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          最小二乘法用于直線,多項式,圓,橢圓的擬合及程序實現(xiàn)

          最小二乘法是一種優(yōu)化算法,最小二乘法名字的緣由有兩個:一是要將誤差最小化,二是將誤差最小化的方法是使誤差的平方和最小化。利用最小二乘法可以簡便地求得未知的數(shù)據(jù),并使得這些求得的數(shù)據(jù)與實際數(shù)據(jù)之間誤差的平方和為最小。最小二乘法還可用于曲線擬合,所擬合的曲線可以是線性擬合與非線性擬合。

          --------------------一元線性函數(shù)--------------------

          形式1:借用案例(http://blog.csdn.net/qll125596718/article/details/8248249)首先以一元線性方程參數(shù)估計為例,樣本回歸模型:


          殘差平方和:


          則通過Q最小確定這條直線,即確定

          ,以
          為變量,把它們看作是Q的函數(shù),就變成了一個求極值的問題,可以通過求導數(shù)得到。求Q對兩個待估參數(shù)的偏導數(shù):


          解得:

          實例(c++):
          1. #include <iostream>
          2. #include <algorithm>
          3. #include <valarray>
          4. #include <vector>
          5. using namespace std;
          6. int main()
          7. {
          8. double x[] = { 1, 2, 3, 4, 5, 6 };
          9. double y[] = { 3, 5.5, 6.8, 8.8, 11, 12};
          10. valarray<double> data_x(x, 6);
          11. valarray<double> data_y(y, 6);
          12. float A = 0.0;
          13. float B = 0.0;
          14. float C = 0.0;
          15. float D = 0.0;
          16. A = (data_x*data_x).sum();
          17. B = data_x.sum();
          18. C = (data_x*data_y).sum();
          19. D = data_y.sum();
          20. float tmp = A*data_x.size() - B*B;
          21. float k, b;
          22. k = (C*data_x.size() - B*D) / tmp;
          23. b = (A*D - C*B) / tmp;
          24. cout << "y=" << k << "x+" << b << endl;
          25. return 0;
          26. }
          運行結果:


          注:valarray類似vector,也是一個模板類,主要用來對一系列元素進行高速的數(shù)字計算,其與vector的主要區(qū)別在于以下兩點:
          1、valarray定義了一組在兩個相同長度和相同類型的valarray類對象之間的數(shù)字計算;
          2、通過重載operater[],可以返回valarray的相關信息(valarray其中某個元素的引用、特定下標的值或者其某個子集)。

          valarray類構造函數(shù):
          valarray( );
          explicit valarray(size_t _Count);
          valarray( const Type& _Val, size_t _Count);
          valarray( const Type* _Ptr, size_t _Count);
          valarray( const slice_array<Type>& _SliceArray);
          valarray( const gslice_array<Type>& _GsliceArray);
          valarray( const mask_array<Type>& _MaskArray);
          valarray( const indirect_array<Type>& _IndArray);

          valarray 類用法:
          1. apply 將 valarray 數(shù)組的每一個值都用 apply 所接受到的函數(shù)進行計算
          2. cshift 將 valarray 數(shù)組的數(shù)據(jù)進行循環(huán)移動,參數(shù)為正者左移為負就右移
          3. max 返回 valarray 數(shù)組的最大值
          4. min 返回 valarray 數(shù)組的最小值
          5. resize 重新設置 valarray 數(shù)組大小,并對其進行初始化
          6. shift 將 valarray 數(shù)組移動,參數(shù)為正者左移,為負者右移,移動后由 0 填充剩余位
          7. size 得到數(shù)組的大小
          8. sum 數(shù)組求和

          --------------------N元線性函數(shù)--------------------

          一元線性方程可以看做多元函數(shù)的特例,現(xiàn)在用矩陣形式表述多元函數(shù)情況下,最小二乘的一般形式:

           設擬合多項式為:

                             

          各點到這條曲線的距離之和,即偏差平方和如下:

                          


          對等式右邊求ai偏導數(shù),得到: 

                        

                       

                                      .......

                       

          把這些等式表示成矩陣的形式,就可以得到下面的矩陣:

                        

            (3)

          進行化簡計算:

                     

          , 

          上面公式(3)可以寫為:

                             

          ,

                        


          1. #include "stdio.h"
          2. #include "stdlib.h"
          3. #include "math.h"
          4. #include "vector"
          5. using namespace std;
          6. struct point
          7. {
          8. double x;
          9. double y;
          10. };
          11. typedef vector<double> doubleVector;
          12. vector<point> getFile(char *File); //獲取文件數(shù)據(jù)
          13. doubleVector getCoeff(vector<point> sample, int n); //矩陣方程
          14. void main()
          15. {
          16. int i, n;
          17. char *File = "XY.txt";
          18. vector<point> sample;
          19. doubleVector coefficient;
          20. sample = getFile(File);
          21. printf("擬合多項式階數(shù)n=");
          22. scanf_s("%d", &n);
          23. coefficient = getCoeff(sample, n);
          24. printf("\n擬合矩陣的系數(shù)為:\n");
          25. for (i = 0; i < coefficient.size(); i++)
          26. printf("a%d = %lf\n", i, coefficient[i]);
          27. }
          28. //矩陣方程
          29. doubleVector getCoeff(vector<point> sample, int n)
          30. {
          31. vector<doubleVector> matFunX; //公式3左矩陣
          32. vector<doubleVector> matFunY; //公式3右矩陣
          33. doubleVector temp;
          34. double sum;
          35. int i, j, k;
          36. //公式3左矩陣
          37. for (i = 0; i <= n; i++)
          38. {
          39. temp.clear();
          40. for (j = 0; j <= n; j++)
          41. {
          42. sum = 0;
          43. for (k = 0; k < sample.size(); k++)
          44. sum += pow(sample[k].x, j + i);
          45. temp.push_back(sum);
          46. }
          47. matFunX.push_back(temp);
          48. }
          49. //printf("matFunX.size=%d\n", matFunX.size());
          50. //printf("matFunX[3][3]=%f\n", matFunX[3][3]);
          51. //公式3右矩陣
          52. for (i = 0; i <= n; i++)
          53. {
          54. temp.clear();
          55. sum = 0;
          56. for (k = 0; k < sample.size(); k++)
          57. sum += sample[k].y*pow(sample[k].x, i);
          58. temp.push_back(sum);
          59. matFunY.push_back(temp);
          60. }
          61. printf("matFunY.size=%d\n", matFunY.size());
          62. //矩陣行列式變換
          63. double num1, num2, ratio;
          64. for (i = 0; i < matFunX.size() - 1; i++)
          65. {
          66. num1 = matFunX[i][i];
          67. for (j = i + 1; j < matFunX.size(); j++)
          68. {
          69. num2 = matFunX[j][i];
          70. ratio = num2 / num1;
          71. for (k = 0; k < matFunX.size(); k++)
          72. matFunX[j][k] = matFunX[j][k] - matFunX[i][k] * ratio;
          73. matFunY[j][0] = matFunY[j][0] - matFunY[i][0] * ratio;
          74. }
          75. }
          76. //計算擬合曲線的系數(shù)
          77. doubleVector coeff(matFunX.size(), 0);
          78. for (i = matFunX.size() - 1; i >= 0; i--)
          79. {
          80. if (i == matFunX.size() - 1)
          81. coeff[i] = matFunY[i][0] / matFunX[i][i];
          82. else
          83. {
          84. for (j = i + 1; j < matFunX.size(); j++)
          85. matFunY[i][0] = matFunY[i][0] - coeff[j] * matFunX[i][j];
          86. coeff[i] = matFunY[i][0] / matFunX[i][i];
          87. }
          88. }
          89. return coeff;
          90. }
          91. //獲取文件數(shù)據(jù)
          92. vector<point> getFile(char *File)
          93. {
          94. int i = 1;
          95. vector<point> dst;
          96. FILE *fp=fopen(File, "r");
          97. if (fp == NULL)
          98. {
          99. printf("Open file error!!!\n");
          100. exit(0);
          101. }
          102. point temp;
          103. double num;
          104. while (fscanf(fp, "%lf", &num) != EOF)
          105. {
          106. if (i % 2 == 0)
          107. {
          108. temp.y = num;
          109. dst.push_back(temp);
          110. }
          111. else
          112. temp.x = num;
          113. i++;
          114. }
          115. fclose(fp);
          116. return dst;
          117. }

          XY.txt內容:

          1. 0 1.0
          2. 0.25 1.28
          3. 0.5 1.65
          4. 0.75 2.12
          5. 1 2.72

          另外在http://blog.csdn.net/lsh_2013/article/details/46697625里也有相關程序:

          1. #include <iostream>
          2. #include <vector>
          3. #include <cmath>
          4. using namespace std;
          5. //最小二乘擬合相關函數(shù)定義
          6. double sum(vector<double> Vnum, int n);
          7. double MutilSum(vector<double> Vx, vector<double> Vy, int n);
          8. double RelatePow(vector<double> Vx, int n, int ex);
          9. double RelateMutiXY(vector<double> Vx, vector<double> Vy, int n, int ex);
          10. void EMatrix(vector<double> Vx, vector<double> Vy, int n, int ex, double coefficient[]);
          11. void CalEquation(int exp, double coefficient[]);
          12. double F(double c[],int l,int m);
          13. double Em[6][4];
          14. //主函數(shù),這里將數(shù)據(jù)擬合成二次曲線
          15. int main(int argc, char* argv[])
          16. {
          17. double arry1[5]={0,0.25,0,5,0.75};
          18. double arry2[5]={1,1.283,1.649,2.212,2.178};
          19. double coefficient[5];
          20. memset(coefficient,0,sizeof(double)*5);
          21. vector<double> vx,vy;
          22. for (int i=0; i<5; i++)
          23. {
          24. vx.push_back(arry1[i]);
          25. vy.push_back(arry2[i]);
          26. }
          27. EMatrix(vx,vy,5,3,coefficient);
          28. printf("擬合方程為:y = %lf + %lfx + %lfx^2 \n",coefficient[1],coefficient[2],coefficient[3]);
          29. return 0;
          30. }
          31. //累加
          32. double sum(vector<double> Vnum, int n)
          33. {
          34. double dsum=0;
          35. for (int i=0; i<n; i++)
          36. {
          37. dsum+=Vnum[i];
          38. }
          39. return dsum;
          40. }
          41. //乘積和
          42. double MutilSum(vector<double> Vx, vector<double> Vy, int n)
          43. {
          44. double dMultiSum=0;
          45. for (int i=0; i<n; i++)
          46. {
          47. dMultiSum+=Vx[i]*Vy[i];
          48. }
          49. return dMultiSum;
          50. }
          51. //ex次方和
          52. double RelatePow(vector<double> Vx, int n, int ex)
          53. {
          54. double ReSum=0;
          55. for (int i=0; i<n; i++)
          56. {
          57. ReSum+=pow(Vx[i],ex);
          58. }
          59. return ReSum;
          60. }
          61. //x的ex次方與y的乘積的累加
          62. double RelateMutiXY(vector<double> Vx, vector<double> Vy, int n, int ex)
          63. {
          64. double dReMultiSum=0;
          65. for (int i=0; i<n; i++)
          66. {
          67. dReMultiSum+=pow(Vx[i],ex)*Vy[i];
          68. }
          69. return dReMultiSum;
          70. }
          71. //計算方程組的增廣矩陣
          72. void EMatrix(vector<double> Vx, vector<double> Vy, int n, int ex, double coefficient[])
          73. {
          74. for (int i=1; i<=ex; i++)
          75. {
          76. for (int j=1; j<=ex; j++)
          77. {
          78. Em[i][j]=RelatePow(Vx,n,i+j-2);
          79. }
          80. Em[i][ex+1]=RelateMutiXY(Vx,Vy,n,i-1);
          81. }
          82. Em[1][1]=n;
          83. CalEquation(ex,coefficient);
          84. }
          85. //求解方程
          86. void CalEquation(int exp, double coefficient[])
          87. {
          88. for(int k=1;k<exp;k++) //消元過程
          89. {
          90. for(int i=k+1;i<exp+1;i++)
          91. {
          92. double p1=0;
          93. if(Em[k][k]!=0)
          94. p1=Em[i][k]/Em[k][k];
          95. for(int j=k;j<exp+2;j++)
          96. Em[i][j]=Em[i][j]-Em[k][j]*p1;
          97. }
          98. }
          99. coefficient[exp]=Em[exp][exp+1]/Em[exp][exp];
          100. for(int l=exp-1;l>=1;l--) //回代求解
          101. coefficient[l]=(Em[l][exp+1]-F(coefficient,l+1,exp))/Em[l][l];
          102. }
          103. //供CalEquation函數(shù)調用
          104. double F(double c[],int l,int m)
          105. {
          106. double sum=0;
          107. for(int i=l;i<=m;i++)
          108. sum+=Em[l-1][i]*c[i];
          109. return sum;
          110. }

          memset相關介紹:

          http://baike.baidu.com/link?url=p7JreiRCj9yPs3r3WAfsXgynjvtGrWoQ_exF9tFGK6fsVP7V6tdm-_13QhCZxqPrfRi0wH0EihhRL_-qVvrewq

          http://c.biancheng.net/cpp/html/157.html

          --------------------擬合圓的方程--------------------






          1. /*
          2. 最小二乘法擬合圓,擬合出的圓以圓心坐標和半徑的形式表示
          3. */
          4. typedef complex<int> POINT;
          5. bool FitCircle(const std::vector<POINT> &points, double &er_x, double &er_y, double &radius)
          6. {
          7. cent_x = 0.0f;
          8. cent_y = 0.0f;
          9. radius = 0.0f;
          10. if (points.size() < 3)
          11. {
          12. return false;
          13. }
          14. double sum_x = 0.0f, sum_y = 0.0f;
          15. double sum_x2 = 0.0f, sum_y2 = 0.0f;
          16. double sum_x3 = 0.0f, sum_y3 = 0.0f;
          17. double sum_xy = 0.0f, sum_x1y2 = 0.0f, sum_x2y1 = 0.0f;
          18. int N = points.size();
          19. for (int i = 0; i < N; i++)
          20. {
          21. double x = points[i].real();
          22. double y = points[i].imag();
          23. double x2 = x * x;
          24. double y2 = y * y;
          25. sum_x += x;
          26. sum_y += y;
          27. sum_x2 += x2;
          28. sum_y2 += y2;
          29. sum_x3 += x2 * x;
          30. sum_y3 += y2 * y;
          31. sum_xy += x * y;
          32. sum_x1y2 += x * y2;
          33. sum_x2y1 += x2 * y;
          34. }
          35. double C, D, E, G, H;
          36. double a, b, c;
          37. C = N * sum_x2 - sum_x * sum_x;
          38. D = N * sum_xy - sum_x * sum_y;
          39. E = N * sum_x3 + N * sum_x1y2 - (sum_x2 + sum_y2) * sum_x;
          40. G = N * sum_y2 - sum_y * sum_y;
          41. H = N * sum_x2y1 + N * sum_y3 - (sum_x2 + sum_y2) * sum_y;
          42. a = (H * D - E * G) / (C * G - D * D);
          43. b = (H * C - E * D) / (D * D - G * C);
          44. c = -(a * sum_x + b * sum_y + sum_x2 + sum_y2) / N;
          45. cent_x = a / (-2);
          46. cent_y = b / (-2);
          47. radius = sqrt(a * a + b * b - 4 * c) / 2;
          48. return true;
          49. }

          --------------------擬合橢圓方程--------------------

          //LSEllipse.h

          1. /*************************************************************************
          2. 功能說明: 對平面上的一些列點給出最小二乘的橢圓擬合,利用奇異值分解法
          3. 解得最小二乘解作為橢圓參數(shù)。
          4. 調用形式: cvFitEllipse2f(arrayx,arrayy,box);
          5. 參數(shù)說明: arrayx: arrayx[n],每個值為x軸一個點
          6. arrayx: arrayy[n],每個值為y軸一個點
          7. n : 點的個數(shù)
          8. box : box[5],橢圓的五個參數(shù),分別為center.x,center.y,2a,2b,xtheta
          9. esp: 解精度,通常取1e-6,這個是解方程用的說
          10. ***************************************************************************/
          11. #include<cstdlib>
          12. #include<float.h>
          13. #include<vector>
          14. using namespace std;
          15. class LSEllipse
          16. {
          17. public:
          18. LSEllipse(void);
          19. ~LSEllipse(void);
          20. vector<double> getEllipseparGauss(vector<CPoint> vec_point);
          21. void cvFitEllipse2f( int *arrayx, int *arrayy,int n,float *box );
          22. private:
          23. int SVD(float *a,int m,int n,float b[],float x[],float esp);
          24. int gmiv(float a[],int m,int n,float b[],float x[],float aa[],float eps,float u[],float v[],int ka);
          25. int ginv(float a[],int m,int n,float aa[],float eps,float u[],float v[],int ka);
          26. int muav(float a[],int m,int n,float u[],float v[],float eps,int ka);
          27. };
          //LSEllipse.cpp
          1. #include "LSEllipse.h"
          2. #include <cmath>
          3. LSEllipse::LSEllipse(void)
          4. {
          5. }
          6. LSEllipse::~LSEllipse(void)
          7. {
          8. }
          9. //列主元高斯消去法
          10. //A為系數(shù)矩陣,x為解向量,若成功,返回true,否則返回false,并將x清空。
          11. bool RGauss(const vector<vector<double> > & A, vector<double> & x)
          12. {
          13. x.clear();
          14. int n = A.size();
          15. int m = A[0].size();
          16. x.resize(n);
          17. //復制系數(shù)矩陣,防止修改原矩陣
          18. vector<vector<double> > Atemp(n);
          19. for (int i = 0; i < n; i++)
          20. {
          21. vector<double> temp(m);
          22. for (int j = 0; j < m; j++)
          23. {
          24. temp[j] = A[i][j];
          25. }
          26. Atemp[i] = temp;
          27. temp.clear();
          28. }
          29. for (int k = 0; k < n; k++)
          30. {
          31. //選主元
          32. double max = -1;
          33. int l = -1;
          34. for (int i = k; i < n; i++)
          35. {
          36. if (abs(Atemp[i][k]) > max)
          37. {
          38. max = abs(Atemp[i][k]);
          39. l = i;
          40. }
          41. }
          42. if (l != k)
          43. {
          44. //交換系數(shù)矩陣的l行和k行
          45. for (int i = 0; i < m; i++)
          46. {
          47. double temp = Atemp[l][i];
          48. Atemp[l][i] = Atemp[k][i];
          49. Atemp[k][i] = temp;
          50. }
          51. }
          52. //消元
          53. for (int i = k+1; i < n; i++)
          54. {
          55. double l = Atemp[i][k]/Atemp[k][k];
          56. for (int j = k; j < m; j++)
          57. {
          58. Atemp[i][j] = Atemp[i][j] - l*Atemp[k][j];
          59. }
          60. }
          61. }
          62. //回代
          63. x[n-1] = Atemp[n-1][m-1]/Atemp[n-1][m-2];
          64. for (int k = n-2; k >= 0; k--)
          65. {
          66. double s = 0.0;
          67. for (int j = k+1; j < n; j++)
          68. {
          69. s += Atemp[k][j]*x[j];
          70. }
          71. x[k] = (Atemp[k][m-1] - s)/Atemp[k][k];
          72. }
          73. return true;
          74. }
          75. vector<double> LSEllipse::getEllipseparGauss(vector<CPoint> vec_point)
          76. {
          77. vector<double> vec_result;
          78. double x3y1 = 0,x1y3= 0,x2y2= 0,yyy4= 0, xxx3= 0,xxx2= 0,x2y1= 0,yyy3= 0,x1y2= 0 ,yyy2= 0,x1y1= 0,xxx1= 0,yyy1= 0;
          79. int N = vec_point.size();
          80. for (int m_i = 0;m_i < N ;++m_i )
          81. {
          82. double xi = vec_point[m_i].x ;
          83. double yi = vec_point[m_i].y;
          84. x3y1 += xi*xi*xi*yi ;
          85. x1y3 += xi*yi*yi*yi;
          86. x2y2 += xi*xi*yi*yi; ;
          87. yyy4 +=yi*yi*yi*yi;
          88. xxx3 += xi*xi*xi ;
          89. xxx2 += xi*xi ;
          90. x2y1 += xi*xi*yi;
          91. x1y2 += xi*yi*yi;
          92. yyy2 += yi*yi;
          93. x1y1 += xi*yi;
          94. xxx1 += xi;
          95. yyy1 += yi;
          96. yyy3 += yi*yi*yi;
          97. }
          98. double resul[5];
          99. resul[0] = -(x3y1);
          100. resul[1] = -(x2y2);
          101. resul[2] = -(xxx3);
          102. resul[3] = -(x2y1);
          103. resul[4] = -(xxx2);
          104. long double Bb[5],Cc[5],Dd[5],Ee[5],Aa[5];
          105. Bb[0] = x1y3, Cc[0] = x2y1, Dd[0] = x1y2, Ee[0] = x1y1, Aa[0] = x2y2;
          106. Bb[1] = yyy4, Cc[1] = x1y2, Dd[1] = yyy3, Ee[1] = yyy2, Aa[1] = x1y3;
          107. Bb[2] = x1y2, Cc[2] = xxx2, Dd[2] = x1y1, Ee[2] = xxx1, Aa[2] = x2y1;
          108. Bb[3] = yyy3, Cc[3]= x1y1, Dd[3] = yyy2, Ee[3] = yyy1, Aa[3] = x1y2;
          109. Bb[4]= yyy2, Cc[4]= xxx1, Dd[4] = yyy1, Ee[4] = N, Aa[4]= x1y1;
          110. vector<vector<double>>Ma(5);
          111. vector<double>Md(5);
          112. for(int i=0;i<5;i++)
          113. {
          114. Ma[i].push_back(Aa[i]);
          115. Ma[i].push_back(Bb[i]);
          116. Ma[i].push_back(Cc[i]);
          117. Ma[i].push_back(Dd[i]);
          118. Ma[i].push_back(Ee[i]);
          119. Ma[i].push_back(resul[i]);
          120. }
          121. RGauss(Ma,Md);
          122. long double A=Md[0];
          123. long double B=Md[1];
          124. long double C=Md[2];
          125. long double D=Md[3];
          126. long double E=Md[4];
          127. double XC=(2*B*C-A*D)/(A*A-4*B);
          128. double YC=(2*D-A*C)/(A*A-4*B);
          129. long double fenzi=2*(A*C*D-B*C*C-D*D+4*E*B-A*A*E);
          130. long double fenmu=(A*A-4*B)*(B-sqrt(A*A+(1-B)*(1-B))+1);
          131. long double fenmu2=(A*A-4*B)*(B+sqrt(A*A+(1-B)*(1-B))+1);
          132. double XA=sqrt(fabs(fenzi/fenmu));
          133. double XB=sqrt(fabs(fenzi/fenmu2));
          134. double Xtheta=0.5*atan(A/(1-B))*180/3.1415926;
          135. if(B<1)
          136. Xtheta+=90;
          137. vec_result.push_back(XC);
          138. vec_result.push_back(YC);
          139. vec_result.push_back(XA);
          140. vec_result.push_back(XB);
          141. vec_result.push_back(Xtheta);
          142. return vec_result;
          143. }
          144. void LSEllipse::cvFitEllipse2f( int *arrayx, int *arrayy,int n,float *box )
          145. {
          146. float cx=0,cy=0;
          147. double rp[5], t;
          148. float *A1=new float[n*5];
          149. float *A2=new float[2*2];
          150. float *A3=new float[n*3];
          151. float *B1=new float[n],*B2=new float[2],*B3=new float[n];
          152. const double min_eps = 1e-6;
          153. int i;
          154. for( i = 0; i < n; i++ )
          155. {
          156. cx += arrayx[i]*1.0;
          157. cy += arrayy[i]*1.0;
          158. }
          159. cx /= n;
          160. cy /= n;
          161. for( i = 0; i < n; i++ )
          162. {
          163. int step=i*5;
          164. float px,py;
          165. px = arrayx[i]*1.0;
          166. py = arrayy[i]*1.0;
          167. px -= cx;
          168. py -= cy;
          169. B1[i] = 10000.0;
          170. A1[step] = -px * px;
          171. A1[step + 1] = -py * py;
          172. A1[step + 2] = -px * py;
          173. A1[step + 3] = px;
          174. A1[step + 4] = py;
          175. }
          176. float *x1=new float[5];
          177. //解出Ax^2+By^2+Cxy+Dx+Ey=10000的最小二乘解!
          178. SVD(A1,n,5,B1,x1,min_eps);
          179. A2[0]=2*x1[0],A2[1]=A2[2]=x1[2],A2[3]=2*x1[1];
          180. B2[0]=x1[3],B2[1]=x1[4];
          181. float *x2=new float[2];
          182. //標準化,將一次項消掉,求出center.x和center.y;
          183. SVD(A2,2,2,B2,x2,min_eps);
          184. rp[0]=x2[0],rp[1]=x2[1];
          185. for( i = 0; i < n; i++ )
          186. {
          187. float px,py;
          188. px = arrayx[i]*1.0;
          189. py = arrayy[i]*1.0;
          190. px -= cx;
          191. py -= cy;
          192. B3[i] = 1.0;
          193. int step=i*3;
          194. A3[step] = (px - rp[0]) * (px - rp[0]);
          195. A3[step+1] = (py - rp[1]) * (py - rp[1]);
          196. A3[step+2] = (px - rp[0]) * (py - rp[1]);
          197. }
          198. //求出A(x-center.x)^2+B(y-center.y)^2+C(x-center.x)(y-center.y)的最小二乘解
          199. SVD(A3,n,3,B3,x1,min_eps);
          200. rp[4] = -0.5 * atan2(x1[2], x1[1] - x1[0]);
          201. t = sin(-2.0 * rp[4]);
          202. if( fabs(t) > fabs(x1[2])*min_eps )
          203. t = x1[2]/t;
          204. else
          205. t = x1[1] - x1[0];
          206. rp[2] = fabs(x1[0] + x1[1] - t);
          207. if( rp[2] > min_eps )
          208. rp[2] = sqrt(2.0 / rp[2]);
          209. rp[3] = fabs(x1[0] + x1[1] + t);
          210. if( rp[3] > min_eps )
          211. rp[3] = sqrt(2.0 / rp[3]);
          212. box[0] = (float)rp[0] + cx;
          213. box[1]= (float)rp[1] + cy;
          214. box[2]= (float)(rp[2]*2);
          215. box[3] = (float)(rp[3]*2);
          216. if( box[2] > box[3] )
          217. {
          218. double tmp=box[2];
          219. box[2]=box[3];
          220. box[3]=tmp;
          221. }
          222. box[4] = (float)(90 + rp[4]*180/3.1415926);
          223. if( box[4] < -180 )
          224. box[4] += 360;
          225. if( box[4] > 360 )
          226. box[4] -= 360;
          227. delete []A1;
          228. delete []A2;
          229. delete []A3;
          230. delete []B1;
          231. delete []B2;
          232. delete []B3;
          233. delete []x1;
          234. delete []x2;
          235. }
          236. int LSEllipse::SVD(float *a,int m,int n,float b[],float x[],float esp)
          237. {
          238. float *aa;
          239. float *u;
          240. float *v;
          241. aa=new float[n*m];
          242. u=new float[m*m];
          243. v=new float[n*n];
          244. int ka;
          245. int flag;
          246. if(m>n)
          247. {
          248. ka=m+1;
          249. }else
          250. {
          251. ka=n+1;
          252. }
          253. flag=gmiv(a,m,n,b,x,aa,esp,u,v,ka);
          254. delete []aa;
          255. delete []u;
          256. delete []v;
          257. return(flag);
          258. }
          259. int LSEllipse::gmiv( float a[],int m,int n,float b[],float x[],float aa[],float eps,float u[],float v[],int ka)
          260. {
          261. int i,j;
          262. i=ginv(a,m,n,aa,eps,u,v,ka);
          263. if (i<0) return(-1);
          264. for (i=0; i<=n-1; i++)
          265. { x[i]=0.0;
          266. for (j=0; j<=m-1; j++)
          267. x[i]=x[i]+aa[i*m+j]*b[j];
          268. }
          269. return(1);
          270. }
          271. int LSEllipse::ginv(float a[],int m,int n,float aa[],float eps,float u[],float v[],int ka)
          272. {
          273. // int muav(float a[],int m,int n,float u[],float v[],float eps,int ka);
          274. int i,j,k,l,t,p,q,f;
          275. i=muav(a,m,n,u,v,eps,ka);
          276. if (i<0) return(-1);
          277. j=n;
          278. if (m<n) j=m;
          279. j=j-1;
          280. k=0;
          281. while ((k<=j)&&(a[k*n+k]!=0.0)) k=k+1;
          282. k=k-1;
          283. for (i=0; i<=n-1; i++)
          284. for (j=0; j<=m-1; j++)
          285. { t=i*m+j; aa[t]=0.0;
          286. for (l=0; l<=k; l++)
          287. { f=l*n+i; p=j*m+l; q=l*n+l;
          288. aa[t]=aa[t]+v[f]*u[p]/a[q];
          289. }
          290. }
          291. return(1);
          292. }
          293. int LSEllipse::muav(float a[],int m,int n,float u[],float v[],float eps,int ka)
          294. { int i,j,k,l,it,ll,kk,ix,iy,mm,nn,iz,m1,ks;
          295. float d,dd,t,sm,sm1,em1,sk,ek,b,c,shh,fg[2],cs[2];
          296. float *s,*e,*w;
          297. //void ppp();
          298. // void sss();
          299. void ppp(float a[],float e[],float s[],float v[],int m,int n);
          300. void sss(float fg[],float cs[]);
          301. s=(float *) malloc(ka*sizeof(float));
          302. e=(float *) malloc(ka*sizeof(float));
          303. w=(float *) malloc(ka*sizeof(float));
          304. it=60; k=n;
          305. if (m-1<n) k=m-1;
          306. l=m;
          307. if (n-2<m) l=n-2;
          308. if (l<0) l=0;
          309. ll=k;
          310. if (l>k) ll=l;
          311. if (ll>=1)
          312. { for (kk=1; kk<=ll; kk++)
          313. { if (kk<=k)
          314. { d=0.0;
          315. for (i=kk; i<=m; i++)
          316. { ix=(i-1)*n+kk-1; d=d+a[ix]*a[ix];}
          317. s[kk-1]=(float)sqrt(d);
          318. if (s[kk-1]!=0.0)
          319. { ix=(kk-1)*n+kk-1;
          320. if (a[ix]!=0.0)
          321. { s[kk-1]=(float)fabs(s[kk-1]);
          322. if (a[ix]<0.0) s[kk-1]=-s[kk-1];
          323. }
          324. for (i=kk; i<=m; i++)
          325. { iy=(i-1)*n+kk-1;
          326. a[iy]=a[iy]/s[kk-1];
          327. }
          328. a[ix]=1.0f+a[ix];
          329. }
          330. s[kk-1]=-s[kk-1];
          331. }
          332. if (n>=kk+1)
          333. { for (j=kk+1; j<=n; j++)
          334. { if ((kk<=k)&&(s[kk-1]!=0.0))
          335. { d=0.0;
          336. for (i=kk; i<=m; i++)
          337. { ix=(i-1)*n+kk-1;
          338. iy=(i-1)*n+j-1;
          339. d=d+a[ix]*a[iy];
          340. }
          341. d=-d/a[(kk-1)*n+kk-1];
          342. for (i=kk; i<=m; i++)
          343. { ix=(i-1)*n+j-1;
          344. iy=(i-1)*n+kk-1;
          345. a[ix]=a[ix]+d*a[iy];
          346. }
          347. }
          348. e[j-1]=a[(kk-1)*n+j-1];
          349. }
          350. }
          351. if (kk<=k)
          352. { for (i=kk; i<=m; i++)
          353. { ix=(i-1)*m+kk-1; iy=(i-1)*n+kk-1;
          354. u[ix]=a[iy];
          355. }
          356. }
          357. if (kk<=l)
          358. { d=0.0;
          359. for (i=kk+1; i<=n; i++)
          360. d=d+e[i-1]*e[i-1];
          361. e[kk-1]=(float)sqrt(d);
          362. if (e[kk-1]!=0.0)
          363. { if (e[kk]!=0.0)
          364. { e[kk-1]=(float)fabs(e[kk-1]);
          365. if (e[kk]<0.0) e[kk-1]=-e[kk-1];
          366. }
          367. for (i=kk+1; i<=n; i++)
          368. e[i-1]=e[i-1]/e[kk-1];
          369. e[kk]=1.0f+e[kk];
          370. }
          371. e[kk-1]=-e[kk-1];
          372. if ((kk+1<=m)&&(e[kk-1]!=0.0))
          373. { for (i=kk+1; i<=m; i++) w[i-1]=0.0;
          374. for (j=kk+1; j<=n; j++)
          375. for (i=kk+1; i<=m; i++)
          376. w[i-1]=w[i-1]+e[j-1]*a[(i-1)*n+j-1];
          377. for (j=kk+1; j<=n; j++)
          378. for (i=kk+1; i<=m; i++)
          379. { ix=(i-1)*n+j-1;
          380. a[ix]=a[ix]-w[i-1]*e[j-1]/e[kk];
          381. }
          382. }
          383. for (i=kk+1; i<=n; i++)
          384. v[(i-1)*n+kk-1]=e[i-1];
          385. }
          386. }
          387. }
          388. mm=n;
          389. if (m+1<n) mm=m+1;
          390. if (k<n) s[k]=a[k*n+k];
          391. if (m<mm) s[mm-1]=0.0;
          392. if (l+1<mm) e[l]=a[l*n+mm-1];
          393. e[mm-1]=0.0;
          394. nn=m;
          395. if (m>n) nn=n;
          396. if (nn>=k+1)
          397. { for (j=k+1; j<=nn; j++)
          398. { for (i=1; i<=m; i++)
          399. u[(i-1)*m+j-1]=0.0;
          400. u[(j-1)*m+j-1]=1.0;
          401. }
          402. }
          403. if (k>=1)
          404. { for (ll=1; ll<=k; ll++)
          405. { kk=k-ll+1; iz=(kk-1)*m+kk-1;
          406. if (s[kk-1]!=0.0)
          407. { if (nn>=kk+1)
          408. for (j=kk+1; j<=nn; j++)
          409. { d=0.0;
          410. for (i=kk; i<=m; i++)
          411. { ix=(i-1)*m+kk-1;
          412. iy=(i-1)*m+j-1;
          413. d=d+u[ix]*u[iy]/u[iz];
          414. }
          415. d=-d;
          416. for (i=kk; i<=m; i++)
          417. { ix=(i-1)*m+j-1;
          418. iy=(i-1)*m+kk-1;
          419. u[ix]=u[ix]+d*u[iy];
          420. }
          421. }
          422. for (i=kk; i<=m; i++)
          423. { ix=(i-1)*m+kk-1; u[ix]=-u[ix];}
          424. u[iz]=1.0f+u[iz];
          425. if (kk-1>=1)
          426. for (i=1; i<=kk-1; i++)
          427. u[(i-1)*m+kk-1]=0.0;
          428. }
          429. else
          430. { for (i=1; i<=m; i++)
          431. u[(i-1)*m+kk-1]=0.0;
          432. u[(kk-1)*m+kk-1]=1.0;
          433. }
          434. }
          435. }
          436. for (ll=1; ll<=n; ll++)
          437. { kk=n-ll+1; iz=kk*n+kk-1;
          438. if ((kk<=l)&&(e[kk-1]!=0.0))
          439. { for (j=kk+1; j<=n; j++)
          440. { d=0.0;
          441. for (i=kk+1; i<=n; i++)
          442. { ix=(i-1)*n+kk-1; iy=(i-1)*n+j-1;
          443. d=d+v[ix]*v[iy]/v[iz];
          444. }
          445. d=-d;
          446. for (i=kk+1; i<=n; i++)
          447. { ix=(i-1)*n+j-1; iy=(i-1)*n+kk-1;
          448. v[ix]=v[ix]+d*v[iy];
          449. }
          450. }
          451. }
          452. for (i=1; i<=n; i++)
          453. v[(i-1)*n+kk-1]=0.0;
          454. v[iz-n]=1.0;
          455. }
          456. for (i=1; i<=m; i++)
          457. for (j=1; j<=n; j++)
          458. a[(i-1)*n+j-1]=0.0;
          459. m1=mm; it=60;
          460. while (1==1)
          461. { if (mm==0)
          462. { ppp(a,e,s,v,m,n);
          463. free(s); free(e); free(w); return(1);
          464. }
          465. if (it==0)
          466. { ppp(a,e,s,v,m,n);
          467. free(s); free(e); free(w); return(-1);
          468. }
          469. kk=mm-1;
          470. while ((kk!=0)&&(fabs(e[kk-1])!=0.0))
          471. { d=(float)(fabs(s[kk-1])+fabs(s[kk]));
          472. dd=(float)fabs(e[kk-1]);
          473. if (dd>eps*d) kk=kk-1;
          474. else e[kk-1]=0.0;
          475. }
          476. if (kk==mm-1)
          477. { kk=kk+1;
          478. if (s[kk-1]<0.0)
          479. { s[kk-1]=-s[kk-1];
          480. for (i=1; i<=n; i++)
          481. { ix=(i-1)*n+kk-1; v[ix]=-v[ix];}
          482. }
          483. while ((kk!=m1)&&(s[kk-1]<s[kk]))
          484. { d=s[kk-1]; s[kk-1]=s[kk]; s[kk]=d;
          485. if (kk<n)
          486. for (i=1; i<=n; i++)
          487. { ix=(i-1)*n+kk-1; iy=(i-1)*n+kk;
          488. d=v[ix]; v[ix]=v[iy]; v[iy]=d;
          489. }
          490. if (kk<m)
          491. for (i=1; i<=m; i++)
          492. { ix=(i-1)*m+kk-1; iy=(i-1)*m+kk;
          493. d=u[ix]; u[ix]=u[iy]; u[iy]=d;
          494. }
          495. kk=kk+1;
          496. }
          497. it=60;
          498. mm=mm-1;
          499. }
          500. else
          501. { ks=mm;
          502. while ((ks>kk)&&(fabs(s[ks-1])!=0.0))
          503. { d=0.0;
          504. if (ks!=mm) d=d+(float)fabs(e[ks-1]);
          505. if (ks!=kk+1) d=d+(float)fabs(e[ks-2]);
          506. dd=(float)fabs(s[ks-1]);
          507. if (dd>eps*d) ks=ks-1;
          508. else s[ks-1]=0.0;
          509. }
          510. if (ks==kk)
          511. { kk=kk+1;
          512. d=(float)fabs(s[mm-1]);
          513. t=(float)fabs(s[mm-2]);
          514. if (t>d) d=t;
          515. t=(float)fabs(e[mm-2]);
          516. if (t>d) d=t;
          517. t=(float)fabs(s[kk-1]);
          518. if (t>d) d=t;
          519. t=(float)fabs(e[kk-1]);
          520. if (t>d) d=t;
          521. sm=s[mm-1]/d; sm1=s[mm-2]/d;
          522. em1=e[mm-2]/d;
          523. sk=s[kk-1]/d; ek=e[kk-1]/d;
          524. b=((sm1+sm)*(sm1-sm)+em1*em1)/2.0f;
          525. c=sm*em1; c=c*c; shh=0.0;
          526. if ((b!=0.0)||(c!=0.0))
          527. { shh=(float)sqrt(b*b+c);
          528. if (b<0.0) shh=-shh;
          529. shh=c/(b+shh);
          530. }
          531. fg[0]=(sk+sm)*(sk-sm)-shh;
          532. fg[1]=sk*ek;
          533. for (i=kk; i<=mm-1; i++)
          534. { sss(fg,cs);
          535. if (i!=kk) e[i-2]=fg[0];
          536. fg[0]=cs[0]*s[i-1]+cs[1]*e[i-1];
          537. e[i-1]=cs[0]*e[i-1]-cs[1]*s[i-1];
          538. fg[1]=cs[1]*s[i];
          539. s[i]=cs[0]*s[i];
          540. if ((cs[0]!=1.0)||(cs[1]!=0.0))
          541. for (j=1; j<=n; j++)
          542. { ix=(j-1)*n+i-1;
          543. iy=(j-1)*n+i;
          544. d=cs[0]*v[ix]+cs[1]*v[iy];
          545. v[iy]=-cs[1]*v[ix]+cs[0]*v[iy];
          546. v[ix]=d;
          547. }
          548. sss(fg,cs);
          549. s[i-1]=fg[0];
          550. fg[0]=cs[0]*e[i-1]+cs[1]*s[i];
          551. s[i]=-cs[1]*e[i-1]+cs[0]*s[i];
          552. fg[1]=cs[1]*e[i];
          553. e[i]=cs[0]*e[i];
          554. if (i<m)
          555. if ((cs[0]!=1.0)||(cs[1]!=0.0))
          556. for (j=1; j<=m; j++)
          557. { ix=(j-1)*m+i-1;
          558. iy=(j-1)*m+i;
          559. d=cs[0]*u[ix]+cs[1]*u[iy];
          560. u[iy]=-cs[1]*u[ix]+cs[0]*u[iy];
          561. u[ix]=d;
          562. }
          563. }
          564. e[mm-2]=fg[0];
          565. it=it-1;
          566. }
          567. else
          568. { if (ks==mm)
          569. { kk=kk+1;
          570. fg[1]=e[mm-2]; e[mm-2]=0.0;
          571. for (ll=kk; ll<=mm-1; ll++)
          572. { i=mm+kk-ll-1;
          573. fg[0]=s[i-1];
          574. sss(fg,cs);
          575. s[i-1]=fg[0];
          576. if (i!=kk)
          577. { fg[1]=-cs[1]*e[i-2];
          578. e[i-2]=cs[0]*e[i-2];
          579. }
          580. if ((cs[0]!=1.0)||(cs[1]!=0.0))
          581. for (j=1; j<=n; j++)
          582. { ix=(j-1)*n+i-1;
          583. iy=(j-1)*n+mm-1;
          584. d=cs[0]*v[ix]+cs[1]*v[iy];
          585. v[iy]=-cs[1]*v[ix]+cs[0]*v[iy];
          586. v[ix]=d;
          587. }
          588. }
          589. }
          590. else
          591. { kk=ks+1;
          592. fg[1]=e[kk-2];
          593. e[kk-2]=0.0;
          594. for (i=kk; i<=mm; i++)
          595. { fg[0]=s[i-1];
          596. sss(fg,cs);
          597. s[i-1]=fg[0];
          598. fg[1]=-cs[1]*e[i-1];
          599. e[i-1]=cs[0]*e[i-1];
          600. if ((cs[0]!=1.0)||(cs[1]!=0.0))
          601. for (j=1; j<=m; j++)
          602. { ix=(j-1)*m+i-1;
          603. iy=(j-1)*m+kk-2;
          604. d=cs[0]*u[ix]+cs[1]*u[iy];
          605. u[iy]=-cs[1]*u[ix]+cs[0]*u[iy];
          606. u[ix]=d;
          607. }
          608. }
          609. }
          610. }
          611. }
          612. }
          613. free(s);free(e);free(w);
          614. return(1);
          615. }
          616. void ppp(float a[],float e[],float s[],float v[],int m,int n)
          617. { int i,j,p,q;
          618. float d;
          619. if (m>=n) i=n;
          620. else i=m;
          621. for (j=1; j<=i-1; j++)
          622. { a[(j-1)*n+j-1]=s[j-1];
          623. a[(j-1)*n+j]=e[j-1];
          624. }
          625. a[(i-1)*n+i-1]=s[i-1];
          626. if (m<n) a[(i-1)*n+i]=e[i-1];
          627. for (i=1; i<=n-1; i++)
          628. for (j=i+1; j<=n; j++)
          629. { p=(i-1)*n+j-1; q=(j-1)*n+i-1;
          630. d=v[p]; v[p]=v[q]; v[q]=d;
          631. }
          632. return;
          633. }
          634. void sss(float fg[],float cs[])
          635. { float r,d;
          636. if ((fabs(fg[0])+fabs(fg[1]))==0.0)
          637. { cs[0]=1.0; cs[1]=0.0; d=0.0;}
          638. else
          639. { d=(float)sqrt(fg[0]*fg[0]+fg[1]*fg[1]);
          640. if (fabs(fg[0])>fabs(fg[1]))
          641. { d=(float)fabs(d);
          642. if (fg[0]<0.0) d=-d;
          643. }
          644. if (fabs(fg[1])>=fabs(fg[0]))
          645. { d=(float)fabs(d);
          646. if (fg[1]<0.0) d=-d;
          647. }
          648. cs[0]=fg[0]/d; cs[1]=fg[1]/d;
          649. }
          650. r=1.0;
          651. if (fabs(fg[0])>fabs(fg[1])) r=cs[1];
          652. else
          653. if (cs[0]!=0.0) r=1.0f/cs[0];
          654. fg[0]=d; fg[1]=r;
          655. return;
          656. }

          參考:

          線性,非線性多項式:

          http://blog.csdn.net/qll125596718/article/details/8248249

          http://blog.csdn.net/poxiaozhuimeng/article/details/41117947

          http://blog.csdn.net/ouyangying123/article/details/53996403

          http://blog.csdn.net/jairuschan/article/details/7517773/

          http://blog.csdn.net/zang141588761/article/details/50523036

          http://www.cnblogs.com/gnuhpc/archive/2012/12/09/2809997.html

          http://download.csdn.net/download/biaobiao11/9755119

          圓擬合:

          http://blog.sina.com.cn/s/blog_b27f71160101gxun.html  

          http://www.cnblogs.com/dotLive/archive/2007/04/06/524633.html

          http://blog.csdn.net/andylao62/article/details/24522365

          http://blog.csdn.net/liyuanbhu/article/details/50889951

          http://blog.csdn.net/liyuanbhu/article/details/50890587

          橢圓擬合:

          http://doc.okbase.net/u013708970/archive/121532.html

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