scala实现的高斯混合聚类，效果还不错，原理参考西瓜书p206-210
import breeze.linalg.{DenseMatrix, DenseVector, det, inv}
import org.apache.spark.{SparkConf, SparkContext}import scala.collection.mutable.ArrayBufferobject GaussCluster {var a1 = 1.0/3.0var a2 = 1.0/3.0var a3 = 1.0/3.0var cov1 = DenseMatrix((0.1, 0.0), (0.0, 0.1))var cov2 = DenseMatrix((0.1, 0.0), (0.0, 0.1))var cov3 = DenseMatrix((0.1, 0.0), (0.0, 0.1))var u1 = DenseVector(0.403, 0.237)var u2 = DenseVector(0.714, 0.346)var u3 = DenseVector(0.532, 0.472)var array = Array(0.0, 0.0, 0.0)var result = new collection.mutable.ArrayBuffer[Array[Double]]()def getPx(cov:DenseMatrix[Double],x:DenseVector[Double],u:DenseVector[Double]):Double = {val exp = (mMv(inv(cov),(x-u))).t * uval px = (-0.5*Math.exp(exp)) / (2*Math.PI)*Math.sqrt(det(cov))px}def mMv(m:DenseMatrix[Double],v:DenseVector[Double]):DenseVector[Double] = {val cols = v.lengthval rows = m.rowsval array = new collection.mutable.ArrayBuffer[Double]()val tv = v.tfor(i <- 0 to cols - 1){var sum = 0.0for(j <- 0 to rows - 1){sum += tv(j)*m(j,i)}array.append(sum)}
// println(array.toArray)DenseVector(array.toArray)}def getPm(x:DenseVector[Double]):Array[Double] = {val p1 = a1 * getPx(cov1,x,u1)val p2 = a2 * getPx(cov2,x,u2)val p3 = a3 * getPx(cov3,x,u3)val pm1 = p1 / (p1 + p2 + p3)val pm2 = p2 / (p1 + p2 + p3)val pm3 = p3 / (p1 + p2 + p3)array = Array(pm1,pm2,pm3)
// var max = 0.0
// for(i <- 0 to array.length-1){
// if(array(i)>max){
// max = array(i)
// }
// }// println(pm1 + ":" + pm2 + ":" + pm3)array}def updateCoef(x:Array[DenseVector[Double]]) = {val pmArray = new collection.mutable.ArrayBuffer[Array[Double]]()val rarray = new collection.mutable.ArrayBuffer[Array[Double]]()val m = x.length.toDoublefor(i <- 0 to x.length-1){pmArray.append(getPm(x(i)))rarray.append(getPm(x(i)))}result = rarrayvar pmSum1 = 0.0var pmX1 = DenseVector(0.0,0.0)for(j <- 0 to pmArray.length-1){pmSum1 += pmArray(j)(0)
// println((x(j) * pmArray(j)(0)))pmX1 = pmX1 :+ (x(j) * pmArray(j)(0))}u1 = pmX1 :/ pmSum1var pmSum2 = 0.0var pmX2 = DenseVector(0.0,0.0)for(j <- 0 to pmArray.length-1){pmSum2 += pmArray(j)(0)pmX2 = pmX2 + (x(j) * pmArray(j)(1))}u2 = pmX2 :/ pmSum2var pmSum3 = 0.0var pmX3 = DenseVector(0.0,0.0)for(j <- 0 to pmArray.length-1){pmSum3 += pmArray(j)(0)pmX3 = pmX3 + (x(j) * pmArray(j)(2))}u3 = pmX3 :/ pmSum3a1 = pmSum1 / ma2 = pmSum2 / ma3 = pmSum3 / mvar Ncov1 = DenseMatrix((0.0,0.0),(0.0,0.0))var Ncov2 = DenseMatrix((0.0,0.0),(0.0,0.0))var Ncov3 = DenseMatrix((0.0,0.0),(0.0,0.0))for(k <- 0 to x.length-1 ){Ncov1 = Ncov1 :+ pmArray(k)(0)*((x(k)-u1) * (x(k)-u1).t)Ncov2 = Ncov2 :+ pmArray(k)(1)*((x(k)-u1) * (x(k)-u2).t)Ncov3 = Ncov3 :+ pmArray(k)(2)*((x(k)-u1) * (x(k)-u3).t)}cov1 = Ncov1 :/ pmSum1cov2 = Ncov2 :/ pmSum2cov3 = Ncov3 :/ pmSum3}def getNewCov(pmSum:Double,pmArray:ArrayBuffer[Double],u:DenseVector[Double],x:Array[DenseVector[Double]]) = {}def main(args: Array[String]): Unit = {
// val m = DenseMatrix((1.0,3.0),(2.0,4.0))
// val v = DenseVector(1.0,2.0)
// val temp = mMv(m,v)
// val result = temp.t * v
// println(result)val conf = new SparkConf().setMaster("local[4]").setAppName(s"${this.getClass.getSimpleName}")val sc = new SparkContext(conf)sc.setLogLevel("ERROR")val oriData = sc.textFile("C:\\Users\\dell\\Desktop\\data\\gaussCluster.txt")val data = oriData.map(_.split(" ")).map(s => s.map(_.toDouble)).map(s => {DenseVector(s(1),s(2))})var c = 0val x = data.collect()while(c<5){for (i <-0 to x.length-1){getPm(x(i))}updateCoef(x)c += 1}for (i <- 0 to result.length-1){val temp = result(i)var max = 0for (j <- 1 to temp.length-1){if(temp(j)>temp(max)){max = j}}println(max)}}}