Java Regression.getOLSRegression方法代码示例

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 1 AFTER converting it to
 * an XYSeries.
 */
public void testOLSRegression1b() {

    final double[][] data = createSampleData1();

    final XYSeries series = new XYSeries("Test");
    for (int i = 0; i < 11; i++) {
        series.add(data[i][0], data[i][1]);
    }
    final XYDataset ds = new XYSeriesCollection(series);
    final double[] result2 = Regression.getOLSRegression(ds, 0);

    assertEquals(.25680930, result2[0], 0.0000001);
    assertEquals(0.72792106, result2[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 2 AFTER converting it to
 * an XYSeries.
 */
public void testOLSRegression2b() {

    final double[][] data = createSampleData2();

    final XYSeries series = new XYSeries("Test");
    for (int i = 0; i < 10; i++) {
        series.add(data[i][0], data[i][1]);
    }
    final XYDataset ds = new XYSeriesCollection(series);
    final double[] result = Regression.getOLSRegression(ds, 0);

    assertEquals(53.9729697, result[0], 0.0000001);
    assertEquals(-4.1823030, result[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 1 AFTER 
 * converting it to an XYSeries.
 */
public void testOLSRegression1b() {

    double[][] data = createSampleData1();

    XYSeries series = new XYSeries("Test");
    for (int i = 0; i < 11; i++) {
        series.add(data[i][0], data[i][1]);
    }
    XYDataset ds = new XYSeriesCollection(series);
    double[] result2 = Regression.getOLSRegression(ds, 0);

    assertEquals(.25680930, result2[0], 0.0000001);
    assertEquals(0.72792106, result2[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 2 AFTER 
 * converting it to an XYSeries.
 */
public void testOLSRegression2b() {

    double[][] data = createSampleData2();

    XYSeries series = new XYSeries("Test");
    for (int i = 0; i < 10; i++) {
        series.add(data[i][0], data[i][1]);
    }
    XYDataset ds = new XYSeriesCollection(series);
    double[] result = Regression.getOLSRegression(ds, 0);

    assertEquals(53.9729697, result[0], 0.0000001);
    assertEquals(-4.1823030, result[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 1 AFTER
 * converting it to an XYSeries.
 */
public void testOLSRegression1b() {

    double[][] data = createSampleData1();

    XYSeries series = new XYSeries("Test");
    for (int i = 0; i < 11; i++) {
        series.add(data[i][0], data[i][1]);
    }
    XYDataset ds = new XYSeriesCollection(series);
    double[] result2 = Regression.getOLSRegression(ds, 0);

    assertEquals(.25680930, result2[0], 0.0000001);
    assertEquals(0.72792106, result2[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 2 AFTER
 * converting it to an XYSeries.
 */
public void testOLSRegression2b() {

    double[][] data = createSampleData2();

    XYSeries series = new XYSeries("Test");
    for (int i = 0; i < 10; i++) {
        series.add(data[i][0], data[i][1]);
    }
    XYDataset ds = new XYSeriesCollection(series);
    double[] result = Regression.getOLSRegression(ds, 0);

    assertEquals(53.9729697, result[0], 0.0000001);
    assertEquals(-4.1823030, result[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/** Handles drawing of the regression line */
private void drawRegression(XYDataset scatterPlotData, XYPlot xyplot) {
	// compute regression function
	double regressionParams[] = Regression.getOLSRegression(
			scatterPlotData, 0);
	Function2D regression = new LineFunction2D(regressionParams[0],
			regressionParams[1]);

	// sample function values
	double lowerBound = xyplot.getDomainAxis().getLowerBound();
	double upperBound = xyplot.getDomainAxis().getUpperBound();
	XYDataset regressionDataset = DatasetUtilities.sampleFunction2D(
			regression, lowerBound, upperBound,
			REGRESSION_LINE_SAMPLE_COUNT, "Fitted Regression Line");
	xyplot.setDataset(1, regressionDataset);

	// render regression line into plot
	XYLineAndShapeRenderer renderer = new XYLineAndShapeRenderer(true,
			false);
	xyplot.setRenderer(1, renderer);
	renderer.setSeriesPaint(0, REGRESSION_LINE_COLOR);
}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
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;
    }
}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
protected boolean plotRel2dChartData(String xAxisName, ArrayList<Double> xarr, String yAxisName, ArrayList<Double> yarr) {
    clear2dChartData();
    if (setXYSeries(runData, xarr, yarr)) {
        double[] ols = Regression.getOLSRegression(chartPanel.getChart().getXYPlot().getDataset(1), 0);
        Function2D curve = new LineFunction2D(ols[0], ols[1]);
        trendData.clear();
        trendData.add(runData.getMinX(), curve.getValue(runData.getMinX()));
        trendData.add(runData.getMaxX(), curve.getValue(runData.getMaxX()));
        double paddingX = runData.getMaxX() * 0.05;
        double paddingY = runData.getMaxY() * 0.05;
        XYPlot plot = chartPanel.getChart().getXYPlot();
        plot.getDomainAxis(0).setRange(runData.getMinX() - paddingX, runData.getMaxX() + paddingX);
        plot.getRangeAxis(0).setRange(runData.getMinY() - paddingY, runData.getMaxY() + paddingY);
        plot.getDomainAxis(0).setLabel(xAxisName);
        plot.getRangeAxis(0).setLabel(yAxisName);
        plot.getRenderer(0).setSeriesVisible(0, true);
        return true;
    }
    return false;
}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 1.
 */
public void testOLSRegression1a() {

    final double[][] data = createSampleData1();
    final double[] result1 = Regression.getOLSRegression(data);
    assertEquals(.25680930, result1[0], 0.0000001);
    assertEquals(0.72792106, result1[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 2.
 */
public void testOLSRegression2a() {

    final double[][] data = createSampleData2();
    final double[] result = Regression.getOLSRegression(data);
    assertEquals(53.9729697, result[0], 0.0000001);
    assertEquals(-4.1823030, result[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 1.
 */
public void testOLSRegression1a() {

    double[][] data = createSampleData1();
    double[] result1 = Regression.getOLSRegression(data);
    assertEquals(.25680930, result1[0], 0.0000001);
    assertEquals(0.72792106, result1[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
/**
 * Checks the results of an OLS regression on sample dataset 2.
 */
public void testOLSRegression2a() {

    double[][] data = createSampleData2();
    double[] result = Regression.getOLSRegression(data);
    assertEquals(53.9729697, result[0], 0.0000001);
    assertEquals(-4.1823030, result[1], 0.0000001);

}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
private boolean plotTrimRpmData() {
    if (setXYSeries(runData, rpmArray, trimArray)) {
        double[] ols = Regression.getOLSRegression(mafChartPanel.getChartPanel().getChart().getXYPlot().getDataset(1), 0);
        Function2D curve = new LineFunction2D(ols[0], ols[1]);
        currMafData.clear();
        currMafData.add(runData.getMinX(), curve.getValue(runData.getMinX()));
        currMafData.add(runData.getMaxX(), curve.getValue(runData.getMaxX()));
        return true;
    }
    return false;
}
 

import org.jfree.data.statistics.Regression; //导入方法依赖的package包/类
private void computeCoefficientOfDetermination() {
    int numberOfItems = scatterpointsDataset.getSeries(0).getItemCount();
    double arithmeticMeanOfX = 0;  //arithmetic mean of X
    double arithmeticMeanOfY = 0;  //arithmetic mean of Y
    double varX = 0;    //variance of X
    double varY = 0;    //variance of Y
    double coVarXY = 0;  //covariance of X and Y;
    //compute arithmetic means
    for (int i = 0; i < numberOfItems; i++) {
        arithmeticMeanOfX += scatterpointsDataset.getXValue(0, i);
        arithmeticMeanOfY += scatterpointsDataset.getYValue(0, i);
    }
    arithmeticMeanOfX /= numberOfItems;
    arithmeticMeanOfY /= numberOfItems;
    //compute variances and covariance
    for (int i = 0; i < numberOfItems; i++) {
        varX += Math.pow(scatterpointsDataset.getXValue(0, i) - arithmeticMeanOfX, 2);
        varY += Math.pow(scatterpointsDataset.getYValue(0, i) - arithmeticMeanOfY, 2);
        coVarXY += (scatterpointsDataset.getXValue(0, i) - arithmeticMeanOfX) * (scatterpointsDataset.getYValue(0,
                                                                                                                i) -
                arithmeticMeanOfY);
    }
    //computation of coefficient of determination
    double r2 = Math.pow(coVarXY, 2) / (varX * varY);
    r2 = MathUtils.round(r2, Math.pow(10.0, 5));

    final double[] coefficients = Regression.getOLSRegression(scatterpointsDataset, 0);
    final double intercept = coefficients[0];
    final double slope = coefficients[1];
    final String linearEquation;
    if (intercept >= 0) {
        linearEquation = "y = " + (float) slope + "x + " + (float) intercept;
    } else {
        linearEquation = "y = " + (float) slope + "x - " + Math.abs((float) intercept);
    }

    TextTitle tt = new TextTitle(linearEquation + "\nR² = " + r2);
    tt.setTextAlignment(HorizontalAlignment.RIGHT);
    tt.setFont(chart.getLegend().getItemFont());
    tt.setBackgroundPaint(new Color(200, 200, 255, 100));
    tt.setFrame(new BlockBorder(Color.white));
    tt.setPosition(RectangleEdge.BOTTOM);

    r2Annotation = new XYTitleAnnotation(0.98, 0.02, tt,
                                         RectangleAnchor.BOTTOM_RIGHT);
    r2Annotation.setMaxWidth(0.48);
    getPlot().addAnnotation(r2Annotation);
}