Make wiki links pretty

mllib/src/main/scala/org/apache/spark/ml/feature/DCT.scala

@@ -33,7 +33,7 @@ import org.apache.spark.sql.types.DataType
  * such that the transform matrix is unitary (aka scaled DCT-II).
  *
  * More information on <a href="https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II">
- * Wikipedia</a>.
+ * DCT-II (Wikipedia)</a>.
  */
 @Since("1.5.0")
 class DCT @Since("1.5.0") (@Since("1.5.0") override val uid: String)

mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala

@@ -30,8 +30,9 @@ import org.apache.spark.sql.types.DataType
 
 /**
  * Perform feature expansion in a polynomial space. As said in wikipedia of Polynomial Expansion,
- * which is available at <a href="http://en.wikipedia.org/wiki/Polynomial_expansion">here</a>,
- * "In mathematics, an expansion of a product of sums expresses it as a sum of products by using
+ * which is available at
+ * <a href="http://en.wikipedia.org/wiki/Polynomial_expansion">Polynomial expansion (Wikipedia)</a>
+ * , "In mathematics, an expansion of a product of sums expresses it as a sum of products by using
  * the fact that multiplication distributes over addition". Take a 2-variable feature vector
  * as an example: `(x, y)`, if we want to expand it with degree 2, then we get
  * `(x, x * x, y, x * y, y * y)`.

mllib/src/main/scala/org/apache/spark/ml/feature/StopWordsRemover.scala

@@ -32,7 +32,7 @@ import org.apache.spark.sql.types.{ArrayType, StringType, StructType}
  * @note null values from input array are preserved unless adding null to stopWords
  * explicitly.
  *
- * @see <a href="http://en.wikipedia.org/wiki/Stop_words">here</a>
+ * @see <a href="http://en.wikipedia.org/wiki/Stop_words">Stop words (Wikipedia)</a>
  */
 @Since("1.5.0")
 class StopWordsRemover @Since("1.5.0") (@Since("1.5.0") override val uid: String)

mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala

@@ -119,7 +119,8 @@ private[regression] trait AFTSurvivalRegressionParams extends Params
 /**
  * :: Experimental ::
  * Fit a parametric survival regression model named accelerated failure time (AFT) model
- * (see <a href="https://en.wikipedia.org/wiki/Accelerated_failure_time_model">here</a>)
+ * (see <a href="https://en.wikipedia.org/wiki/Accelerated_failure_time_model">
+ * Accelerated failure time model (Wikipedia)</a>)
  * based on the Weibull distribution of the survival time.
  */
 @Experimental

mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala

@@ -124,7 +124,8 @@ private[regression] trait GeneralizedLinearRegressionBase extends PredictorParam
  * :: Experimental ::
  *
  * Fit a Generalized Linear Model
- * (see <a href="https://en.wikipedia.org/wiki/Generalized_linear_model">here</a>)
+ * (see <a href="https://en.wikipedia.org/wiki/Generalized_linear_model">
+ * Generalized linear model (Wikipedia)</a>)
  * specified by giving a symbolic description of the linear
  * predictor (link function) and a description of the error distribution (family).
  * It supports "gaussian", "binomial", "poisson" and "gamma" as family.

mllib/src/main/scala/org/apache/spark/mllib/evaluation/RegressionMetrics.scala

@@ -74,7 +74,8 @@ class RegressionMetrics @Since("2.0.0") (
   /**
    * Returns the variance explained by regression.
    * explainedVariance = $\sum_i (\hat{y_i} - \bar{y})^2^ / n$
-   * @see <a href="https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained">here</a>
+   * @see <a href="https://en.wikipedia.org/wiki/Fraction_of_variance_unexplained">
+   * Fraction of variance unexplained (Wikipedia)</a>
    */
   @Since("1.2.0")
   def explainedVariance: Double = {
@@ -110,7 +111,8 @@ class RegressionMetrics @Since("2.0.0") (
 
   /**
    * Returns R^2^, the unadjusted coefficient of determination.
-   * @see <a href="http://en.wikipedia.org/wiki/Coefficient_of_determination">here</a>
+   * @see <a href="http://en.wikipedia.org/wiki/Coefficient_of_determination">
+   * Coefficient of determination (Wikipedia)</a>
    * In case of regression through the origin, the definition of R^2^ is to be modified.
    * @see <a href="https://online.stat.psu.edu/~ajw13/stat501/SpecialTopics/Reg_thru_origin.pdf">
    * J. G. Eisenhauer, Regression through the Origin. Teaching Statistics 25, 76-80 (2003)</a>

mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala

@@ -38,7 +38,7 @@ import org.apache.spark.mllib.linalg.{Vector, Vectors}
  * For weighted instances, the unbiased estimation of variance is defined by the reliability
  * weights:
  * see <a href="https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Reliability_weights">
- * here</a>.
+ * Reliability weights (Wikipedia)</a>.
  */
 @Since("1.1.0")
 @DeveloperApi

mllib/src/main/scala/org/apache/spark/mllib/stat/Statistics.scala

@@ -186,7 +186,8 @@ object Statistics {
    * distribution of the sample data and the theoretical distribution we can provide a test for the
    * the null hypothesis that the sample data comes from that theoretical distribution.
    * For more information on KS Test:
-   * @see <a href="https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test">here</a>
+   * @see <a href="https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test">
+   * Kolmogorov-Smirnov test (Wikipedia)</a>
    *
    * @param data an `RDD[Double]` containing the sample of data to test
    * @param cdf a `Double => Double` function to calculate the theoretical CDF at a given value

mllib/src/main/scala/org/apache/spark/mllib/stat/distribution/MultivariateGaussian.scala

@@ -29,7 +29,7 @@ import org.apache.spark.mllib.util.MLUtils
  * the event that the covariance matrix is singular, the density will be computed in a
  * reduced dimensional subspace under which the distribution is supported.
  * (see <a href="http://en.wikipedia.org/wiki/Multivariate_normal_distribution#Degenerate_case">
- * here</a>)
+ * Multivariate normal distribution (Wikipedia)</a>)
  *
  * @param mu The mean vector of the distribution
  * @param sigma The covariance matrix of the distribution