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__init__.py
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__init__.py
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'''
Oger is a Python toolbox for rapidly building, training and evaluating modular learning architectures on large datasets. It builds functionality on top of the Modular toolkit for Data Processing (MDP). Oger builds functionality on top of MDP, such as:
- Cross-validation of datasets
- Grid-searching large parameter spaces
- Processing of temporal datasets
- Gradient-based training of deep learning architectures
- Interface to the Speech Processing, Recognition, and Automatic Annotation Kit (SPRAAK)
In addition, several additional MDP nodes are provided by Oger, such as a:
- Reservoir node
- Leaky reservoir node
- Ridge regression node
- Conditional Restricted Boltzmann Machine (CRBM) node
'''
import utils
import nodes
import datasets
import gradient
import evaluation
#print 'Warning: importing parallel subpackage - still in development!'
import parallel
import copy_reg
import numpy
def ufunc_pickler(ufunc):
''' utility function for pickling ufuncs
'''
return ufunc.__name__
def ufunc_unpickler(name):
''' utility function for pickling ufuncs
'''
import numpy
return getattr(numpy, name)
copy_reg.pickle(numpy.ufunc, ufunc_pickler, ufunc_unpickler)
del copy_reg
del ufunc_pickler
del ufunc_unpickler
del numpy
__all__ = ['utils', 'nodes', 'datasets', 'gradient', 'evaluation', 'parallel']