Examples of Mean

de.lmu.ifi.dbs.elki.math.Mean
Compute the mean using a numerically stable online algorithm. This class can repeatedly be fed with data using the add() methods, the resulting values for mean can be queried at any time using getMean(). Trivial code, but replicated a lot. The class is final so it should come at low cost. Related Literature:
B. P. Welford
Note on a method for calculating corrected sums of squares and products
in: Technometrics 4(3)

D.H.D. West
Updating Mean and Variance Estimates: An Improved Method
In: Communications of the ACM, Volume 22 Issue 9
@author Erich Schubert
jscicalc.pobject.Mean
Container to hold most recently evaluated mean value. @author J. D. Lamb @version $Revision: 1.6 $
opennlp.tools.util.eval.Mean
Calculates the arithmetic mean of values added with the {@link #add(double)} method.
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) $
org.apache.commons.math.stat.univariate.moment.Mean
ycoon.com/arithmetic_mean.htm"> arithmetic mean of the available values. @version $Revision: 1.13 $ $Date: 2003/11/19 03:28:24 $
org.apache.commons.math3.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.
tv.floe.metronome.clustering.kmeans.Mean
zdenekdrahos.Statistics.Characteristics.Mean

Examples of de.lmu.ifi.dbs.elki.math.Mean

    // regression using the covariance matrix
    CovarianceMatrix covm = new CovarianceMatrix(2);
    for(DBID id : relation.iterDBIDs()) {
      final double local = relation.get(id).doubleValue(1);
      // Compute mean of neighbors
      Mean mean = new Mean();
      DBIDs neighbors = npred.getNeighborDBIDs(id);
      for(DBID n : neighbors) {
        if(id.equals(n)) {
          continue;
        }
        mean.put(relation.get(n).doubleValue(1));
      }
      final double m;
      if(mean.getCount() > 0) {
        m = mean.getMean();
      }
      else {
        // if object id has no neighbors ==> avg = non-spatial attribute of id
        m = local;
      }

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Examples of jscicalc.pobject.Mean

    private double statSize(){
  return statMemory.size() - statMemoryNeg.size();
    }


    public Mean statMean(){
  Mean mean = new Mean();
  Complex d = new Complex();
  for( Complex o : statMemory ){
      d = d.add( o );
  }
  for( Complex o : statMemoryNeg ){
      d = d.subtract( o );
  }
  if( statSize() > 0 ){
     mean.setValue( d.divide( new Complex( statSize(), 0 ) ) );
  } else {
      mean.setError( true );
   }
  return mean;
    }

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Examples of jscicalc.pobject.Mean

   }
  return mean;
    }
    
    public Complex statSumSquares(){
  Mean m = statMean();
  if( m.error() || !(m.value() instanceof Complex) )
      throw new RuntimeException( "Stat Error" );
  Complex e = (Complex)(m.value());
  Complex d = new Complex();
  for( Complex o : statMemory ){
      Complex c = o.subtract( e );
      d = d.add( c.square() );
  }

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Examples of opennlp.tools.util.eval.Mean

    }


    // Clears the adaptive data which was created for the current document
    mNameFinder.clearAdaptiveData();


    documentConfidence = new Mean();
  }

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Examples of opennlp.tools.util.eval.Mean

 */
public class MeanTest {


  @Test
  public void testMeanCalculation() {
    Mean a = new Mean();
    a.add(1);
    assertEquals(1, a.count());
    assertEquals(1d, a.mean(), 0.00001d);
    
    a.add(1);
    assertEquals(2, a.count());
    assertEquals(1d, a.mean(), 0.00001d);
    a.toString();
    
    Mean b = new Mean();
    b.add(0.5);
    assertEquals(1, b.count());
    assertEquals(0.5d, b.mean(), 0.00001d);
    
    b.add(2);
    assertEquals(2, b.count());
    assertEquals(1.25d, b.mean(), 0.00001d);
    b.toString();
    
    Mean c = new Mean();
    assertEquals(0, c.count());
    assertEquals(0d, c.mean(), 0.00001d);
    c.toString();
  }

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Examples of opennlp.tools.util.eval.Mean

    }


    // Clears the adaptive data which was created for the current document
    mNameFinder.clearAdaptiveData();


    documentConfidence = new Mean();
  }

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Examples of opennlp.tools.util.eval.Mean

     *          the predicted pos tag
     */
    private void add(String tok, String ref, String pred) {
      // token stats
      if (!tokAccuracies.containsKey(tok)) {
        tokAccuracies.put(tok, new Mean());
        tokOcurrencies.put(tok, new Counter());
        tokErrors.put(tok, new Counter());
      }
      tokOcurrencies.get(tok).increment();

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


  public Stat() {
    min = new Min();
    max = new Max();
    sum = new Sum();
    mean = new Mean();
    sd = new StandardDeviation();


    stats = new StorelessUnivariateStatistic[] {min, max, sum, mean, sd};
  }

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

        u.clear();
        u.addValue(1);
        u.addValue(2);
        assertEquals(3, u.getMean(), 1E-14);
        u.clear();
        u.setMeanImpl(new Mean()); // OK after clear
    }

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




    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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