Fix README headings, add more Linux instructions

README.md

@@ -1,20 +1,26 @@
-#LargeVis
+# LargeVis
 This is the *official* implementation of the **LargeVis** model by the original authors, which is used to visualize large-scale and high-dimensional data [(Tang, Liu, Zhang and Mei)](https://arxiv.org/abs/1602.00370). It now supports visualizing both high-dimensional feature vectors and networks. The package also contains a very efficient algorithm for constructing K-nearest neighbor graph (K-NNG).
 
 Contact person: Jian Tang, [email protected]. This work is done when the author is in Microsoft Research Asia.
 
-##Install
+## Install
 Both C++ source codes and Python wrapper are provided on Linux, OS X and Windows. To install the package, external packages are required, including [GSL (GNU Scientific Library)](http://www.gnu.org/software/gsl/) on Linux and OS X or [BOOST](http://www.boost.org/) on Windows for generating random numbers.
 
-####Linux
+#### Linux
+
+On Ubuntu, install the G++ and GSL dependencies:
+
+`sudo apt-get install g++ libgsl-dev`
+
 Compile the source files via:
 ```
+cd Linux
 g++ LargeVis.cpp main.cpp -o LargeVis -lm -pthread -lgsl -lgslcblas -Ofast -march=native -ffast-math
 ```
 
 To install the Python wrapper, modify ```setup.py``` to make sure that the GSL path is correctly set and then run ```sudo python setup.py install```.
 
-####OS X
+#### OS X
 Install gsl using [Homebrew](http://brew.sh/):
 ```
 brew install gsl
@@ -27,12 +33,12 @@ g++ LargeVis.cpp main.cpp -o LargeVis -lm -pthread -lgsl -lgslcblas -Ofast -marc
 
 To install the Python wrapper, run ```sudo python setup.py install```.
 
-####Windows
+#### Windows
 To compile the source files, use Microsoft Visual Studio, where you need to set the BOOST path.
 
 To install the Python wrapper, modify ```setup.py``` to make sure that the BOOST path is correctly set and then run ```python setup.py install```.
 
-##Usage
+## Usage
 LargeVis is suitable for visualizing both high-dimensional feature vectors and networks. For high-dimensional feature vectors, the format of input file should be as follows: the first line specifies the number of feature vectors and the dimensionality (500 vectors with 10 dimensions in the following example), and each of the next 500 lines describes one feature vector with 10 float numbers.
 ```
 500 10
@@ -79,7 +85,7 @@ Besides the two parameters, other optional parameters include:
 * `-gamma`: The weights assigned to negative edges. Default is 7.
 * `-perp`: The perplexity used for deciding edge weights in K-NNG. Default is 50.
 
-##Examples
+## Examples
 We provide some examples including MNIST(high-dimensional feature vectors) and CondMat(networks) in the ```Examples/``` folder.
 
 For example, to visualize the MNIST dataset,
@@ -91,7 +97,7 @@ python plot.py -input mnist_vec2D.txt -label mnist_label.txt -output mnist_vec2D
 ![plot of mnist](Examples/MNIST/mnist_plot.png)
 
 Please cite the following paper if you use LargeVis to visualize your data.
-##Citation
+## Citation
 ```
 @inproceedings{tang2016visualizing,
   title={Visualizing Large-scale and High-dimensional Data},
@@ -102,5 +108,5 @@ Please cite the following paper if you use LargeVis to visualize your data.
   organization={International World Wide Web Conferences Steering Committee}
 }
 ```
-##Acknowledgement
+## Acknowledgement
 Some methods of this package are from a previous work of the LargeVis authors, [LINE (Large-scale Information Network Embedding)](https://github.com/tangjianpku/LINE).