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author | Jagadeesan <as2@us.ibm.com> | 2016-08-23 12:23:30 +0100 |
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committer | Sean Owen <sowen@cloudera.com> | 2016-08-23 12:23:30 +0100 |
commit | 97d461b75badbfa323d7f1508b20600ea189bb95 (patch) | |
tree | c58958008c2f37193349f54a7e86a9586724f2de | |
parent | 9d376ad76ca702ae3fc6ffd0567e7590d9a8daf3 (diff) | |
download | spark-97d461b75badbfa323d7f1508b20600ea189bb95.tar.gz spark-97d461b75badbfa323d7f1508b20600ea189bb95.tar.bz2 spark-97d461b75badbfa323d7f1508b20600ea189bb95.zip |
[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.
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 |