本文整理汇总了Java中org.apache.commons.math.stat.StatUtils.mean方法的典型用法代码示例。如果您正苦于以下问题：Java StatUtils.mean方法的具体用法？Java StatUtils.mean怎么用？Java StatUtils.mean使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.apache.commons.math.stat.StatUtils的用法示例。
在下文中一共展示了StatUtils.mean方法的8个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: testSample
import org.apache.commons.math.stat.StatUtils; //导入方法依赖的package包/类
public void testSample() {
final double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
final int length = values.length;
final double mean = StatUtils.mean(values); // 6.333...
final SemiVariance sv = new SemiVariance(); // Default bias correction is true
final double downsideSemiVariance = sv.evaluate(values); // Downside is the default
assertEquals(TestUtils.sumSquareDev(new double[] {-2d, 2d, 4d, -2d, 3d, 5d}, mean) / (length - 1),
downsideSemiVariance, 1E-14);
sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
final double upsideSemiVariance = sv.evaluate(values);
assertEquals(TestUtils.sumSquareDev(new double[] {22d, 11d, 14d}, mean) / (length - 1),
upsideSemiVariance, 1E-14);
// Verify that upper + lower semivariance against the mean sum to variance
assertEquals(StatUtils.variance(values), downsideSemiVariance + upsideSemiVariance, 10e-12);
}
示例2: testSample
import org.apache.commons.math.stat.StatUtils; //导入方法依赖的package包/类
@Test
public void testSample() {
final double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
final int length = values.length;
final double mean = StatUtils.mean(values); // 6.333...
final SemiVariance sv = new SemiVariance(); // Default bias correction is true
final double downsideSemiVariance = sv.evaluate(values); // Downside is the default
Assert.assertEquals(TestUtils.sumSquareDev(new double[] {-2d, 2d, 4d, -2d, 3d, 5d}, mean) / (length - 1),
downsideSemiVariance, 1E-14);
sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
final double upsideSemiVariance = sv.evaluate(values);
Assert.assertEquals(TestUtils.sumSquareDev(new double[] {22d, 11d, 14d}, mean) / (length - 1),
upsideSemiVariance, 1E-14);
// Verify that upper + lower semivariance against the mean sum to variance
Assert.assertEquals(StatUtils.variance(values), downsideSemiVariance + upsideSemiVariance, 10e-12);
}
示例3: testSample
import org.apache.commons.math.stat.StatUtils; //导入方法依赖的package包/类
public void testSample() {
final double[] values = { -2.0d, 2.0d, 4.0d, -2.0d, 22.0d, 11.0d, 3.0d, 14.0d, 5.0d };
final int length = values.length;
final double mean = StatUtils.mean(values); // 6.333...
final SemiVariance sv = new SemiVariance(); // Default bias correction is true
final double downsideSemiVariance = sv.evaluate(values); // Downside is the default
assertEquals(TestUtils.sumSquareDev(new double[] {-2d, 2d, 4d, -2d, 3d, 5d}, mean) / (length - 1),
downsideSemiVariance, 1E-14);
sv.setVarianceDirection(SemiVariance.UPSIDE_VARIANCE);
final double upsideSemiVariance = sv.evaluate(values);
assertEquals(TestUtils.sumSquareDev(new double[] {22d, 11d, 14d}, mean) / (length - 1),
upsideSemiVariance, 1E-14);
// Verify that upper + lower semivariance against the mean sum to variance
assertEquals(StatUtils.variance(values), downsideSemiVariance + upsideSemiVariance, 10e-12);
}
示例4: createLeaf
import org.apache.commons.math.stat.StatUtils; //导入方法依赖的package包/类
@Override
protected <T extends ITrainingPoint<R, N>> DTNode<R, Double> createLeaf(Collection<T> set)
{
double[] values = new double[set.size()];
int i = 0;
for (T point : set)
{
values[i] = point.getContent().doubleValue();
i++;
}
double mean = StatUtils.mean(values);
// Create the leaf
DTNode<R, Double> leaf = new DTNode<R, Double>();
leaf.setContent(mean);
return leaf;
}
示例5: process
import org.apache.commons.math.stat.StatUtils; //导入方法依赖的package包/类
@Override
public void process(DoubleTimeSeries series) {
final long[] times = series.getTimes();
final double[] data = series.getData();
final int size = series.size();
for (int i = size - 1; i >= 0; i--) {
final long latest = times[i];
final long earliest = latest - length;
final DoubleTimeSeries spanoftime = series.get(earliest, latest);
data[i] = StatUtils.mean(spanoftime.getData());
}
}
示例6: getMean
import org.apache.commons.math.stat.StatUtils; //导入方法依赖的package包/类
/**
*
* @param list
* @return
*/
public double getMean(List<SynsetOut> list) {
double[] scores = new double[list.size()];
int l = 0;
for (SynsetOut out : list) {
scores[l] = out.getScore();
l++;
}
return StatUtils.mean(scores);
}
示例7: evaluate
import org.apache.commons.math.stat.StatUtils; //导入方法依赖的package包/类
@Override
public Double evaluate(ArrayList<C> leafsContent)
{
double[] evaluations = new double[leafsContent.size()];
for (int i = 0; i < evaluations.length; i++)
{
double value = leafsContent.get(i).doubleValue();
evaluations[i] = value;
}
double mean = StatUtils.mean(evaluations);
return mean;
}
示例8: compute
import org.apache.commons.math.stat.StatUtils; //导入方法依赖的package包/类
public static PairedEndStats compute(Iterator<Alignment> alignments, double minPercentile, double maxPercentile) {
double[] insertSizes = new double[MAX_PAIRS];
int nPairs = 0;
while (alignments.hasNext()) {
Alignment al = alignments.next();
if (isProperPair(al)) {
insertSizes[nPairs] = Math.abs(al.getInferredInsertSize());
nPairs++;
}
if (nPairs >= MAX_PAIRS) {
break;
}
}
if(nPairs == 0) {
log.error("Error computing insert size distribution. No alignments in sample interval.");
return null;
}
double mean = StatUtils.mean(insertSizes, 0, nPairs);
double median = StatUtils.percentile(insertSizes, 0, nPairs, 50);
double stdDev = Math.sqrt(StatUtils.variance(insertSizes, 0, nPairs));
double[] deviations = new double[nPairs];
for (int i = 0; i < nPairs; i++) {
deviations[i] = Math.abs(insertSizes[i] - median);
}
// MAD, as defined at http://stat.ethz.ch/R-manual/R-devel/library/stats/html/mad.html
double mad = 1.4826 * StatUtils.percentile(deviations, 50);
double sec = StatUtils.percentile(insertSizes, 0, nPairs, minPercentile);
double max = StatUtils.percentile(insertSizes, 0, nPairs, maxPercentile);
PairedEndStats stats = new PairedEndStats(mean, median, stdDev, mad, sec, max);
return stats;
}