本文整理汇总了Java中org.apache.commons.math3.stat.descriptive.DescriptiveStatistics.getKurtosis方法的典型用法代码示例。如果您正苦于以下问题：Java DescriptiveStatistics.getKurtosis方法的具体用法？Java DescriptiveStatistics.getKurtosis怎么用？Java DescriptiveStatistics.getKurtosis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.math3.stat.descriptive.DescriptiveStatistics的用法示例。
在下文中一共展示了DescriptiveStatistics.getKurtosis方法的3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: collectData
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
public void collectData()
{
collectPopulationData();
List<DescriptiveStatistics> allTdata = collectMoreTolData();
DescriptiveStatistics tolstats = allTdata.get(0);
DescriptiveStatistics ntolstats = allTdata.get(1);
DescriptiveStatistics mtolstats = allTdata.get(2);
double morans = calculateMoransI(meanBlue);
double moranTol = calculateMoransT(tolstats.getMean());
mC = morans;
mT = moranTol;
//print("<<collectData>> morans is " + morans + ", moranTol is " + moranTol);
String moranStr = "" + morans;
String moranTolStr = "" + moranTol;
String globalHappyStr = "" + globalHappiness;
String globalBlueHappy = "" + globalHappinessB;
String globalGreenHappy = "" + globalHappinessG;
String numAgentStr = "" + allAgents.size();
String totalMoveStr = String.valueOf(globalMoveCounter);
String numblue = String.valueOf(population[0]);
String numgreen = String.valueOf(population[1]);
String changedMindStr = "" + changedMind;
String tolN = "" + tolstats.getN();
String tolmean = "" + tolstats.getMean();
String tolvar = "" + tolstats.getVariance();
String tolstdDev = "" + tolstats.getStandardDeviation();
String tolKurt = "" + tolstats.getKurtosis();
String tolSkew = "" + tolstats.getSkewness();
String ntolN = "" + ntolstats.getN();
String ntolmean = "" + ntolstats.getMean();
String ntolvar = "" + ntolstats.getVariance();
String ntolstdDev = "" + ntolstats.getStandardDeviation();
String ntolKurt = "" + ntolstats.getKurtosis();
String ntolSkew = "" + ntolstats.getSkewness();
String mtolN = "" + mtolstats.getN();
String mtolmean = "" + mtolstats.getMean();
String mtolvar = "" + mtolstats.getVariance();
String mtolstdDev = "" + mtolstats.getStandardDeviation();
String mtolKurt = "" + mtolstats.getKurtosis();
String mtolSkew = "" + mtolstats.getSkewness();
String theString[] = null;
theString = new String[] { moranStr, moranTolStr, totalMoveStr, globalHappyStr, globalBlueHappy, globalGreenHappy, numAgentStr, numblue, numgreen, changedMindStr, tolN, tolmean, tolvar,
tolstdDev, tolKurt, tolSkew, ntolN, ntolmean, ntolvar, ntolstdDev, ntolKurt, ntolSkew, mtolN, mtolmean, mtolvar, mtolstdDev, mtolKurt, mtolSkew };
writer.writeNext(theString);
}
示例2: process
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
@Override
public Data process(Data input) {
Utils.isKeyValid(input, "NPIX", Integer.class);
npix = (Integer) input.get("NPIX");
int roi = (Integer) input.get("NROI");
Utils.checkWindow(searchWindowLeft, searchWindowRight - searchWindowLeft, 0, roi);
Utils.mapContainsKeys(input, dataKey);
double[] data = (double[]) input.get(dataKey);
double[] movingAverage;
if (movingAverageKey != null) {
movingAverage = (double[]) input.get(movingAverageKey);
} else {
movingAverage = new double[data.length];
}
double[] mean = new double[npix];
double[] median = new double[npix];
double[] mode = new double[npix];
double[] std = new double[npix];
double[] kurtosis = new double[npix];
double[] skewness = new double[npix];
double[] min = new double[npix];
double[] max = new double[npix];
double[] quantil25 = new double[npix];
double[] quantil75 = new double[npix];
for (int pix = 0; pix < npix; pix++) {
double[] values = new double[searchWindowRight - searchWindowLeft];
for (int sl = searchWindowLeft; sl < searchWindowRight; sl++) {
int slice = pix * roi + sl;
values[sl - searchWindowLeft] = data[slice];
int binNumber = findBinNumber((data[slice] - movingAverage[slice]));
histogram[binNumber] += 1;
}
DescriptiveStatistics stats = new DescriptiveStatistics(values);
mean[pix] = stats.getMean();
min[pix] = stats.getMin();
max[pix] = stats.getMax();
std[pix] = stats.getStandardDeviation();
skewness[pix] = stats.getSkewness();
kurtosis[pix] = stats.getKurtosis();
Percentile percentile = new Percentile();
median[pix] = percentile.evaluate(values, 0.5);
quantil25[pix] = percentile.evaluate(values, 0.25);
quantil75[pix] = percentile.evaluate(values, 0.75);
double[] modeArray = StatUtils.mode(values);
mode[pix] = modeArray[0];
}
input.put(outputKey + "_mean", mean);
input.put(outputKey + "_median", median);
input.put(outputKey + "_mode", mode);
input.put(outputKey + "_std", std);
input.put(outputKey + "_kurtosis", kurtosis);
input.put(outputKey + "_skewness", skewness);
input.put(outputKey + "_min", min);
input.put(outputKey + "_max", max);
input.put(outputKey + "_quantil25", quantil25);
input.put(outputKey + "_quantil75", quantil75);
input.put(outputKey + "_histogram", histogram);
return input;
}
示例3: process
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //导入方法依赖的package包/类
@Override
public Data process(Data item) {
Utils.isKeyValid(item, "NPIX", Integer.class);
npix = (Integer) item.get("NPIX");
Utils.mapContainsKeys(item, dataKey);
int[][] data = (int[][]) item.get(dataKey);
double[] mean = new double[npix];
double[] median = new double[npix];
double[] mode = new double[npix];
double[] std = new double[npix];
double[] kurtosis = new double[npix];
double[] skewness = new double[npix];
double[] min = new double[npix];
double[] max = new double[npix];
double[] quantil25 = new double[npix];
double[] quantil75 = new double[npix];
for (int pix = 0; pix < npix; pix++) {
double[] values = Utils.toDoubleArray(data[pix]);
///FIXME: fill nans instead of continue
if (values.length == 0) {
continue;
}
DescriptiveStatistics stats = new DescriptiveStatistics(values);
mean[pix] = stats.getMean();
min[pix] = stats.getMin();
max[pix] = stats.getMax();
std[pix] = stats.getStandardDeviation();
skewness[pix] = stats.getSkewness();
kurtosis[pix] = stats.getKurtosis();
quantil25[pix] = stats.getPercentile(0.25);
quantil75[pix] = stats.getPercentile(0.75);
median[pix] = stats.getPercentile(0.5);
double[] modeArray = StatUtils.mode(values);
mode[pix] = modeArray[0];
}
item.put(outputKey + "_mean", mean);
item.put(outputKey + "_median", median);
item.put(outputKey + "_mode", mode);
item.put(outputKey + "_std", std);
item.put(outputKey + "_kurtosis", kurtosis);
item.put(outputKey + "_skewness", skewness);
item.put(outputKey + "_min", min);
item.put(outputKey + "_max", max);
item.put(outputKey + "_quantil25", quantil25);
item.put(outputKey + "_quantil75", quantil75);
return item;
}