Skip to content
Branch: master
Find file History
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
..
Failed to load latest commit information.
doc
somoclu
tests
CITATION
MANIFEST.in Added CITATION file Aug 23, 2016
README.rst
setup.py Version number bumped Mar 1, 2018

README.rst

Somoclu - Python Interface

Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution and it can be accelerated by CUDA. The topology of map is either planar or toroid, the grid is rectangular or hexagonal. Currently a subset of the command line version is supported with this Python module.

Key features of the Python interface:

  • Fast execution by parallelization: OpenMP and CUDA are supported.
  • Multi-platform: Linux, macOS, and Windows are supported.
  • Planar and toroid maps.
  • Rectangular and hexagonal grids.
  • Gaussian or bubble neighborhood functions.
  • Visualization of maps, including those that were trained outside of Python.
  • PCA initialization of codebook.

The documentation is available on Read the Docs. Further details are found in the manuscript describing the library [1].

Usage

A simple example is below. For more example, please refer to the documentation and a more thorough ipython notebook example at Somoclu in Python.ipynb.

import somoclu
import numpy as np
import matplotlib.pyplot as plt

c1 = np.random.rand(50, 2)/5
c2 = (0.2, 0.5) + np.random.rand(50, 2)/5
c3 = (0.4, 0.1) + np.random.rand(50, 2)/5
data = np.float32(np.concatenate((c1, c2, c3)))
colors = ["red"] * 50
colors.extend(["green"] * 50)
colors.extend(["blue"] * 50)

labels = list(range(150))
n_rows, n_columns = 30, 50
som = somoclu.Somoclu(n_columns, n_rows, maptype="planar",
                      gridtype="rectangular")
som.train(data, epochs=10)
som.view_umatrix(bestmatches=True, bestmatchcolors=colors, labels=labels)

Installation

The code is available on PyPI, hence it can be installed by

$ pip install somoclu

Alternatively, it is also available on [conda-forge](https://github.com/conda-forge/somoclu-feedstock):

$ conda install somoclu

Some pre-built binaries in the wheel format or windows installer are provided at PyPI Dowloads, they are tested with Anaconda distributions. If you encounter errors like ImportError: DLL load failed: The specified module could not be found when import somoclu, you may need to use Dependency Walker as shown here on _somoclu_wrap.pyd to find out missing DLLs and place them at the write place. Usually right version (32/64bit) of vcomp90.dll, msvcp90.dll, msvcr90.dll should be put to C:\Windows\System32 or C:\Windows\SysWOW64.

If you want the latest git version, first git clone the repo, install swig and run:

$ ./autogen.sh
$ ./configure [options]
$ make
$ make python

to generate Python interface files.

Then follow the standard procedure for installing Python modules:

$ python setup.py install

Build with CUDA support on Linux and macOS:

If the CUDAHOME variable is set, the usual install command will build and install the library:

$ sudo python setup.py install

Build with CUDA support on Windows:

You should first follow the instructions to build the Windows binary with HAVE_MPI and CLI disabled with the same version Visual Studio as your Python is built with.(Since currently Python is built by VS2008 by default and CUDA v6.5 removed VS2008 support, you may use CUDA 6.0 with VS2008 or find a Python prebuilt with VS2010. And remember to install VS2010 or Windows SDK7.1 to get the option in Platform Toolset if you use VS2013.) The recommended configuration is VS2010 Platform Toolset with Python 3.4. Then you should copy the .obj files generated in the release build path to the Python\somoclu\src folder.

Then modify the environment variable CUDA_PATH or win_cuda_dir in setup.py to your CUDA path and run the install command

$ sudo python setup.py install

Then it should be able to build and install the module.

Citation

  1. Peter Wittek, Shi Chao Gao, Ik Soo Lim, Li Zhao (2017). Somoclu: An Efficient Parallel Library for Self-Organizing Maps. Journal of Statistical Software, 78(9), pp.1--21. DOI:10.18637/jss.v078.i09. arXiv:1305.1422.
You can’t perform that action at this time.