[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, 34 insertions, 0 deletions

diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md
index 6acfc71d7b..2c7c9ed693 100644
--- a/docs/mllib-statistics.md
+++ b/docs/mllib-statistics.md
@@ -38,6 +38,8 @@ available in `Statistics`.
 which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
 total count.
 
+Refer to the [`MultivariateStatisticalSummary` Scala docs](api/scala/index.html#org.apache.spark.mllib.stat.MultivariateStatisticalSummary) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}
@@ -60,6 +62,8 @@ println(summary.numNonzeros) // number of nonzeros in each column
 which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
 total count.
 
+Refer to the [`MultivariateStatisticalSummary` Java docs](api/java/org/apache/spark/mllib/stat/MultivariateStatisticalSummary.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -86,6 +90,8 @@ System.out.println(summary.numNonzeros()); // number of nonzeros in each column
 which contains the column-wise max, min, mean, variance, and number of nonzeros, as well as the
 total count.
 
+Refer to the [`MultivariateStatisticalSummary` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.stat.MultivariateStatisticalSummary) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.stat import Statistics
 
@@ -116,6 +122,8 @@ correlation methods are currently Pearson's and Spearman's correlation.
 calculate correlations between series. Depending on the type of input, two `RDD[Double]`s or 
 an `RDD[Vector]`, the output will be a `Double` or the correlation `Matrix` respectively.
 
+Refer to the [`Statistics` Scala docs](api/scala/index.html#org.apache.spark.mllib.stat.Statistics) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.SparkContext
 import org.apache.spark.mllib.linalg._
@@ -144,6 +152,8 @@ val correlMatrix: Matrix = Statistics.corr(data, "pearson")
 calculate correlations between series. Depending on the type of input, two `JavaDoubleRDD`s or 
 a `JavaRDD<Vector>`, the output will be a `Double` or the correlation `Matrix` respectively.
 
+Refer to the [`Statistics` Java docs](api/java/org/apache/spark/mllib/stat/Statistics.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.api.java.JavaDoubleRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -173,6 +183,8 @@ Matrix correlMatrix = Statistics.corr(data.rdd(), "pearson");
 calculate correlations between series. Depending on the type of input, two `RDD[Double]`s or 
 an `RDD[Vector]`, the output will be a `Double` or the correlation `Matrix` respectively.
 
+Refer to the [`Statistics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.stat import Statistics
 
@@ -338,6 +350,8 @@ featureTestResults.foreach { result =>
 run Pearson's chi-squared tests. The following example demonstrates how to run and interpret 
 hypothesis tests.
 
+Refer to the [`ChiSqTestResult` Java docs](api/java/org/apache/spark/mllib/stat/test/ChiSqTestResult.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -385,6 +399,8 @@ for (ChiSqTestResult result : featureTestResults) {
 run Pearson's chi-squared tests. The following example demonstrates how to run and interpret
 hypothesis tests.
 
+Refer to the [`Statistics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics) for more details on the API.
+
 {% highlight python %}
 from pyspark import SparkContext
 from pyspark.mllib.linalg import Vectors, Matrices
@@ -437,6 +453,8 @@ message.
 run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstrates how to run
 and interpret the hypothesis tests.
 
+Refer to the [`Statistics` Scala docs](api/scala/index.html#org.apache.spark.mllib.stat.Statistics) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.mllib.stat.Statistics
 
@@ -459,6 +477,8 @@ val testResult2 = Statistics.kolmogorovSmirnovTest(data, myCDF)
 run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstrates how to run
 and interpret the hypothesis tests.
 
+Refer to the [`Statistics` Java docs](api/java/org/apache/spark/mllib/stat/Statistics.html) for details on the API.
+
 {% highlight java %}
 import java.util.Arrays;
 
@@ -483,6 +503,8 @@ System.out.println(testResult);
 run a 1-sample, 2-sided Kolmogorov-Smirnov test. The following example demonstrates how to run
 and interpret the hypothesis tests.
 
+Refer to the [`Statistics` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.stat.Statistics) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.stat import Statistics
 
@@ -513,6 +535,8 @@ 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)`.
 
+Refer to the [`RandomRDDs` Scala docs](api/scala/index.html#org.apache.spark.mllib.random.RandomRDDs) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.SparkContext
 import org.apache.spark.mllib.random.RandomRDDs._
@@ -533,6 +557,8 @@ 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)`.
 
+Refer to the [`RandomRDDs` Java docs](api/java/org/apache/spark/mllib/random/RandomRDDs) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.SparkContext;
 import org.apache.spark.api.JavaDoubleRDD;
@@ -559,6 +585,8 @@ 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)`.
 
+Refer to the [`RandomRDDs` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.random.RandomRDDs) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.random import RandomRDDs
 
@@ -589,6 +617,8 @@ mean of PDFs of normal distributions centered around each of the samples.
 to compute kernel density estimates from an RDD of samples. The following example demonstrates how
 to do so.
 
+Refer to the [`KernelDensity` Scala docs](api/scala/index.html#org.apache.spark.mllib.stat.KernelDensity) for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.mllib.stat.KernelDensity
 import org.apache.spark.rdd.RDD
@@ -611,6 +641,8 @@ val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
 to compute kernel density estimates from an RDD of samples. The following example demonstrates how
 to do so.
 
+Refer to the [`KernelDensity` Java docs](api/java/org/apache/spark/mllib/stat/KernelDensity.html) for details on the API.
+
 {% highlight java %}
 import org.apache.spark.mllib.stat.KernelDensity;
 import org.apache.spark.rdd.RDD;
@@ -633,6 +665,8 @@ double[] densities = kd.estimate(new double[] {-1.0, 2.0, 5.0});
 to compute kernel density estimates from an RDD of samples. The following example demonstrates how
 to do so.
 
+Refer to the [`KernelDensity` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.stat.KernelDensity) for more details on the API.
+
 {% highlight python %}
 from pyspark.mllib.stat import KernelDensity