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[人工智能/神经网络/深度学习] pneumonia

利用朴实贝叶斯方法求解有关肺炎的问题。肺炎对应有四个特征:发烧、疼痛、咳嗽和血细胞异常,当确定了患肺炎与否时,四个特征条件独立。假设患肺炎与否和四个特征都可表示为Ture和False。 根据pneumonia.tex文件中的数据(500行,每行前4个数对应4个特征变量,第5个数对应患肺炎是否为真,以0表示False,1表示Ture),编程求解: 第a问: 文件中患肺炎为真和为假的比例; 当患肺炎为真时,发烧、疼痛、咳嗽、血细胞异常为真的比例; 当患肺炎为假时,发烧、疼痛、咳嗽、血细胞异常为真的比例。 第b问: 求当出现发烧和咳嗽症状但没有疼痛和血细胞异常症状时,患肺炎的可能性。 第c问: 求当出现发烧和咳嗽症状但疼痛和血细胞状况未知时,患肺炎的可能性。
Simple Bayesian method to solve the question of pneumonia. Pneumonia corresponding four characteristics: fever, pain, cough and blood cell abnormalities, determine the risk of pneumonia or not, the four characteristics of conditional independence. Assumptions suffering from pneumonia or not and four features can be represented as Ture and False. The pneumonia.tex file data (500 lines, each line of the first 4 numbers corresponding to the four characteristic variables, number 5 corresponds suffering from pneumonia, is true, and 0 represents False and 1 indicates Ture), programming solving: a Q: file suffering from pneumonia for true and for false proportion suffering from pneumonia as true, fever, pain, cough, blood cell abnormalities for the true proportion suffering from pneumonia is false, fever, pain, cough, blood cell abnormalities as Really ratio. B Q: demand when there is the possibility of suffering from pneumonia symptoms of fever and cough, but no pain and blood cell abnorma (2012-09-14, C/C++, 3KB, 下载7次)

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总计:181