From 23af691be41b6b18b3655f01f6755789ff891c7a Mon Sep 17 00:00:00 2001 From: Shuai Lin Date: Thu, 14 Jul 2016 12:05:55 +0800 Subject: [PATCH] [SPARK-16485][DOC][ML] Fixed several inline formatting in ml features doc. --- 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 88fd291b4be50..a7b0281bb9879 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.