Topic_Models_Cpp

Cheng Zhang's Topic Models C++ library

Install

Tested on Ubuntu 12.04

GCC 4.8

sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-4.8 g++-4.8
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.8 50
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 50

Then add this to your .bashrc

export CC=gcc-4.8
export CXX=g++-4.8

NLopt

A dependency from Buola

cd {INSTALL_ROOT}
wget http://ab-initio.mit.edu/nlopt/nlopt-2.3.tar.gz
tar -xzf nlopt-2.3.tar.gz && rm nlopt-2.3.tar.gz
cd nlopt-2.3
./configure
make
sudo make install

Buola

A matrix manipulation libary (among others)

sudo apt-get install libeigen3-dev libxml2-dev libdbus-1-dev libncurses5-dev
cd {INSTALL_ROOT}
wget http://www.csc.kth.se/~chengz/minibuola.tar.gz
tar -xzf minibuola.tar.gz && rm minibuola.tar.gz
cd minibuola

cd build
cmake ..
make -j5
sudo make install

TopicModel

cd {INSTALL_ROOT}
git clone https://github.com/renaud/Topic_Models_Cpp.git
cd Topic_Models_Cpp
mkdir build
cd build
cmake ..
make

Play with Topic Models

Dataset

3class KTH action data for fun This data is preprocessed with bag-of-STIP

To check the options:

./TopicModel --help

Example 1: SLDA

./TopicModel --slda --alpha 0.1 --corpus_name KTH --data YOURPATH/KTH/Train.dat --label YOURPATH/KTH/ImgLabel.txt --test YOURPATH/KTH/Test.dat --shuffle --num_classes 3   -k 30  --truth YOURPATH/KTH/GroundTruth.txt --seed 2

The result will be:

0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 0 2 2 2
accuracy:0.84745762711864403016

Example 2: SHDP

./TopicModel  --corpus_name KTH --data YOURPATH/KTH/Train.dat --label YOURPATH/KTH/ImgLabel.txt --test YOURPATH/KTH/Test.dat --shuffle --num_classes 3  -k 80 -t 20 --truth YOURPATH/KTH/GroundTruth.txt --seed 2

The result will be:

0 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 0 0 2 2
accuracy:0.864407

To use LDA use --lda To use HDP use --hdp. In this case the label document is not needed anymore For the onlineSHDP and onlineHDP, it need large data to converge. So it does not work for the KTH data that we used here as example.

References

• LDA: D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993–1022, 2003.
• SLDA: C. Wang, D. M. Blei, and L. Fei-Fei. Simultaneous image classification and annotation. In CVPR, 2009.
• HDP: Y. W. Teh, M. I. Jordan, M. J. Beal, and D. M. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566–1581, 2006.
• SHDP&onlineSHDP: C. Zhang, C.H. Ek, X. Gratal, F. Pokorny and H. Kjellström, Supervised Hierarchical Dirichlet Process with Variational Inference, In ICCV,2013 PS: The suplement of this paper gives the computation of the bound and update equation in detail. Recomand for beginners.
• OnlineHDP: C. Wang, J. Paisley, and D. Blei. Online variational inference for the Hierarchical Dirichlet Process. In AISTATS, 2011.