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→由布尔网络生成TPM的代码
== 由布尔网络生成TPM的代码 ==
== 由布尔网络生成TPM的代码 ==
python:<syntaxhighlight lang="python3">
import numpy as np
import networkx as nx
def bool2tpm(G, noise_prob=0.3):
def update_state(node, state, G, noise_prob):
"""
result：该节点下一时刻状态返回为1的概率
"""
predecessors = list(G.predecessors(node))
pred_state = [state[nodes.index(pred)] for pred in predecessors]
result = 1
for val in pred_state:
result &= val # AND
if result < 1:
result += noise_prob # 带噪音的AND门
return result
# 构建布尔网络的状态空间
nodes = list(G.nodes())
num_nodes = len(nodes)
state_space = list(np.array(np.meshgrid(*[[0, 1]] * num_nodes)).T.reshape(-1, num_nodes))
# 计算TPM
num_states = len(state_space)
TPM = np.zeros((num_states, num_states))
for idx, state in enumerate(state_space):
p1 = np.zeros((1, num_nodes))
# 为每个节点更新下一时刻为1的概率p1
for node in nodes:
p1[0, nodes.index(node)] = update_state(node, state, G, noise_prob)
for next_idx, next_state in enumerate(state_space):
TrueP = p1 * next_state + (1-p1) * (1-next_state)
TPM[idx, next_idx] = np.prod(TrueP)
# 归一化TPM
TPM /= TPM.sum(axis=1, keepdims=True)
return TPM
# 创建布尔网络
G = nx.DiGraph()
G.add_nodes_from(['A', 'B', 'C', 'D'])
G.add_edges_from([('A', 'C'), ('A', 'D'), ('B', 'C'), ('B', 'D'), ('C', 'A'), ('C', 'B'), ('D', 'A'), ('D', 'B')])
TPM = bool2tpm(G, noise_prob=0.3)
</syntaxhighlight>
== 应用 ==
== 应用 ==