From 97d461b75badbfa323d7f1508b20600ea189bb95 Mon Sep 17 00:00:00 2001
From: Jagadeesan <as2@us.ibm.com>
Date: Tue, 23 Aug 2016 12:23:30 +0100
Subject: [PATCH] [SPARK-17095] [Documentation] [Latex and Scala doc do not
play nicely]
## What changes were proposed in this pull request?
In Latex, it is common to find "}}}" when closing several expressions at once. [SPARK-16822](https://issues.apache.org/jira/browse/SPARK-16822) added Mathjax to render Latex equations in scaladoc. However, when scala doc sees "}}}" or "{{{" it treats it as a special character for code block. This results in some very strange output.
Author: Jagadeesan <as2@us.ibm.com>
Closes #14688 from jagadeesanas2/SPARK-17095.
---
.../spark/ml/feature/PolynomialExpansion.scala | 8 +++++---
.../ml/regression/GeneralizedLinearRegression.scala | 8 +++++---
.../spark/ml/regression/LinearRegression.scala | 9 ++++++---
.../spark/mllib/clustering/StreamingKMeans.scala | 12 ++++++++----
4 files changed, 24 insertions(+), 13 deletions(-)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
index 6e872c1f2c..25fb6be5af 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala
@@ -76,9 +76,11 @@ class PolynomialExpansion @Since("1.4.0") (@Since("1.4.0") override val uid: Str
* (n + d choose d) (including 1 and first-order values). For example, let f([a, b, c], 3) be the
* function that expands [a, b, c] to their monomials of degree 3. We have the following recursion:
*
- * {{{
- * f([a, b, c], 3) = f([a, b], 3) ++ f([a, b], 2) * c ++ f([a, b], 1) * c^2 ++ [c^3]
- * }}}
+ * <p><blockquote>
+ * $$
+ * f([a, b, c], 3) &= f([a, b], 3) ++ f([a, b], 2) * c ++ f([a, b], 1) * c^2 ++ [c^3]
+ * $$
+ * </blockquote></p>
*
* To handle sparsity, if c is zero, we can skip all monomials that contain it. We remember the
* current index and increment it properly for sparse input.
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
index 1d4dfd1147..02b27fb650 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala
@@ -196,9 +196,11 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val
/**
* Sets the regularization parameter for L2 regularization.
* The regularization term is
- * {{{
- * 0.5 * regParam * L2norm(coefficients)^2
- * }}}
+ * <p><blockquote>
+ * $$
+ * 0.5 * regParam * L2norm(coefficients)^2
+ * $$
+ * </blockquote></p>
* Default is 0.0.
*
* @group setParam
diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index b1bb9b9fe0..7fddfd9b10 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -338,9 +338,12 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
/*
Note that in Linear Regression, the objective history (loss + regularization) returned
from optimizer is computed in the scaled space given by the following formula.
- {{{
- L = 1/2n||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2 + regTerms
- }}}
+ <p><blockquote>
+ $$
+ L &= 1/2n||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2
+ + regTerms \\
+ $$
+ </blockquote></p>
*/
val arrayBuilder = mutable.ArrayBuilder.make[Double]
var state: optimizer.State = null
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
index 52bdccb919..f20ab09bf0 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala
@@ -39,10 +39,14 @@ import org.apache.spark.util.random.XORShiftRandom
* generalized to incorporate forgetfullness (i.e. decay).
* The update rule (for each cluster) is:
*
- * {{{
- * c_t+1 = [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t]
- * n_t+t = n_t * a + m_t
- * }}}
+ * <p><blockquote>
+ * $$
+ * \begin{align}
+ * c_t+1 &= [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t] \\
+ * n_t+t &= n_t * a + m_t
+ * \end{align}
+ * $$
+ * </blockquote></p>
*
* Where c_t is the previously estimated centroid for that cluster,
* n_t is the number of points assigned to it thus far, x_t is the centroid
--
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