本文整理匯總了Java中org.apache.commons.math3.stat.descriptive.DescriptiveStatistics.getMin方法的典型用法代碼示例。如果您正苦於以下問題：Java DescriptiveStatistics.getMin方法的具體用法？Java DescriptiveStatistics.getMin怎麽用？Java DescriptiveStatistics.getMin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.DescriptiveStatistics的用法示例。
在下文中一共展示了DescriptiveStatistics.getMin方法的3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: getMin
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //導入方法依賴的package包/類
public Double getMin(final String feature) {
final DescriptiveStatistics stat = stats.get(feature);
return stat == null ? null : stat.getMin();
}
示例2: process
import org.apache.commons.math3.stat.descriptive.DescriptiveStatistics; //導入方法依賴的package包/類
@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; //導入方法依賴的package包/類
@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;
}