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* Extraction of minimal domiant and absorbent choices             *
* by reduction of X to dominant hyperkernels and characterisation *
* with Pirlot's fixpoint algorithm                                *
* allprekernelsPirlot.py                                          *
* R. Bisdorff, July 2005                                          *
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--->>>
Hypergraph source: socialChoice33normh1.py

actionset(['a', 'c', 'b', 'e', 'd', '_c_b_d_'])

direct neighbors :  {'a': (set(['a', 'e']), set(['a', 'c', 'b',
'_c_b_d_', 'd'])), 'c': (set(['a', '_c_b_d_', 'c', 'e', 'd']),
set(['c', 'b', '_c_b_d_'])), 'b': (set(['a', '_c_b_d_', 'c', 'b',
'e']), set(['b', '_c_b_d_', 'd'])), 'e': (set(['e']), set(['a', 'c',
'b', 'e', 'd', '_c_b_d_'])), 'd': (set(['a', '_c_b_d_', 'b', 'e',
'd']), set(['c', '_c_b_d_', 'd'])), '_c_b_d_': (set(['a', 'c', 'b',
'e', 'd']), set(['c', 'b', 'd']))}

not direct neighbors :  {'a': (set(['c', 'b', '_c_b_d_', 'd']),
set(['e'])), 'c': (set(['b']), set(['a', 'e', 'd'])), 'b':
(set(['d']), set(['a', 'c', 'e'])), 'e': (set(['a', 'c', 'b',
'_c_b_d_', 'd']), set([])), 'd': (set(['c']), set(['a', 'b', 'e'])),
'_c_b_d_': (set([]), set(['a', 'e']))}


Connected Components:

1: set(['a', 'c', 'b', 'e', 'd', '_c_b_d_'])

--->>> Kernel extraction by maximising independent choices
--- Iterations
1 ------------ 
memory in use:  0.0 Mb
2 ------------ 
memory in use:  0.0 Mb
3 ------------ 
memory in use:  0.0 Mb
4 ------------
Minimal absorbent kernel:  frozenset(['e'])
memory in use:  0.0 Mb
5 ------------
memory in use:  0.0 Mb
6 ------------
Minimal dominant kernel:  frozenset(['_c_b_d_'])
memory in use:  0.0 Mb
--- Statistics
Time     : 0.0
History  : 6
Solutions: 2

--->>> Global Results

--- Potentially good choices

valuationdomain {'med': 50, 'min': 0, 'max': 100}
Dominant kernel : ['_c_b_d_']
L-domain         : {'med': 50, 'min': 0, 'max': 100}
+irredundance    : 100
independence     : 100
dominance        : 57
absorbency       : 43
characterization :
'_c_b_d_':  57.0
'a':  43.0
'b':  43.0
'c':  43.0
'd':  43.0
'e':  43.0

--- Potentially bad choices

valuationdomain {'med': 50, 'min': 0, 'max': 100}
Absorbent kernel : ['e']
valuation domain : {'med': 50, 'min': 0, 'max': 100}
+-irredundance   : 100
independence     : 100
dominance        : 14
absorbency       : 57
characterization :
'_c_b_d_':  43.0
'a':  43.0
'b':  43.0
'c':  43.0
'd':  43.0
'e':  57.0


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* allprekernelsPirlot.py  *
* R.B. June 2005          *
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