megaman is a scalable manifold learning package implemented in
python. It has a front-end API designed to be familiar
to scikit-learn but harnesses
the C++ Fast Library for Approximate Nearest Neighbors (FLANN)
and the Sparse Symmetric Positive Definite (SSPD) solver
Locally Optimal Block Precodition Gradient (LOBPCG) method
to scale manifold learning algorithms to large data sets.
On a personal computer megaman can embed 1 million data points
with hundreds of dimensions in 10 minutes.
megaman is designed for researchers and as such caches intermediary
steps and indices to allow for fast re-computation with new parameters.
Package documentation can be found at http://mmp2.github.io/megaman/
If you use our software please cite the following JMLR paper:
McQueen, Meila, VanderPlas, & Zhang, "Megaman: Scalable Manifold Learning in Python", Journal of Machine Learning Research, Vol 17 no. 14, 2016. http://jmlr.org/papers/v17/16-109.html
You can also find our arXiv paper at http://arxiv.org/abs/1603.02763
Due to the change of API,
$ conda install -c conda-forge megaman
is no longer supported.
We are currently working on fixing the bug.
Please see the full install instructions below to build
megaman from source.
To install megaman from source requires the following:
Optional requirements include
These requirements can be installed on Linux and MacOSX using the following conda command:
conda create -n manifold_env python=3.5 -y can also use python=2.7 or python=3.6 source activate manifold_env conda install --channel=conda-forge -y pip nose coverage cython numpy scipy \ scikit-learn pyflann pyamg h5py plotly
Clone this repository and
cd into source repository
cd /tmp/ git clone https://github.com/mmp2/megaman.git cd megaman
Finally, within the source repository, run this command to install the
megaman package itself:
python setup.py install
nose for unit tests. With
nose installed, type
to run the unit tests.
megaman is tested on Python versions 2.7, 3.4, and 3.5.
See this issues list for what we have planned for upcoming releases: