scala下串行实现em算法

本人编写的一维em算法

初学em算法时被各种公式吓到，学的过程也不是很顺利（本人数学渣渣），好不容易弄懂了大概，现在来编写个程序来检验下

import scala.mathobject Main {  val data=Array(1.0,1.3,2.2,2.6,2.8,5.0,7.3,7.4,7.5,7.7,7.9)//点的数据，这里数据只要一维，当然可以为n维  var means=Array(4.0,8.0)//均值  var variances=Array(1.0,1.0)//方差  var probs=Array(0.5,0.5)//数据在每个类的概率,这里  var p=Array.ofDim[Double](2,11)//每个点属于每个类的后验概率  //求得一个特定的点在相应的高斯函数中的概率  def gaussianPro(point:Double,means:Double,probs:Double):Double={    1/(math.sqrt(2*math.Pi)*math.sqrt(probs))*math.exp(-(point-means)*(point-means)/(2*probs))  }  //计算每个点属于每个类的后验概率  def Computepostpro(){    //计算分母    var denominator=new Array[Double](data.size)    for(i<- 0 until data.length){     for(j<- 0 until probs.length)       denominator(i)+=gaussianPro(data(i), means(j), variances(j))*probs(j)    }    //denominator.foreach(println)    //计算每个点属于每个类的后验概率    for(i<- 0 until probs.length){       for(j<- 0 until data.length)          p(i)(j)=probs(i)*gaussianPro(data(j), means(i), variances(i))/denominator(j)    }  }  def GuessMean(){    var m1=new Array[Double](2)    var m2=new Array[Double](2)    for(i<- 0 until 2){     p(i).foreach(m2(i)+=_)     p(i).zip(data).foreach(f=>m1(i)+=f._1*f._2)     means(i)=m1(i)/m2(i)    }  }  def Guessvariances(){    var m1=new Array[Double](2)    var m2=new Array[Double](2)    for(i<- 0 until 2){     p(i).foreach(m2(i)+=_)     p(i).zip(data).foreach(f=>m1(i)+=f._1*(f._2-means(i))*(f._2-means(i)))     variances(i)=m1(i)/m2(i)    }  }  def ComputeProbs(){    for(i<-0 until 2){      probs(i)=p(i).reduce(_+_)/p(i).length    }  }  def E_Step(){    Computepostpro()  }  def M_Step(){    GuessMean()    Guessvariances()    ComputeProbs()  }     def Threshold(localmean:Array[Double],localvariances:Array[Double]):Boolean={    var m1,m2=0.0    for(i<-0 until means.length){      m1+=localmean(i)-means(i)      m2+=localvariances(i)-variances(i)    }    (math.abs(m1)<0.01)&&(math.abs(m2)<0.01)  }  def Output(){    for(i<-0 until 2)   println("第"+i+"个类的均值为："+means(i)+" 方差为："+variances(i)+" 选择这个类的概率为："+probs(i));  }  def main(args: Array[String]): Unit = {    var k=20//迭代次数      var f=true    while(k>0&&f)   {      k-=1     var localmean=means     var localvariances=variances     E_Step()     M_Step()     f=Threshold(localmean, localvariances)   }    Output  }  }