Bayesian Nonparametric Small-Variance Asymptotic Clustering
This is a library of Bayesian nonparametric small-variance asymptotic clustering algorithms: DP-means, Dynamic means, DP-vMF-means, DDP-vMF-means. For comparison reasons the library also implements k-means as well as spherical k-means.
This library comes with an executable that allows batch clustering using DP-vMF-means, DP-means, spherical k-means and k-means.
The pure python implementation of DP-vMF-means shows the simplicity of the algorithm.
For an example of using DDP-vMF-means refer to rtDDPvMF, which relies on this package's dpMMlowVar library to perform real-time directional segmentation from Kinect RGB-D streams using DDP-vMF-means.
If you use DP-vMF-means or DDP-vMF-means please cite:
Julian Straub, Trevor Campbell, Jonathan P. How, John W. Fisher III. "Small-Variance Nonparametric Clustering on the Hypersphere", In CVPR, 2015.
If you use Dynamic-means please cite:
T. Campbell, M. Liu, B. Kulis, J. How, and L. Carin. "Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture". In Advances in Neural Information Processing Systems (NIPS), 2013.
This code is dependent on Eigen3, Boost, CUDA, OpenCV, OpenMP and PCL. It has been tested on Ubuntu 14.04 with
- Eigen3 (3.0.5)
- Boost (1.54)
- CUDA (6.5)
- OpenCV (2.4)
- PCL (1.7)
This package uses the pods build system. Used widely at CSAIL MIT the build system makes it easy to break up software projects into small packages that can be checked out and compiled automatically (see below).
Install Eigen3, Boost, OpenCV, and PCL
sudo apt-get install libeigen3-dev libboost-dev libopencv-dev libpcl-1.7-all-dev
Install the appropriate CUDA version matching with your nvidia drivers. On our machines we use
libcuda1-340 cuda-6-5 cuda-toolkit-6-5
Clone this repository and compile the code:
git clone firstname.lastname@example.org:jstraub/dpMMlowVar; cd dpMMlowVar; make checkout; make configure; make -j6; make install;
Note that this will checkout several other necessary repositories. To update all repositories run
make update; make configure; make -j6; make install;
After you have compiled the code you can run clustering of the surface normals of an example data set in the ./data/ folder by running:
cd ./python; python dpView.py
Note that the extraction of surface normals necessitates matlab. An alternative is to directly run segmentation from Kinect RGB-D frames using the rtDDPvMF package.
./dpMMlowVarCluster -h Allowed options: -h [ --help ] produce help message --seed arg seed for random number generator -N [ --N ] arg number of input datapoints -D [ --D ] arg number of dimensions of the data -T [ --T ] arg iterations -a [ --alpha ] arg alpha parameter of the DP (if single value assumes all alpha_i are the same -K [ --K ] arg number of initial clusters --base arg which base measure to use (only spkm, kmeans, DPvMFmeans right now) -p [ --params ] arg parameters of the base measure -i [ --input ] arg path to input dataset .csv file (rows: dimensions; cols: different datapoints) -o [ --output ] arg path to output labels .csv file (rows: time; cols: different datapoints) --mlInds output ml indices --centroids output centroids of clusters --silhouette output average silhouette --shuffle shuffle the data before processing
- DPvMFmeans clustering: First compute the lambda parameter of DPvMFmeans as lambda = cos(angleInRadians) - 1. so for example lambda = -0.06 for an angle of 20deg.
./dpMMlowVarCluster -N 10000 -D 3 --base DPvMFmeans -p -0.06 -i ./data/rndSphereDataIwUncertain.csv -o test
Julian Straub and Trevor D. Campbell