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#--------------------- # Installation #--------------------- First make sure that you have Scons installed (http://www.scons.org/), and installed the following dependencies: cython numpy scipy matplotlib h5py multiprocessing Then type "scons" in the neuroparser directory to build the code. Note that for the moment you will first have to install the following dependencies: #--------------------- # Warning #--------------------- This code is currently in an early state of development for public use, and will undergo many changes in the coming months. Use it at your own risk! It will likely be good to download the latest code with some frequency and rebuild by typing scons -c to clear away old files, and then scons to rebuild them. Note that the more standard setup.py install is not currently recommended. #--------------------- # Usage #--------------------- Some basic examples of how the code can be called are in: /neuroparser/examples/graphnet_example.py and some more use cases can be found in: /neuroparser/optimization/cwpath/tests/profile.py /neuroparser/optimization/cwpath/tests/test_graphnet.py Please report bugs or feature requests to logang@stanford.edu or on the GitHub site. --L.G. Dec 2012.
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Sparse and structured methods for supervised and unsupervised learning on large neuroimaging data
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