ExpectationSum

logLikelihood：似然函数

weights：每个类的权重

means：每个类的均值

sigmas：每个类的covariance matrix

package org.apache.spark.mllib.clusteringimport breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV, Vector => BV}import org.apache.spark.mllib.linalg.{DenseMatrix, Matrices, Vectors, BLAS}import org.apache.spark.mllib.stat.distribution.MultivariateGaussianimport org.apache.spark.mllib.util.MLUtils/**  * Created by fhqplzj on 16-7-29 at 下午3:29.  */class MyExpectationSum(var logLikelihood: Double,                       val weights: Array[Double],                       val means: Array[BDV[Double]],                       val sigmas: Array[BDM[Double]]) extends Serializable {  def +=(x: MyExpectationSum) = {    for (i <- weights.indices) {      weights(i) += x.weights(i)      means(i) += x.means(i)      sigmas(i) += x.sigmas(i)    }    logLikelihood += x.logLikelihood    this  }}object MyExpectationSum {  def zero(k: Int, d: Int) = {    new MyExpectationSum(0, Array.fill(k)(0), Array.fill(k)(BDV.zeros(d)), Array.fill(k)(BDM.zeros(d, d)))  }  def add(weights: Array[Double], dists: Array[MultivariateGaussian])(sums: MyExpectationSum, x: BV[Double]) = {    val p = weights.zip(dists).map {      case (weight, dist) =>        MLUtils.EPSILON + weight * dist.pdf(x)    }    val pSum = p.sum    sums.logLikelihood += math.log(pSum)    for (i <- p.indices) {      p(i) /= pSum      sums.weights(i) += p(i)      sums.means(i) += p(i) * x      BLAS.syr(p(i), Vectors.fromBreeze(x), Matrices.fromBreeze(sums.sigmas(i)).asInstanceOf[DenseMatrix])    }    sums  }}