def inner_node_match(node1, node2, match_final, f, t):
if node1.label == node2.label:
common = 0 # 记录以node1为根的子树和以node2为根的子树中，匹配的叶子节点数
node_list1, inner_node_list1, leaf_node_list1 = node_get(node1)
node_list2, inner_node_list2, leaf_node_list2 = node_get(node2)
for node in leaf_node_list1:
if node.matched == 0: # 若该叶子节点不存在与之匹配的节点，跳过
continue
else:
id1 = node.id
for item in match_final: # 在match_final中找node的匹配信息，计算common
if item[0] == id1:
id2 = item[1]
for leaf_node in leaf_node_list2:
if leaf_node.id == id2:
common += 1
max_num = len(leaf_node_list1) if len(leaf_node_list1) > len(leaf_node_list2) else len(leaf_node_list2)
# 阈值t的大小根据子树规模动态改变
if len(leaf_node_list1) <= 4 or len(leaf_node_list2) <= 4:
t = 0.4
sim_inner = common / max_num # 好像没有int/double的问题
sim_value = string_similarity_ngram(node1.value, node2.value, 2)
# 为中间节点相似度设置权重，common leaves function有更高的权重
# 即使中间节点value的相似度小于阈值f，但是若子树相似度远大于t，也认为两个中间节点相似
if sim_inner >= 0.8:
match_final.append((node1.id, node2.id, sim_value))
node1.matched = 1
node2.matched = 1
elif sim_inner > t and sim_value > f:
match_final.append((node1.id, node2.id, sim_value))
node1.matched = 1
node2.matched = 1
def inner_node_match(node1, node2, match_final, f, t):
if node1.label == node2.label:
common = 0 # 记录以node1为根的子树和以node2为根的子树中，匹配的叶子节点数
node_list1, inner_node_list1, leaf_node_list1 = node_get(node1)
node_list2, inner_node_list2, leaf_node_list2 = node_get(node2)
for node in leaf_node_list1:
if node.matched == 0: # 若该叶子节点不存在与之匹配的节点，跳过
continue
else:
id1 = node.id
for item in match_final: # 在match_final中找node的匹配信息，计算common
if item[0] == id1:
id2 = item[1]
for leaf_node in leaf_node_list2:
if leaf_node.id == id2:
common += 1
max_num = len(leaf_node_list1) if len(leaf_node_list1) > len(
leaf_node_list2) else len(leaf_node_list2)
# 阈值t的大小根据子树规模动态改变
if len(leaf_node_list1) <= 4 or len(leaf_node_list2) <= 4:
t = 0.4
sim_inner = common / max_num # 好像没有int/double的问题
sim_value = string_similarity_ngram(node1.value, node2.value, 2)
# 为中间节点相似度设置权重，common leaves function有更高的权重
# 即使中间节点value的相似度小于阈值f，但是若子树相似度远大于t，也认为两个中间节点相似
if sim_inner >= 0.8:
match_final.append((node1.id, node2.id, sim_value))
node1.matched = 1
node2.matched = 1
elif sim_inner > t and sim_value > f:
match_final.append((node1.id, node2.id, sim_value))
node1.matched = 1
node2.matched = 1
def leaf_match(left_leaf_node_list, right_leaf_node_list, f):
match_temp = []
for i in range(len(left_leaf_node_list)):
for item in right_leaf_node_list:
if left_leaf_node_list[i].label == item.label:
sim = string_similarity_ngram(left_leaf_node_list[i].value, item.value, 2)
if sim > f:
match_temp.append((left_leaf_node_list[i].id, item.id, sim))
return match_temp
def leaf_match(left_leaf_node_list, right_leaf_node_list, f):
match_temp = []
for i in range(len(left_leaf_node_list)):
for item in right_leaf_node_list:
if left_leaf_node_list[i].label == item.label:
sim = string_similarity_ngram(left_leaf_node_list[i].value,
item.value, 2)
if sim > f:
match_temp.append(
(left_leaf_node_list[i].id, item.id, sim))
return match_temp
