math.py 文件源码

def weighted_quantile(x, weights, quantile):
    I = np.argsort(x)
    sort_x = x[I]
    sort_w = weights[I]
    acum_w = np.add.accumulate(sort_w)
    norm_w = (acum_w - 0.5*sort_w)/acum_w[-1] 
    interpq = np.searchsorted(norm_w, [quantile])[0] 
    if interpq == 0:
        return sort_x[0]
    elif interpq == len(x):
        return sort_x[-1]
    else:
        tmp1 = (norm_w[interpq] - quantile)/(norm_w[interpq] - norm_w[interpq-1])
        tmp2 = (quantile - norm_w[interpq-1])/(norm_w[interpq] - norm_w[interpq-1])
        assert tmp1>=0 and tmp2>=0 and tmp1<=1 and tmp2<=1 
        return sort_x[interpq-1]*tmp1 + sort_x[interpq]*tmp2