package com.bigchange.test
import com.bigchange.datamining.CustomNaiveBayes
import scala.collection.mutable
import scala.collection.mutable.ListBuffer
import scala.io.Source
/**
* Created by C.J.YOU on 2016/9/13.
*/
object NaiveBayesTest {
/**
* 数据 :
* feature1: 健康（health）、外表（appearance）、两者皆是（both）
* feature2: 很少运动（sedentary）、一般（moderate）、经常运动（active）
* feature3: 热情是高（aggressive）还是一般（moderate）；
* feature4: 最后，我们会问他是否适应使用高科技产品
*/
def main(args: Array[String]) {
// val Array(dataPath) = args
val data = Source.fromFile("src/main/resources/nbData/i100-i500").getLines().toList
// 可实现打完包后读取jar包中对应文件数据
val data2 = Source.fromInputStream(this.getClass.getResourceAsStream("src/main/resources/nbData/i100-i500")).getLines().toList
// 十折交叉验证(index,List(item1,item2))
val splitData = data.zipWithIndex.map(x => (x._2 % 10,x._1)).groupBy(_._1).mapValues(x => x.map(_._2))
val modelMap = new mutable.HashMap[Int,String]()
val model = CustomNaiveBayes.model(0, splitData)
var list = List((0,model))
for (id <- 1 until 10) {
// 训练
val model = CustomNaiveBayes.model(id, splitData)
list = list ::: List((id,model))
}
// 分类
val listP = new ListBuffer[(String, Double)]
list.foreach(x => {
println("model:" + x)
val pred = CustomNaiveBayes.predict(Array("health", "moderate", "moderate1", "yes"), x._2)
listP.+=(pred)
})
println("tobe:"+ listP)
println("tobe:"+ listP.max)
}
}
