def get_probabilities_Q_possible(self, possible_configs):
"""
Get the probability Q(l;n) for each configuration in the possible_configs dictionnary.
The implementation is based on the recursive equation of the paper.
Input
----------------------
possible_configs: Dictionnary where keys are the config as a string (i.e. "0101011011") and value are sets of the parent configurations.
Return
----------------------
Dictionnary of probabilities where the key is the configuration as a string (e.g. "10101110") and the value is Q(l;n)
"""
#the master dictionnary
dict_config = {}
prob_tot = 1.0
#order by size to simplify the calculation
possible_config_ordered_by_size = self.regroup_config_by_size(possible_configs)
N = len(self.G.nodes())
#Initial configuration
initial_config = [0]*N
if self.symbolic_:
dict_config[self.list_to_string(initial_config)] = "<prod>"+";".join(["G("+str(node)+","+str(0)+")" for node in self.G.nodes()])+"</prod>"
else:
dict_config[self.list_to_string(initial_config)] = np.float128(np.product([(1.0-self.G.nodes[node]["rf"].resp_func(0)) for node in self.G.nodes()]))
prob_tot -= dict_config[self.list_to_string(initial_config)]
for size in range(1,N+1):
if size in possible_config_ordered_by_size:
for config_str in possible_config_ordered_by_size[size]:
Q_ln = self.solve_specific_configuration(config_str, dict_config, possible_configs)
dict_config[config_str] = Q_ln
prob_tot -= Q_ln
dict_config["1"*N] = prob_tot
return dict_config
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