巴菲特芒格的精准医疗AI投资：个性化治疗的新时代

    # 安装Python
    sudo apt-get install python3 python3-pip
    
    
      
      
    

    # 安装Pandas、NumPy、Matplotlib、Scikit-learn、Keras
    pip install pandas numpy matplotlib scikit-learn keras
    
    
      
      
    

    import requests
    from bs4 import BeautifulSoup
    
    def fetch_data(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    data = []
    for item in soup.find_all('div', class_='data-item'):
        data.append({
            'gene': item.find('span', class_='gene').text,
            'disease': item.find('span', class_='disease').text,
            'treatment': item.find('span', class_='treatment').text
        })
    return data
    
    if __name__ == '__main__':
    url = 'https://example.com/medical-data'
    data = fetch_data(url)
    print(data)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    import pandas as pd
    from sklearn.decomposition import PCA
    
    def preprocess_data(data):
    df = pd.DataFrame(data)
    # 数据清洗
    df = df.dropna()
    # 特征提取
    pca = PCA(n_components=3)
    principal_components = pca.fit_transform(df[['gene', 'disease', 'treatment']])
    return principal_components
    
    if __name__ == '__main__':
    data = [{'gene': 'BRCA1', 'disease': 'Breast Cancer', 'treatment': 'Targeted Therapy'},
            {'gene': 'EGFR', 'disease': 'Lung Cancer', 'treatment': 'Immunotherapy'}]
    processed_data = preprocess_data(data)
    print(processed_data)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    from keras.models import Sequential
    from keras.layers import Dense
    
    def build_model(input_dim):
    model = Sequential()
    model.add(Dense(64, activation='relu', input_dim=input_dim))
    model.add(Dense(32, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model
    
    if __name__ == '__main__':
    input_dim = 3
    model = build_model(input_dim)
    print(model.summary())
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    import numpy as np
    
    def invest_strategy(predicted_price, current_price, threshold=0.05):
    if predicted_price > current_price * (1 + threshold):
        return 'Buy'
    elif predicted_price < current_price * (1 - threshold):
        return 'Sell'
    else:
        return 'Hold'
    
    if __name__ == '__main__':
    current_price = 100
    predicted_price = 105
    print(invest_strategy(predicted_price, current_price))
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    import matplotlib.pyplot as plt
    
    def analyze_results(actual_prices, predicted_prices):
    plt.plot(actual_prices, label='Actual Prices')
    plt.plot(predicted_prices, label='Predicted Prices')
    plt.xlabel('Time')
    plt.ylabel('Price')
    plt.legend()
    plt.show()
    
    if __name__ == '__main__':
    actual_prices = [100, 105, 110, 115, 120]
    predicted_prices = [102, 107, 112, 117, 122]
    analyze_results(actual_prices, predicted_prices)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 精准医疗AI投资系统代码示例
    import pandas as pd
    from sklearn.decomposition import PCA
    from keras.models import Sequential
    from keras.layers import Dense
    import matplotlib.pyplot as plt
    
    # 数据采集模块
    def fetch_medical_data(url):
    # 简单示例，实际实现需根据具体数据源进行调整
    response = requests.get(url)
    data = response.json()
    return data
    
    # 数据处理模块
    def preprocess_data(data):
    df = pd.DataFrame(data)
    df = df.dropna()
    pca = PCA(n_components=3)
    principal_components = pca.fit_transform(df[['gene', 'disease', 'treatment']])
    return principal_components
    
    # 模型训练模块
    def build_model(input_dim):
    model = Sequential()
    model.add(Dense(64, activation='relu', input_dim=input_dim))
    model.add(Dense(32, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    return model
    
    # 投资决策模块
    def investment_strategy(predicted_price, current_price, threshold=0.05):
    if predicted_price > current_price * (1 + threshold):
        return 'Buy'
    elif predicted_price < current_price * (1 - threshold):
        return 'Sell'
    else:
        return 'Hold'
    
    # 结果分析模块
    def analyze_results(actual_prices, predicted_prices):
    plt.plot(actual_prices, label='Actual Prices')
    plt.plot(predicted_prices, label='Predicted Prices')
    plt.xlabel('Time')
    plt.ylabel('Price')
    plt.legend()
    plt.show()
    
    # 系统集成
    def main():
    # 数据采集
    data = fetch_medical_data('https://example.com/medical-api')
    # 数据处理
    processed_data = preprocess_data(data)
    # 模型训练
    model = build_model(processed_data.shape[1])
    # 模型预测
    predicted_prices = model.predict(processed_data)
    # 投资决策
    current_price = 100  # 假设当前价格为100
    strategy = investment_strategy(predicted_prices[-1][0], current_price)
    print(f'投资策略：{strategy}')
    # 结果分析
    actual_prices = [100, 105, 110, 115, 120]
    analyze_results(actual_prices, predicted_prices)
    
    if __name__ == '__main__':
    main()