From 3b6e1d094e153599e158331b10d33d74a667be5a Mon Sep 17 00:00:00 2001
From: Shuai Lin <linshuai2012@gmail.com>
Date: Mon, 25 Jul 2016 20:26:55 +0100
Subject: [PATCH] [SPARK-16485][DOC][ML] Fixed several inline formatting in ml
features doc
## What changes were proposed in this pull request?
Fixed several inline formatting in ml features doc.
Before:
<img width="475" alt="screen shot 2016-07-14 at 12 24 57 pm" src="https://cloud.githubusercontent.com/assets/717363/16827974/1e1b6e04-49be-11e6-8aa9-4a0cb6cd3b4e.png">
After:
<img width="404" alt="screen shot 2016-07-14 at 12 25 48 pm" src="https://cloud.githubusercontent.com/assets/717363/16827976/2576510a-49be-11e6-96dd-92a1fa464d36.png">
## How was this patch tested?
Genetate the docs locally by `SKIP_API=1 jekyll build` and view it in the browser.
Author: Shuai Lin <linshuai2012@gmail.com>
Closes #14194 from lins05/fix-docs-formatting.
---
docs/ml-features.md | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/docs/ml-features.md b/docs/ml-features.md
index e7d7ddfe28..6020114845 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -216,7 +216,7 @@ for more details on the API.
[RegexTokenizer](api/scala/index.html#org.apache.spark.ml.feature.RegexTokenizer) allows more
advanced tokenization based on regular expression (regex) matching.
- By default, the parameter "pattern" (regex, default: \\s+) is used as delimiters to split the input text.
+ By default, the parameter "pattern" (regex, default: `"\\s+"`) is used as delimiters to split the input text.
Alternatively, users can set parameter "gaps" to false indicating the regex "pattern" denotes
"tokens" rather than splitting gaps, and find all matching occurrences as the tokenization result.
@@ -815,7 +815,7 @@ The rescaled value for a feature E is calculated as,
`\begin{equation}
Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
\end{equation}`
-For the case `E_{max} == E_{min}`, `Rescaled(e_i) = 0.5 * (max + min)`
+For the case `$E_{max} == E_{min}$`, `$Rescaled(e_i) = 0.5 * (max + min)$`
Note that since zero values will probably be transformed to non-zero values, output of the transformer will be `DenseVector` even for sparse input.
--
GitLab