| 最近在写一个荧光图像分析软件，需要自己拟合方程。一元回归线公式的算法参考了《Java数值方法》，拟合度R^2（绝对系数）是自己写的，欢迎讨论。计算结果和Excel完全一致。 总共三个文件： DataPoint.java /**
* A data point for interpolation and regression.
*/
public class DataPoint
{
/** the x value */ public float x;
/** the y value */ public float y; /**
* Constructor.
* @param x the x value
* @param y the y value
*/
public DataPoint(float x, float y)
{
this.x = x;
this.y = y; }
} /**
* A least-squares regression line function.
*/ import java.util.*;
import java.math.BigDecimal; public class RegressionLine
//implements Evaluatable
{
/** sum of x */ private double sumX;
/** sum of y */ private double sumY;
/** sum of x*x */ private double sumXX;
/** sum of x*y */ private double sumXY;
/** sum of y*y */ private double sumYY;
/** sum of yi-y */ private double sumDeltaY;
/** sum of sumDeltaY^2 */ private double sumDeltaY2;
/**误差 */
private double sse;
private double sst;
private double E;
private String[] xy ;
private ArrayList listX ;
private ArrayList listY ;
private int XMin,XMax,YMin,YMax;
/** line coefficient a0 */ private float a0;
/** line coefficient a1 */ private float a1; /** number of data points */ private int pn ;
/** true if coefficients valid */ private boolean coefsValid; /**
* Constructor.
*/
public RegressionLine() {
XMax = 0;
YMax = 0;
pn = 0;
xy =new String[2];
listX = new ArrayList();
listY = new ArrayList();
} /**
* Constructor.
* @param data the array of data points
*/
public RegressionLine(DataPoint data[])
{
pn = 0;
xy =new String[2];
listX = new ArrayList();
listY = new ArrayList();
for (int i = 0; i < data.length; ++i) {
addDataPoint(data[i]);
}
} /**
* Return the current number of data points.
* @return the count
*/
public int getDataPointCount() { return pn; } /**
* Return the coefficient a0.
* @return the value of a0
*/
public float getA0()
{
validateCoefficients();
return a0;
} /**
* Return the coefficient a1.
* @return the value of a1
*/
public float getA1()
{
validateCoefficients();
return a1;
} /**
* Return the sum of the x values.
* @return the sum
*/
public double getSumX() { return sumX; } /**
* Return the sum of the y values.
* @return the sum
*/
public double getSumY() { return sumY; } /**
* Return the sum of the x*x values.
* @return the sum
*/
public double getSumXX() { return sumXX; } /**
* Return the sum of the x*y values.
* @return the sum
*/
public double getSumXY() { return sumXY; }
public double getSumYY() { return sumYY; }
public int getXMin() {
return XMin;
} public int getXMax() {
return XMax;
} public int getYMin() {
return YMin;
} public int getYMax() {
return YMax;
}
/**
* Add a new data point: Update the sums.
* @param dataPoint the new data point
*/
public void addDataPoint(DataPoint dataPoint)
{
sumX += dataPoint.x;
sumY += dataPoint.y;
sumXX += dataPoint.x*dataPoint.x;
sumXY += dataPoint.x*dataPoint.y;
sumYY += dataPoint.y*dataPoint.y;
if(dataPoint.x > XMax){
XMax = (int)dataPoint.x;
}
if(dataPoint.y > YMax){
YMax = (int)dataPoint.y;
}
//把每个点的具体坐标存入ArrayList中，备用
xy[0] = (int)dataPoint.x+ "";
xy[1] = (int)dataPoint.y+ "";
if(dataPoint.x!=0 && dataPoint.y != 0){
System.out.print(xy[0]+",");
System.out.println(xy[1]);
try{
//System.out.println("n:"+n);
listX.add(pn,xy[0]);
listY.add(pn,xy[1]);
}
catch(Exception e){
e.printStackTrace();
}
/*
System.out.println("N:" + n);
System.out.println("ArrayList listX:"+ listX.get(n));
System.out.println("ArrayList listY:"+ listY.get(n));
*/
}
++pn;
coefsValid = false;
} /**
* Return the value of the regression line function at x.
* (Implementation of Evaluatable.)
* @param x the value of x
* @return the value of the function at x
*/
public float at(int x)
{
if (pn < 2) return Float.NaN; validateCoefficients();
return a0 + a1*x;
}
public float at(float x)
{
if (pn < 2) return Float.NaN; validateCoefficients();
return a0 + a1*x;
} /**
* Reset.
*/
public void reset()
{
pn = 0;
sumX = sumY = sumXX = sumXY = 0;
coefsValid = false;
} /**
* Validate the coefficients.
* 计算方程系数 y=ax+b 中的a
*/
private void validateCoefficients()
{
if (coefsValid) return; if (pn >= 2) {
float xBar = (float) sumX/pn;
float yBar = (float) sumY/pn; a1 = (float) ((pn*sumXY - sumX*sumY)
/(pn*sumXX - sumX*sumX));
a0 = (float) (yBar - a1*xBar);
}
else {
a0 = a1 = Float.NaN;
} coefsValid = true;
}
/**
* 返回误差
*/
public double getR(){
//遍历这个list并计算分母
for(int i = 0; i < pn -1; i++) {
float Yi= (float)Integer.parseInt(listY.get(i).toString());
float Y = at(Integer.parseInt(listX.get(i).toString()));
float deltaY = Yi - Y;
float deltaY2 = deltaY*deltaY;
/*
System.out.println("Yi:" + Yi);
System.out.println("Y:" + Y);
System.out.println("deltaY:" + deltaY);
System.out.println("deltaY2:" + deltaY2);
*/
sumDeltaY2 += deltaY2;
//System.out.println("sumDeltaY2:" + sumDeltaY2);
}
sst = sumYY - (sumY*sumY)/pn;
//System.out.println("sst:" + sst);
E =1- sumDeltaY2/sst;
return round(E,4) ;
}
//用于实现精确的四舍五入
public double round(double v,int scale){ if(scale<0){
throw new IllegalArgumentException(
"The scale must be a positive integer or zero");
}
BigDecimal b = new BigDecimal(Double.toString(v));
BigDecimal one = new BigDecimal("1");
return b.divide(one,scale,BigDecimal.ROUND_HALF_UP).doubleValue(); }
public float round(float v,int scale){ if(scale<0){
throw new IllegalArgumentException(
"The scale must be a positive integer or zero");
}
BigDecimal b = new BigDecimal(Double.toString(v));
BigDecimal one = new BigDecimal("1");
return b.divide(one,scale,BigDecimal.ROUND_HALF_UP).floatValue(); }
} 演示程序： LinearRegression.java /**
* <p><b>Linear Regression</b>
* <br>
* Demonstrate linear regression by constructing the regression line for a set
* of data points.
*
* <p>require DataPoint.java,RegressionLine.java
*
* <p>为了计算对于给定数据点的最小方差回线，需要计算SumX,SumY,SumXX,SumXY; (注：SumXX = Sum (X^2))
* <p><b>回归直线方程如下： f(x)=a1x+a0 </b>
* <p><b>斜率和截距的计算公式如下：</b>
* <br>n: 数据点个数
* <p>a1=(n(SumXY)-SumX*SumY)/(n*SumXX-(SumX)^2)
* <br>a0=(SumY - SumY * a1)/n
* <br>(也可表达为a0=averageY-a1*averageX)
*
* <p><b>画线的原理：两点成一直线，只要能确定两个点即可</b><br>
* 第一点：(0,a0) 再随意取一个x1值代入方程，取得y1，连结(0,a0)和(x1,y1)两点即可。
* 为了让线穿过整个图,x1可以取横坐标的最大值Xmax，即两点为(0,a0),(Xmax,Y)。如果y=a1*Xmax+a0,y大于
* 纵坐标最大值Ymax，则不用这个点。改用y取最大值Ymax，算得此时x的值，使用(X,Ymax)， 即两点为(0,a0),(X,Ymax)
*
* <p><b>拟合度计算：(即Excel中的R^2)</b>
* <p> *R2 = 1 - E
* <p>误差E的计算：E = SSE/SST
* <p>SSE=sum((Yi-Y)^2) SST=sumYY - (sumY*sumY)/n;
* <p>
*/
public class LinearRegression
{
private static final int MAX_POINTS = 10;
private double E; /**
* Main program.
*
* @param args
* the array of runtime arguments
*/
public static void main(String args[])
{
RegressionLine line = new RegressionLine(); line.addDataPoint(new DataPoint(20, 136));
line.addDataPoint(new DataPoint(40, 143));
line.addDataPoint(new DataPoint(60, 152));
line.addDataPoint(new DataPoint(80, 162));
line.addDataPoint(new DataPoint(100, 167));
printSums(line);
printLine(line);
} /**
* Print the computed sums.
*
* @param line
* the regression line
*/
private static void printSums(RegressionLine line)
{
System.out.println("\n数据点个数 n = " + line.getDataPointCount());
System.out.println("\nSum x = " + line.getSumX());
System.out.println("Sum y = " + line.getSumY());
System.out.println("Sum xx = " + line.getSumXX());
System.out.println("Sum xy = " + line.getSumXY());
System.out.println("Sum yy = " + line.getSumYY());
} /**
* Print the regression line function.
*
* @param line
* the regression line
*/
private static void printLine(RegressionLine line)
{
System.out.println("\n回归线公式: y = " +
line.getA1() +
"x + " + line.getA0());
System.out.println("拟合度： R^2 = " + line.getR());
}
} |
