import java.awt.*;abstract class Mixture {  final int maxkp = 20;  final int typegauss = 0;  final int typeuniform = 1;  final int typecurvedgauss = 2;  final int typescaleshift = 3;  final int typeline = 101;  Object[] kernel = new Object[maxkp];  int type[] = new int[maxkp];  double[] weight = new double[maxkp];  int nk;  int xsiz, ysiz;  double[] px;  Database db;  public Mixture(int xSize, int ySize, Database DB) {    xsiz = xSize;    ysiz = ySize;      db = DB;  }  public void initKernel(Object mod, int tp, int pos) {    kernel[pos] =  mod;    type[pos] = tp;  }  public void setnk(int nK) {    if(nk == nK) return;    nk = nK;    randomKernels();  }  public void setnk(int nK, double[] ws) {	double sum;	int i;        double[] w;	nk = nK;        w = new double[nk];	sum = 0;	for(i = 0;  i < nk; i++) {	    sum += ws[i];	}	for(i = 0; i < nk; i++) {	    w[i] = ws[i] / sum;	}	randomKernels(w);    }  public void randomKernels(double[] ws) {    double sum, tmp;    int i;        sum = 0;     for(i = 0; i < nk; i++) {	sum += ws[i];    }    for(i = 0; i < nk; i++) {      tmp = ws[i] / sum;      switch(type[i]) {/*        case typegauss :          ((Gaussian) kernel[i]).randomKernel(tmp);          break; */        case typecurvedgauss :          ((CurvedGaussian) kernel[i]).randomKernel(tmp);          break;	case typeuniform :          ((Uniform) kernel[i]).randomKernel(tmp);          break;/*	case typeline :          ((Line) kernel[i]).randomKernel(tmp); 	  break; */      }    }  }  public void randomKernels() {    double[] ws = new double[nk];    for(int i = 0; i < nk; i++) ws[i] = 1.0 / nk;    randomKernels(ws);  }  public void paint(Graphics g) {    int i;    for(i = 0; i < nk; i++) {      switch(type[i]) {/*	case typegauss :          ((Gaussian) kernel[i]).paint(g, db);          break; */	case typecurvedgauss :          ((CurvedGaussian) kernel[i]).paint(g, db);          break;	case typeuniform :          ((Uniform) kernel[i]).paint(g, db);          break;/*	case typeline :          ((Line) kernel[i]).paint(g, db); 	  break; */      }    }     g.setColor(Color.red);    g.drawString("Mean Likelihood = " + likelihood(), 0, 30);  }  private void calcpx() {    int i, j, np;    double[] probs;    np = db.nPoints();    px = new double[np];    for(j = 0; j < np; j++) {      px[j] = 0;    }    for(i = 0; i < nk; i++) {          probs = ((Module)kernel[i]).calcp(db);      for(j = 0; j < np; j++) {        px[j] += probs[j];      }    }  }  public void EM(double[] ws) {    EMmain(ws);  }  public void EMmain(double[] ws) {    double sum, newlike;    int i, j;        if(db.nPoints() <= 2) return;    sum = 0;    for(i = 0; i < nk; i++) {      sum += ws[i];    }    calcpx();    for(i = 0; i < nk; i++) {      ((Module)kernel[i]).EMprob(px, db);      switch(type[i]) {/*	case typegauss :          ((Gaussian)kernel[i]).EMpar(db, ws[i] / sum);	  break; */	case typecurvedgauss :          ((CurvedGaussian)kernel[i]).EMpar(db, ws[i] / sum);	  break;	case typeuniform :          ((Uniform)kernel[i]).EMpar(db, ws[i] / sum);	  break;/*	case typeline :              ((Line)kernel[i]).EMpar(db, ws[i] / sum);	  break; */	}    }    return;  }  public double likelihood() {    int i, j, np;    double tmp;    np = db.nPoints();     if(np == 0) return 0;    calcpx();    tmp = 0;    for(j = 0; j < np; j++) {      tmp += Math.log(px[j]);    }    return tmp / np;  }}