我现在明白我需要枚举我的数据,以便我使用 spark mllib naive bayes 来处理向量。我要处理的数据如下所示。
Male,Suspicion of Alcohol,Weekday,12am-4am,75,30-39
Male,Moving Traffic Violation,Weekday,12am-4am,0,20-24
Male,Suspicion of Alcohol,Weekend,4am-8am,12,40-49
Male,Suspicion of Alcohol,Weekday,12am-4am,0,50-59
Female,Road Traffic Collision,Weekend,12pm-4pm,0,20-24
Male,Road Traffic Collision,Weekday,12pm-4pm,0,25-29
Male,Road Traffic Collision,Weekday,8pm-12pm,0,Other
Male,Other,Weekday,8am-12pm,23,60-69
Male,Moving Traffic Violation,Weekend,12pm-4pm,26,30-39
Female,Road Traffic Collision,Weekend,4am-8am,61,16-19
Male,Moving Traffic Violation,Weekend,4pm-8pm,74,25-29
Male,Road Traffic Collision,Weekday,12am-4am,0,Other
Male,Moving Traffic Violation,Weekday,8pm-12pm,0,16-19
Male,Road Traffic Collision,Weekday,8pm-12pm,0,Other
Male,Moving Traffic Violation,Weekend,4am-8am,0,30-39
幸运的是,这是英国警方的交通违规数据,所有字段都包含一组值,即男性/女性/未知。因此,如果我为每列中的每个数据项分配数值,我最终会得到这样的数据集
0,3 0 0 75 3
0,0 0 0 0 1
0,3 1 1 12 4
0,3 0 0 0 5
1,2 1 3 0 1
0,2 0 3 0 2
0,2 0 5 0 8
0,1 0 2 23 6
0,0 1 3 26 3
1,2 1 1 61 0
0,0 1 4 74 2
0,2 0 0 0 8
0,0 0 5 0 0
0,2 0 5 0 8
0,0 1 1 0 3
我知道我可以直接在 scala 中运行朴素贝叶斯。