int main()
{
int n;
printf("Enter the Number of points to be input\n");
scanf("%d",&n);
linreg(n);
return 0;
}
// Compute the difference between the samples in d1 and d2, returning
// the slope of a line through the points, (i, d1[i] - d2[i])
// for i in [0,n)
// Returns the y intercept in aa.
// Return value is the slope of the fitted line. If it decides we're
// done, returns 0.0
double
find_slope(double *d1, double *d2, int n, double *aa,
double period) {
int i;
double a,b,r;
double yi;
double *err = calloc(n, sizeof(*err));
double *x = calloc(n, sizeof(*x));
double fit_err, tot_err;
double var;
double thresh = period / 2.0;
int addct = 0;
int n2 = 0;
if(n == 1) {
*aa = d1[0] - d2[0];
return 0.0;
}
if(n == 2) {
*aa = 0.0;
return d1[1] - d2[1];
}
for(i = 1; i < n; i++) {
yi = d1[i] - d2[i];
if(-thresh <= yi && yi <= thresh) {
// printf("%3d\t%f\n", i, yi);
err[n2] = yi;
x[n2] = i;
n2++;
}
}
r = linreg(x, err, n2, &a, &b);
for(i = 0; i < n2; i++) {
printf("%3d\t%f\n", (int)x[i], err[i]);
}
var = vari(err,n);
printf("%s() a = %f, b = %f, r = %f, var = %f\n", __func__, a, b, r, var);
*aa = a;
free(err);
free(x);
return b;
}
int main(int argc, char *argv[])
{
int n = 10;
float x[10]= {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
float y[10]= {2, 5, 9, 10, 14, 18, 20, 22, 25, 29};
float m, b, r;
linreg(n, x, y, &m, &b, &r);
printf("m=%g b=%g r=%g\n",m, b, r);
return 0;
}
void main() {
//* Initialize data to be fit. Data is quadratic plus random number.
Matrix c(3);
cout << "Curve fit data is created using the quadratic" << endl;
cout << " y(x) = c(1) + c(2)*x + c(3)*x^2" << endl;
cout << "Enter the coefficients:" << endl;
cout << "c(1) = "; cin >> c(1);
cout << "c(2) = "; cin >> c(2);
cout << "c(3) = "; cin >> c(3);
double alpha;
cout << "Enter estimated error bar: "; cin >> alpha;
int i, N = 50; // Number of data points
long seed = 1234; // Seed for random number generator
Matrix x(N), y(N), sigma(N);
for( i=1; i<=N; i++ ) {
x(i) = i; // x = [1, 2, ..., N]
y(i) = c(1) + c(2)*x(i) + c(3)*x(i)*x(i) + alpha*randn(seed);
sigma(i) = alpha; // Constant error bar
}
//* Fit the data to a straight line or a more general polynomial
cout << "Enter number of fit parameters (=2 for line): ";
int M; cin >> M;
Matrix a_fit(M), sig_a(M), yy(N); double chisqr;
if( M == 2 ) //* Linear regression (Straight line) fit
linreg( x, y, sigma, a_fit, sig_a, yy, chisqr);
else //* Polynomial fit
pollsf( x, y, sigma, M, a_fit, sig_a, yy, chisqr);
//* Print out the fit parameters, including their error bars.
cout << "Fit parameters:" << endl;
for( i=1; i<=M; i++ )
cout << " a(" << i << ") = " << a_fit(i) <<
" +/- " << sig_a(i) << endl;
cout << "Chi square = " << chisqr << "; N-M = " << N-M << endl;
//* Print out the plotting variables: x, y, sigma, yy
ofstream xOut("x.txt"), yOut("y.txt"),
sigmaOut("sigma.txt"), yyOut("yy.txt");
for( i=1; i<=N; i++ ) {
xOut << x(i) << endl;
yOut << y(i) << endl;
sigmaOut << sigma(i) << endl;
yyOut << yy(i) << endl;
}
}
void cvLineFitter::fitLine(ofxPoint2f * pts, int nPts, float &slope, float &intercept, float &chiSqr){
Matrix x(nPts), y(nPts), sigma(nPts);
for(int i=1; i<=nPts; i++ ) {
x(i) = pts[i-1].x;
y(i) = pts[i-1].y;
sigma(i) = 1.0f; //error bar? what is this?
}
Matrix a_fit(2), sig_a(2), yy(nPts);
double chiSqrd;
linreg( x, y, sigma, a_fit, sig_a, yy, chiSqrd);
chiSqr = (double)chiSqrd;
intercept = a_fit(1);
slope= a_fit(2);
chiSqr = sig_a(1);
//printf("fit: %f %f \n", a_fit(1), sig_a(1));
//printf("sig 1: %f %f\n", sig_a(1), sig_a(2));
}
int Line(Global::Image *mImageParser, float* pParser_CSV, Sample &mSample, const bool bSw = false)
{
//float fRandY0 = GlobalTool::Max(pParser_CSV[nRandX0], pParser_CSV[nRandX1]);
//float fRandY1 = GlobalTool::Min(pParser_CSV[nRandX0], pParser_CSV[nRandX1]);
float fLSX[2000] = { 0, };
float fLSY[2000] = { 0, };
int nCount = 0;
mSample.nSn = 0;
float fRandY0 = pParser_CSV[mSample.nX0];
float fRandY1 = pParser_CSV[mSample.nX1];
//int nMaxArray = GlobalTool::Max(mSample.nX0, mSample.nX1);
//int nMinArray = GlobalTool::Min(mSample.nX0, mSample.nX1);
float fU = mSample.nX1 - mSample.nX0;
float fV = fRandY1 - fRandY0;
float fM = (fV / fU);
float nSurchDiff0;
if (fM < 0)
{
nSurchDiff0 = fSurchDiff;
//printf("slope : %f %f \n",90 + degree, (atan(fM) * 180) / M_PI);
}
else
{
nSurchDiff0 = -fSurchDiff;
//printf("slope : %f %f \n", 90 - degree, (atan(fM) * 180) / M_PI);
}
float fDegree = (atan(fM) * 180) / M_PI;
//double rad = atan2(fU, fV);
//double degree = ((rad * 180) / M_PI);
if (abs(fDegree) >= 30)
{
//printf("height : %f %f \n", fRandY0, fRandY1);
//printf("slope : %f \n", (atan(fM) * 180) / M_PI);
return 0;
}
if (mImageParser && bSw)
{
Global::Circle(mImageParser, mSample.nX0, fRandY0, 15, RGB8(0, 255, 255));
Global::Circle(mImageParser, mSample.nX1, fRandY1, 15, RGB8(0, 255, 255));
//printf("slope : %f \n", atan(fM));
}
for (int i = 1; i < nWidthParser - 1; i++)
{
float nY0 = fM * ((i - 1) - mSample.nX0) + fRandY0;
float nY1 = fM * ((i + 1) - mSample.nX0) + fRandY0;
//printf("x: %d y : %d y : %d \n", i, nY0, nY1);
if (nY0 < nheightParser || nY0 > 0)
{
if (mImageParser)
{
//Global::Line(mImageParser, (i - 1), nY0, (i + 1), nY1, RGB8(0, 255, 0));
//Global::Line(mImageParser, (i - 1) - nSurchDiff0, nY0 - fSurchDiff, (i + 1) - nSurchDiff0, nY1 - fSurchDiff, RGB8(0, 255, 0));
//Global::Line(mImageParser, (i - 1) + nSurchDiff0, nY0 + fSurchDiff, (i + 1) + nSurchDiff0, nY1 + fSurchDiff, RGB8(0, 255, 0));
}
if (pParser_CSV[i] != 0)
{
float fSurchPointMin = fM * ((i + 1) - mSample.nX0 + nSurchDiff0) + fRandY0;
float fSurchPointMax = fM * ((i + 1) - mSample.nX0 - nSurchDiff0) + fRandY0;
if (pParser_CSV[i + 1] > fSurchPointMin - nSurchDiff0 && pParser_CSV[i + 1] < fSurchPointMax + nSurchDiff0)
{
fLSX[nCount] = i + 1;
fLSY[nCount++] = pParser_CSV[i + 1];
mSample.nSn++;
if (mImageParser && bSw)
{
Global::Circle(mImageParser, i + 1, pParser_CSV[i + 1], 2, RGB8(255, 0, 0));
}
}
//Sleep(10);
//Global::Show("Parser ", mImageParser);
}
}
}
if (bSw)
{
linreg(nCount, fLSX, fLSY, &mSample.m, &mSample.b, &mSample.r);
//Global::Line(mImageParser, 1, m + b, (mImageParser->width - nSurchDiff0), m*(mImageParser->width - nSurchDiff0) + b, RGB8(255, 0, 0));
printf("m=%g b=%g r=%g Ag=%f MeanAg=%f \n", mSample.m, mSample.b, mSample.r, fDegree, (atan(mSample.m) * 180) / M_PI);
}
if (mImageParser && bSw)
{
for (int i = 1; i < nWidthParser - 1; i++)
{
float nY0 = mSample.m * (i - 1) + mSample.b;
float nY1 = mSample.m * (i + 1) + mSample.b;
if (nY0 < mImageParser->height || nY0 > 0)
{
Global::Line(mImageParser, (i - 1), nY0, (i + 1), nY1, RGB8(0, 255, 0));
}
}
}
return 1;
}
static int linear_regression(int m, int n) {
double *Y, *X, *S, *U, *T, *B, *R;
double *oma;
int real_m; // ,real_n;
int i,j,k,a;
int tulos;
double tulosterm;
int tulosl;
// RS REM struct murtoluku tulosml;
oma=regressor[0];
if (n>=m) n=m-1;
nterm_type=UNIDENTIFIED;
strcpy(nterm_output_buffer,comment_1);
Y=matrixmalloc(m,1);
if (Y==NULL) return(-1);
for (i=0;i<m;i++) {
*(Y+i)=sequence[i];
}
X=matrixmalloc(m,n);
if (X==NULL) return(-1);
S=matrixmalloc(m,n);
if (S==NULL) return(-1);
U=matrixmalloc(n,n);
if (U==NULL) return(-1);
T=matrixmalloc(m,n);
if (T==NULL) return(-1);
B=matrixmalloc(m,1);
if (B==NULL) return(-1);
R=matrixmalloc(m,1);
if (R==NULL) return(-1);
m-=1;n-=1;
i=0;
tulos=FALSE;
k=0;
polyfill(X,m,n,k);
tulos=linreg(X,Y,S,U,T,B,R,m,n);
if (tulos<0) return(-1);
if (!tulos) {
j=k;
k++;
do {
j++;
oma=regressor[j-1];
for (i=0;i<m;i++) {
*(X+i)=*(oma+i);
}
muutnim=data.v[j-1];
polyfill(X,m,n,k);
/*
showmatrix(X,m,n);
*/
tulos=linreg(X,Y,S,U,T,B,R,m,n);
if (tulos<0) return(-1);
} while (tulos!=TRUE && j<data.m_act);
}
/*
showmatrix(B,m,1);
*/
if (tulos) {
if (k>0) *(X+0)=*(oma+m);
polyrow(X,1,n,0,k,m+1);
/*
showmatrix(X,1,n);
*/
if ( pow( *(Y+m)-solve_term(X,B,1,n,0) ,2) < MINTOLERANCE ) {
if (k>0) *(X+0)=*(oma+m+1);
polyrow(X,1,n,0,k,m+2);
tulosterm=solve_term(X,B,1,n,0);
tulosl=(int)floor(tulosterm);
if (fabs(tulosterm-tulosl)>.5) tulosl=(int)ceil(tulosterm);
muste_ltoa(tulosl,nterm_output_buffer,10);
nterm_type=POLYNOMIAL;
construct_formula(B,k,n);
}
}
else {
real_m=m; // real_n=n;
for (n=1;n<(real_m-n-1);n++) {
a=n+1;
m=real_m-n;
for (i=0;i<m+1;i++) {
*(Y+i)=sequence[i+n];
}
/*
showmatrix(Y,m,1);
*/
for (i=0;i<m;i++) *(X+i)=1;
for (j=1;j<=n;j++) {
for (i=0;i<m;i++) {
*(X+i+m*j)=sequence[n-j+i];
}
}
/*
showmatrix(X,m,a);
*/
tulos=linreg(X,Y,S,U,T,B,R,m,a);
if (tulos<0) return(-1);
/*
showmatrix(B,a,1);
*/
if (tulos) {
tulosterm=*(B+0);
for (k=0;k<n;k++) {
tulosterm+=(*(Y+m-1-k))*(*(B+1+k));
}
/*
printf("\ntark:%f\n",pow(tulosterm-*(Y+m+1),2));WAIT;
*/
if (pow(tulosterm-*(Y+m+1),2) < MINTOLERANCE) {
tulosterm=*(B+0);
for (k=0;k<n;k++) {
tulosterm+=(*(Y+m-k))*(*(B+1+k));
}
tulosl=(int)floor(tulosterm);
if (fabs(tulosterm-tulosl)>.5) tulosl=(int)ceil(tulosterm);
muste_ltoa(tulosl,nterm_output_buffer,10);
nterm_type=FIBONACCI;
construct_formula(B,0,n);
break;
}
}
}
}
return(1);
}
