k -> new XYIntervalSeries(moveType)); double yValue = levelValues[i];
k -> new XYIntervalSeries(moveType)); double yValue = levelValues[i];
int seriesIndex = 0; for (SingleBenchmarkResult singleBenchmarkResult : problemBenchmarkResult.getSingleBenchmarkResultList()) { XYIntervalSeries series = new XYIntervalSeries(singleBenchmarkResult.getSolverBenchmarkResult().getNameWithFavoriteSuffix()); XYItemRenderer renderer = new YIntervalRenderer(); if (singleBenchmarkResult.hasAllSuccess()) {
if(lDataSets!=null && lDataSets.size()>0) XYIntervalSeries series = new XYIntervalSeries(i); for(int j=1;j<=lDataSets.size();j++)
String groupName = it2.next(); int[] population = map.get(groupName); XYIntervalSeries pop = new XYIntervalSeries(groupName); if (inverted) { for (int i = numberOfCategories - 1; i >= 0; i--) {
private XYIntervalSeries computeRegressionData(double xStart, double xEnd) { if (scatterpointsDataset.getItemCount(0) > 1) { final double[] coefficients = Regression.getOLSRegression(scatterpointsDataset, 0); final Function2D curve = new LineFunction2D(coefficients[0], coefficients[1]); final XYSeries regressionData = DatasetUtilities.sampleFunction2DToSeries( curve, xStart, xEnd, 100, "regression line"); final XYIntervalSeries xyIntervalRegression = new XYIntervalSeries(regressionData.getKey()); final List<XYDataItem> regressionDataItems = regressionData.getItems(); for (XYDataItem item : regressionDataItems) { final double x = item.getXValue(); final double y = item.getYValue(); xyIntervalRegression.add(x, x, x, y, y, y); } return xyIntervalRegression; } else { JOptionPane.showMessageDialog(this, "Unable to compute regression line.\n" + "At least 2 values are needed to compute regression coefficients."); return null; } }
series[i] = new XYIntervalSeries(states[i]); dataset.addSeries(series[i]);
private XYIntervalSeries computeAcceptableDeviationData(double lowerBound, double upperBound) { final Function2D identityFunction = new Function2D() { @Override public double getValue(double x) { return x; } }; final XYSeries identity = DatasetUtilities.sampleFunction2DToSeries(identityFunction, lowerBound, upperBound, 100, "1:1 line"); final XYIntervalSeries xyIntervalSeries = new XYIntervalSeries(identity.getKey()); final List<XYDataItem> items = identity.getItems(); for (XYDataItem item : items) { final double x = item.getXValue(); final double y = item.getYValue(); if (scatterPlotModel.showAcceptableDeviation) { final double acceptableDeviation = scatterPlotModel.acceptableDeviationInterval; final double xOff = acceptableDeviation * x / 100; final double yOff = acceptableDeviation * y / 100; xyIntervalSeries.add(x, x - xOff, x + xOff, y, y - yOff, y + yOff); } else { xyIntervalSeries.add(x, x, x, y, y, y); } } return xyIntervalSeries; }
private XYIntervalSeries computeAcceptableDeviationData(double lowerBound, double upperBound) { final XYSeries identity = DatasetUtilities.sampleFunction2DToSeries(x -> x, lowerBound, upperBound, 100, "1:1 line"); final XYIntervalSeries xyIntervalSeries = new XYIntervalSeries(identity.getKey()); for (int i = 0; i < identity.getItemCount(); i++) { XYDataItem item = identity.getDataItem(i); final double x = item.getXValue(); final double y = item.getYValue(); if (scatterPlotModel.showAcceptableDeviation) { final double acceptableDeviation = scatterPlotModel.acceptableDeviationInterval; final double xOff = acceptableDeviation * x / 100; final double yOff = acceptableDeviation * y / 100; xyIntervalSeries.add(x, x - xOff, x + xOff, y, y - yOff, y + yOff); } else { xyIntervalSeries.add(x, x, x, y, y, y); } } return xyIntervalSeries; }
final float[] sampleValues = profileData.getSampleValues(); final float[] sampleSigmas = profileData.getSampleSigmas(); XYIntervalSeries series = new XYIntervalSeries(getRaster() != null ? getRaster().getName() : DEFAULT_SAMPLE_DATASET_NAME); for (int x = 0; x < sampleValues.length; x++) { final float y = sampleValues[x]; && dataSourceConfig.dataField != null) { XYIntervalSeries corrSeries = new XYIntervalSeries(getCorrelativeDataLabel(dataSourceConfig.pointDataSource, dataSourceConfig.dataField)); int[] shapeVertexIndexes = profileData.getShapeVertexIndexes(); SimpleFeature[] simpleFeatures = dataSourceConfig.pointDataSource.getFeatureCollection().toArray(new SimpleFeature[0]);
final float[] sampleValues = profileData.getSampleValues(); final float[] sampleSigmas = profileData.getSampleSigmas(); XYIntervalSeries series = new XYIntervalSeries(getRaster() != null ? getRaster().getName() : DEFAULT_SAMPLE_DATASET_NAME); for (int x = 0; x < sampleValues.length; x++) { final float y = sampleValues[x]; && dataSourceConfig.dataField != null) { XYIntervalSeries corrSeries = new XYIntervalSeries( StatisticChartStyling.getCorrelativeDataLabel(dataSourceConfig.pointDataSource, dataSourceConfig.dataField)); int[] shapeVertexIndexes = profileData.getShapeVertexIndexes();
private XYIntervalSeries computeRegressionData(double xStart, double xEnd) { if (scatterpointsDataset.getItemCount(0) > 1) { final double[] coefficients = Regression.getOLSRegression(scatterpointsDataset, 0); final Function2D curve = new LineFunction2D(coefficients[0], coefficients[1]); final XYSeries regressionData = DatasetUtilities.sampleFunction2DToSeries(curve, xStart, xEnd, 100, "regression line"); final XYIntervalSeries xyIntervalRegression = new XYIntervalSeries(regressionData.getKey()); for (int i = 0; i < regressionData.getItemCount(); i++) { XYDataItem item = regressionData.getDataItem(i); final double x = item.getXValue(); final double y = item.getYValue(); xyIntervalRegression.add(x, x, x, y, y, y); } return xyIntervalRegression; } else { Dialogs.showInformation("Unable to compute regression line.\n" + "At least 2 values are needed to compute regression coefficients."); return null; } }
final XYIntervalSeries scatterValues = new XYIntervalSeries(getCorrelativeDataName()); for (ComputedData computedData : computedDatas) { final float rasterMean = computedData.rasterMean;
final XYIntervalSeries scatterValues = new XYIntervalSeries(getCorrelativeDataName()); for (ComputedData computedData : computedDatas) { final float rasterMean = computedData.rasterMean;