int main()
{
const int n=40, m=3; // 40 measurements, 3 parameters
double p[m], x[n], opts[LM_OPTS_SZ], info[LM_INFO_SZ];
register int i;
int ret;
/* generate some measurement using the exponential model with
* parameters (5.0, 0.1, 1.0), corrupted with zero-mean
* Gaussian noise of s=0.1
*/
INIT_RANDOM(0);
for(i=0; i<n; ++i)
x[i]=(5.0*exp(-0.1*i) + 1.0) + gNoise(0.0, 0.1);
/* initial parameters estimate: (1.0, 0.0, 0.0) */
p[0]=1.0; p[1]=0.0; p[2]=0.0;
/* optimization control parameters; passing to levmar NULL instead of opts reverts to defaults */
opts[0]=LM_INIT_MU; opts[1]=1E-15; opts[2]=1E-15; opts[3]=1E-20;
opts[4]=LM_DIFF_DELTA; // relevant only if the finite difference Jacobian version is used
/* invoke the optimization function */
//ret=dlevmar_der(expfunc, jacexpfunc, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
ret=dlevmar_dif(expfunc, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // without Jacobian
printf("Levenberg-Marquardt returned in %g iter, reason %g, sumsq %g [%g]\n", info[5], info[6], info[1], info[0]);
printf("Best fit parameters: %.7g %.7g %.7g\n", p[0], p[1], p[2]);
exit(0);
}
void Test::ExpFit(int nSample)
{
LOGI("Fitting %d samples..", nSample);
LOGI(" LM_OPTS_SZ=%d", LM_OPTS_SZ);
LOGI(" LM_INFO_SZ=%d", LM_INFO_SZ);
LOGI(" LM_INIT_MU=%f", LM_INIT_MU);
LOGI(" LM_DIFF_DELTA=%f", LM_DIFF_DELTA);
const int n=nSample, m=3; // 40 measurements, 3 parameters
double p[m], x[n], opts[LM_OPTS_SZ], info[LM_INFO_SZ];
register int i;
int ret;
/* generate some measurement using the exponential model with
* parameters (5.0, 0.1, 1.0), corrupted with zero-mean
* Gaussian noise of s=0.1
*/
INIT_RANDOM(0);
for(i=0; i<n; ++i)
x[i]=(5.0*exp(-0.1*i) + 1.0) + gNoise(0.0, 0.1);
/* initial parameters estimate: (1.0, 0.0, 0.0) */
p[0]=1.0; p[1]=0.0; p[2]=0.0;
LOGI("Initial fit parameters: %.7g %.7g %.7g\n", p[0], p[1], p[2]);
/* optimization control parameters; passing to levmar NULL instead of opts reverts to defaults */
opts[0]=LM_INIT_MU; opts[1]=1E-15; opts[2]=1E-15; opts[3]=1E-20;
opts[4]=LM_DIFF_DELTA; // relevant only if the finite difference Jacobian version is used
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t1 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t1 = std::chrono::monotonic_clock::now();
#endif
/* invoke the optimization function */
ret=dlevmar_der(expfunc, jacexpfunc, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // with analytic Jacobian
#ifdef COMPILEDWITHC11
std::chrono::steady_clock::time_point t2 = std::chrono::steady_clock::now();
#else
std::chrono::monotonic_clock::time_point t2 = std::chrono::monotonic_clock::now();
#endif
double dt= std::chrono::duration_cast<std::chrono::duration<double> >(t2 - t1).count();
LOGI("time elapsed for optimization = %f ms", dt*1000.0);
//ret=dlevmar_dif(expfunc, p, x, m, n, 1000, opts, info, NULL, NULL, NULL); // without Jacobian
LOGI("Levenberg-Marquardt returned in %g iter, reason %g, sumsq %g [%g]\n", info[5], info[6], info[1], info[0]);
LOGI("Best fit parameters: %.7g %.7g %.7g\n", p[0], p[1], p[2]);
//DD::
FILE *fp = fopen("/mnt/sdcard/dev/out_params.txt", "w+");
fprintf(fp, "%f\t%f\t%f\n", p[0], p[1], p[2]);
fclose(fp);
fp = fopen("/mnt/sdcard/dev/out_data.txt", "w+");
for(i=0; i<n; i++)
{
float xest = p[0]*exp(-p[1]*i) + p[2];
fprintf(fp, "%d\t%f\t%f\n",i, x[i], xest);
}
fclose(fp);
}
