Regression Success!
with least square fitting
with gnu scientific library

1. random number generation with gsl
gsl_rng_env_setup();
rngtype = gsl_rng_default;
rng = gsl_rng_alloc(rngtype);
y[i] = x[i] + 3.0 * gsl_rng_uniform(rng);
REF: Random Number Generator Examples

2. gnuplot_plot_xy()

3. gsl_fit_linear()

Further information are in code below.
Basics are in trackbacks below.



always19@always19-desktop:~/project/SystemMonitor/gnuplot_i-2.10/test$ gcc -o drawlsf19 drawlsf19.c ../gnuplot_i.o -lgsl -lm -lgslcblas -L"/usr/lib" -I"../src"



#include <stdio.h>
#include "gnuplot_i.h"

#include <gsl/gsl_fit.h>
#include <gsl/gsl_rng.h>


#define NUM_SAMPLES 50

int
main(void)
{
double x[NUM_SAMPLES];
double y[NUM_SAMPLES];

const gsl_rng_type *rngtype;
gsl_rng * rng;

gnuplot_ctrl *h1;


int i;
double c0, c1, cov00, cov01, cov11, chisq;

gsl_rng_env_setup();
h1 = gnuplot_init();
gnuplot_setstyle(h1, "points");

rngtype = gsl_rng_default;
rng = gsl_rng_alloc(rngtype);

for (i = 0; i < NUM_SAMPLES ; i++)
{
double u = gsl_rng_uniform (rng);
x[i] = i ;
y[i] = x[i] + 3.0 * u;
}

gsl_fit_linear(x, 1, y, 1, NUM_SAMPLES, &c0, &c1, &cov00, &cov01, &cov11, &chisq);

gnuplot_plot_xy(h1, x, y, NUM_SAMPLES, "linear");

gnuplot_setstyle(h1, "lines");
gnuplot_plot_slope(h1, c1, c0, "regression");
gnuplot_plot_slope(h1, 1.0, 0.0, "slope");

getchar();

gnuplot_close(h1);

gsl_rng_free(rng);
return 0;
}