Examples of org.apache.commons.math.stat.regression.SimpleRegression.addData()

Package org.apache.commons.math.stat.regression

Class org.apache.commons.math.stat.regression.SimpleRegression

Examples of org.apache.commons.math.stat.regression.SimpleRegression.addData()

org.apache.commons.math.stat.regression.SimpleRegression.addData()
Adds the observation (x,y) to the regression data set.
Uses updating formulas for means and sums of squares defined in "Algorithms for Computing the Sample Variance: Analysis and Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J. 1983, American Statistician, vol. 37, pp. 242-247, referenced in Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985.
@param x independent variable value @param y dependent variable value

    SimpleRegression simple = new SimpleRegression();
    for(IValue v : dataValues){
      ITuple t = (ITuple) v;
      INumber x = (INumber) t.get(0);
      INumber y = (INumber) t.get(1);
      simple.addData(x.toReal().doubleValue(), y.toReal().doubleValue());
    }
    return simple;
  }

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        } else if (xArray.length < 2) {
            throw MathRuntimeException.createIllegalArgumentException(
                  LocalizedFormats.INSUFFICIENT_DIMENSION, xArray.length, 2);
        } else {
            for(int i=0; i<xArray.length; i++) {
                regression.addData(xArray[i], yArray[i]);
            }
            return regression.getR();
        }
    }

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      //saves.put(s, mutualInformation(bigBag, b));
      //Bag<String> bb = new Bag<String>(b);
      //bb.discardInfrequent(10);
      //if(bb.size() == 0) continue;
      System.out.println(s + "\t" + b.totalCount() + "\t" + b.size());
      sr.addData(Math.log(b.totalCount()), Math.log(b.size()));
    }
    System.out.println(sr.getRSquare());
    double m = sr.getSlope();
    double c = sr.getIntercept();
    for(String s : bigBag.getList()) {

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    if(xs.length != ys.length){
      throw new SSErrorXelException(SSError.NA);
    }else{
      SimpleRegression sr = new SimpleRegression();
      for(int i =0; i < xs.length; i++){
        sr.addData(xs[i], ys[i]);
      }
      return sr;
    }
    
  }

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        positive.addData(evolData.m_positiveXPoints.get(i), evolData.m_positiveYPoints.get(i));
      }
      
      for(int i=0; i<evolData.m_negativeXPoints.size(); i++)
      {
        negative.addData(evolData.m_negativeXPoints.get(i),evolData. m_negativeYPoints.get(i));
      }
      
      logger.debug("Number of negative points: " + evolData.m_negativeXPoints.size());
      logger.debug("Number of positive points: " + evolData.m_positiveXPoints.size());

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     */
    public double correlation(final double[] xArray, final double[] yArray) throws IllegalArgumentException {
        SimpleRegression regression = new SimpleRegression();
        if(xArray.length == yArray.length && xArray.length > 1) {
            for(int i=0; i<xArray.length; i++) {
                regression.addData(xArray[i], yArray[i]);
            }
            return regression.getR();
        }
        else {
            throw MathRuntimeException.createIllegalArgumentException(

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     */
    public double correlation(final double[] xArray, final double[] yArray) throws IllegalArgumentException {
        SimpleRegression regression = new SimpleRegression();
        if(xArray.length == yArray.length && xArray.length > 1) {
            for(int i=0; i<xArray.length; i++) {
                regression.addData(xArray[i], yArray[i]);
            }
            return regression.getR();
        }
        else {
            throw MathRuntimeException.createIllegalArgumentException(

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    for (int i = 0; i < list.size() - 1; i++) {
      RTs point1 = list.get(i);
      RTs point2 = list.get(i + 1);
      if (point1.RT < point2.RT - 2) {
        SimpleRegression regression = new SimpleRegression();
        regression.addData(point1.RT, point1.RT2);
        regression.addData(point2.RT, point2.RT2);
        double rt = point1.RT + 1;
        while (rt < point2.RT) {
          RTs newPoint = new RTs(rt, regression.predict(rt));
          list.add(newPoint);

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      RTs point1 = list.get(i);
      RTs point2 = list.get(i + 1);
      if (point1.RT < point2.RT - 2) {
        SimpleRegression regression = new SimpleRegression();
        regression.addData(point1.RT, point1.RT2);
        regression.addData(point2.RT, point2.RT2);
        double rt = point1.RT + 1;
        while (rt < point2.RT) {
          RTs newPoint = new RTs(rt, regression.predict(rt));
          list.add(newPoint);
          rt++;

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    for (int i = 0; i < list.size() - 1; i++) {
      RTs point1 = list.get(i);
      RTs point2 = list.get(i + 1);
      if (point1.RT < point2.RT - 2) {
        SimpleRegression regression = new SimpleRegression();
        regression.addData(point1.RT, point1.RT2);
        regression.addData(point2.RT, point2.RT2);
        double rt = point1.RT + 1;
        while (rt < point2.RT) {
          RTs newPoint = new RTs(rt, regression.predict(rt));
          list.add(newPoint);

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