SPARK-3359 [DOCS] sbt/sbt unidoc doesn't work with Java 8

This follows https://github.com/apache/spark/pull/2893 , but does not completely fix SPARK-3359 either. This fixes minor scaladoc/javadoc issues that Javadoc 8 will treat as errors.

Author: Sean Owen <sowen@cloudera.com>

Closes #2909 from srowen/SPARK-3359 and squashes the following commits:

f62c347 [Sean Owen] Fix some javadoc issues that javadoc 8 considers errors. This is not all of the errors turned up when javadoc 8 runs on output of genjavadoc.

3 files changed, 9 insertions, 8 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
index 4734251127..dfad25d57c 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala
@@ -26,7 +26,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}
  * :: Experimental ::
  * Normalizes samples individually to unit L^p^ norm
  *
- * For any 1 <= p < Double.PositiveInfinity, normalizes samples using
+ * For any 1 &lt;= p &lt; Double.PositiveInfinity, normalizes samples using
  * sum(abs(vector).^p^)^(1/p)^ as norm.
  *
  * For p = Double.PositiveInfinity, max(abs(vector)) will be used as norm for normalization.
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
index ec2d481dcc..10a515af88 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala
@@ -152,7 +152,7 @@ class RowMatrix(
    * storing the right singular vectors, is computed via matrix multiplication as
    * U = A * (V * S^-1^), if requested by user. The actual method to use is determined
    * automatically based on the cost:
-   *  - If n is small (n < 100) or k is large compared with n (k > n / 2), we compute the Gramian
+   *  - If n is small (n &lt; 100) or k is large compared with n (k > n / 2), we compute the Gramian
    *    matrix first and then compute its top eigenvalues and eigenvectors locally on the driver.
    *    This requires a single pass with O(n^2^) storage on each executor and on the driver, and
    *    O(n^2^ k) time on the driver.
@@ -169,7 +169,8 @@ class RowMatrix(
    * @note The conditions that decide which method to use internally and the default parameters are
    *       subject to change.
    *
-   * @param k number of leading singular values to keep (0 < k <= n). It might return less than k if
+   * @param k number of leading singular values to keep (0 &lt; k &lt;= n).
+   *          It might return less than k if
    *          there are numerically zero singular values or there are not enough Ritz values
    *          converged before the maximum number of Arnoldi update iterations is reached (in case
    *          that matrix A is ill-conditioned).
@@ -192,7 +193,7 @@ class RowMatrix(
   /**
    * The actual SVD implementation, visible for testing.
    *
-   * @param k number of leading singular values to keep (0 < k <= n)
+   * @param k number of leading singular values to keep (0 &lt; k &lt;= n)
    * @param computeU whether to compute U
    * @param rCond the reciprocal condition number
    * @param maxIter max number of iterations (if ARPACK is used)
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
index ca35100aa9..dce0adffa6 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala
@@ -196,8 +196,8 @@ object MLUtils {
 
   /**
    * Load labeled data from a file. The data format used here is
-   * <L>, <f1> <f2> ...
-   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
+   * L, f1 f2 ...
+   * where f1, f2 are feature values in Double and L is the corresponding label as Double.
    *
    * @param sc SparkContext
    * @param dir Directory to the input data files.
@@ -219,8 +219,8 @@ object MLUtils {
 
   /**
    * Save labeled data to a file. The data format used here is
-   * <L>, <f1> <f2> ...
-   * where <f1>, <f2> are feature values in Double and <L> is the corresponding label as Double.
+   * L, f1 f2 ...
+   * where f1, f2 are feature values in Double and L is the corresponding label as Double.
    *
    * @param data An RDD of LabeledPoints containing data to be saved.
    * @param dir Directory to save the data.