def novel_node_similarity_calculation(self, novel_node_x, novel_node_y):
"""
计算两本小说的相似性
"""
min_length = min(len(novel_node_x.chapter_list), len(novel_node_y.chapter_list))
if min_length <= 2:
return 0.0, []
similarity_matirx = defaultdict(list)
for index_x, chapter_x in enumerate(novel_node_x.chapter_list):
if len(chapter_x.chapter_title) == 0:
continue
for index_y, chapter_y in enumerate(novel_node_y.chapter_list):
if len(chapter_y.chapter_title) == 0:
continue
chapter_similarity = self.novel_chapter_similarity_calculation(chapter_x, chapter_y)
if chapter_similarity >= 0.8:
similarity_matirx[index_x].append(index_y)
match = BipartiteGraph()
match_number, match_list = match.bipartite_graph_max_match(
len(novel_node_x.chapter_list),
len(novel_node_y.chapter_list),
similarity_matirx
)
similarity = match_number * 1.0 / min_length
return similarity, match_list
def novel_node_similarity_calculation(self, novel_node_x, novel_node_y):
"""
计算两本小说的相似性
"""
long_list = novel_node_x.chapter_list
short_list = novel_node_y.chapter_list
if len(novel_node_y.chapter_list) > len(novel_node_x.chapter_list):
long_list = novel_node_y.chapter_list
short_list = novel_node_x.chapter_list
if len(short_list) <= 2:
return 0.0, []
similarity_matirx = defaultdict(list)
for index_x, chapter_x in enumerate(long_list):
if chapter_x.chapter_title == '':
continue
for index_y, chapter_y in enumerate(short_list):
chapter_similarity = self.novel_chapter_similarity_calculation(chapter_x, chapter_y)
if chapter_similarity >= 0.8:
similarity_matirx[index_x].append(index_y)
match = BipartiteGraph()
match_number, match_list = match.bipartite_graph_max_match(len(long_list), len(short_list), similarity_matirx)
similarity = match_number * 1.0 / len(short_list)
return similarity, match_list