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基于有效信息的因果涌现理论 (查看源代码)

添加369字节、 2024年11月24日 (日) 14:42

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the EI value of micro and macro level.

"""

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# generate the statue list of boolean nodes, based on the number of micro elements

Nmi = 2 ** micro

node_list = list(itertools.product(['0', '1'], repeat=micro))

S_m = torch.zeros((Nmi, Nmi), dtype=torch.float)

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# calculate the transition probability matrix of micro level

for i, pre in enumerate(node_list):

for j, tmp in enumerate(node_list):

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S_m[i][j] = tr_pr

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# calculate the grouping matrix from micro to macro level, based on the elem_group and mech_group.

unique = set(mech_group.values())

Nma = len(unique) ** len(elem_group)

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group[i, int(Sma, base=len(unique))] = 1

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# calculate the transition probability matrix of macro level

S_M = group.t() @ S_m @ group

S_M = S_M / torch.sum(S_M, axis=1).unsqueeze(1)

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# calculate the EI of micro and macro level

micro_ei = effective_information(S_m)

macro_ei = effective_information(S_M)

return micro_ei, macro_ei

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# Example：提高确定性实例

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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)

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