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初稿

单特征(只按照累计确诊人数分析,数据来源截止到3.31)

1、logistic回归拟合疫情曲线

生物中学的增长模型,类似S曲线的拟合,阅读文章: https://www.jianshu.com/p/4c90f8f7c1c1 https://blog.csdn.net/Zengmeng1998/article/details/104208284 https://blog.csdn.net/z_ccsdn/article/details/104134358?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-1.nonecase 代码为logistic.py拟合结果如下图(横坐标代表1月20号开始累增):

image-20200511221956915

2、简单的神经网络拟合

构建一个简单的神经网络来拟合并预测未来疫情结果,代码为dpAsy.py https://blog.csdn.net/weixin_44000193/article/details/104091767#comments_12149374 拟合效果如下:

image-20200511222051196

多特征(累计七个特征)

和单特征类似,增加特征拟合提高可信度 https://blog.csdn.net/weixin_44000193/article/details/104091767#comments_12149374 image-20200511222819455

1、7个特征构建简单的神经网络拟合新冠曲线,参见manyFeatures.py

image-20200511222840659

2、xgboost中选用7个特征拟合的新冠曲线,参见xgboost.py

image-20200511223007798

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