本文整理匯總了Java中org.apache.commons.math3.stat.descriptive.SummaryStatistics.clear方法的典型用法代碼示例。如果您正苦於以下問題：Java SummaryStatistics.clear方法的具體用法？Java SummaryStatistics.clear怎麽用？Java SummaryStatistics.clear使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類org.apache.commons.math3.stat.descriptive.SummaryStatistics的用法示例。
在下文中一共展示了SummaryStatistics.clear方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Java代碼示例。
示例1: populateStatistics
import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
/**
* Handles the process of injecting data into the summary statistics class for calculation.
*
* @param summaryStatistics The summary statistics object
* @param someData The data set to calculate statistics with
* @return The summary statistics object
*/
private SummaryStatistics populateStatistics(SummaryStatistics summaryStatistics, List<Data> someData)
{
summaryStatistics.clear();
for (Data someDatum : someData)
{
summaryStatistics.addValue(someDatum.getTemperature());
}
return summaryStatistics;
}
示例2: noisySeasonalTest
import org.apache.commons.math3.stat.descriptive.SummaryStatistics; //導入方法依賴的package包/類
@Test
public void noisySeasonalTest() {
long seed = 1234567L; // System.nanoTime() // change this to do random stress test
int numAverages = 1; // Increase to tighten the statistics on the sample statistics
int trials = 100;
double start = 1.5;
double delta = 0.0;
SummaryStatistics seasonalZScoreStats = new SummaryStatistics();
SummaryStatistics varianceFractionStats = new SummaryStatistics();
SummaryStatistics fractionClassifiedStats = new SummaryStatistics();
SummaryStatistics sampleZScoreStats = new SummaryStatistics();
SummaryStatistics sampleVarFracStats = new SummaryStatistics();
for (int j = 0; j < numAverages; ++j) {
int count = 0;
double seasonalAmplitude = start + delta * j;
double noiseSigma = 3.0;
for (int i = 0; i < trials; ++i) {
double[] data = testDataGenerator.createNoisySeasonalData(
168 * 4, 168, seasonalAmplitude, 0.0, noiseSigma, seed++);
Decomposition stl = SeasonalTrendLoess.performRobustPeriodicDecomposition(data, 168);
StlFitStats stats = new StlFitStats(stl);
assertTrue(stats.getTrendinessZScore() < 3.0);
stl.smoothSeasonal(15);
StlFitStats smoothedStats = new StlFitStats(stl);
double vf = smoothedStats.getSeasonalVariance() / smoothedStats.getResidualVariance();
varianceFractionStats.addValue(vf);
double z = smoothedStats.getSeasonalZScore();
seasonalZScoreStats.addValue(z);
if (z > 3.0)
++count;
}
double rate = ((double) count) / trials;
fractionClassifiedStats.addValue(rate);
sampleZScoreStats.addValue(seasonalZScoreStats.getMean());
sampleVarFracStats.addValue(varianceFractionStats.getMean());
seasonalZScoreStats.clear();
varianceFractionStats.clear();
}
assertTrue("Min Average Z-Score", sampleZScoreStats.getMin() > 3.13);
assertTrue("Avg Average Z-Score", Math.abs(sampleZScoreStats.getMean() - 3.64) < 0.06);
assertTrue("Max Average Z-Score", sampleZScoreStats.getMax() < 4.13);
assertTrue("Min Var Frac", sampleVarFracStats.getMin() > 0.173);
assertTrue("Avg Var Frac", Math.abs(sampleVarFracStats.getMean() - 0.193) < 0.01);
assertTrue("Max Var Frac", sampleVarFracStats.getMax() < 0.213);
}