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feat(python): best sol candidate in phenotype space #221

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Mar 21, 2021
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feat(python): best sol candidate in phenotype space #221

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11 changes: 11 additions & 0 deletions python/lcmaes.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -97,6 +97,16 @@ boost::python::list get_candidate_x(const Candidate &c)
return x;
}

boost::python::list get_best_candidate_pheno(const CMASolutions &s,
const GenoPheno<pwqBoundStrategy> &gp)
{
boost::python::list gpx;
dVec d = gp.pheno(s.best_candidate().get_x_dvec());
for (int i=0;i<d.rows();++i)
gpx.append(d[i]);
return gpx;
}

PyObject* get_solution_cov_py(const CMASolutions &s)
{
npy_intp shape[2] = {s.dim(),s.dim()};
Expand Down Expand Up @@ -443,6 +453,7 @@ BOOST_PYTHON_MODULE(lcmaes)
.def("set_fvalue",&Candidate::set_fvalue,"sets candidate's objective function value")
;
def("get_candidate_x",get_candidate_x,args("cand"),"returns candidate's parameter vector");
def("get_best_candidate_pheno",get_best_candidate_pheno,args("cmasol","gp"),"returns candidate's parameter vector in phenotype space");

/*- genopheno object -*/
class_<GenoPheno<NoBoundStrategy>>("GenoPhenoNB","genotype/phenotype transformation object for problem with unbounded parameters")
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2 changes: 2 additions & 0 deletions python/ptest_bounds.py
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Expand Up @@ -25,8 +25,10 @@ def nfitfunc(x,n):

# collect and inspect results
bcand = cmasols.best_candidate()
bcand_pheno = lcmaes.get_best_candidate_pheno(cmasols,gp)
bx = lcmaes.get_candidate_x(bcand)
print("best x=",bx)
print('best x in phenotype space=',bcand_pheno)
print("distribution mean=",lcmaes.get_solution_xmean(cmasols))
cov = lcmaes.get_solution_cov(cmasols) # numpy array
print("cov=",cov)
Expand Down