在CUDA的共识聚类

Harnessing the power of the GPU to mine numerical data Current Version - 0.2 You can find the most current version for your operating system in the "Featured" box on the left of this page. Installation Required, all OS * Python 2.6 (3.0 NOT currently supported) * Python-numpy >= 1.4.0 * Python-scipy * Python-matplotlib >= 0.98.0 * Python-pil >= 1.1.6 * Python-pygtk * Python-PyCUDA >= 2011.2.2 * A CUDA-enabled GPU of compute capability 1.2 or higher Quick Start Guide Create a tab-delimited file for your data. Put the "samples" to be clustered in the columns, and the "features" that define them in the rows. Make sure the first row and the first column are labels. Example MyData.txt |SAMPLE_ID|Sample 1|Sample 2|...| |:---------|:-------|:-------|:--| |Gene 1 |3.4234 |1.4244 | |... | Then, try the following command: python cluster.py -f MyData.

analysis
normalise.py
__init__.py
snr.py
pca.py
cluster.py
clustio
helvB08.pbm
helvB08.pil
logstats.py
parsers.py
__init__.py
display.py
writeutils.py
console.py
ioutils.py
kernels
kmeans.py
som_kernels.py
consensus.py
transpose.py
nw.py
som.py
__init__.py
nw_kernels.py
sampler.py
treetype
cu_twister
hierarchical.py
km_kernels.py
kernel_helpers.py
LICENSE
recursive_cluster.py
scripts.py