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→状态空间的因果涌现
python代码实现:<syntaxhighlight lang="python3">
python代码实现:<syntaxhighlight lang="python3">
import torch
def Entropia(a):
b = torch.nonzero(a > 0).squeeze(1)
return -torch.sum(a[b]*torch.log2(a[b]))
def effective_information(G1):
"""
Calculate EI value of transition probability matrix
G1: TPM matrix
return the EI value
"""
N = G1.shape[0]
G = torch.zeros_like(G1)
nozero_number = 0
for i in range(N):
if torch.sum(G1[i]) != 0:
G[i] = G1[i] / torch.sum(G1[i])
nozero_number += 1
Wout = torch.zeros(N)
Win = torch.zeros(N)
for i in range(N):
if torch.sum(G[i]) != 0:
Wout[i] = torch.tensor(Entropia(G[i]))
for i in range(N):
for j in range(N):
if G[i][j] != 0:
Win[j] += G[i][j] / nozero_number
Wout_average = torch.sum(Wout) / nozero_number
Win_entropy = Entropia(Win)
return Win_entropy - Wout_average
def calc_tpm(micro_tpm, group):
def calc_tpm(micro_tpm, group):
"""
"""