SVM实例

本文主要是介绍SVM实例，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

数据源：R自带的iris数据（R的e1071包没装下来，so用Scala写了；鸢尾花(iris)是数据挖掘常用到的一个数据集，包含150种鸢尾花的信息，每50种取自三个鸢尾花种之一（setosa,versicolour或virginica)。每个花的特征用下面的5种属性描述萼片长度(Sepal.Length)、萼片宽度(Sepal.Width)、花瓣长度(Petal.Length)、花瓣宽度(Petal.Width)、类(Species)。）；

spark mlib代码：

 def svmTest(sc:SparkContext,sqlContext:SQLContext): Unit ={import org.apache.spark.mllib.classification.SVMWithSGDimport org.apache.spark.mllib.regression.LabeledPointimport sqlContext.implicits._import breeze.linalg._// Load and parse the data file
    val data = sc.textFile("file:///D://cs3.txt")val trainData = data.map { line =>val parts = line.split(",")val y=parts(0).toDoubleval vd0=Vectors.dense(parts(1).toDouble,parts(2).toDouble,parts(3).toDouble,parts(4).toDouble)val v1=Vectors.dense(-2.0919917512589015,7.089178225784549,5.567376955110936,0.8621925858604499)
//      println(parts(1).toDouble*(-2.0919917512589015)+parts(2).toDouble*(7.089178225784549)+parts(3).toDouble*(5.567376955110936)+parts(4).toDouble*(0.8621925858604499) )
      LabeledPoint(y,vd0)}val testData = sc.textFile("file:///D://cs4.txt").map { line =>val parts = line.split(",")val y=parts(0).toDoubleval vd0=Vectors.dense(parts(1).toDouble,parts(2).toDouble,parts(3).toDouble,parts(4).toDouble)LabeledPoint(y,vd0)}

    // Run training algorithm to build the model
    val numIterations = 20
    val model = SVMWithSGD.train(trainData, numIterations)// Evaluate model on training examples and compute training error
    val trainLabelPreds = trainData.map { point =>val prediction = model.predict(point.features)(point.label, prediction)}println(model.toString())trainLabelPreds.toDF("label","prediction").showval trainErr = trainLabelPreds.filter(r => r._1 != r._2).count.toDouble / trainData.countprintln("Training Error = " + trainErr)// Compute raw scores on the test set.
    val testPredictLabels = testData.map { point =>
//      println("feature="+point.features)
      val score = model.predict(point.features)(score, point.label)}
//    testPredictLabels.collect.foreach(println)
    val testErr = testPredictLabels.filter(r => r._1 != r._2).count.toDouble / testData.countprintln("test Error = " + testErr)}

运行结果（错误率0，准确率100%）：

其实可以加个打印看下wx+b的值（然后发现wx<0的为label=0，大于0的为label=1，b似乎没有？松弛变量的常数C也没见到？）      

println(parts(1).toDouble*(0.4672035760731836)+parts(2).toDouble*(1.6471825085309382)+parts(3).toDouble*(-2.317158274255798)+parts(4).toDouble*(-0.978114663957106) )

附：

traindata 训练集cs3.txt（格式：label标签[只能是0/1]，特征1......特征4）：

1,5.1,3.5,1.4,0.2
1,4.9,3,1.4,0.2
1,4.7,3.2,1.3,0.2
1,4.6,3.1,1.5,0.2
1,5,3.6,1.4,0.2
1,5.4,3.9,1.7,0.4
1,4.6,3.4,1.4,0.3
1,5,3.4,1.5,0.2
1,4.4,2.9,1.4,0.2
1,4.9,3.1,1.5,0.1
1,5.4,3.7,1.5,0.2
1,4.8,3.4,1.6,0.2
1,4.8,3,1.4,0.1
1,4.3,3,1.1,0.1
1,5.8,4,1.2,0.2
1,5.7,4.4,1.5,0.4
1,5.4,3.9,1.3,0.4
1,5.1,3.5,1.4,0.3
1,5.7,3.8,1.7,0.3
1,5.1,3.8,1.5,0.3
1,5.4,3.4,1.7,0.2
1,5.1,3.7,1.5,0.4
1,4.6,3.6,1,0.2
1,5.1,3.3,1.7,0.5
1,4.8,3.4,1.9,0.2
0,7,3.2,4.7,1.4
0,6.4,3.2,4.5,1.5
0,6.9,3.1,4.9,1.5
0,5.5,2.3,4,1.3
0,6.5,2.8,4.6,1.5
0,5.7,2.8,4.5,1.3
0,6.3,3.3,4.7,1.6
0,4.9,2.4,3.3,1
0,6.6,2.9,4.6,1.3
0,5.2,2.7,3.9,1.4
0,5,2,3.5,1
0,5.9,3,4.2,1.5
0,6,2.2,4,1
0,6.1,2.9,4.7,1.4
0,5.6,2.9,3.6,1.3
0,6.7,3.1,4.4,1.4
0,5.6,3,4.5,1.5
0,5.8,2.7,4.1,1
0,6.2,2.2,4.5,1.5
0,5.6,2.5,3.9,1.1
0,5.9,3.2,4.8,1.8
0,6.1,2.8,4,1.3
0,6.3,2.5,4.9,1.5
0,6.1,2.8,4.7,1.2

0,6.4,2.9,4.3,1.3

testdata 测试集(cs4.txt)：

0,6.6,3,4.4,1.4
0,6.8,2.8,4.8,1.4
0,6.7,3,5,1.7
0,6,2.9,4.5,1.5
0,5.7,2.6,3.5,1
0,5.5,2.4,3.8,1.1
0,5.5,2.4,3.7,1
0,5.8,2.7,3.9,1.2
0,6,2.7,5.1,1.6
0,5.4,3,4.5,1.5
0,6,3.4,4.5,1.6
0,6.7,3.1,4.7,1.5
0,6.3,2.3,4.4,1.3
0,5.6,3,4.1,1.3
0,5.5,2.5,4,1.3
0,5.5,2.6,4.4,1.2
0,6.1,3,4.6,1.4
0,5.8,2.6,4,1.2
0,5,2.3,3.3,1
0,5.6,2.7,4.2,1.3
0,5.7,3,4.2,1.2
0,5.7,2.9,4.2,1.3
0,6.2,2.9,4.3,1.3
0,5.1,2.5,3,1.1
0,5.7,2.8,4.1,1.3
1,5,3,1.6,0.2
1,5,3.4,1.6,0.4
1,5.2,3.5,1.5,0.2
1,5.2,3.4,1.4,0.2
1,4.7,3.2,1.6,0.2
1,4.8,3.1,1.6,0.2
1,5.4,3.4,1.5,0.4
1,5.2,4.1,1.5,0.1
1,5.5,4.2,1.4,0.2
1,4.9,3.1,1.5,0.2
1,5,3.2,1.2,0.2
1,5.5,3.5,1.3,0.2
1,4.9,3.6,1.4,0.1
1,4.4,3,1.3,0.2
1,5.1,3.4,1.5,0.2
1,5,3.5,1.3,0.3
1,4.5,2.3,1.3,0.3
1,4.4,3.2,1.3,0.2
1,5,3.5,1.6,0.6
1,5.1,3.8,1.9,0.4
1,4.8,3,1.4,0.3
1,5.1,3.8,1.6,0.2
1,4.6,3.2,1.4,0.2
1,5.3,3.7,1.5,0.2
1,5,3.3,1.4,0.2

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