org.apache.commons.math.stat.descriptive.moment.Variance Java Examples

The following examples show how to use org.apache.commons.math.stat.descriptive.moment.Variance. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #2
Source File: InteractionTest.java    From cacheonix-core with GNU Lesser General Public License v2.1 6 votes vote down vote up 
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);

}
Example #3
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #4
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
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);

    }
Example #5
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
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);

}
Example #6
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
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);

    }
Example #7
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
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);

    }
Example #8
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #9
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
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);

    }
Example #10
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
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);

}
Example #11
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #12
Source File: Math_43_SummaryStatistics_s.java    From coming with MIT License 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #13
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #14
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #15
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #16
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #17
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
@Test
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]);
    }

    Assert.assertEquals(mean,m.getResult(),tolerance);
    Assert.assertEquals(var,v.getResult(),tolerance);
    Assert.assertEquals(skew ,s.getResult(),tolerance);
    Assert.assertEquals(kurt,k.getResult(),tolerance);

}
Example #18
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
/**
 * Add a value to the data
 * @param value the value to add
 */
public void addValue(double value) {
    sumImpl.increment(value);
    sumsqImpl.increment(value);
    minImpl.increment(value);
    maxImpl.increment(value);
    sumLogImpl.increment(value);
    secondMoment.increment(value);
    // If mean, variance or geomean have been overridden,
    // need to increment these
    if (!(meanImpl instanceof Mean)) {
        meanImpl.increment(value);
    }
    if (!(varianceImpl instanceof Variance)) {
        varianceImpl.increment(value);
    }
    if (!(geoMeanImpl instanceof GeometricMean)) {
        geoMeanImpl.increment(value);
    }
    n++;
}
Example #19
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
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);

    }
Example #20
Source File: InteractionTest.java    From astor with GNU General Public License v2.0 6 votes vote down vote up 
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);

    }
Example #21
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Returns the variance of the values that have been added.
 * <p>
 * Double.NaN is returned if no values have been added.
 * </p>
 * @return the variance
 */
public double getVariance() {
    if (varianceImpl == variance) {
        return new Variance(secondMoment).getResult();
    } else {
        return varianceImpl.getResult();
    }
}
Example #22
Source File: Covariance.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Compute a covariance matrix from a matrix whose columns represent
 * covariates.
 * @param matrix input matrix (must have at least two columns and two rows)
 * @param biasCorrected determines whether or not covariance estimates are bias-corrected
 * @return covariance matrix
 */
protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {
    int dimension = matrix.getColumnDimension();
    Variance variance = new Variance(biasCorrected);
    RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
    for (int i = 0; i < dimension; i++) {
        for (int j = 0; j < i; j++) {
          double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
          outMatrix.setEntry(i, j, cov);
          outMatrix.setEntry(j, i, cov);
        }
        outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
    }
    return outMatrix;
}
Example #23
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Returns the variance of the values that have been added.
 * <p>
 * Double.NaN is returned if no values have been added.
 * </p>
 * @return the variance
 */
public double getVariance() {
    if (varianceImpl == variance) {
        return new Variance(secondMoment).getResult();
    } else {
        return varianceImpl.getResult();
    }
}
Example #24
Source File: Covariance.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Compute a covariance matrix from a matrix whose columns represent
 * covariates.
 * @param matrix input matrix (must have at least two columns and two rows)
 * @param biasCorrected determines whether or not covariance estimates are bias-corrected
 * @return covariance matrix
 */
protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {
    int dimension = matrix.getColumnDimension();
    Variance variance = new Variance(biasCorrected);
    RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
    for (int i = 0; i < dimension; i++) {
        for (int j = 0; j < i; j++) {
          double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
          outMatrix.setEntry(i, j, cov);
          outMatrix.setEntry(j, i, cov);
        }
        outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
    }
    return outMatrix;
}
Example #25
Source File: Covariance.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Compute a covariance matrix from a matrix whose columns represent
 * covariates.
 * @param matrix input matrix (must have at least two columns and two rows)
 * @param biasCorrected determines whether or not covariance estimates are bias-corrected
 * @return covariance matrix
 */
protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {
    int dimension = matrix.getColumnDimension();
    Variance variance = new Variance(biasCorrected);
    RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
    for (int i = 0; i < dimension; i++) {
        for (int j = 0; j < i; j++) {
          double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
          outMatrix.setEntry(i, j, cov);
          outMatrix.setEntry(j, i, cov);
        }
        outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
    }
    return outMatrix;
}
Example #26
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Returns the variance of the values that have been added.
 * <p>
 * Double.NaN is returned if no values have been added.
 * </p>
 * @return the variance
 */
public double getVariance() {
    if (varianceImpl == variance) {
        return new Variance(secondMoment).getResult();
    } else {
        return varianceImpl.getResult();
    }
}
Example #27
Source File: SummaryStatistics.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Returns the variance of the values that have been added.
 * <p>
 * Double.NaN is returned if no values have been added.
 * </p>
 * @return the variance
 */
public double getVariance() {
    if (varianceImpl == variance) {
        return new Variance(secondMoment).getResult();
    } else {
        return varianceImpl.getResult();
    }
}
Example #28
Source File: KMeansPlusPlusClusterer.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Get a random point from the {@link Cluster} with the largest distance variance.
 *
 * @param clusters the {@link Cluster}s to search
 * @return a random point from the selected cluster
 */
private T getPointFromLargestVarianceCluster(final Collection<Cluster<T>> clusters) {

    double maxVariance = Double.NEGATIVE_INFINITY;
    Cluster<T> selected = null;
    for (final Cluster<T> cluster : clusters) {
        if (!cluster.getPoints().isEmpty()) {

            // compute the distance variance of the current cluster
            final T center = cluster.getCenter();
            final Variance stat = new Variance();
            for (final T point : cluster.getPoints()) {
                stat.increment(point.distanceFrom(center));
            }
            final double variance = stat.getResult();

            // select the cluster with the largest variance
            if (variance > maxVariance) {
                maxVariance = variance;
                selected = cluster;
            }

        }
    }

    // did we find at least one non-empty cluster ?
    if (selected == null) {
        throw new ConvergenceException(LocalizedFormats.EMPTY_CLUSTER_IN_K_MEANS);
    }

    // extract a random point from the cluster
    final List<T> selectedPoints = selected.getPoints();
    return selectedPoints.remove(random.nextInt(selectedPoints.size()));

}
Example #29
Source File: Covariance.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Compute a covariance matrix from a matrix whose columns represent
 * covariates.
 * @param matrix input matrix (must have at least two columns and two rows)
 * @param biasCorrected determines whether or not covariance estimates are bias-corrected
 * @return covariance matrix
 */
protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {
    int dimension = matrix.getColumnDimension();
    Variance variance = new Variance(biasCorrected);
    RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
    for (int i = 0; i < dimension; i++) {
        for (int j = 0; j < i; j++) {
          double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
          outMatrix.setEntry(i, j, cov);
          outMatrix.setEntry(j, i, cov);
        }
        outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
    }
    return outMatrix;
}
Example #30
Source File: Covariance.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * Compute a covariance matrix from a matrix whose columns represent
 * covariates.
 * @param matrix input matrix (must have at least two columns and two rows)
 * @param biasCorrected determines whether or not covariance estimates are bias-corrected
 * @return covariance matrix
 */
protected RealMatrix computeCovarianceMatrix(RealMatrix matrix, boolean biasCorrected) {
    int dimension = matrix.getColumnDimension();
    Variance variance = new Variance(biasCorrected);
    RealMatrix outMatrix = new BlockRealMatrix(dimension, dimension);
    for (int i = 0; i < dimension; i++) {
        for (int j = 0; j < i; j++) {
          double cov = covariance(matrix.getColumn(i), matrix.getColumn(j), biasCorrected);
          outMatrix.setEntry(i, j, cov);
          outMatrix.setEntry(j, i, cov);
        }
        outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
    }
    return outMatrix;
}