Spark ML包，数据挖掘示例数据Affairs
1.数据字段解释
affairs：一年来婚外情的频率
gender：性别
age：年龄
yearsmarried：婚龄
children：是否有小孩
religiousness：宗教信仰程度（5分制，1分表示反对，5分表示非常信仰）
education：学历
occupation：职业（逆向编号的戈登7种分类）
rating：对婚姻的自我评分（5分制，1表示非常不幸福，5表示非常幸福）
2.数据列表
val dataList: List[(Double, String, Double, Double, String, Double, Double, Double, Double)] = List(
(0, "male", 37, 10, "no", 3, 18, 7, 4),
(0, "female", 27, 4, "no", 4, 14, 6, 4),
(0, "female", 32, 15, "yes", 1, 12, 1, 4),
(0, "male", 57, 15, "yes", 5, 18, 6, 5),
(0, "male", 22, 0.75, "no", 2, 17, 6, 3),
(0, "female", 32, 1.5, "no", 2, 17, 5, 5),
(0, "female", 22, 0.75, "no", 2, 12, 1, 3),
(0, "male", 57, 15, "yes", 2, 14, 4, 4),
(0, "female", 32, 15, "yes", 4, 16, 1, 2),
(0, "male", 22, 1.5, "no", 4, 14, 4, 5),
(0, "male", 37, 15, "yes", 2, 20, 7, 2),
(0, "male", 27, 4, "yes", 4, 18, 6, 4),
(0, "male", 47, 15, "yes", 5, 17, 6, 4),
(0, "female", 22, 1.5, "no", 2, 17, 5, 4),
(0, "female", 27, 4, "no", 4, 14, 5, 4),
(0, "female", 37, 15, "yes", 1, 17, 5, 5),
(0, "female", 37, 15, "yes", 2, 18, 4, 3),
(0, "female", 22, 0.75, "no", 3, 16, 5, 4),
(0, "female", 22, 1.5, "no", 2, 16, 5, 5),
(0, "female", 27, 10, "yes", 2, 14, 1, 5),
(0, "female", 22, 1.5, "no", 2, 16, 5, 5),
(0, "female", 22, 1.5, "no", 2, 16, 5, 5),
(0, "female", 27, 10, "yes", 4, 16, 5, 4),
(0, "female", 32, 10, "yes", 3, 14, 1, 5),
(0, "male", 37, 4, "yes", 2, 20, 6, 4),
(0, "female", 22, 1.5, "no", 2, 18, 5, 5),
(0, "female", 27, 7, "no", 4, 16, 1, 5),
(0, "male", 42, 15, "yes", 5, 20, 6, 4),
(0, "male", 27, 4, "yes", 3, 16, 5, 5),
(0, "female", 27, 4, "yes", 3, 17, 5, 4),
(0, "male", 42, 15, "yes", 4, 20, 6, 3),
(0, "female", 22, 1.5, "no", 3, 16, 5, 5),
(0, "male", 27, 0.417, "no", 4, 17, 6, 4),
(0, "female", 42, 15, "yes", 5, 14, 5, 4),
(0, "male", 32, 4, "yes", 1, 18, 6, 4),
(0, "female", 22, 1.5, "no", 4, 16, 5, 3),
(0, "female", 42, 15, "yes", 3, 12, 1, 4),
(0, "female", 22, 4, "no", 4, 17, 5, 5),
(0, "male", 22, 1.5, "yes", 1, 14, 3, 5),
(0, "female", 22, 0.75, "no", 3, 16, 1, 5),
(0, "male", 32, 10, "yes", 5, 20, 6, 5),
(0, "male", 52, 15, "yes", 5, 18, 6, 3),
(0, "female", 22, 0.417, "no", 5, 14, 1, 4),
(0, "female", 27, 4, "yes", 2, 18, 6, 1),
(0, "female", 32, 7, "yes", 5, 17, 5, 3),
(0, "male", 22, 4, "no", 3, 16, 5, 5),
(0, "female", 27, 7, "yes", 4, 18, 6, 5),
(0, "female", 42, 15, "yes", 2, 18, 5, 4),
(0, "male", 27, 1.5, "yes", 4, 16, 3, 5),
(0, "male", 42, 15, "yes", 2, 20, 6, 4),
(0, "female", 22, 0.75, "no", 5, 14, 3, 5),
(0, "male", 32, 7, "yes", 2, 20, 6, 4),
(0, "male", 27, 4, "yes", 5, 20, 6, 5),
(0, "male", 27, 10, "yes", 4, 20, 6, 4),
(0, "male", 22, 4, "no", 1, 18, 5, 5),
(0, "female", 37, 15, "yes", 4, 14, 3, 1),
(0, "male", 22, 1.5, "yes", 5, 16, 4, 4),
(0, "female", 37, 15, "yes", 4, 17, 1, 5),
(0, "female", 27, 0.75, "no", 4, 17, 5, 4),
(0, "male", 32, 10, "yes", 4, 20, 6, 4),
(0, "female", 47, 15, "yes", 5, 14, 7, 2),
(0, "male", 37, 10, "yes", 3, 20, 6, 4),
(0, "female", 22, 0.75, "no", 2, 16, 5, 5),
(0, "male", 27, 4, "no", 2, 18, 4, 5),
(0, "male", 32, 7, "no", 4, 20, 6, 4),
(0, "male", 42, 15, "yes", 2, 17, 3, 5),
(0, "male", 37, 10, "yes", 4, 20, 6, 4),
(0, "female", 47, 15, "yes", 3, 17, 6, 5),
(0, "female", 22, 1.5, "no", 5, 16, 5, 5),
(0, "female", 27, 1.5, "no", 2, 16, 6, 4),
(0, "female", 27, 4, "no", 3, 17, 5, 5),
(0, "female", 32, 10, "yes", 5, 14, 4, 5),
(0, "female", 22, 0.125, "no", 2, 12, 5, 5),
(0, "male", 47, 15, "yes", 4, 14, 4, 3),
(0, "male", 32, 15, "yes", 1, 14, 5, 5),
(0, "male", 27, 7, "yes", 4, 16, 5, 5),
(0, "female", 22, 1.5, "yes", 3, 16, 5, 5),
(0, "male", 27, 4, "yes", 3, 17, 6, 5),
(0, "female", 22, 1.5, "no", 3, 16, 5, 5),
(0, "male", 57, 15, "yes", 2, 14, 7, 2),
(0, "male", 17.5, 1.5, "yes", 3, 18, 6, 5),
(0, "male", 57, 15, "yes", 4, 20, 6, 5),
(0, "female", 22, 0.75, "no", 2, 16, 3, 4),
(0, "male", 42, 4, "no", 4, 17, 3, 3),
(0, "female", 22, 1.5, "yes", 4, 12, 1, 5),
(0, "female", 22, 0.417, "no", 1, 17, 6, 4),
(0, "female", 32, 15, "yes", 4, 17, 5, 5),
(0, "female", 27, 1.5, "no", 3, 18, 5, 2),
(0, "female", 22, 1.5, "yes", 3, 14, 1, 5),
(0, "female", 37, 15, "yes", 3, 14, 1, 4),
(0, "female", 32, 15, "yes", 4, 14, 3, 4),
(0, "male", 37, 10, "yes", 2, 14, 5, 3),
(0, "male", 37, 10, "yes", 4, 16, 5, 4),
(0, "male", 57, 15, "yes", 5, 20, 5, 3),
(0, "male", 27, 0.417, "no", 1, 16, 3, 4),
(0, "female", 42, 15, "yes", 5, 14, 1, 5),
(0, "male", 57, 15, "yes", 3, 16, 6, 1),
(0, "male", 37, 10, "yes", 1, 16, 6, 4),
(0, "male", 37, 15, "yes", 3, 17, 5, 5),
(0, "male", 37, 15, "yes", 4, 20, 6, 5),
(0, "female", 27, 10, "yes", 5, 14, 1, 5),
(0, "male", 37, 10, "yes", 2, 18, 6, 4),
(0, "female", 22, 0.125, "no", 4, 12, 4, 5),
(0, "male", 57, 15, "yes", 5, 20, 6, 5),
(0, "female", 37, 15, "yes", 4, 18, 6, 4),
(0, "male", 22, 4, "yes", 4, 14, 6, 4),
(0, "male", 27, 7, "yes", 4, 18, 5, 4),
(0, "male", 57, 15, "yes", 4, 20, 5, 4),
(0, "male", 32, 15, "yes", 3, 14, 6, 3),
(0, "female", 22, 1.5, "no", 2, 14, 5, 4),
(0, "female", 32, 7, "yes", 4, 17, 1, 5),
(0, "female", 37, 15, "yes", 4, 17, 6, 5),
(0, "female", 32, 1.5, "no", 5, 18, 5, 5),
(0, "male", 42, 10, "yes", 5, 20, 7, 4),
(0, "female", 27, 7, "no", 3, 16, 5, 4),
(0, "male", 37, 15, "no", 4, 20, 6, 5),
(0, "male", 37, 15, "yes", 4, 14, 3, 2),
(0, "male", 32, 10, "no", 5, 18, 6, 4),
(0, "female", 22, 0.75, "no", 4, 16, 1, 5),
(0, "female", 27, 7, "yes", 4, 12, 2, 4),
(0, "female", 27, 7, "yes", 2, 16, 2, 5),
(0, "female", 42, 15, "yes", 5, 18, 5, 4),
(0, "male", 42, 15, "yes", 4, 17, 5, 3),
(0, "female", 27, 7, "yes", 2, 16, 1, 2),
(0, "female", 22, 1.5, "no", 3, 16, 5, 5),
(0, "male", 37, 15, "yes", 5, 20, 6, 5),
(0, "female", 22, 0.125, "no", 2, 14, 4, 5),
(0, "male", 27, 1.5, "no", 4, 16, 5, 5),
(0, "male", 32, 1.5, "no", 2, 18, 6, 5),
(0, "male", 27, 1.5, "no", 2, 17, 6, 5),
(0, "female", 27, 10, "yes", 4, 16, 1, 3),
(0, "male", 42, 15, "yes", 4, 18, 6, 5),
(0, "female", 27, 1.5, "no", 2, 16, 6, 5),
(0, "male", 27, 4, "no", 2, 18, 6, 3),
(0, "female", 32, 10, "yes", 3, 14, 5, 3),
(0, "female", 32, 15, "yes", 3, 18, 5, 4),
(0, "female", 22, 0.75, "no", 2, 18, 6, 5),
(0, "female", 37, 15, "yes", 2, 16, 1, 4),
(0, "male", 27, 4, "yes", 4, 20, 5, 5),
(0, "male", 27, 4, "no", 1, 20, 5, 4),
(0, "female", 27, 10, "yes", 2, 12, 1, 4),
(0, "female", 32, 15, "yes", 5, 18, 6, 4),
(0, "male", 27, 7, "yes", 5, 12, 5, 3),
(0, "male", 52, 15, "yes", 2, 18, 5, 4),
(0, "male", 27, 4, "no", 3, 20, 6, 3),
(0, "male", 37, 4, "yes", 1, 18, 5, 4),
(0, "male", 27, 4, "yes", 4, 14, 5, 4),
(0, "female", 52, 15, "yes", 5, 12, 1, 3),
(0, "female", 57, 15, "yes", 4, 16, 6, 4),
(0, "male", 27, 7, "yes", 1, 16, 5, 4),
(0, "male", 37, 7, "yes", 4, 20, 6, 3),
(0, "male", 22, 0.75, "no", 2, 14, 4, 3),
(0, "male", 32, 4, "yes", 2, 18, 5, 3),
(0, "male", 37, 15, "yes", 4, 20, 6, 3),
(0, "male", 22, 0.75, "yes", 2, 14, 4, 3),
(0, "male", 42, 15, "yes", 4, 20, 6, 3),
(0, "female", 52, 15, "yes", 5, 17, 1, 1),
(0, "female", 37, 15, "yes", 4, 14, 1, 2),
(0, "male", 27, 7, "yes", 4, 14, 5, 3),
(0, "male", 32, 4, "yes", 2, 16, 5, 5),
(0, "female", 27, 4, "yes", 2, 18, 6, 5),
(0, "female", 27, 4, "yes", 2, 18, 5, 5),
(0, "male", 37, 15, "yes", 5, 18, 6, 5),
(0, "female", 47, 15, "yes", 5, 12, 5, 4),
(0, "female", 32, 10, "yes", 3, 17, 1, 4),
(0, "female", 27, 1.5, "yes", 4, 17, 1, 2),
(0, "female", 57, 15, "yes", 2, 18, 5, 2),
(0, "female", 22, 1.5, "no", 4, 14, 5, 4),
(0, "male", 42, 15, "yes", 3, 14, 3, 4),
(0, "male", 57, 15, "yes", 4, 9, 2, 2),
(0, "male", 57, 15, "yes", 4, 20, 6, 5),
(0, "female", 22, 0.125, "no", 4, 14, 4, 5),
(0, "female", 32, 10, "yes", 4, 14, 1, 5),
(0, "female", 42, 15, "yes", 3, 18, 5, 4),
(0, "female", 27, 1.5, "no", 2, 18, 6, 5),
(0, "male", 32, 0.125, "yes", 2, 18, 5, 2),
(0, "female", 27, 4, "no", 3, 16, 5, 4),
(0, "female", 27, 10, "yes", 2, 16, 1, 4),
(0, "female", 32, 7, "yes", 4, 16, 1, 3),
(0, "female", 37, 15, "yes", 4, 14, 5, 4),
(0, "female", 42, 15, "yes", 5, 17, 6, 2),
(0, "male", 32, 1.5, "yes", 4, 14, 6, 5),
(0, "female", 32, 4, "yes", 3, 17, 5, 3),
(0, "female", 37, 7, "no", 4, 18, 5, 5),
(0, "female", 22, 0.417, "yes", 3, 14, 3, 5),
(0, "female", 27, 7, "yes", 4, 14, 1, 5),
(0, "male", 27, 0.75, "no", 3, 16, 5, 5),
(0, "male", 27, 4, "yes", 2, 20, 5, 5),
(0, "male", 32, 10, "yes", 4, 16, 4, 5),
(0, "male", 32, 15, "yes", 1, 14, 5, 5),
(0, "male", 22, 0.75, "no", 3, 17, 4, 5),
(0, "female", 27, 7, "yes", 4, 17, 1, 4),
(0, "male", 27, 0.417, "yes", 4, 20, 5, 4),
(0, "male", 37, 15, "yes", 4, 20, 5, 4),
(0, "female", 37, 15, "yes", 2, 14, 1, 3),
(0, "male", 22, 4, "yes", 1, 18, 5, 4),
(0, "male", 37, 15, "yes", 4, 17, 5, 3),
(0, "female", 22, 1.5, "no", 2, 14, 4, 5),
(0, "male", 52, 15, "yes", 4, 14, 6, 2),
(0, "female", 22, 1.5, "no", 4, 17, 5, 5),
(0, "male", 32, 4, "yes", 5, 14, 3, 5),
(0, "male", 32, 4, "yes", 2, 14, 3, 5),
(0, "female", 22, 1.5, "no", 3, 16, 6, 5),
(0, "male", 27, 0.75, "no", 2, 18, 3, 3),
(0, "female", 22, 7, "yes", 2, 14, 5, 2),
(0, "female", 27, 0.75, "no", 2, 17, 5, 3),
(0, "female", 37, 15, "yes", 4, 12, 1, 2),
(0, "female", 22, 1.5, "no", 1, 14, 1, 5),
(0, "female", 37, 10, "no", 2, 12, 4, 4),
(0, "female", 37, 15, "yes", 4, 18, 5, 3),
(0, "female", 42, 15, "yes", 3, 12, 3, 3),
(0, "male", 22, 4, "no", 2, 18, 5, 5),
(0, "male", 52, 7, "yes", 2, 20, 6, 2),
(0, "male", 27, 0.75, "no", 2, 17, 5, 5),
(0, "female", 27, 4, "no", 2, 17, 4, 5),
(0, "male", 42, 1.5, "no", 5, 20, 6, 5),
(0, "male", 22, 1.5, "no", 4, 17, 6, 5),
(0, "male", 22, 4, "no", 4, 17, 5, 3),
(0, "female", 22, 4, "yes", 1, 14, 5, 4),
(0, "male", 37, 15, "yes", 5, 20, 4, 5),
(0, "female", 37, 10, "yes", 3, 16, 6, 3),
(0, "male", 42, 15, "yes", 4, 17, 6, 5),
(0, "female", 47, 15, "yes", 4, 17, 5, 5),
(0, "male", 22, 1.5, "no", 4, 16, 5, 4),
(0, "female", 32, 10, "yes", 3, 12, 1, 4),
(0, "female", 22, 7, "yes", 1, 14, 3, 5),
(0, "female", 32, 10, "yes", 4, 17, 5, 4),
(0, "male", 27, 1.5, "yes", 2, 16, 2, 4),
(0, "male", 37, 15, "yes", 4, 14, 5, 5),
(0, "male", 42, 4, "yes", 3, 14, 4, 5),
(0, "female", 37, 15, "yes", 5, 14, 5, 4),
(0, "female", 32, 7, "yes", 4, 17, 5, 5),
(0, "female", 42, 15, "yes", 4, 18, 6, 5),
(0, "male", 27, 4, "no", 4, 18, 6, 4),
(0, "male", 22, 0.75, "no", 4, 18, 6, 5),
(0, "male", 27, 4, "yes", 4, 14, 5, 3),
(0, "female", 22, 0.75, "no", 5, 18, 1, 5),
(0, "female", 52, 15, "yes", 5, 9, 5, 5),
(0, "male", 32, 10, "yes", 3, 14, 5, 5),
(0, "female", 37, 15, "yes", 4, 16, 4, 4),
(0, "male", 32, 7, "yes", 2, 20, 5, 4),
(0, "female", 42, 15, "yes", 3, 18, 1, 4),
(0, "male", 32, 15, "yes", 1, 16, 5, 5),
(0, "male", 27, 4, "yes", 3, 18, 5, 5),
(0, "female", 32, 15, "yes", 4, 12, 3, 4),
(0, "male", 22, 0.75, "yes", 3, 14, 2, 4),
(0, "female", 22, 1.5, "no", 3, 16, 5, 3),
(0, "female", 42, 15, "yes", 4, 14, 3, 5),
(0, "female", 52, 15, "yes", 3, 16, 5, 4),
(0, "male", 37, 15, "yes", 5, 20, 6, 4),
(0, "female", 47, 15, "yes", 4, 12, 2, 3),
(0, "male", 57, 15, "yes", 2, 20, 6, 4),
(0, "male", 32, 7, "yes", 4, 17, 5, 5),
(0, "female", 27, 7, "yes", 4, 17, 1, 4),
(0, "male", 22, 1.5, "no", 1, 18, 6, 5),
(0, "female", 22, 4, "yes", 3, 9, 1, 4),
(0, "female", 22, 1.5, "no", 2, 14, 1, 5),
(0, "male", 42, 15, "yes", 2, 20, 6, 4),
(0, "male", 57, 15, "yes", 4, 9, 2, 4),
(0, "female", 27, 7, "yes", 2, 18, 1, 5),
(0, "female", 22, 4, "yes", 3, 14, 1, 5),
(0, "male", 37, 15, "yes", 4, 14, 5, 3),
(0, "male", 32, 7, "yes", 1, 18, 6, 4),
(0, "female", 22, 1.5, "no", 2, 14, 5, 5),
(0, "female", 22, 1.5, "yes", 3, 12, 1, 3),
(0, "male", 52, 15, "yes", 2, 14, 5, 5),
(0, "female", 37, 15, "yes", 2, 14, 1, 1),
(0, "female", 32, 10, "yes", 2, 14, 5, 5),
(0, "male", 42, 15, "yes", 4, 20, 4, 5),
(0, "female", 27, 4, "yes", 3, 18, 4, 5),
(0, "male", 37, 15, "yes", 4, 20, 6, 5),
(0, "male", 27, 1.5, "no", 3, 18, 5, 5),
(0, "female", 22, 0.125, "no", 2, 16, 6, 3),
(0, "male", 32, 10, "yes", 2, 20, 6, 3),
(0, "female", 27, 4, "no", 4, 18, 5, 4),
(0, "female", 27, 7, "yes", 2, 12, 5, 1),
(0, "male", 32, 4, "yes", 5, 18, 6, 3),
(0, "female", 37, 15, "yes", 2, 17, 5, 5),
(0, "male", 47, 15, "no", 4, 20, 6, 4),
(0, "male", 27, 1.5, "no", 1, 18, 5, 5),
(0, "male", 37, 15, "yes", 4, 20, 6, 4),
(0, "female", 32, 15, "yes", 4, 18, 1, 4),
(0, "female", 32, 7, "yes", 4, 17, 5, 4),
(0, "female", 42, 15, "yes", 3, 14, 1, 3),
(0, "female", 27, 7, "yes", 3, 16, 1, 4),
(0, "male", 27, 1.5, "no", 3, 16, 4, 2),
(0, "male", 22, 1.5, "no", 3, 16, 3, 5),
(0, "male", 27, 4, "yes", 3, 16, 4, 2),
(0, "female", 27, 7, "yes", 3, 12, 1, 2),
(0, "female", 37, 15, "yes", 2, 18, 5, 4),
(0, "female", 37, 7, "yes", 3, 14, 4, 4),
(0, "male", 22, 1.5, "no", 2, 16, 5, 5),
(0, "male", 37, 15, "yes", 5, 20, 5, 4),
(0, "female", 22, 1.5, "no", 4, 16, 5, 3),
(0, "female", 32, 10, "yes", 4, 16, 1, 5),
(0, "male", 27, 4, "no", 2, 17, 5, 3),
(0, "female", 22, 0.417, "no", 4, 14, 5, 5),
(0, "female", 27, 4, "no", 2, 18, 5, 5),
(0, "male", 37, 15, "yes", 4, 18, 5, 3),
(0, "male", 37, 10, "yes", 5, 20, 7, 4),
(0, "female", 27, 7, "yes", 2, 14, 4, 2),
(0, "male", 32, 4, "yes", 2, 16, 5, 5),
(0, "male", 32, 4, "yes", 2, 16, 6, 4),
(0, "male", 22, 1.5, "no", 3, 18, 4, 5),
(0, "female", 22, 4, "yes", 4, 14, 3, 4),
(0, "female", 17.5, 0.75, "no", 2, 18, 5, 4),
(0, "male", 32, 10, "yes", 4, 20, 4, 5),
(0, "female", 32, 0.75, "no", 5, 14, 3, 3),
(0, "male", 37, 15, "yes", 4, 17, 5, 3),
(0, "male", 32, 4, "no", 3, 14, 4, 5),
(0, "female", 27, 1.5, "no", 2, 17, 3, 2),
(0, "female", 22, 7, "yes", 4, 14, 1, 5),
(0, "male", 47, 15, "yes", 5, 14, 6, 5),
(0, "male", 27, 4, "yes", 1, 16, 4, 4),
(0, "female", 37, 15, "yes", 5, 14, 1, 3),
(0, "male", 42, 4, "yes", 4, 18, 5, 5),
(0, "female", 32, 4, "yes", 2, 14, 1, 5),
(0, "male", 52, 15, "yes", 2, 14, 7, 4),
(0, "female", 22, 1.5, "no", 2, 16, 1, 4),
(0, "male", 52, 15, "yes", 4, 12, 2, 4),
(0, "female", 22, 0.417, "no", 3, 17, 1, 5),
(0, "female", 22, 1.5, "no", 2, 16, 5, 5),
(0, "male", 27, 4, "yes", 4, 20, 6, 4),
(0, "female", 32, 15, "yes", 4, 14, 1, 5),
(0, "female", 27, 1.5, "no", 2, 16, 3, 5),
(0, "male", 32, 4, "no", 1, 20, 6, 5),
(0, "male", 37, 15, "yes", 3, 20, 6, 4),
(0, "female", 32, 10, "no", 2, 16, 6, 5),
(0, "female", 32, 10, "yes", 5, 14, 5, 5),
(0, "male", 37, 1.5, "yes", 4, 18, 5, 3),
(0, "male", 32, 1.5, "no", 2, 18, 4, 4),
(0, "female", 32, 10, "yes", 4, 14, 1, 4),
(0, "female", 47, 15, "yes", 4, 18, 5, 4),
(0, "female", 27, 10, "yes", 5, 12, 1, 5),
(0, "male", 27, 4, "yes", 3, 16, 4, 5),
(0, "female", 37, 15, "yes", 4, 12, 4, 2),
(0, "female", 27, 0.75, "no", 4, 16, 5, 5),
(0, "female", 37, 15, "yes", 4, 16, 1, 5),
(0, "female", 32, 15, "yes", 3, 16, 1, 5),
(0, "female", 27, 10, "yes", 2, 16, 1, 5),
(0, "male", 27, 7, "no", 2, 20, 6, 5),
(0, "female", 37, 15, "yes", 2, 14, 1, 3),
(0, "male", 27, 1.5, "yes", 2, 17, 4, 4),
(0, "female", 22, 0.75, "yes", 2, 14, 1, 5),
(0, "male", 22, 4, "yes", 4, 14, 2, 4),
(0, "male", 42, 0.125, "no", 4, 17, 6, 4),
(0, "male", 27, 1.5, "yes", 4, 18, 6, 5),
(0, "male", 27, 7, "yes", 3, 16, 6, 3),
(0, "female", 52, 15, "yes", 4, 14, 1, 3),
(0, "male", 27, 1.5, "no", 5, 20, 5, 2),
(0, "female", 27, 1.5, "no", 2, 16, 5, 5),
(0, "female", 27, 1.5, "no", 3, 17, 5, 5),
(0, "male", 22, 0.125, "no", 5, 16, 4, 4),
(0, "female", 27, 4, "yes", 4, 16, 1, 5),
(0, "female", 27, 4, "yes", 4, 12, 1, 5),
(0, "female", 47, 15, "yes", 2, 14, 5, 5),
(0, "female", 32, 15, "yes", 3, 14, 5, 3),
(0, "male", 42, 7, "yes", 2, 16, 5, 5),
(0, "male", 22, 0.75, "no", 4, 16, 6, 4),
(0, "male", 27, 0.125, "no", 3, 20, 6, 5),
(0, "male", 32, 10, "yes", 3, 20, 6, 5),
(0, "female", 22, 0.417, "no", 5, 14, 4, 5),
(0, "female", 47, 15, "yes", 5, 14, 1, 4),
(0, "female", 32, 10, "yes", 3, 14, 1, 5),
(0, "male", 57, 15, "yes", 4, 17, 5, 5),
(0, "male", 27, 4, "yes", 3, 20, 6, 5),
(0, "female", 32, 7, "yes", 4, 17, 1, 5),
(0, "female", 37, 10, "yes", 4, 16, 1, 5),
(0, "female", 32, 10, "yes", 1, 18, 1, 4),
(0, "female", 22, 4, "no", 3, 14, 1, 4),
(0, "female", 27, 7, "yes", 4, 14, 3, 2),
(0, "male", 57, 15, "yes", 5, 18, 5, 2),
(0, "male", 32, 7, "yes", 2, 18, 5, 5),
(0, "female", 27, 1.5, "no", 4, 17, 1, 3),
(0, "male", 22, 1.5, "no", 4, 14, 5, 5),
(0, "female", 22, 1.5, "yes", 4, 14, 5, 4),
(0, "female", 32, 7, "yes", 3, 16, 1, 5),
(0, "female", 47, 15, "yes", 3, 16, 5, 4),
(0, "female", 22, 0.75, "no", 3, 16, 1, 5),
(0, "female", 22, 1.5, "yes", 2, 14, 5, 5),
(0, "female", 27, 4, "yes", 1, 16, 5, 5),
(0, "male", 52, 15, "yes", 4, 16, 5, 5),
(0, "male", 32, 10, "yes", 4, 20, 6, 5),
(0, "male", 47, 15, "yes", 4, 16, 6, 4),
(0, "female", 27, 7, "yes", 2, 14, 1, 2),
(0, "female", 22, 1.5, "no", 4, 14, 4, 5),
(0, "female", 32, 10, "yes", 2, 16, 5, 4),
(0, "female", 22, 0.75, "no", 2, 16, 5, 4),
(0, "female", 22, 1.5, "no", 2, 16, 5, 5),
(0, "female", 42, 15, "yes", 3, 18, 6, 4),
(0, "female", 27, 7, "yes", 5, 14, 4, 5),
(0, "male", 42, 15, "yes", 4, 16, 4, 4),
(0, "female", 57, 15, "yes", 3, 18, 5, 2),
(0, "male", 42, 15, "yes", 3, 18, 6, 2),
(0, "female", 32, 7, "yes", 2, 14, 1, 2),
(0, "male", 22, 4, "no", 5, 12, 4, 5),
(0, "female", 22, 1.5, "no", 1, 16, 6, 5),
(0, "female", 22, 0.75, "no", 1, 14, 4, 5),
(0, "female", 32, 15, "yes", 4, 12, 1, 5),
(0, "male", 22, 1.5, "no", 2, 18, 5, 3),
(0, "male", 27, 4, "yes", 5, 17, 2, 5),
(0, "female", 27, 4, "yes", 4, 12, 1, 5),
(0, "male", 42, 15, "yes", 5, 18, 5, 4),
(0, "male", 32, 1.5, "no", 2, 20, 7, 3),
(0, "male", 57, 15, "no", 4, 9, 3, 1),
(0, "male", 37, 7, "no", 4, 18, 5, 5),
(0, "male", 52, 15, "yes", 2, 17, 5, 4),
(0, "male", 47, 15, "yes", 4, 17, 6, 5),
(0, "female", 27, 7, "no", 2, 17, 5, 4),
(0, "female", 27, 7, "yes", 4, 14, 5, 5),
(0, "female", 22, 4, "no", 2, 14, 3, 3),
(0, "male", 37, 7, "yes", 2, 20, 6, 5),
(0, "male", 27, 7, "no", 4, 12, 4, 3),
(0, "male", 42, 10, "yes", 4, 18, 6, 4),
(0, "female", 22, 1.5, "no", 3, 14, 1, 5),
(0, "female", 22, 4, "yes", 2, 14, 1, 3),
(0, "female", 57, 15, "no", 4, 20, 6, 5),
(0, "male", 37, 15, "yes", 4, 14, 4, 3),
(0, "female", 27, 7, "yes", 3, 18, 5, 5),
(0, "female", 17.5, 10, "no", 4, 14, 4, 5),
(0, "male", 22, 4, "yes", 4, 16, 5, 5),
(0, "female", 27, 4, "yes", 2, 16, 1, 4),
(0, "female", 37, 15, "yes", 2, 14, 5, 1),
(0, "female", 22, 1.5, "no", 5, 14, 1, 4),
(0, "male", 27, 7, "yes", 2, 20, 5, 4),
(0, "male", 27, 4, "yes", 4, 14, 5, 5),
(0, "male", 22, 0.125, "no", 1, 16, 3, 5),
(0, "female", 27, 7, "yes", 4, 14, 1, 4),
(0, "female", 32, 15, "yes", 5, 16, 5, 3),
(0, "male", 32, 10, "yes", 4, 18, 5, 4),
(0, "female", 32, 15, "yes", 2, 14, 3, 4),
(0, "female", 22, 1.5, "no", 3, 17, 5, 5),
(0, "male", 27, 4, "yes", 4, 17, 4, 4),
(0, "female", 52, 15, "yes", 5, 14, 1, 5),
(0, "female", 27, 7, "yes", 2, 12, 1, 2),
(0, "female", 27, 7, "yes", 3, 12, 1, 4),
(0, "female", 42, 15, "yes", 2, 14, 1, 4),
(0, "female", 42, 15, "yes", 4, 14, 5, 4),
(0, "male", 27, 7, "yes", 4, 14, 3, 3),
(0, "male", 27, 7, "yes", 2, 20, 6, 2),
(0, "female", 42, 15, "yes", 3, 12, 3, 3),
(0, "male", 27, 4, "yes", 3, 16, 3, 5),
(0, "female", 27, 7, "yes", 3, 14, 1, 4),
(0, "female", 22, 1.5, "no", 2, 14, 4, 5),
(0, "female", 27, 4, "yes", 4, 14, 1, 4),
(0, "female", 22, 4, "no", 4, 14, 5, 5),
(0, "female", 22, 1.5, "no", 2, 16, 4, 5),
(0, "male", 47, 15, "no", 4, 14, 5, 4),
(0, "male", 37, 10, "yes", 2, 18, 6, 2),
(0, "male", 37, 15, "yes", 3, 17, 5, 4),
(0, "female", 27, 4, "yes", 2, 16, 1, 4),
(3, "male", 27, 1.5, "no", 3, 18, 4, 4),
(3, "female", 27, 4, "yes", 3, 17, 1, 5),
(7, "male", 37, 15, "yes", 5, 18, 6, 2),
(12, "female", 32, 10, "yes", 3, 17, 5, 2),
(1, "male", 22, 0.125, "no", 4, 16, 5, 5),
(1, "female", 22, 1.5, "yes", 2, 14, 1, 5),
(12, "male", 37, 15, "yes", 4, 14, 5, 2),
(7, "female", 22, 1.5, "no", 2, 14, 3, 4),
(2, "male", 37, 15, "yes", 2, 18, 6, 4),
(3, "female", 32, 15, "yes", 4, 12, 3, 2),
(1, "female", 37, 15, "yes", 4, 14, 4, 2),
(7, "female", 42, 15, "yes", 3, 17, 1, 4),
(12, "female", 42, 15, "yes", 5, 9, 4, 1),
(12, "male", 37, 10, "yes", 2, 20, 6, 2),
(12, "female", 32, 15, "yes", 3, 14, 1, 2),
(3, "male", 27, 4, "no", 1, 18, 6, 5),
(7, "male", 37, 10, "yes", 2, 18, 7, 3),
(7, "female", 27, 4, "no", 3, 17, 5, 5),
(1, "male", 42, 15, "yes", 4, 16, 5, 5),
(1, "female", 47, 15, "yes", 5, 14, 4, 5),
(7, "female", 27, 4, "yes", 3, 18, 5, 4),
(1, "female", 27, 7, "yes", 5, 14, 1, 4),
(12, "male", 27, 1.5, "yes", 3, 17, 5, 4),
(12, "female", 27, 7, "yes", 4, 14, 6, 2),
(3, "female", 42, 15, "yes", 4, 16, 5, 4),
(7, "female", 27, 10, "yes", 4, 12, 7, 3),
(1, "male", 27, 1.5, "no", 2, 18, 5, 2),
(1, "male", 32, 4, "no", 4, 20, 6, 4),
(1, "female", 27, 7, "yes", 3, 14, 1, 3),
(3, "female", 32, 10, "yes", 4, 14, 1, 4),
(3, "male", 27, 4, "yes", 2, 18, 7, 2),
(1, "female", 17.5, 0.75, "no", 5, 14, 4, 5),
(1, "female", 32, 10, "yes", 4, 18, 1, 5),
(7, "female", 32, 7, "yes", 2, 17, 6, 4),
(7, "male", 37, 15, "yes", 2, 20, 6, 4),
(7, "female", 37, 10, "no", 1, 20, 5, 3),
(12, "female", 32, 10, "yes", 2, 16, 5, 5),
(7, "male", 52, 15, "yes", 2, 20, 6, 4),
(7, "female", 42, 15, "yes", 1, 12, 1, 3),
(1, "male", 52, 15, "yes", 2, 20, 6, 3),
(2, "male", 37, 15, "yes", 3, 18, 6, 5),
(12, "female", 22, 4, "no", 3, 12, 3, 4),
(12, "male", 27, 7, "yes", 1, 18, 6, 2),
(1, "male", 27, 4, "yes", 3, 18, 5, 5),
(12, "male", 47, 15, "yes", 4, 17, 6, 5),
(12, "female", 42, 15, "yes", 4, 12, 1, 1),
(7, "male", 27, 4, "no", 3, 14, 3, 4),
(7, "female", 32, 7, "yes", 4, 18, 4, 5),
(1, "male", 32, 0.417, "yes", 3, 12, 3, 4),
(3, "male", 47, 15, "yes", 5, 16, 5, 4),
(12, "male", 37, 15, "yes", 2, 20, 5, 4),
(7, "male", 22, 4, "yes", 2, 17, 6, 4),
(1, "male", 27, 4, "no", 2, 14, 4, 5),
(7, "female", 52, 15, "yes", 5, 16, 1, 3),
(1, "male", 27, 4, "no", 3, 14, 3, 3),
(1, "female", 27, 10, "yes", 4, 16, 1, 4),
(1, "male", 32, 7, "yes", 3, 14, 7, 4),
(7, "male", 32, 7, "yes", 2, 18, 4, 1),
(3, "male", 22, 1.5, "no", 1, 14, 3, 2),
(7, "male", 22, 4, "yes", 3, 18, 6, 4),
(7, "male", 42, 15, "yes", 4, 20, 6, 4),
(2, "female", 57, 15, "yes", 1, 18, 5, 4),
(7, "female", 32, 4, "yes", 3, 18, 5, 2),
(1, "male", 27, 4, "yes", 1, 16, 4, 4),
(7, "male", 32, 7, "yes", 4, 16, 1, 4),
(2, "male", 57, 15, "yes", 1, 17, 4, 4),
(7, "female", 42, 15, "yes", 4, 14, 5, 2),
(7, "male", 37, 10, "yes", 1, 18, 5, 3),
(3, "male", 42, 15, "yes", 3, 17, 6, 1),
(1, "female", 52, 15, "yes", 3, 14, 4, 4),
(2, "female", 27, 7, "yes", 3, 17, 5, 3),
(12, "male", 32, 7, "yes", 2, 12, 4, 2),
(1, "male", 22, 4, "no", 4, 14, 2, 5),
(3, "male", 27, 7, "yes", 3, 18, 6, 4),
(12, "female", 37, 15, "yes", 1, 18, 5, 5),
(7, "female", 32, 15, "yes", 3, 17, 1, 3),
(7, "female", 27, 7, "no", 2, 17, 5, 5),
(1, "female", 32, 7, "yes", 3, 17, 5, 3),
(1, "male", 32, 1.5, "yes", 2, 14, 2, 4),
(12, "female", 42, 15, "yes", 4, 14, 1, 2),
(7, "male", 32, 10, "yes", 3, 14, 5, 4),
(7, "male", 37, 4, "yes", 1, 20, 6, 3),
(1, "female", 27, 4, "yes", 2, 16, 5, 3),
(12, "female", 42, 15, "yes", 3, 14, 4, 3),
(1, "male", 27, 10, "yes", 5, 20, 6, 5),
(12, "male", 37, 10, "yes", 2, 20, 6, 2),
(12, "female", 27, 7, "yes", 1, 14, 3, 3),
(3, "female", 27, 7, "yes", 4, 12, 1, 2),
(3, "male", 32, 10, "yes", 2, 14, 4, 4),
(12, "female", 17.5, 0.75, "yes", 2, 12, 1, 3),
(12, "female", 32, 15, "yes", 3, 18, 5, 4),
(2, "female", 22, 7, "no", 4, 14, 4, 3),
(1, "male", 32, 7, "yes", 4, 20, 6, 5),
(7, "male", 27, 4, "yes", 2, 18, 6, 2),
(1, "female", 22, 1.5, "yes", 5, 14, 5, 3),
(12, "female", 32, 15, "no", 3, 17, 5, 1),
(12, "female", 42, 15, "yes", 2, 12, 1, 2),
(7, "male", 42, 15, "yes", 3, 20, 5, 4),
(12, "male", 32, 10, "no", 2, 18, 4, 2),
(12, "female", 32, 15, "yes", 3, 9, 1, 1),
(7, "male", 57, 15, "yes", 5, 20, 4, 5),
(12, "male", 47, 15, "yes", 4, 20, 6, 4),
(2, "female", 42, 15, "yes", 2, 17, 6, 3),
(12, "male", 37, 15, "yes", 3, 17, 6, 3),
(12, "male", 37, 15, "yes", 5, 17, 5, 2),
(7, "male", 27, 10, "yes", 2, 20, 6, 4),
(2, "male", 37, 15, "yes", 2, 16, 5, 4),
(12, "female", 32, 15, "yes", 1, 14, 5, 2),
(7, "male", 32, 10, "yes", 3, 17, 6, 3),
(2, "male", 37, 15, "yes", 4, 18, 5, 1),
(7, "female", 27, 1.5, "no", 2, 17, 5, 5),
(3, "female", 47, 15, "yes", 2, 17, 5, 2),
(12, "male", 37, 15, "yes", 2, 17, 5, 4),
(12, "female", 27, 4, "no", 2, 14, 5, 5),
(2, "female", 27, 10, "yes", 4, 14, 1, 5),
(1, "female", 22, 4, "yes", 3, 16, 1, 3),
(12, "male", 52, 7, "no", 4, 16, 5, 5),
(2, "female", 27, 4, "yes", 1, 16, 3, 5),
(7, "female", 37, 15, "yes", 2, 17, 6, 4),
(2, "female", 27, 4, "no", 1, 17, 3, 1),
(12, "female", 17.5, 0.75, "yes", 2, 12, 3, 5),
(7, "female", 32, 15, "yes", 5, 18, 5, 4),
(7, "female", 22, 4, "no", 1, 16, 3, 5),
(2, "male", 32, 4, "yes", 4, 18, 6, 4),
(1, "female", 22, 1.5, "yes", 3, 18, 5, 2),
(3, "female", 42, 15, "yes", 2, 17, 5, 4),
(1, "male", 32, 7, "yes", 4, 16, 4, 4),
(12, "male", 37, 15, "no", 3, 14, 6, 2),
(1, "male", 42, 15, "yes", 3, 16, 6, 3),
(1, "male", 27, 4, "yes", 1, 18, 5, 4),
(2, "male", 37, 15, "yes", 4, 20, 7, 3),
(7, "male", 37, 15, "yes", 3, 20, 6, 4),
(3, "male", 22, 1.5, "no", 2, 12, 3, 3),
(3, "male", 32, 4, "yes", 3, 20, 6, 2),
(2, "male", 32, 15, "yes", 5, 20, 6, 5),
(12, "female", 52, 15, "yes", 1, 18, 5, 5),
(12, "male", 47, 15, "no", 1, 18, 6, 5),
(3, "female", 32, 15, "yes", 4, 16, 4, 4),
(7, "female", 32, 15, "yes", 3, 14, 3, 2),
(7, "female", 27, 7, "yes", 4, 16, 1, 2),
(12, "male", 42, 15, "yes", 3, 18, 6, 2),
(7, "female", 42, 15, "yes", 2, 14, 3, 2),
(12, "male", 27, 7, "yes", 2, 17, 5, 4),
(3, "male", 32, 10, "yes", 4, 14, 4, 3),
(7, "male", 47, 15, "yes", 3, 16, 4, 2),
(1, "male", 22, 1.5, "yes", 1, 12, 2, 5),
(7, "female", 32, 10, "yes", 2, 18, 5, 4),
(2, "male", 32, 10, "yes", 2, 17, 6, 5),
(2, "male", 22, 7, "yes", 3, 18, 6, 2),
(1, "female", 32, 15, "yes", 3, 14, 1, 5))
3.定义列名
val colArray: Array[String] = Array("affairs", "gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")
val data = dataList.toDF(colArray:_*)
data.printSchema()
root
|-- affairs: double (nullable = false)
|-- gender: string (nullable = true)
|-- age: double (nullable = false)
|-- yearsmarried: double (nullable = false)
|-- children: string (nullable = true)
|-- religiousness: double (nullable = false)
|-- education: double (nullable = false)
|-- occupation: double (nullable = false)
|-- rating: double (nullable = false)
data.show(10)
+-------+------+----+------------+--------+-------------+---------+----------+------+
|affairs|gender| age|yearsmarried|children|religiousness|education|occupation|rating|
+-------+------+----+------------+--------+-------------+---------+----------+------+
| 0.0| male|37.0| 10.0| no| 3.0| 18.0| 7.0| 4.0|
| 0.0|female|27.0| 4.0| no| 4.0| 14.0| 6.0| 4.0|
| 0.0|female|32.0| 15.0| yes| 1.0| 12.0| 1.0| 4.0|
| 0.0| male|57.0| 15.0| yes| 5.0| 18.0| 6.0| 5.0|
| 0.0| male|22.0| 0.75| no| 2.0| 17.0| 6.0| 3.0|
| 0.0|female|32.0| 1.5| no| 2.0| 17.0| 5.0| 5.0|
| 0.0|female|22.0| 0.75| no| 2.0| 12.0| 1.0| 3.0|
| 0.0| male|57.0| 15.0| yes| 2.0| 14.0| 4.0| 4.0|
| 0.0|female|32.0| 15.0| yes| 4.0| 16.0| 1.0| 2.0|
| 0.0| male|22.0| 1.5| no| 4.0| 14.0| 4.0| 5.0|
+-------+------+----+------------+--------+-------------+---------+----------+------+
only showing top 10 rows
4.查看数据的统计分布情况
val descrDF = data.describe(colArray:_*) descrDF.printSchema()
root
|-- summary: string (nullable = true)
|-- affairs: string (nullable = true)
|-- gender: string (nullable = true)
|-- age: string (nullable = true)
|-- yearsmarried: string (nullable = true)
|-- children: string (nullable = true)
|-- religiousness: string (nullable = true)
|-- education: string (nullable = true)
|-- occupation: string (nullable = true)
|-- rating: string (nullable = true) descrDF.selectExpr("summary",
"round(affairs,2) as affairs",
"round(age,2) as age",
"round(yearsmarried,2) as yearsmarried",
"children",
"round(religiousness,2) as religiousness",
"round(education,2) as education",
"round(occupation,2) as occupation",
"round(rating,2) as rating").show(10, truncate = false)
+-------+-------+-----+------------+--------+-------------+---------+----------+------+
|summary|affairs|age |yearsmarried|children|religiousness|education|occupation|rating|
+-------+-------+-----+------------+--------+-------------+---------+----------+------+
|count |601.0 |601.0|601.0 |601 |601.0 |601.0 |601.0 |601.0 |
|mean |1.46 |32.49|8.18 |null |3.12 |16.17 |4.19 |3.93 |
|stddev |3.3 |9.29 |5.57 |null |1.17 |2.4 |1.82 |1.1 |
|min |0.0 |17.5 |0.13 |no |1.0 |9.0 |1.0 |1.0 |
|max |12.0 |57.0 |15.0 |yes |5.0 |20.0 |7.0 |5.0 |
+-------+-------+-----+------------+--------+-------------+---------+----------+------+
Spark ML包，数据挖掘示例数据Affairs的更多相关文章
- Spark ML源码分析之三 分类器
前面跟大家扯了这么多废话,终于到具体的机器学习模型了.大部分机器学习的教程,总要从监督学习开始讲起,而监督学习的众多算法当中,又以分类算法最为基础,原因在于分类问题非常的单纯直接,几乎 ...
- Spark ML机器学习库评估指标示例
本文主要对 Spark ML库下模型评估指标的讲解,以下代码均以Jupyter Notebook进行讲解,Spark版本为2.4.5.模型评估指标位于包org.apache.spark.ml.eval ...
- Spark2 ML包之决策树分类Decision tree classifier详细解说
所用数据源,请参考本人博客http://www.cnblogs.com/wwxbi/p/6063613.html 1.导入包 import org.apache.spark.sql.SparkSess ...
- Spark ML机器学习
Spark提供了常用机器学习算法的实现, 封装于spark.ml和spark.mllib中. spark.mllib是基于RDD的机器学习库, spark.ml是基于DataFrame的机器学习库. ...
- Spark ML下实现的多分类adaboost+naivebayes算法在文本分类上的应用
1. Naive Bayes算法 朴素贝叶斯算法算是生成模型中一个最经典的分类算法之一了,常用的有Bernoulli和Multinomial两种.在文本分类上经常会用到这两种方法.在词袋模型中,对于一 ...
- Spark ML 几种 归一化(规范化)方法总结
规范化,有关之前都是用 python写的, 偶然要用scala 进行写, 看到这位大神写的, 那个网页也不错,那个连接图做的还蛮不错的,那天也将自己的博客弄一下那个插件. 本文来源 原文地址:htt ...
- Spark ML Pipeline简介
Spark ML Pipeline基于DataFrame构建了一套High-level API,我们可以使用MLPipeline构建机器学习应用,它能够将一个机器学习应用的多个处理过程组织起来,通过在 ...
- Spark ML源码分析之一 设计框架解读
本博客为作者原创,如需转载请注明参考 在深入理解Spark ML中的各类算法之前,先理一下整个库的设计框架,是非常有必要的,优秀的框架是对复杂问题的抽象和解剖,对这种抽象的学习本身 ...
- Spark ML源码分析之二 从单机到分布式
前一节从宏观角度给大家介绍了Spark ML的设计框架(链接:http://www.cnblogs.com/jicanghai/p/8570805.html),本节我们将介绍,Spar ...
随机推荐
- notepad++ 文本替换功能，解决excel批量修改数据库的数据操作
^ \(' 开始\t ',' 制表符$ '\) 行末 复制出来之后,使用文本替换功能,转换为SQL,添加到临时表中.然后多表关联修改. SELECT COUNT(*) FROM orderno; # ...
- QT基础：QT 定时器学习
定时器在编程中经常要用到,有必要学习一下,记记笔记! Qt中定时器的使用有两种方法,一种是使用QObject类提供的定时器,还有一种就是使用QTimer类. 1.QObject中的定时器的使用,需要用 ...
- Bootstrap——导航居中
这是采用了栅格,设置缩进,使看起来居中,但是手机浏览会靠到最左边.另外center-block类好像也不管用. <div class="row"> <ul cla ...
- 深入理解css3中的flex-grow、flex-shrink、flex-basis
https://www.cnblogs.com/ghfjj/p/6529733.html
- OpenSift源代码编译过程记录
本文记录了在CentOS6.5上编译Sift的开源实现OpenSift的编译过程,同一时候记录了编译过程中的几个问题. sift的理论已经有非常多了,以下会给出链接: 1.Requirements a ...
- iOS Ad hoc
There's one situation in which you need an Ad Hoc profile, and that's when you want to test Push Not ...
- MySQL 安装与配置
Linux 安装 MySQL Windows 安装 MySQL 如何连接 MySQL 如何修改 MySQL 密码 如何重置 MySQL 密码
- react native (一)
开始接触app方面的工作,真心塞~ 又开始了周而复始的死磕一个问题专坐一整天的节奏,关键是还没有成绩,实在无语.╮(╯▽╰)╭,还是总结一下最近心塞历程吧-- react native中文网:http ...
- STL——算法简介
一.算法概观 以有限的步骤,解决逻辑或数学上的问题,这一专门科目我们称为算法.特定的算法往往搭配特定的数据结构,例如binary search tree(二叉搜索树)和 RB-tree 便是为了解决查 ...
- codeforces水题100道 第九题 Codeforces Beta Round #63 (Div. 2) Young Physicist (math)
题目链接:http://www.codeforces.com/problemset/problem/69/A题意:给你n个三维空间矢量,求这n个矢量的矢量和是否为零.C++代码: #include & ...