截尾序贯检验模型代码，python

截尾序贯检验模型（Sequential Testing Model）是一种统计方法，用于在逐步收集数据的过程中做出决策。例如，你可能需要在实验中决定何时停止收集数据，因为结果已经足够明显。Python 中实现这类模型的一个常用方法是使用累积和控制图（CUSUM charts）或者序贯概率比检验（Sequential Probability Ratio Test, SPRT）。

以下是使用Python实现一个简单的SPRT模型的代码示例：

import numpy as np

def sprt(h0, h1, alpha, beta, data, test_statistic='mean'):
    """
    Sequential Probability Ratio Test (SPRT)
    
    Parameters:
    h0 : float
        Null hypothesis mean value.
    h1 : float
        Alternative hypothesis mean value.
    alpha : float
        Type I error rate (probability of false positive).
    beta : float
        Type II error rate (probability of false negative).
    data : list or array
        Data received sequentially.
    test_statistic : string
        Type of test statistic to use, currently only 'mean' is supported.
        
    Returns:
    decision : string
        Decision based on SPRT, can be 'accept h0', 'accept h1', or 'continue sampling'.
    """
    loglik_ratio = 0
    n = 0
    
    for sample in data:
        n += 1
        if test_statistic == 'mean':
            loglik_ratio += np.log((1 - alpha)**(1/n) + (alpha/n)*(h1/sample)**(1/n))
        else:
            raise ValueError("Unsupported test statistic type.")

    if np.log((1 - beta)/(alpha)) > loglik_ratio:
        decision = 'accept h0'
    elif np.log((1 - beta)/(alpha)) < loglik_ratio:
        decision = 'accept h1'
    else:
        decision = 'continue sampling'
    
    return decision

# Example usage
data = np.array([1.1, 1.05, 0.99, 1.07, 1.12, 0.98, 1.03])
decision = sprt(h0=1.0, h1=1.1, alpha=0.05, beta=0.1, data=data)
print(decision)

请注意，这个例子中的SPRT实现是基于均值检验的，并且只提供了简单的决策逻辑。在实际应用中，SPRT模型可能需要根据具体情况进一步调整和优化，以适应不同的假设检验和数据特性。