spark算法实现——svm支持向量机

package mllib

import org.apache.spark.mllib.classification.{SVMModel, SVMWithSGD}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SQLContext
import org.apache.spark.{SparkContext, SparkConf}


//二分类
object SVMwithSGD {
    def main(args: Array[String]) {
        val conf = new SparkConf().setAppName("test").setMaster("local")
        val sc = new SparkContext(conf)
        val sql  = new SQLContext(sc);
        val data: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc, "svm.txt")
        val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
        val training = splits(0).cache()
        val test = splits(1)
//        data.foreach( x => println(x.toString()))
//        data.foreach( x => println(x.label))
        data.foreach( x => println(x.features))
        val numIterations = 100
        val model: SVMModel = SVMWithSGD.train(training, numIterations)
        model.clearThreshold()//为了模型拿到评分 不是处理过之后的分类结果

        val scoreAndLabels: RDD[(Double, Double)] = test.map { point =>
//                大于0 小于0 两类
            val score = model.predict(point.features)
            (score, point.label)
        }
        scoreAndLabels.foreach(println)

    }

}