List of usage examples for org.apache.commons.math3.stat.descriptive DescriptiveStatistics getStandardDeviation
public double getStandardDeviation()
From source file:org.matsim.contrib.taxi.util.stats.TaxiStatsWriter.java
private void addStats(CSVLineBuilder lineBuilder, String format1, String format2, DescriptiveStatistics stats) { lineBuilder.addf(format1, stats.getMean()).// addf(format1, stats.getStandardDeviation()).// addEmpty().// addf(format2, stats.getMin()). // addf(format2, stats.getPercentile(2)). // addf(format2, stats.getPercentile(5)). // addf(format2, stats.getPercentile(25)). // addf(format2, stats.getPercentile(50)). // addf(format2, stats.getPercentile(75)). // addf(format2, stats.getPercentile(95)). // addf(format2, stats.getPercentile(98)). // addf(format2, stats.getMax()); }
From source file:org.mot.common.math.CalculatorFactory.java
public Double getTimeWindowDeviation(Number[] data, Integer windowSize) { // Create a DescriptiveStats instance and set the window size to 100 DescriptiveStatistics stats = new DescriptiveStatistics(); stats.setWindowSize(windowSize);/* w ww . j a va2 s . c o m*/ if (windowSize == 0) { windowSize = data.length; } for (int i = 0; i < data.length; i++) { if (data[i] != null) { stats.addValue((Double) data[i]); } else { break; } } return stats.getStandardDeviation(); }
From source file:org.mskcc.cbio.portal.scripts.ImportProteinArrayData.java
private double[] convertToZscores(String[] strs) { double[] data = new double[strs.length - 1]; boolean nan = false; for (int i = 1; i < strs.length; i++) { // ignore the first column try {/*from w w w . j ava2s . c om*/ data[i - 1] = Double.parseDouble(strs[i]); } catch (Exception e) { data[i - 1] = Double.NaN; nan = true; } } DescriptiveStatistics ds = new DescriptiveStatistics(nan ? copyWithNoNaN(data) : data); double mean = ds.getMean(); double std = ds.getStandardDeviation(); for (int i = 0; i < data.length; i++) { if (!Double.isNaN(data[i])) { data[i] = (data[i] - mean) / std; } } return data; }
From source file:org.obiba.opal.web.magma.Dtos.java
@SuppressWarnings({ "OverlyLongMethod", "PMD.NcssMethodCount" })
public static Math.ContinuousSummaryDto.Builder asDto(ContinuousVariableSummary summary) {
DescriptiveStatistics descriptiveStats = summary.getDescriptiveStats();
Math.DescriptiveStatsDto.Builder descriptiveBuilder = Math.DescriptiveStatsDto.newBuilder()
.setN(descriptiveStats.getN()).addAllPercentiles(summary.getPercentiles());
if (isNumeric(descriptiveStats.getMin()))
descriptiveBuilder.setMin(descriptiveStats.getMin());
if (isNumeric(descriptiveStats.getMax()))
descriptiveBuilder.setMax(descriptiveStats.getMax());
if (isNumeric(descriptiveStats.getMean()))
descriptiveBuilder.setMean(descriptiveStats.getMean());
if (isNumeric(descriptiveStats.getSum()))
descriptiveBuilder.setSum(descriptiveStats.getSum());
if (isNumeric(descriptiveStats.getSumsq()))
descriptiveBuilder.setSumsq(descriptiveStats.getSumsq());
if (isNumeric(descriptiveStats.getStandardDeviation())) {
descriptiveBuilder.setStdDev(descriptiveStats.getStandardDeviation());
}//from w w w . j ava2 s.c o m
if (isNumeric(descriptiveStats.getVariance()))
descriptiveBuilder.setVariance(descriptiveStats.getVariance());
if (isNumeric(descriptiveStats.getSkewness()))
descriptiveBuilder.setSkewness(descriptiveStats.getSkewness());
if (isNumeric(descriptiveStats.getGeometricMean())) {
descriptiveBuilder.setGeometricMean(descriptiveStats.getGeometricMean());
}
if (isNumeric(descriptiveStats.getKurtosis()))
descriptiveBuilder.setKurtosis(descriptiveStats.getKurtosis());
double median = descriptiveStats.apply(new Median());
if (isNumeric(median))
descriptiveBuilder.setMedian(median);
if (isNumeric(descriptiveStats.getVariance()))
descriptiveBuilder.setVariance(descriptiveStats.getVariance());
Math.ContinuousSummaryDto.Builder continuousBuilder = Math.ContinuousSummaryDto.newBuilder()
.addAllDistributionPercentiles(summary.getDistributionPercentiles());
for (IntervalFrequency.Interval interval : summary.getIntervalFrequencies()) {
Math.IntervalFrequencyDto.Builder freqBuilder = Math.IntervalFrequencyDto.newBuilder()
.setFreq(interval.getFreq());
if (isNumeric(interval.getLower()))
freqBuilder.setLower(interval.getLower());
if (isNumeric(interval.getUpper()))
freqBuilder.setUpper(interval.getUpper());
if (isNumeric(interval.getDensity()))
freqBuilder.setDensity(interval.getDensity());
if (isNumeric(interval.getDensityPct()))
freqBuilder.setDensityPct(interval.getDensityPct());
continuousBuilder.addIntervalFrequency(freqBuilder);
}
for (ContinuousVariableSummary.Frequency frequency : summary.getFrequencies()) {
Math.FrequencyDto.Builder freqBuilder = Math.FrequencyDto.newBuilder() //
.setValue(frequency.getValue()) //
.setFreq(frequency.getFreq())//
.setMissing(frequency.isMissing());
if (isNumeric(frequency.getPct()))
freqBuilder.setPct(frequency.getPct());
continuousBuilder.addFrequencies(freqBuilder);
}
return continuousBuilder.setSummary(descriptiveBuilder);
}
From source file:org.specvis.view.screenandlumscale.ViewFitLumScaleController.java
private void setValuesForTextFields() { // 1. Get luminance scale. LuminanceScale luminanceScale;// w w w. ja v a 2 s .c o m if (StartApplication.getSpecvisData().getUiSettingsScreenAndLuminanceScale() .isThisWindowOpenedForStimulus()) { luminanceScale = StartApplication.getSpecvisData().getUiSettingsScreenAndLuminanceScale() .getStimulusLuminanceScale(); } else { luminanceScale = StartApplication.getSpecvisData().getUiSettingsScreenAndLuminanceScale() .getBackgroundLuminanceScale(); } // 2. Get measured and fitted luminance values. double[] measuredLuminances = new double[] { luminanceScale.getLuminanceForBrightness0(), luminanceScale.getLuminanceForBrightness20(), luminanceScale.getLuminanceForBrightness40(), luminanceScale.getLuminanceForBrightness60(), luminanceScale.getLuminanceForBrightness80(), luminanceScale.getLuminanceForBrightness100() }; double[] fittedLuminances = new double[] { luminanceScale.getFittedLuminanceForEachBrightnessValue()[0], luminanceScale.getFittedLuminanceForEachBrightnessValue()[20], luminanceScale.getFittedLuminanceForEachBrightnessValue()[40], luminanceScale.getFittedLuminanceForEachBrightnessValue()[60], luminanceScale.getFittedLuminanceForEachBrightnessValue()[80], luminanceScale.getFittedLuminanceForEachBrightnessValue()[100] }; // 2.1. Set negative fitted values to 0.1. Chi-squared analysis don't "take" negative expected values. for (int i = 0; i < fittedLuminances.length; i++) { if (fittedLuminances[i] < 0.0) { fittedLuminances[i] = 0.1; } } // 3. Calculate difference between measured and fitted values. double[] differenceBetweenMeasuredAndFittedLuminances = new double[measuredLuminances.length]; for (int i = 0; i < measuredLuminances.length; i++) { differenceBetweenMeasuredAndFittedLuminances[i] = functions .round(measuredLuminances[i] - fittedLuminances[i], 2); } // 4. Calculate standard deviation between measured and fitted values. DescriptiveStatistics descriptiveStatistics = new DescriptiveStatistics(); for (double differenceBetweenMeasuredAndFittedLuminance : differenceBetweenMeasuredAndFittedLuminances) { descriptiveStatistics.addValue(differenceBetweenMeasuredAndFittedLuminance); } double standardDeviation = functions.round(descriptiveStatistics.getStandardDeviation(), 2); textFieldSTD.setText(String.valueOf(standardDeviation)); // 4. Calculate Chi-squared and P-value for fitted luminance values. double[] expectedValues = fittedLuminances; double[] observedValues = measuredLuminances; int degreesOfFreedom = observedValues.length - 1; ChiSquared chiSquared = new ChiSquared(); double criticalValue = functions.round(chiSquared.calculateCriticalValue(expectedValues, observedValues), 4); double pValue = functions.round(chiSquared.calculateChiSquaredPValue(degreesOfFreedom, criticalValue), 4); textFieldChiSquared.setText(String.valueOf(criticalValue)); textFieldPValue.setText(String.valueOf(pValue)); }
From source file:org.uma.jmetal.util.experiment.component.GenerateLatexTablesWithStatistics.java
/** * Computes the statistical values//www . java 2 s . c om * @param values * @return */ private Map<String, Double> computeStatistics(List<Double> values) { Map<String, Double> results = new HashMap<>(); DescriptiveStatistics stats = new DescriptiveStatistics(); for (Double value : values) { stats.addValue(value); } results.put("mean", stats.getMean()); results.put("median", stats.getPercentile(50.0)); results.put("stdDeviation", stats.getStandardDeviation()); results.put("iqr", stats.getPercentile(75) - stats.getPercentile(25)); results.put("max", stats.getMax()); results.put("min", stats.getMean()); results.put("numberOfElements", (double) values.size()); return results; }
From source file:org.wildfly.swarm.proc.CSVCollector.java
public void onFinish(String id) { List<Object> record = new ArrayList<>(); record.add(id);/*from ww w. j a v a 2 s .c o m*/ record.add(Paths.get(id).getFileName()); for (Measure m : Measure.values()) { if (!results.containsKey(m)) { throw new RuntimeException("Measurement is missing " + m); } DescriptiveStatistics stats = results.get(m); record.add(stats.getN()); record.add(stats.getMin()); record.add(stats.getMax()); record.add(stats.getMean()); record.add(stats.getStandardDeviation()); record.add(stats.getPercentile(50)); record.add(stats.getPercentile(75)); } try { csvOutput.printRecord(record); csvOutput.flush(); } catch (IOException e) { throw new RuntimeException("Failed to write data", e); } }
From source file:org.wso2.carbon.ml.database.internal.MLDatabaseService.java
/** * Create the JSON string with summary statistics for a column. * * @param type Data-type of the column/*from w w w . ja v a 2 s.com*/ * @param graphFrequencies Bin frequencies of the column * @param missing Number of missing values in the column * @param unique Number of unique values in the column * @param descriptiveStats DescriptiveStats object of the column * @return JSON representation of the summary statistics of the column */ private JSONArray createJson(String type, SortedMap<?, Integer> graphFrequencies, int missing, int unique, DescriptiveStatistics descriptiveStats) throws JSONException { JSONObject json = new JSONObject(); JSONArray freqs = new JSONArray(); Object[] categoryNames = graphFrequencies.keySet().toArray(); // Create an array with intervals/categories and their frequencies. for (int i = 0; i < graphFrequencies.size(); i++) { JSONArray temp = new JSONArray(); temp.put(categoryNames[i].toString()); temp.put(graphFrequencies.get(categoryNames[i])); freqs.put(temp); } // Put the statistics to a json object json.put("unique", unique); json.put("missing", missing); DecimalFormat decimalFormat = new DecimalFormat("#.###"); if (descriptiveStats.getN() != 0) { json.put("mean", decimalFormat.format(descriptiveStats.getMean())); json.put("min", decimalFormat.format(descriptiveStats.getMin())); json.put("max", decimalFormat.format(descriptiveStats.getMax())); json.put("median", decimalFormat.format(descriptiveStats.getPercentile(50))); json.put("std", decimalFormat.format(descriptiveStats.getStandardDeviation())); if (type.equalsIgnoreCase(FeatureType.NUMERICAL)) { json.put("skewness", decimalFormat.format(descriptiveStats.getSkewness())); } } json.put("values", freqs); json.put("bar", true); json.put("key", "Frequency"); JSONArray summaryStatArray = new JSONArray(); summaryStatArray.put(json); return summaryStatArray; }
From source file:org.wso2.carbon.ml.dataset.internal.DatabaseHandler.java
/** * Create the JSON string with summary statistics for a column. * * @param type Data-type of the column * @param graphFrequencies Bin frequencies of the column * @param missing Number of missing values in the column * @param unique Number of unique values in the column * @param descriptiveStats DescriptiveStats object of the column * @return JSON representation of the summary statistics of the column */// www.j a v a2 s. co m private JSONArray createJson(String type, SortedMap<?, Integer> graphFrequencies, int missing, int unique, DescriptiveStatistics descriptiveStats) { JSONObject json = new JSONObject(); JSONArray freqs = new JSONArray(); Object[] categoryNames = graphFrequencies.keySet().toArray(); // Create an array with intervals/categories and their frequencies. for (int i = 0; i < graphFrequencies.size(); i++) { JSONArray temp = new JSONArray(); temp.put(categoryNames[i].toString()); temp.put(graphFrequencies.get(categoryNames[i])); freqs.put(temp); } // Put the statistics to a json object json.put("unique", unique); json.put("missing", missing); DecimalFormat decimalFormat = new DecimalFormat("#.###"); if (descriptiveStats.getN() != 0) { json.put("mean", decimalFormat.format(descriptiveStats.getMean())); json.put("median", decimalFormat.format(descriptiveStats.getPercentile(50))); json.put("std", decimalFormat.format(descriptiveStats.getStandardDeviation())); if (type.equalsIgnoreCase(FeatureType.NUMERICAL)) { json.put("skewness", decimalFormat.format(descriptiveStats.getSkewness())); } } json.put("values", freqs); json.put("bar", true); json.put("key", "Frequency"); JSONArray summaryStatArray = new JSONArray(); summaryStatArray.put(json); return summaryStatArray; }
From source file:performancestatisticsasset.DistributionSet.java
public void setDistributionSet(String targetGroup, String targetTask, String targetMeasure, RecordList dataSet) {/*from w w w. j ava 2 s . c o m*/ //Determine the number of trials selected by group and by task int trials = 0; int len = dataSet.records.size(); for (int i = 0; i < len; i++) { if (dataSet.records.get(i).getTrialNumber() > trials) trials = dataSet.records.get(i).getTrialNumber(); } Distribution tempDist; DescriptiveStatistics tempStat; //For each trial of the set do for (int i = 0; i < trials; i++) { tempDist = new Distribution(); tempStat = new DescriptiveStatistics(); //Select data for (int j = 0; j < len; j++) { //If the current record is of the correct trial if ((dataSet.records.get(j).getTrialNumber() == i + 1) && (targetGroup.equals(dataSet.records.get(j).getGroupID())) && (targetTask.equals(dataSet.records.get(j).getTaskID()))) { //Fill distribution switch (targetMeasure) { case "time": tempStat.addValue(dataSet.records.get(j).getTimeToComplete()); break; case "perf": tempStat.addValue(dataSet.records.get(j).getPerformance()); break; } } } //Transfer the computed statistics to tempDist tempDist.max = tempStat.getMax(); tempDist.min = tempStat.getMin(); tempDist.sum = tempStat.getSum(); tempDist.variance = tempStat.getVariance(); tempDist.mean = tempStat.getMean(); tempDist.stdDev = tempStat.getStandardDeviation(); tempDist.skewness = tempStat.getSkewness(); tempDist.kurtosis = tempStat.getKurtosis(); tempDist.n = tempStat.getN(); //Add tempDist to distributionSet distributionSet.add(tempDist); } }