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));
}
virtual raft::kstatus run()
{
A a;
B b;
raft::signal sig_a( raft::none ), sig_b( raft::none );
input[ "input_a" ].pop( a, &sig_a );
input[ "input_b" ].pop( b, &sig_b );
assert( sig_a == sig_b );
C c( a + b );
output[ "sum" ].push( c , sig_a );
if( sig_b == raft::eof )
{
return( raft::stop );
}
return( raft::proceed );
}
