人工智能基础概念与发展历程

    # 示例：基于规则的简单AI决策系统
    def ai_decision(temperature, humidity):
    if temperature > 30 and humidity > 70:
        return "开启空调"
    elif temperature < 10:
        return "开启暖气"
    else:
        return "保持当前状态"
    
    # 测试
    print(ai_decision(35, 75))  # 输出：开启空调
    print(ai_decision(8, 50))   # 输出：开启暖气
    
    
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 示例：简单的强化学习框架（模拟AlphaGo的学习过程）
    import random
    
    # 定义奖励函数
    def reward(action):
    if action == "正确落子":
        return 1
    else:
        return -1
    
    # 模拟AI学习
    def ai_learn():
    actions = ["正确落子", "错误落子"]
    chosen_action = random.choice(actions)
    return reward(chosen_action)
    
    # 测试
    print(ai_learn())  # 输出：1 或 -1
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 示例：简单的医疗诊断专家系统
    def diagnose(symptom):
    if symptom == "发烧":
        return "可能感染流感"
    elif symptom == "胸痛":
        return "建议检查心脏"
    else:
        return "无法确定，请咨询医生"
    
    # 测试
    print(diagnose("发烧"))  # 输出：可能感染流感
    
    
      
      
      
      
      
      
      
      
      
      
      
    

    # 示例：使用Scikit-learn实现简单的分类模型
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier
    
    # 加载数据集
    data = load_iris()
    X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2)
    
    # 训练模型
    model = RandomForestClassifier()
    model.fit(X_train, y_train)
    
    # 测试模型
    accuracy = model.score(X_test, y_test)
    print(f"模型准确率：{accuracy:.2f}")
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 示例：使用Keras构建简单的神经网络
    from keras.models import Sequential
    from keras.layers import Dense
    
    # 定义模型
    model = Sequential()
    model.add(Dense(10, input_dim=8, activation='relu'))  # 输入层
    model.add(Dense(1, activation='sigmoid'))            # 输出层
    
    # 编译模型
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    print("神经网络模型构建完成！")
    
    
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 示例：简单的Q-learning算法
    import numpy as np
    
    # 定义Q表
    q_table = np.zeros((5, 5))  # 5个状态，5个动作
    
    # 定义奖励矩阵
    rewards = np.array([
    [0, -1, 0, -1, 100],
    [-1, 0, -1, 0, -1],
    [0, -1, 0, -1, 0],
    [-1, 0, -1, 0, -1],
    [0, -1, 0, -1, 100]
    ])
    
    # Q-learning算法
    def q_learning(q_table, rewards, episodes=1000, alpha=0.1, gamma=0.9):
    for _ in range(episodes):
        state = np.random.randint(0, 5)
        while state != 4:  # 目标状态
            action = np.argmax(q_table[state])
            next_state = np.random.choice(np.where(rewards[state] >= 0)[0])
            q_table[state, action] += alpha * (rewards[state, action] + gamma * np.max(q_table[next_state]) - q_table[state, action])
            state = next_state
    return q_table
    
    # 运行算法
    q_table = q_learning(q_table, rewards)
    print("Q表：\n", q_table)