void minim_bayes(density_t *ndft)
{
int i;
double minf;
double *x;
bayes_par par_input;
nlopt_function_data function_data;
bopt_params par;
/* Initializing the bounds for the optimization.
We need two vectors with size ipf.npar to parse
them to bayesopt */
par_input.lb = (double *)malloc(ipf.npar*sizeof(double));
parse_double ("bayes_lb", par_input.lb);
for(i = 1; i < ipf.npar; i++) par_input.lb[i] = par_input.lb[0];
par_input.ub = (double *)malloc(ipf.npar*sizeof(double));
parse_double ("bayes_ub", par_input.ub);
for(i = 1; i < ipf.npar; i++) par_input.ub[i] = par_input.ub[0];
x = (double *)malloc(ipf.npar*sizeof(double));
for(i = 0; i < ipf.npar; i++) x[i] = gsl_vector_get(ipf.gamma, i);
/* Initializing all the parameters to default, and then modifying them */
par = initialize_parameters_to_default();
/* The use of the auxiliar data.n_iter, data.init_samples and data.init_method
parameters is caused by their par.* equivalents being defined as size_t
instead of int, which caused some warnings and errors in the parser */
par.n_iterations = 190;
parse_int("bayes_iter", &par_input.n_iter); par.n_iterations = par_input.n_iter;
par.n_init_samples = 10;
parse_int("bayes_init_samples", &par_input.init_samples); par.n_init_samples = par_input.init_samples;
par.init_method = 1;
parse_int("bayes_init_method", &par_input.init_method); par.init_method = par_input.init_method;
par.random_seed = 0;
par.verbose_level = 1; parse_int("bayes_verbose", &par.verbose_level);
par.noise = 1e-10; parse_double("bayes_noise", &par.noise);
par_input.learning = "L_MCMC"; parse_string("bayes_learning", &par_input.learning);
set_learning(&par, par_input.learning);
function_data.counter = 0;
function_data.ndft = density_get_val(ndft);
messages_basic("\n\n\nStarting the optimization.\n\n\n");
bayes_optimization(ipf.npar, nlopt_gfunction, &function_data, par_input.lb, par_input.ub, x, &minf, par);
if(myid == 0) printf("Success: iterations = %d, G(gamma) = %15.10f\n", function_data.counter, minf);
if(myid == 0) printf("gamma = ");
if(myid == 0) {for(i = 0; i < ipf.npar; i++) printf("%.5f ", x[i]);}
if(myid == 0) printf("\n");
for(i = 0; i < ipf.npar; i++) gsl_vector_set(ipf.gamma, i, x[i]);
ipf_write(ipf, ipf_ref, "pot");
parallel_end(EXIT_SUCCESS);
}
void mexFunction(int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[])
{
double *xptr;
mxArray *xopt;
const mxArray *func_name, *params;
user_function_data udata;
size_t nDim;
unsigned int ii;
bopt_params parameters;
double *ub, *lb; /* Upper and lower bound */
double fmin;
int error_code;
/* Check correct number of parameters */
CHECK0(nlhs != 2 || nrhs != 3 || nrhs != 5,
"wrong number of arguments");
/* TODO: Change This */
udata.neval = 0;
udata.verbose = 0;
/* First term is the function handle or name */
func_name = prhs[0];
if (mxIsChar(func_name))
{
CHECK0(mxGetString(func_name, udata.f, FLEN) == 0,
"error reading function name string (too long?)");
udata.nrhs = 1;
udata.xrhs = 0;
}
#ifndef HAVE_OCTAVE
else if (mxIsFunctionHandle(func_name))
{
udata.prhs[0] = (mxArray *)func_name;
strcpy(udata.f, "feval");
udata.nrhs = 2;
udata.xrhs = 1;
}
#endif
else
{
mexErrMsgTxt("First term should be a function name or function handle");
}
/* Second parameter. nDim */
CHECK0(mxIsNumeric(prhs[1]) && !mxIsComplex(prhs[1])
&& mxGetM(prhs[1]) * mxGetN(prhs[1]) == 1,
"nDim must be a scalar");
nDim = (unsigned int) mxGetScalar(prhs[1]);
udata.prhs[udata.xrhs] = mxCreateDoubleMatrix(1, nDim, mxREAL);
xopt = mxCreateDoubleMatrix(1, nDim, mxREAL);
xptr = mxGetPr(xopt);
/* Third term. Parameters */
if (nrhs != 2)
{
CHECK0(mxIsStruct(prhs[2]), "3rd element must be a struct");
params = prhs[2];
}
else
{
params = mxCreateStructMatrix(1,1,0,NULL);
}
parameters = load_parameters(params);
if(nrhs == 5)
{
/* Load bounds */
mexPrintf("Loading bounds...");
CHECK0(mxIsDouble(prhs[3]) && !mxIsComplex(prhs[3])
&& (mxGetM(prhs[3]) == 1 || mxGetN(prhs[3]) == 1)
&& (mxGetM(prhs[3]) == nDim || mxGetN(prhs[3]) == nDim),
"lowerBound must be real row or column vector");
lb = mxGetPr(prhs[3]);
CHECK0(mxIsDouble(prhs[4]) && !mxIsComplex(prhs[4])
&& (mxGetM(prhs[4]) == 1 || mxGetN(prhs[4]) == 1)
&& (mxGetM(prhs[4]) == nDim || mxGetN(prhs[4]) == nDim),
"upperBound must be real row or column vector");
ub = mxGetPr(prhs[4]);
mexPrintf("done. \n");
}
else
{
lb = (double*)(mxCalloc(nDim,sizeof(double)));
ub = (double*)(mxCalloc(nDim,sizeof(double)));
for (ii = 0; ii < nDim; ++ii)
{
lb[ii] = 0.;
ub[ii] = 1.;
}
}
error_code = bayes_optimization(nDim,user_function,&udata,lb,ub,xptr,
&fmin,parameters);
if(nrhs != 5)
{
mxFree(lb);
mxFree(ub);
}
mxDestroyArray(udata.prhs[udata.xrhs]);
plhs[0] = xopt;
if (nlhs > 1)
{
plhs[1] = mxCreateDoubleMatrix(1, 1, mxREAL);
*(mxGetPr(plhs[1])) = fmin;
}
if (nlhs > 2)
{
plhs[2] = mxCreateDoubleMatrix(1, 1, mxREAL);
*(mxGetPr(plhs[2])) = (double)(error_code);
}
}
