颠覆在线教育格局：Deepseek引领个性化学习新纪元

 import numpy as np

    
 from tensorflow.keras.models import Sequential
    
 from tensorflow.keras.layers import LSTM, Dense
    
 from tensorflow.keras.optimizers import Adam
    
  
    
 # 构建模拟的用户行为数据序列
    
 data = np.random.rand(1000, 10)  # 1000个样本，每个样本10个特征
    
 target = np.random.randint(2, size=(1000, 1))  # 二分类目标
    
  
    
 # 数据预处理：将数据reshape为LSTM输入格式 [样本数, 时间步长, 特征数]
    
 data = data.reshape((data.shape[0], 1, data.shape[1]))
    
  
    
 # 定义LSTM模型
    
 model = Sequential([
    
     LSTM(64, input_shape=(1, 10), return_sequences=False),
    
     Dense(1, activation='sigmoid')
    
 ])
    
 model.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])
    
  
    
 # 模型训练
    
 model.fit(data, target, epochs=10, batch_size=32)
    
    
    
    

 import numpy as np

    
 import random
    
  
    
 class AdaptiveLearningAgent:
    
     def __init__(self, state_size, action_size, learning_rate=0.01, discount_factor=0.95):
    
     self.state_size = state_size
    
     self.action_size = action_size
    
     self.learning_rate = learning_rate
    
     self.discount_factor = discount_factor
    
     self.q_table = np.zeros((state_size, action_size))
    
  
    
     def choose_action(self, state, epsilon=0.1):
    
     if random.uniform(0, 1) < epsilon:
    
         return random.randint(0, self.action_size - 1)
    
     else:
    
         return np.argmax(self.q_table[state])
    
  
    
     def learn(self, state, action, reward, next_state):
    
     predict = self.q_table[state, action]
    
     target = reward + self.discount_factor * np.max(self.q_table[next_state])
    
     self.q_table[state, action] += self.learning_rate * (target - predict)
    
  
    
 # 模拟学习环境与自适应路径生成
    
 state_size = 100  # 代表不同知识状态
    
 action_size = 5   # 代表不同学习任务
    
 agent = AdaptiveLearningAgent(state_size, action_size)
    
  
    
 # 训练示例：模拟1000次学习过程
    
 for _ in range(1000):
    
     current_state = random.randint(0, state_size - 1)
    
     action = agent.choose_action(current_state)
    
     # 模拟奖励机制：根据行动对知识掌握的正面影响
    
     reward = random.uniform(0, 1) if action == (current_state % action_size) else random.uniform(-0.5, 0)
    
     next_state = (current_state + 1) % state_size
    
     agent.learn(current_state, action, reward, next_state)
    
  
    
 print("Adaptive Q-table trained successfully!")