void boundary_transformation_inverse(boundary_transformation_t *t,
double const *x, double *y, unsigned long len)
{
double lb, ub, al, au;
unsigned long i;
for (i = 0; i < len; ++i) {
lb = t->lower_bounds[_index(t, i)];
ub = t->upper_bounds[_index(t, i)];
al = t->al[_index(t, i)];
au = t->au[_index(t, i)];
y[i] = x[i];
if (y[i] < lb + al)
y[i] = (lb - al) + 2 * sqrt(al * (y[i] - lb));
else if (y[i] > ub - au)
y[i] = (ub + au) - 2 * sqrt(au * (ub - y[i]));
}
if (11 < 3 || do_assertions) {
double *z = calloc(len, sizeof(double));
for (i = 0; i < len; ++i)
z[i] = y[i];
boundary_transformation(t, z, y, len);
for (i = 0; i < len; ++i)
if (fabs(y[i] - x[i]) > 1e-14)
printf(" difference for index %ld should be zero, is %f ", i, y[i] - x[i]);
for (i = 0; i < len; ++i)
y[i] = z[i];
free(z);
}
}
/* the optimization loop */
int main(int argn, char **args) {
cmaes_t evo; /* an CMA-ES type struct or "object" */
boundary_transformation_t boundaries;
double *arFunvals, *x_in_bounds, *const*pop;
double lowerBounds[] = {1.0, DBL_MAX / -1e2}; /* last number is recycled for all remaining coordinates */
double upperBounds[] = {3, 2e22};
int nb_bounds = 1; /* numbers used from lower and upperBounds */
unsigned long dimension;
int i;
/* initialize boundaries, be sure that initialSigma is smaller than upper minus lower bound */
boundary_transformation_init(&boundaries, lowerBounds, upperBounds, nb_bounds);
/* Initialize everything into the struct evo, 0 means default */
arFunvals = cmaes_init(&evo, 0, NULL, NULL, 0, 0, "cmaes_initials.par");
dimension = (unsigned long)cmaes_Get(&evo, "dimension");
printf("%s\n", cmaes_SayHello(&evo));
x_in_bounds = cmaes_NewDouble(dimension); /* calloc another vector */
cmaes_ReadSignals(&evo, "cmaes_signals.par"); /* write header and initial values */
/* Iterate until stop criterion holds */
while(!cmaes_TestForTermination(&evo))
{
/* generate lambda new search points, sample population */
pop = cmaes_SamplePopulation(&evo); /* do not change content of pop */
/* transform into bounds and evaluate the new search points */
for (i = 0; i < cmaes_Get(&evo, "lambda"); ++i) {
boundary_transformation(&boundaries, pop[i], x_in_bounds, dimension);
/* this loop can be omitted if is_feasible is invariably true */
while(!is_feasible(x_in_bounds, dimension)) { /* is_feasible needs to be user-defined, in case, and can change/repair x */
cmaes_ReSampleSingle(&evo, i);
boundary_transformation(&boundaries, pop[i], x_in_bounds, dimension);
}
arFunvals[i] = fitfun(x_in_bounds, dimension); /* evaluate */
}
/* update the search distribution used for cmaes_SampleDistribution() */
cmaes_UpdateDistribution(&evo, arFunvals); /* assumes that pop[i] has not been modified */
/* read instructions for printing output or changing termination conditions */
cmaes_ReadSignals(&evo, "cmaes_signals.par");
fflush(stdout); /* useful in MinGW */
}
printf("Stop:\n%s\n", cmaes_TestForTermination(&evo)); /* print termination reason */
cmaes_WriteToFile(&evo, "all", "allcmaes.dat"); /* write final results */
/* get best estimator for the optimum, xmean */
boundary_transformation(&boundaries,
(double const *) cmaes_GetPtr(&evo, "xmean"), /* "xbestever" might be used as well */
x_in_bounds, dimension);
/* do something with final solution x_in_bounds */
/* ... */
/* and finally release memory */
cmaes_exit(&evo); /* release memory */
boundary_transformation_exit(&boundaries); /* release memory */
free(x_in_bounds);
return 0;
}
