Examples of org.apache.commons.math.stat.descriptive.moment.Mean

Package org.apache.commons.math.stat.descriptive.moment

Examples of org.apache.commons.math.stat.descriptive.moment.Mean

org.apache.commons.math.stat.descriptive.moment.Mean
Computes the arithmetic mean of a set of values. Uses the definitional formula:

mean = sum(x_i) / n

where n is the number of observations.

When {@link #increment(double)} is used to add data incrementally from astream of (unstored) values, the value of the statistic that {@link #getResult()} returns is computed using the following recursiveupdating algorithm:
1. Initialize m = the first value
2. For each additional value, update using
  m = m + (new value - m) / (number of observations)
If {@link #evaluate(double[])} is used to compute the mean of an arrayof stored values, a two-pass, corrected algorithm is used, starting with the definitional formula computed using the array of stored values and then correcting this by adding the mean deviation of the data values from the arithmetic mean. See, e.g. "Comparison of Several Algorithms for Computing Sample Means and Variances," Robert F. Ling, Journal of the American Statistical Association, Vol. 69, No. 348 (Dec., 1974), pp. 859-866.

Returns Double.NaN if the dataset is empty.
Note that this implementation is not synchronized. If multiple threads access an instance of this class concurrently, and at least one of the threads invokes the increment() or clear() method, it must be synchronized externally. @version $Revision: 1006299 $ $Date: 2010-10-10 16:47:17 +0200 (dim. 10 oct. 2010) $

            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

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     * </p>
     * @return the mean
     */
    public double getMean() {
        if (mean == meanImpl) {
            return new Mean(secondMoment).getResult();
        } else {
            return meanImpl.getResult();
        }
    }

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      int i = 0;
      for (Integer rc : partitionRecordCounts) {
        values[i++] = rc.doubleValue();
      }
      StandardDeviation stdDev = new StandardDeviation();
      Mean mean = new Mean();
      double std = stdDev.evaluate(values);
      double m = mean.evaluate(values);
      System.out.println("mean: " + m + " std dev: " + std);
      //Assert.assertTrue(std < 0.1 * m);
      assertEquals(31000, count);
    }
  }

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    return stdevCalculator.evaluate(values);
  }
  
  public static Double mean(double[] values)
  {
    Mean meanCalculator = new Mean();
    return meanCalculator.evaluate(values);
  }

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     *  Double.NaN is returned if no values have been added.</p>
     * 
     * @return the mean
     */
    public double getMean() {
      return new Mean(secondMoment).getResult();
    }

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     * Returns the <a href="http://www.xycoon.com/arithmetic_mean.htm">
     * arithmetic mean </a> of the available values 
     * @return The mean or Double.NaN if no values have been added.
     */
    public double getMean() {
        return apply(new Mean());
    }

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    public void testInteraction() {
        
        FourthMoment m4 = new FourthMoment();
        Mean m = new Mean(m4);
        Variance v = new Variance(m4);
        Skewness s= new Skewness(m4);
        Kurtosis k = new Kurtosis(m4);


        for (int i = 0; i < testArray.length; i++){
            m4.increment(testArray[i]);
        }
        
        assertEquals(mean,m.getResult(),tolerance);
        assertEquals(var,v.getResult(),tolerance);
        assertEquals(skew ,s.getResult(),tolerance);
        assertEquals(kurt,k.getResult(),tolerance);


    }

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  @Override
  public int getMeanStateStringSize() {
    readLock.lock();
    try {
      final Mean mean = new Mean();


      for (StateVertex state : sfg.vertexSet()) {
        mean.increment(state.getDom().getBytes().length);
      }


      return (int) mean.getResult();
    } finally {
      readLock.unlock();
    }
  }

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     * @throws  IllegalArgumentException if the arrays lengths do not match or
     * there is insufficient data
     */
    public double covariance(final double[] xArray, final double[] yArray, boolean biasCorrected)
        throws IllegalArgumentException {
        Mean mean = new Mean();
        double result = 0d;
        int length = xArray.length;
        if(length == yArray.length && length > 1) {
            double xMean = mean.evaluate(xArray);
            double yMean = mean.evaluate(yArray);
            for (int i = 0; i < length; i++) {
                double xDev = xArray[i] - xMean;
                double yDev = yArray[i] - yMean;
                result += (xDev * yDev - result) / (i + 1);
            }

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            sumSqImpl[i]   = new SumOfSquares();
            minImpl[i]     = new Min();
            maxImpl[i]     = new Max();
            sumLogImpl[i]  = new SumOfLogs();
            geoMeanImpl[i] = new GeometricMean();
            meanImpl[i]    = new Mean();
        }


        covarianceImpl =
            new VectorialCovariance(k, isCovarianceBiasCorrected);

View Full Code Here

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