Deepseek：智能信息检索引领未来 —— 探索颠覆性技术与创新代码

 import numpy as np

    
 from sklearn.feature_extraction.text import TfidfVectorizer
    
  
    
 # 示例文本数据
    
 documents = [
    
     "Deepseek引领智能信息检索新时代",
    
     "传统检索方法与现代深度学习的融合",
    
     "大数据时代的信息筛选与精确匹配"
    
 ]
    
  
    
 # 初始化TF-IDF向量化器
    
 vectorizer = TfidfVectorizer(stop_words='english')
    
 tfidf_matrix = vectorizer.fit_transform(documents)
    
  
    
 print("TF-IDF特征矩阵：\n", tfidf_matrix.toarray())
    
    
    
    

 from transformers import BertTokenizer, BertModel

    
 import torch
    
  
    
 # 初始化BERT模型与分词器
    
 tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    
 model = BertModel.from_pretrained('bert-base-uncased')
    
  
    
 # 示例句子
    
 text = "Deepseek transforms intelligent information retrieval."
    
  
    
 # 分词与编码
    
 inputs = tokenizer(text, return_tensors="pt")
    
 outputs = model(**inputs)
    
  
    
 # 提取句子嵌入
    
 sentence_embedding = outputs.last_hidden_state.mean(dim=1)
    
 print("BERT句子嵌入：\n", sentence_embedding)
    
    
    
    

 import numpy as np

    
  
    
 class RLAgent:
    
     def __init__(self, num_actions, learning_rate=0.1, discount_factor=0.95):
    
     self.num_actions = num_actions
    
     self.q_table = np.zeros(num_actions)
    
     self.lr = learning_rate
    
     self.df = discount_factor
    
  
    
     def choose_action(self, state):
    
     # 简单的ε-贪心策略（固定ε值）
    
     epsilon = 0.1
    
     if np.random.rand() < epsilon:
    
         return np.random.randint(0, self.num_actions)
    
     else:
    
         return np.argmax(self.q_table)
    
  
    
     def update(self, state, action, reward, next_state):
    
     best_next_action = np.argmax(self.q_table)
    
     td_target = reward + self.df * self.q_table[best_next_action]
    
     self.q_table[action] += self.lr * (td_target - self.q_table[action])
    
  
    
 # 示例：模拟动态反馈更新
    
 agent = RLAgent(num_actions=5)
    
 state = 0
    
 action = agent.choose_action(state)
    
 reward = 1.0  # 模拟用户正反馈
    
 next_state = 0
    
 agent.update(state, action, reward, next_state)
    
  
    
 print("更新后的Q表：", agent.q_table)