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

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

Note that adding values using increment or incrementAll and then executing getResult will sometimes give a different, less accurate, result than executing evaluate with the full array of values. The former approach should only be used when the full array of values is not available.

The "population variance" ( sum((x_i - mean)^2) / n ) can also be computed using this statistic. The isBiasCorrected property determines whether the "population" or "sample" value is returned by the evaluate and getResult methods. To compute population variances, set this property to false.

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) $
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
    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);

    }
View Full Code Here
233
234
235
236
237
238
239
240
241
242
     * </p>
     * @return the variance
     */
    public double getVariance() {
        if (varianceImpl == variance) {
            return new Variance(secondMoment).getResult();
        } else {
            return varianceImpl.getResult();
        }
    }
View Full Code Here
326
327
328
329
330
331
332
     * Calculates the variance of the y values.
     *
     * @return Y variance
     */
    protected double calculateYVariance() {
        return new Variance().evaluate(Y.getData());
    }
View Full Code Here
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
     * @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;
    }
View Full Code Here
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
        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;
View Full Code Here
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
    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);

    }
View Full Code Here
158
159
160
161
162
163
164
     *  Double.NaN is returned if no values have been added.</p>
     *
     * @return the variance
     */
    public double getVariance() {
        return new Variance(secondMoment).getResult();
    }
View Full Code Here
109
110
111
112
113
114
115
     * Returns the variance of the available values.
     * @return The variance, Double.NaN if no values have been added
     * or 0.0 for a single value set. 
     */
    public double getVariance() {
        return apply(new Variance());
    }
View Full Code Here
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
    public void testConsistency() {
        final RealMatrix matrix = createRealMatrix(swissData, 47, 5);
        final RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();

        // Variances on the diagonal
        Variance variance = new Variance();
        for (int i = 0; i < 5; i++) {
            assertEquals(variance.evaluate(matrix.getColumn(i)), covarianceMatrix.getEntry(i,i), 10E-14);
        }

        // Symmetry, column-consistency
        assertEquals(covarianceMatrix.getEntry(2, 3),
                new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14);
        assertEquals(covarianceMatrix.getEntry(2, 3), covarianceMatrix.getEntry(3, 2), Double.MIN_VALUE);

        // All columns same -> all entries = column variance
        RealMatrix repeatedColumns = new Array2DRowRealMatrix(47, 3);
        for (int i = 0; i < 3; i++) {
            repeatedColumns.setColumnMatrix(i, matrix.getColumnMatrix(0));
        }
        RealMatrix repeatedCovarianceMatrix = new Covariance(repeatedColumns).getCovarianceMatrix();
        double columnVariance = variance.evaluate(matrix.getColumn(0));
        for (int i = 0; i < 3; i++) {
            for (int j = 0; j < 3; j++) {
                assertEquals(columnVariance, repeatedCovarianceMatrix.getEntry(i, j), 10E-14);
            }
        }
View Full Code Here
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
    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);

    }
View Full Code Here
TOP

Related Classes of org.apache.commons.math.stat.descriptive.moment.Variance

  • com.facebook.presto.operator.aggregation.TestDoubleVarianceAggregation
  • com.facebook.presto.operator.aggregation.TestDoubleVariancePopAggregation
  • com.facebook.presto.operator.aggregation.TestLongVarianceAggregation
  • com.facebook.presto.operator.aggregation.TestLongVariancePopAggregation
  • org.apache.commons.math.stat.clustering.KMeansPlusPlusClusterer
  • org.apache.commons.math.stat.correlation.Covariance
  • org.apache.commons.math.stat.correlation.CovarianceTest
  • org.apache.commons.math.stat.descriptive.DescriptiveStatistics
  • org.apache.commons.math.stat.descriptive.InteractionTest
  • org.apache.commons.math.stat.descriptive.SummaryStatistics
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.