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
