import java.awt.*;class CurvedGaussian extends Module {  double kmx, kmy, ksx, ksy, ksxy;  final int mins = 8;  boolean plotcircle = true;    public  CurvedGaussian(int xSize, int ySize, double w) {      super(xSize, ySize, w);    }  public void randomKernel(double w) {    weight = w;    kmx = xsiz * Math.random();    kmy = ysiz * Math.random();    ksx = xsiz / 4 + mins;    ksy = ysiz / 4 + mins;    ksxy = 0;  }  public void setplotline() {    plotcircle = false;  }  public double[] getPar() {    double pars[];    pars = new double[6];    pars[0] = weight;    pars[1] = kmx;    pars[2] = kmy;    pars[3] = ksx;    pars[4] = ksy;    pars[5] = ksxy;    return pars;  }  public void paint(Graphics g, Database db) {    int j;    double theta, u1x, u1y, u2x, u2y, r1, r2, tmp, varx, vary;    if(Math.abs(ksxy) <= 1e-4) {      if(ksx > ksy) {        u1x = u2y = 1;        u1y = u2x = 0;        r1 = ksx;        r2 = ksy;      } else {        u1x = u2y = 0;        u1y = u2x = 1;        r1 = ksy;        r2 = ksx;      }    } else {      varx = ksx * ksx;      vary = ksy * ksy;      // eigen value      tmp = varx - vary;      tmp = Math.sqrt(tmp * tmp + 4 * ksxy * ksxy);      r1 = Math.sqrt((varx + vary + tmp) / 2);      r2 = Math.sqrt((varx + vary - tmp) / 2);      // eigen vectors      u1x = r1 * r1 - vary;      tmp = Math.sqrt(u1x * u1x + ksxy * ksxy);      u1x /= tmp;      u1y = ksxy / tmp;      u2x = r2 * r2 - vary;      tmp = Math.sqrt(u2x * u2x + ksxy * ksxy);      u2x /= tmp;      u2y = ksxy / tmp;    }        g.setColor(Color.red);    g.drawString(weight + "", (int) kmx, (int) kmy);    g.setColor(Color.blue);    if(plotcircle) {      for(j = 1; j < 4; j++) {        drawCurvedOval(g, u1x, u1y, u2x, u2y, r1 * j, r2 * j);      }    } else {      g.drawLine((int)(kmx + 3 * r1 * u1x), (int)(kmy + 3 * r1 * u1y),               (int)(kmx - 3 * r1 * u1x), (int)(kmy - 3 * r1 * u1y));    }  }  public void drawCurvedOval(Graphics g,       double x1, double y1, double x2, double y2, double r1, double r2) {    int fx, fy, tx, ty;    double w1, w2;    fx = (int) (kmx + r1 * x1);    fy = (int) (kmy + r1 * y1);    for(double th = 0.1; th < 6.4; th += 0.1) {      w1 = Math.cos(th);      w2 = Math.sin(th);      tx = (int) (kmx + r1 * x1 * w1 + r2 * x2 * w2);      ty = (int) (kmy + r1 * y1 * w1 + r2 * y2 * w2);      g.drawLine(fx, fy, tx, ty);      fx = tx;      fy = ty;    }  }  public double density(int x, int y) {    double tmpx, tmpy, tmpxy, det, varx, vary;    varx = ksx * ksx;    vary = ksy * ksy;    det = varx * vary - ksxy * ksxy;    tmpx = (x - kmx) * (x - kmx);    tmpy = (y - kmy) * (y - kmy);    tmpxy = (x - kmx) * (y - kmy);    return weight / Math.sqrt(det) / 6.28319 *      Math.exp(-(tmpx * vary + tmpy * varx - 2 * tmpxy * ksxy) / det / 2);  }  public void EMpar(Database db, double prior) {    int j, np;    double x, y, tmp, tmpx, tmpy, tmpsx, tmpsy, tmpsxy;    np = db.nPoints();    tmpsx = tmpsy = tmpsxy = kmx = kmy = 0;    for(j = 0; j < np; j++) {      x = db.xVal(j);      y = db.yVal(j);      kmx += probs[j] * x;      kmy += probs[j] * y;      tmpsx += probs[j] * x * x;      tmpsy += probs[j] * y * y;      tmpsxy += probs[j] * x * y;    }    tmp = np * weight;    kmx /= tmp;    kmy /= tmp;    ksx = Math.sqrt(tmpsx / tmp - kmx * kmx);    ksy = Math.sqrt(tmpsy / tmp - kmy * kmy);    ksxy = tmpsxy / tmp - kmx * kmy;    if(ksx < mins) ksx = mins;    if(ksy < mins) ksy = mins;    weight = 0.9 * weight + 0.1 * prior;  }}