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the EI value of micro and macro level.
the EI value of micro and macro level.
"""
"""
# generate the statue list of boolean nodes, based on the number of micro elements
Nmi = 2 ** micro
Nmi = 2 ** micro
node_list = list(itertools.product(['0', '1'], repeat=micro))
node_list = list(itertools.product(['0', '1'], repeat=micro))
S_m = torch.zeros((Nmi, Nmi), dtype=torch.float)
S_m = torch.zeros((Nmi, Nmi), dtype=torch.float)
# calculate the transition probability matrix of micro level
for i, pre in enumerate(node_list):
for i, pre in enumerate(node_list):
for j, tmp in enumerate(node_list):
for j, tmp in enumerate(node_list):
S_m[i][j] = tr_pr
S_m[i][j] = tr_pr
# calculate the grouping matrix from micro to macro level, based on the elem_group and mech_group.
unique = set(mech_group.values())
unique = set(mech_group.values())
Nma = len(unique) ** len(elem_group)
Nma = len(unique) ** len(elem_group)
group[i, int(Sma, base=len(unique))] = 1
group[i, int(Sma, base=len(unique))] = 1
# calculate the transition probability matrix of macro level
S_M = group.t() @ S_m @ group
S_M = group.t() @ S_m @ group
S_M = S_M / torch.sum(S_M, axis=1).unsqueeze(1)
S_M = S_M / torch.sum(S_M, axis=1).unsqueeze(1)
# calculate the EI of micro and macro level
micro_ei = effective_information(S_m)
micro_ei = effective_information(S_m)
macro_ei = effective_information(S_M)
macro_ei = effective_information(S_M)
return micro_ei, macro_ei
return micro_ei, macro_ei
# Example：提高确定性实例
# Example：提高确定性实例
mech_group = {'00': '0', '01': '0', '10': '0', '11': '1'}
mech_group = {'00': '0', '01': '0', '10': '0', '11': '1'}
micro_ei, macro_ei = calc_bn_ei(micro_mech, micro, edges, elem_group, mech_group)
micro_ei, macro_ei = calc_bn_ei(micro_mech, micro, edges, elem_group, mech_group)
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