Repository: spark
Updated Branches:
refs/heads/branch-2.0 929fa287e -> 2c74b6d73
[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 <weichenxu...@outlook.com>
Closes #14246 from WeichenXu123/fix_formular_err.
(cherry picked from commit 8310c0741c0ca805ec74c1a78ba4a0f18e82d459)
Signed-off-by: Sean Owen <so...@cloudera.com>
Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2c74b6d7
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2c74b6d7
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2c74b6d7
Branch: refs/heads/branch-2.0
Commit: 2c74b6d73beab4510fa7933dde9c0a5c218cce92
Parents: 929fa28
Author: WeichenXu <weichenxu...@outlook.com>
Authored: Tue Jul 19 12:07:40 2016 +0100
Committer: Sean Owen <so...@cloudera.com>
Committed: Tue Jul 19 12:07:49 2016 +0100
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.../org/apache/spark/ml/regression/LinearRegression.scala | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/2c74b6d7/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
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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 401f2c6..0a155e1 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.
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