/**
   * Constructor. The Gaussian mixture model will be learned from the given data with the EM
   * algorithm. The number of components will be selected by BIC.
   *
   * @param data the training data.
   */
  @SuppressWarnings("unchecked")
  public GaussianMixture(double[] data) {
    if (data.length < 20) throw new IllegalArgumentException("Too few samples.");

    ArrayList<Component> mixture = new ArrayList<Component>();
    Component c = new Component();
    c.priori = 1.0;
    c.distribution = new GaussianDistribution(data);
    mixture.add(c);

    int freedom = 0;
    for (int i = 0; i < mixture.size(); i++) freedom += mixture.get(i).distribution.npara();

    double bic = 0.0;
    for (double x : data) {
      double p = c.distribution.p(x);
      if (p > 0) bic += Math.log(p);
    }
    bic -= 0.5 * freedom * Math.log(data.length);

    double b = Double.NEGATIVE_INFINITY;
    while (bic > b) {
      b = bic;
      components = (ArrayList<Component>) mixture.clone();

      split(mixture);
      bic = EM(mixture, data);

      freedom = 0;
      for (int i = 0; i < mixture.size(); i++) freedom += mixture.get(i).distribution.npara();

      bic -= 0.5 * freedom * Math.log(data.length);
    }
  }
Example #2
0
  /** Round the bounds for axis i. */
  public void extendBound(int i) {
    if (i < 0 || i >= dimension) {
      throw new IllegalArgumentException("Invalid bound index: " + i);
    }

    extendBound[i] = true;
    lowerBound[i] = precisionUnit[i] * (Math.floor(originalLowerBound[i] / precisionUnit[i]));
    upperBound[i] = precisionUnit[i] * (Math.ceil(originalUpperBound[i] / precisionUnit[i]));
  }
  /**
   * Constructor. The Gaussian mixture model will be learned from the given data with the EM
   * algorithm.
   *
   * @param data the training data.
   * @param k the number of components.
   */
  public GaussianMixture(double[] data, int k) {
    if (k < 2) throw new IllegalArgumentException("Invalid number of components in the mixture.");

    double min = Math.min(data);
    double max = Math.max(data);
    double step = (max - min) / (k + 1);

    for (int i = 0; i < k; i++) {
      Component c = new Component();
      c.priori = 1.0 / k;
      c.distribution = new GaussianDistribution(min += step, step);
      components.add(c);
    }

    EM(components, data);
  }
  public ExponentialDistributionDemo() {
    super(new BorderLayout());

    Hashtable<Integer, JLabel> labelTable = new Hashtable<Integer, JLabel>();
    for (int i = 0; i <= 50; i += 10) {
      labelTable.put(new Integer(i), new JLabel(String.valueOf(i / 10)));
    }

    lambdaSlider = new JSlider(0, 50, (int) Math.round(lambda * 10));
    lambdaSlider.addChangeListener(this);
    lambdaSlider.setLabelTable(labelTable);
    lambdaSlider.setMajorTickSpacing(10);
    lambdaSlider.setMinorTickSpacing(2);
    lambdaSlider.setPaintTicks(true);
    lambdaSlider.setPaintLabels(true);

    optionPane = new JPanel(new FlowLayout(FlowLayout.LEFT));
    optionPane.setBorder(BorderFactory.createRaisedBevelBorder());
    optionPane.add(new JLabel("\u03BB:"));
    optionPane.add(lambdaSlider);

    add(optionPane, BorderLayout.NORTH);

    canvas = new JPanel(new GridLayout(2, 2));
    add(canvas, BorderLayout.CENTER);

    ExponentialDistribution dist = new ExponentialDistribution(lambda);

    double[][] p = new double[100][2];
    double[][] q = new double[100][2];
    for (int i = 0; i < p.length; i++) {
      p[i][0] = i / 20.0;
      p[i][1] = dist.p(p[i][0]);
      q[i][0] = i / 20.0;
      q[i][1] = dist.cdf(p[i][0]);
    }

    pdf = LinePlot.plot(p, Line.Style.SOLID, Color.BLUE);
    pdf.setTitle("PDF");
    canvas.add(pdf);

    cdf = LinePlot.plot(q, Line.Style.SOLID, Color.BLUE);
    cdf.setTitle("CDF");
    canvas.add(cdf);

    double[] data = new double[500];
    for (int i = 0; i < data.length; i++) {
      data[i] = dist.rand();
    }

    histogram = Histogram.plot(data, 20);
    histogram.setTitle("Histogram");
    canvas.add(histogram);

    qqplot = QQPlot.plot(data, dist);
    qqplot.setTitle("Q-Q Plot");
    canvas.add(qqplot);
  }
Example #5
0
  /** Set the precision unit for axis i. */
  void setPrecisionUnit(int i) {
    if (upperBound[i] > lowerBound[i]) {
      double digits = Math.log10(Math.abs(upperBound[i] - lowerBound[i]));
      double residual = digits - Math.floor(digits);
      if (residual < 0.2) {
        // If the range is less than 15 units, we reduce one level.
        digits -= 1.0;
      }

      precisionDigits[i] = (int) Math.floor(digits);
      precisionUnit[i] = Math.pow(10, precisionDigits[i]);

      if (residual >= 0.2 && residual <= 0.7) {
        // In case of too few grids, we use a half of precision unit.
        precisionUnit[i] /= 2;
        precisionDigits[i] -= 1;
      }
    } else {
      precisionUnit[i] = 0.1;
    }
  }