`. - -{% highlight java %} -import org.apache.spark.SparkContext; -import org.apache.spark.api.JavaDoubleRDD; -import static org.apache.spark.mllib.random.RandomRDDs.*; - -JavaSparkContext jsc = ... - -// Generate a random double RDD that contains 1 million i.i.d. values drawn from the -// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions. -JavaDoubleRDD u = normalJavaRDD(jsc, 1000000L, 10); -// Apply a transform to get a random double RDD following `N(1, 4)`. -JavaDoubleRDD v = u.map( - new Function<Double, Double>() { - public Double call(Double x) { - return 1.0 + 2.0 * x; - } - }); -{% endhighlight %} -</div> - -<div data-lang="python" markdown="1"> -[`RandomRDDs`](api/python/pyspark.mllib.random.RandomRDDs-class.html) provides factory -methods to generate random double RDDs or vector RDDs. -The following example generates a random double RDD, whose values follows the standard normal -distribution `N(0, 1)`, and then map it to `N(1, 4)`. - -{% highlight python %} -from pyspark.mllib.random import RandomRDDs - -sc = ... # SparkContext - -# Generate a random double RDD that contains 1 million i.i.d. values drawn from the -# standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions. -u = RandomRDDs.uniformRDD(sc, 1000000L, 10) -# Apply a transform to get a random double RDD following `N(1, 4)`. -v = u.map(lambda x: 1.0 + 2.0 * x) -{% endhighlight %} -</div> - -</div> - -## Correlations calculation +## Correlations Calculating the correlation between two series of data is a common operation in Statistics. In MLlib we provide the flexibility to calculate pairwise correlations among many series. The supported @@ -455,3 +381,77 @@ for (ChiSqTestResult result : featureTestResults) { </div> </div> + +## Random data generation + +Random data generation is useful for randomized algorithms, prototyping, and performance testing. +MLlib supports generating random RDDs with i.i.d. values drawn from a given distribution: +uniform, standard normal, or Poisson. + +<div class="codetabs"> +<div data-lang="scala" markdown="1"> +[`RandomRDDs`](api/scala/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory +methods to generate random double RDDs or vector RDDs. +The following example generates a random double RDD, whose values follows the standard normal +distribution `N(0, 1)`, and then map it to `N(1, 4)`. + +{% highlight scala %} +import org.apache.spark.SparkContext +import org.apache.spark.mllib.random.RandomRDDs._ + +val sc: SparkContext = ... + +// Generate a random double RDD that contains 1 million i.i.d. values drawn from the +// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions. +val u = normalRDD(sc, 1000000L, 10) +// Apply a transform to get a random double RDD following `N(1, 4)`. +val v = u.map(x => 1.0 + 2.0 * x) +{% endhighlight %} +</div> + +<div data-lang="java" markdown="1"> +[`RandomRDDs`](api/java/index.html#org.apache.spark.mllib.random.RandomRDDs) provides factory +methods to generate random double RDDs or vector RDDs. +The following example generates a random double RDD, whose values follows the standard normal +distribution `N(0, 1)`, and then map it to `N(1, 4)`. + +{% highlight java %} +import org.apache.spark.SparkContext; +import org.apache.spark.api.JavaDoubleRDD; +import static org.apache.spark.mllib.random.RandomRDDs.*; + +JavaSparkContext jsc = ... + +// Generate a random double RDD that contains 1 million i.i.d. values drawn from the +// standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions. +JavaDoubleRDD u = normalJavaRDD(jsc, 1000000L, 10); +// Apply a transform to get a random double RDD following `N(1, 4)`. +JavaDoubleRDD v = u.map( + new Function<Double, Double>() { + public Double call(Double x) { + return 1.0 + 2.0 * x; + } + }); +{% endhighlight %} +</div> + +<div data-lang="python" markdown="1"> +[`RandomRDDs`](api/python/pyspark.mllib.random.RandomRDDs-class.html) provides factory +methods to generate random double RDDs or vector RDDs. +The following example generates a random double RDD, whose values follows the standard normal +distribution `N(0, 1)`, and then map it to `N(1, 4)`. + +{% highlight python %} +from pyspark.mllib.random import RandomRDDs + +sc = ... # SparkContext + +# Generate a random double RDD that contains 1 million i.i.d. values drawn from the +# standard normal distribution `N(0, 1)`, evenly distributed in 10 partitions. +u = RandomRDDs.uniformRDD(sc, 1000000L, 10) +# Apply a transform to get a random double RDD following `N(1, 4)`. +v = u.map(lambda x: 1.0 + 2.0 * x) +{% endhighlight %} +</div> + +</div> |