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a cluster-oriented implementation of self-organizing maps

Project Description

Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. The topology of the grid is rectangular.

Currently a subset of the C++ version is supported with this package.

Homepage: https://github.com/peterwittek/somoclu

Example, in which the data file rgbs.txt can be found at https://github.com/peterwittek/somoclu/tree/master/data

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
import somoclu
import numpy as np

data = np.loadtxt('rgbs.txt')
print(data)
data = np.float32(data)
nSomX = 50
nSomY = 50
nVectors = data.shape[0]
nDimensions = data.shape[1]
data1D = np.ndarray.flatten(data)
nEpoch = 10
radius0 = 0
radiusN = 0
radiusCooling = "linear"
scale0 = 0
scaleN = 0.01
scaleCooling = "linear"
kernelType = 0
mapType = "planar"
snapshots = 0
initialCodebookFilename = ''
codebook_size = nSomY * nSomX * nDimensions
codebook = np.zeros(codebook_size, dtype=np.float32)
globalBmus_size = int(nVectors * int(np.ceil(nVectors/nVectors))*2)
globalBmus = np.zeros(globalBmus_size, dtype=np.intc)
uMatrix_size = nSomX * nSomY
uMatrix = np.zeros(uMatrix_size, dtype=np.float32)
somoclu.trainWrapper(data1D, nEpoch, nSomX, nSomY,
                     nDimensions, nVectors,
                     radius0, radiusN,
                     radiusCooling, scale0, scaleN,
                     scaleCooling, snapshots,
                     kernelType, mapType,
                     initialCodebookFilename,
                     codebook, globalBmus, uMatrix)
print codebook
print globalBmus
print uMatrix

Get it now

$ sudo pip install somoclu

Build on Mac OS X:

Before installing using pip, gcc should be installed first. As of OS X 10.9, gcc is just symlink to clang. To build somoclu and this extension correctly, it is recommended to install gcc using something like:

$ brew install gcc48

and set environment using:

export CC=/usr/local/bin/gcc
export CXX=/usr/local/bin/g++
export CPP=/usr/local/bin/cpp
export LD=/usr/local/bin/gcc
alias c++=/usr/local/bin/c++
alias g++=/usr/local/bin/g++
alias gcc=/usr/local/bin/gcc
alias cpp=/usr/local/bin/cpp
alias ld=/usr/local/bin/gcc
alias cc=/usr/local/bin/gcc

Then you can

$ sudo pip install somoclu

Build with CUDA support on Linux:

You need to clone this repo or download the latest release tarball, and

$ cd src/Python
$ bash makepy.sh

Then if your CUDA installation is located at /opt/cuda and 64bit, you can do the following to install:

$ sudo python2 setup_with_CUDA.py install

Otherwise, you should modify the setup_with_CUDA.py , change the path to CUDA installation accordingly:

call(["./configure", "--without-mpi","--with-cuda=/opt/cuda/"])

and

library_dirs=['/opt/cuda/lib64']

Then run the install command

$ sudo python2 setup_with_CUDA.py install

Then you can use the python interface like before, with CUDA support.

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Filename, size & hash SHA256 hash help File type Python version Upload date
somoclu-1.4-py2.7-win-amd64.egg (33.0 kB) Copy SHA256 hash SHA256 Egg 2.7 Oct 17, 2014
somoclu-1.4.tar.gz (55.3 kB) Copy SHA256 hash SHA256 Source None Oct 17, 2014
somoclu-1.4.win-amd64.zip (32.7 kB) Copy SHA256 hash SHA256 Windows Installer 2.7 Oct 17, 2014
somoclu-1.4.zip (76.2 kB) Copy SHA256 hash SHA256 Source None Oct 17, 2014

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