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import concurrent.futures
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from collections import Counter
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import cppy.cp_util as util
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# 价值不大，就是多线程的一个表现，说明松耦合不如消息驱动的组件
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class WordFrequencyAgent:
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def __init__(self, words):
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self.words = words
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def compute_word_frequency(self):
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self.word_freq = Counter(self.words)
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def get_word_frequency(self):
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return self.word_freq
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# 将文本分割成多个部分，并为每个部分创建一个Agent
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def create_agents(words, num_agents = 4 ):
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text_chunks = [ words[i::num_agents] for i in range(num_agents) ]
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agents = [ WordFrequencyAgent(chunk) for chunk in text_chunks ]
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return agents
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def compute_all_word_frequencies(agents):
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with concurrent.futures.ThreadPoolExecutor() as executor:
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# 使用线程池来并行计算词频
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future_to_agent = {executor.submit(agent.compute_word_frequency): agent for agent in agents}
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for future in concurrent.futures.as_completed(future_to_agent):
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agent = future_to_agent[future]
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data = future.result() # 词频被保存在agent中
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# 所有Agent计算完成后，合并它们的词频结果
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def merge_word_frequencies(agents):
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merged_freq = Counter()
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for agent in agents:
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merged_freq.update(agent.get_word_frequency())
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return merged_freq
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if __name__ == '__main__':
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words = util.extract_file_words(util.testfilepath) # 从文本抽词
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agents = create_agents(words) # 创建代理
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compute_all_word_frequencies(agents) # 计算
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merged_word_freq = merge_word_frequencies(agents) # 合并结果
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util.print_word_freqs(merged_word_freq.most_common(10)) # 排序输出 |
