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Machine learning library for fast and efficient Gaussian mixture models

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This repository contains code which implements the Stochastic Gaussian Mixture Model (S-GMM) for event-based datasets

Dependencies

Running a classification task

  1. Generate time-surfaces using: time_surfaces.ipynb (look at tonic for more details on what datasets are support)
  2. Compile C++14 source code
premake4 gmake && cd build && make
  1. Main entry point for clustering and classification: variational-gmm.ipynb

Installation

Tonic

pip install tonic

CMake and Premake4

On Mac

You can install cmake and premake via the homebrew package manager

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
brew install cmake premake
On Linux
sudo apt-get install cmake premake4

Blaze

Make sure you have BLAS and LAPACK installed first

sudo apt-get install libopenblas-dev

Proceed to the blaze installation

git clone https://bitbucket.org/blaze-lib/blaze.git
cd blaze && cmake -DCMAKE_INSTALL_PREFIX=/usr/local/
sudo make install

Intel TBB

On Mac

You can install intel TBB via the homebrew package manager

brew install tbb
On Linux
sudo apt install libtbb-dev

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Machine learning library for fast and efficient Gaussian mixture models

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  • C++ 83.9%
  • Jupyter Notebook 11.9%
  • Python 3.4%
  • Lua 0.8%