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Showing content from https://github.com/mmp2/megaman below:

mmp2/megaman: megaman: Manifold Learning for Millions of Points

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

• Tutorial Notebook

Below it's a tutorial to install megaman on Google Colab, through Conda environment.

It also provides tutorial of using megaman to build spectral embedding on uniform swiss roll dataset.

• Install & Example script
• You can download the Jupyter Notebook version here

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:

• python tested with versions 2.7, 3.5 and 3.6
• numpy version 1.8 or higher
• scipy version 0.16.0 or higher
• scikit-learn
• FLANN
• pyflann which offers another method of computing distance matrices (this is bundled with the FLANN source code)
• cython
• a C++ compiler such as gcc/g++

Optional requirements include

• pyamg, which allows for faster decompositions of large matrices
• nose for running the unit tests
• h5py for reading testing .mat files
• plotly an graphing library for interactive plot

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

megaman uses 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.

• James McQueen
• Marina Meila
• Zhongyue Zhang
• Jake VanderPlas
• Yu-Chia Chen

• Xiao Wang: lazy rmetric, Nystrom Extension
• Hangliang Ren (Harry): Installation tutorials, Spectral Embedding

See this issues list for what we have planned for upcoming releases:

Future Work

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