Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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56 lines
1.6 KiB
56 lines
1.6 KiB
#
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# Author: Pearu Peterson, March 2002
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#
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__all__ = ['get_flinalg_funcs']
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# The following ensures that possibly missing flavor (C or Fortran) is
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# replaced with the available one. If none is available, exception
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# is raised at the first attempt to use the resources.
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try:
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from . import _flinalg
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except ImportError:
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_flinalg = None
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# from numpy.distutils.misc_util import PostponedException
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# _flinalg = PostponedException()
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# print _flinalg.__doc__
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has_column_major_storage = lambda a:0
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def has_column_major_storage(arr):
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return arr.flags['FORTRAN']
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_type_conv = {'f':'s', 'd':'d', 'F':'c', 'D':'z'} # 'd' will be default for 'i',..
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def get_flinalg_funcs(names,arrays=(),debug=0):
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"""Return optimal available _flinalg function objects with
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names. Arrays are used to determine optimal prefix."""
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ordering = []
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for i in range(len(arrays)):
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t = arrays[i].dtype.char
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if t not in _type_conv:
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t = 'd'
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ordering.append((t,i))
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if ordering:
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ordering.sort()
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required_prefix = _type_conv[ordering[0][0]]
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else:
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required_prefix = 'd'
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# Some routines may require special treatment.
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# Handle them here before the default lookup.
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# Default lookup:
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if ordering and has_column_major_storage(arrays[ordering[0][1]]):
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suffix1,suffix2 = '_c','_r'
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else:
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suffix1,suffix2 = '_r','_c'
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funcs = []
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for name in names:
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func_name = required_prefix + name
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func = getattr(_flinalg,func_name+suffix1,
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getattr(_flinalg,func_name+suffix2,None))
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funcs.append(func)
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return tuple(funcs)
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