From 56068b54535f0dd49c99d3f564c49431912ae6ac Mon Sep 17 00:00:00 2001
From: El Potaeto <pommedeterresautee@msn.com>
Date: Sun, 22 Feb 2015 00:17:37 +0100
Subject: [PATCH] text vignette

---
 R-package/vignettes/vignette.css            | 12 ++++++++----
 R-package/vignettes/xgboostPresentation.Rmd | 16 ++++++++--------
 2 files changed, 16 insertions(+), 12 deletions(-)

diff --git a/R-package/vignettes/vignette.css b/R-package/vignettes/vignette.css
index 7d370f2f2..b9967535c 100644
--- a/R-package/vignettes/vignette.css
+++ b/R-package/vignettes/vignette.css
@@ -25,7 +25,7 @@ body{
 
     line-height: 1;
     max-width: 800px;
-    padding: 20px;
+    padding: 10px;
     font-size: 17px;
     text-align: justify;
     text-justify: inter-word;
@@ -33,9 +33,10 @@ body{
 
 
 p   {
-    line-height: 150%;
+    line-height: 140%;
 /    max-width: 540px;
     max-width: 960px;
+    margin-bottom: 5px;
     font-weight: 400;    
 /    color: #333333
 }
@@ -46,7 +47,7 @@ h1, h2, h3, h4 {
     font-weight: 400;
 }
 
-h2, h3, h4, h5, p {
+h2, h3, h4, h5 {
     margin-bottom: 20px;
     padding: 0;
 }
@@ -86,6 +87,7 @@ h6 {
     font-variant:small-caps;
     font-style: italic;
 }
+
 a {
     color: #606AAA;
     margin: 0;
@@ -101,6 +103,7 @@ a:hover {
 a:visited {
     color: gray;
 }
+
 ul, ol {
     padding: 0;
     margin: 0px 0px 0px 50px;
@@ -138,9 +141,10 @@ code {
 }
 
 
-p code {
+li code, p code {
   background: #CDCDCD;
   color: #606AAA;
+  padding: 0px 5px 0px 5px;
 }
 
 code.r, code.cpp {
diff --git a/R-package/vignettes/xgboostPresentation.Rmd b/R-package/vignettes/xgboostPresentation.Rmd
index e9efa56b0..63954f18a 100644
--- a/R-package/vignettes/xgboostPresentation.Rmd
+++ b/R-package/vignettes/xgboostPresentation.Rmd
@@ -22,8 +22,8 @@ This is an introductory document for using the \verb@xgboost@ package in *R*.
 
 It is an efficient and scalable implementation of gradient boosting framework by @friedman2001greedy. Two solvers are included:
 
-- *linear model*
-- *tree learning* algorithm
+- *linear* model ;
+- *tree learning* algorithm.
 
 It supports various objective functions, including *regression*, *classification* and *ranking*. The package is made to be extendible, so that users are also allowed to define their own objective function easily. 
 
@@ -48,7 +48,7 @@ Installation
 
 The first step is to install the package.
 
-For up-to-date version (which is *highly* recommended), install from Github:
+For up-to-date version (which is *highly* recommended), install from *Github*:
 
 ```{r installGithub, eval=FALSE}
 devtools::install_github('tqchen/xgboost',subdir='R-package')
@@ -56,7 +56,7 @@ devtools::install_github('tqchen/xgboost',subdir='R-package')
 
 > *Windows* user will need to install [RTools](http://cran.r-project.org/bin/windows/Rtools/) first.
 
-For stable version on CRAN, run:
+For stable version on *CRAN*, run:
 
 ```{r installCran, eval=FALSE}
 install.packages('xgboost')
@@ -194,11 +194,11 @@ print(paste("test-error=", err))
 
 > We remind you that the algorithm has never seen the `test` data before.
 
-Here, we have just computed a simple metric: the average error:
+Here, we have just computed a simple metric, the average error.
 
-* `as.numeric(pred > 0.5)` applies our rule that when the probability (== prediction == regression) is over `0.5` the observation is classified as `1` and `0` otherwise ;
-* `probabilityVectorPreviouslyComputed != test$label` computes the vector of error between true data and computed probabilities ;
-* `mean(vectorOfErrors)` computes the average error itself.
+1. `as.numeric(pred > 0.5)` applies our rule that when the probability (== prediction == regression) is over `0.5` the observation is classified as `1` and `0` otherwise ;
+2. `probabilityVectorPreviouslyComputed != test$label` computes the vector of error between true data and computed probabilities ;
+3. `mean(vectorOfErrors)` computes the average error itself.
 
 The most important thing to remember is that **to do a classification basically, you just do a regression and then apply a threeshold**.