From 8310c0741c0ca805ec74c1a78ba4a0f18e82d459 Mon Sep 17 00:00:00 2001
From: WeichenXu <WeichenXu123@outlook.com>
Date: Tue, 19 Jul 2016 12:07:40 +0100
Subject: [SPARK-16600][MLLIB] fix some latex formula syntax error

## What changes were proposed in this pull request?

`\partial\x` ==> `\partial x`
`har{x_i}` ==> `hat{x_i}`

## How was this patch tested?

N/A

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #14246 from WeichenXu123/fix_formular_err.
---
 .../scala/org/apache/spark/ml/regression/LinearRegression.scala   | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

(limited to 'mllib')

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 401f2c673f..0a155e1844 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
@@ -794,16 +794,16 @@ class LinearRegressionSummary private[regression] (
  *
  * Now, the first derivative of the objective function in scaled space is
  * {{{
- * \frac{\partial L}{\partial\w_i} = diff/N (x_i - \bar{x_i}) / \hat{x_i}
+ * \frac{\partial L}{\partial w_i} = diff/N (x_i - \bar{x_i}) / \hat{x_i}
  * }}}
  * However, ($x_i - \bar{x_i}$) will densify the computation, so it's not
  * an ideal formula when the training dataset is sparse format.
  *
- * This can be addressed by adding the dense \bar{x_i} / \har{x_i} terms
+ * This can be addressed by adding the dense \bar{x_i} / \hat{x_i} terms
  * in the end by keeping the sum of diff. The first derivative of total
  * objective function from all the samples is
  * {{{
- * \frac{\partial L}{\partial\w_i} =
+ * \frac{\partial L}{\partial w_i} =
  *     1/N \sum_j diff_j (x_{ij} - \bar{x_i}) / \hat{x_i}
  *   = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) - diffSum \bar{x_i}) / \hat{x_i})
  *   = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i}) + correction_i)
@@ -822,7 +822,7 @@ class LinearRegressionSummary private[regression] (
  * the training dataset, which can be easily computed in distributed fashion, and is
  * sparse format friendly.
  * {{{
- * \frac{\partial L}{\partial\w_i} = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i})
+ * \frac{\partial L}{\partial w_i} = 1/N ((\sum_j diff_j x_{ij} / \hat{x_i})
  * }}},
  *
  * @param coefficients The coefficients corresponding to the features.
-- 
cgit v1.2.3