Jfreechart实现多项式趋势线

package com.huawei.lda.chart.xy; import java.awt.Graphics2D; import java.awt.geom.Rectangle2D; import java.util.ArrayList; import java.util.List; import java.util.Vector; import org.jfree.chart.axis.ValueAxis; import org.jfree.chart.plot.PlotOrientation; import org.jfree.chart.plot.XYPlot; import org.jfree.chart.renderer.xy.XYItemRendererState; import org.jfree.chart.renderer.xy.XYSplineRenderer; import org.jfree.data.xy.XYDataset; import org.jfree.ui.RectangleEdge; public class XYThrendlineRenderer extends XYSplineRenderer { List<Double> x = new ArrayList<Double>(); List<Double> y = new ArrayList<Double>(); /** * To collect data points for later splining. */ private Vector points; /** * Resolution of splines (number of line segments between points) */ private int precision; private int step = 6; /** * Creates a new instance with the 'precision' attribute defaulting to 5. */ public XYThrendlineRenderer() { this(5); } /** * Creates a new renderer with the specified precision. * * @param precision * the number of points between data items. */ public XYThrendlineRenderer(int precision) { super(); if (precision <= 0) { throw new IllegalArgumentException("Requires precision > 0."); } this.precision = precision; } /** * Creates a new renderer with the specified precision. * * @param precision * the number of points between data items. */ public XYThrendlineRenderer(int precision, int step) { super(); if (precision <= 0) { throw new IllegalArgumentException("Requires precision > 0."); } if (step < 2) { throw new IllegalArgumentException("Requires step > 1"); } this.step = step; this.precision = precision; } protected void drawPrimaryLineAsPath(XYItemRendererState state, Graphics2D g2, XYPlot plot, XYDataset dataset, int pass, int series, int item, ValueAxis domainAxis, ValueAxis rangeAxis, Rectangle2D dataArea) { RectangleEdge xAxisLocation = plot.getDomainAxisEdge(); RectangleEdge yAxisLocation = plot.getRangeAxisEdge(); // get the data points double x1 = dataset.getXValue(series, item); double y1 = dataset.getYValue(series, item); double transX1 = domainAxis.valueToJava2D(x1, dataArea, xAxisLocation); double transY1 = rangeAxis.valueToJava2D(y1, dataArea, yAxisLocation); // collect points if (!Double.isNaN(transX1) && !Double.isNaN(transY1)) { x.add(transX1); y.add(transY1); } int itemCount = dataset.getItemCount(series); if (item == itemCount - 1) { State s = (State) state; double[] aa = PolyFit(x.toArray(new Double[itemCount]), y.toArray(new Double[itemCount]), itemCount, step); double oldx = x.get(0); double oldy = getY(oldx, x.toArray(new Double[itemCount]), aa, step); s.seriesPath.moveTo(oldx, oldy); for (int i = 0; i < itemCount - 1; i++) { double start = x.get(i); double end = x.get(i + 1); double rg = end - start; double stepValue = rg / precision; for (int j = 0; j < precision; j++) { double tx = start + j * stepValue; double ty = getY(tx, x.toArray(new Double[itemCount]), aa, step); s.seriesPath.lineTo(tx, ty); } } drawFirstPassShape(g2, pass, series, item, s.seriesPath); x.clear(); y.clear(); } } /** * <p> * 函数功能：最小二乘法曲线拟合 * </p> * * @param x * 实型一维数组，长度为 n 。存放给定 n 个数据点的　X　坐标 * @param y * 实型一维数组，长度为 n 。存放给定 n 个数据点的　Y　坐标 * @param n * 变量。给定数据点的个数 * @param a * 实型一维数组，长度为 m 。返回 m-1　次拟合多项式的 m 个系数 * @param m * 拟合多项式的项数，即拟合多项式的最高次数为 m-1. 要求 m<=n 且m<=20。若 m>n 或 m>20 * ，则本函数自动按 m=min{n,20} 处理. * <p> * Date:2007-12-25 16:21 PM * </p> * @author qingbao-gao * @return */ public static double[] PolyFit(Double x[], Double y[], int n, int m) { double a[] = new double[m]; int i, j, k; double z, p, c, g, q = 0, d1, d2; double[] s = new double[20]; double[] t = new double[20]; double[] b = new double[20]; double[] dt = new double[3]; for (i = 0; i <= m - 1; i++) { a[i] = 0.0; } if (m > n) { m = n; } if (m > 20) { m = 20; } z = 0.0; for (i = 0; i <= n - 1; i++) { z = z + x[i] / (1.0 * n); } b[0] = 1.0; d1 = 1.0 * n; p = 0.0; c = 0.0; for (i = 0; i <= n - 1; i++) { p = p + (x[i] - z); c = c + y[i]; } c = c / d1; p = p / d1; a[0] = c * b[0]; if (m > 1) { t[1] = 1.0; t[0] = -p; d2 = 0.0; c = 0.0; g = 0.0; for (i = 0; i <= n - 1; i++) { q = x[i] - z - p; d2 = d2 + q * q; c = c + y[i] * q; g = g + (x[i] - z) * q * q; } c = c / d2; p = g / d2; q = d2 / d1; d1 = d2; a[1] = c * t[1]; a[0] = c * t[0] + a[0]; } for (j = 2; j <= m - 1; j++) { s[j] = t[j - 1]; s[j - 1] = -p * t[j - 1] + t[j - 2]; if (j >= 3) for (k = j - 2; k >= 1; k--) { s[k] = -p * t[k] + t[k - 1] - q * b[k]; } s[0] = -p * t[0] - q * b[0]; d2 = 0.0; c = 0.0; g = 0.0; for (i = 0; i <= n - 1; i++) { q = s[j]; for (k = j - 1; k >= 0; k--) { q = q * (x[i] - z) + s[k]; } d2 = d2 + q * q; c = c + y[i] * q; g = g + (x[i] - z) * q * q; } c = c / d2; p = g / d2; q = d2 / d1; d1 = d2; a[j] = c * s[j]; t[j] = s[j]; for (k = j - 1; k >= 0; k--) { a[k] = c * s[k] + a[k]; b[k] = t[k]; t[k] = s[k]; } } dt[0] = 0.0; dt[1] = 0.0; dt[2] = 0.0; for (i = 0; i <= n - 1; i++) { q = a[m - 1]; for (k = m - 2; k >= 0; k--) { q = a[k] + q * (x[i] - z); } p = q - y[i]; if (Math.abs(p) > dt[2]) { dt[2] = Math.abs(p); } dt[0] = dt[0] + p * p; dt[1] = dt[1] + Math.abs(p); } return a; } /** * <p> * 对X轴数据节点球平均值 * </p> * * @param x * 存储X轴节点的数组 * <p> * Date:2007-12-25 20:21 PM * </p> * @author qingbao-gao * @return 平均值 */ public static double ave(Double[] x) { double ave = 0; double sum = 0; if (x != null) { for (int i = 0; i < x.length; i++) { sum += x[i]; } ave = sum / x.length; } return ave; } /** * <p> * 由X值获得Y值 * </p> * * @param x * 当前X轴输入值,即为预测的月份 * @param xx * 当前X轴输入值的前X数据点 * @param a * 存储多项式系数的数组 * @param m * 存储多项式的最高次数的数组 * <p> * Date:2007-12-25 PM 20:07 * </p> * <P> * Author:qingbao-gao * </P> * @return 对应X轴节点值的Y轴值 */ public static double getY(Double x, Double[] xx, double[] a, int m) { double y = 0; double ave = ave(xx); double l = 0; for (int i = 0; i < m; i++) { l = a[0]; if (i > 0) { y += a[i] * Math.pow((x - ave), i); } } return (y + l); } /** * Returns the parameters 'a' and 'b' for an equation y = a + bx, fitted to * the data using ordinary least squares regression. The result is returned * as a double[], where result[0] --> a, and result[1] --> b. * * @param data * the data. * * @return The parameters. */ public static double[] getOLSRegression(double[][] data) { int n = data.length; if (n < 2) { throw new IllegalArgumentException("Not enough data."); } double sumX = 0; double sumY = 0; double sumXX = 0; double sumXY = 0; for (int i = 0; i < n; i++) { double x = data[i][0]; double y = data[i][1]; sum