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Due to ongoing technical difficulties with the Active Harmony integration, we've implemented our own nelder mead search. It still needs some debugging but appears to be working for discretized input sets. Still needs refactoring to handle continuous values from the Kokkos tuning interface. That's the next step.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,95 @@ | ||
| #include "nelder_mead.hpp" | ||
| #include <algorithm> | ||
| #include <iostream> | ||
| #include <fstream> | ||
| #include <iomanip> | ||
| #include <cmath> | ||
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| namespace apex { | ||
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| namespace nelder_mead { | ||
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| void NelderMead::start(void) { | ||
| // create a starting point | ||
| std::vector<double> init_point; | ||
| for (auto& v : vars) { | ||
| init_point.push_back(v.second.get_init()); | ||
| } | ||
| // create a lower limit | ||
| std::vector<double> lower_limit; | ||
| std::vector<double> upper_limit; | ||
| for (auto& v : vars) { | ||
| auto& limits = v.second.get_limits(); | ||
| lower_limit.push_back(limits[0]); | ||
| upper_limit.push_back(limits[1]); | ||
| } | ||
| // create a starting simplex - random values in the space, nvars+1 of them | ||
| std::vector<std::vector<double>> init_simplex; | ||
| for (size_t i = 0 ; i < (vars.size() + 1) ; i++) { | ||
| std::vector<double> tmp; | ||
| for (auto& v : vars) { | ||
| double r = ((double) std::rand() / (RAND_MAX)); | ||
| auto& limits = v.second.get_limits(); | ||
| tmp.push_back(r * ((limits[1] + limits[0]) / 2.0)); | ||
| } | ||
| init_simplex.push_back(tmp); | ||
| } | ||
| searcher = new apex::internal::nelder_mead::Searcher<double>(init_point, init_simplex, lower_limit, upper_limit, true); | ||
| searcher->function_tolerance(100000); | ||
| searcher->point_tolerance(0.01); | ||
| } | ||
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| /* | ||
| static std::string vector_to_string(std::vector<double> val) { | ||
| std::stringstream ss; | ||
| ss << "["; | ||
| for (size_t i = 0; i < val.size(); i++) { | ||
| ss << val[i]; | ||
| ss << (i == val.size() - 1 ? "]" : ","); | ||
| } | ||
| return ss.str(); | ||
| } | ||
| */ | ||
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| void NelderMead::getNewSettings() { | ||
| if (searcher == nullptr) start(); | ||
| // get the next point from the simplex search | ||
| auto point = searcher->get_next_point(); | ||
| //std::cout << "Next point: " << vector_to_string(point) << std::endl; | ||
| size_t i{0}; | ||
| for (auto& v : vars) { | ||
| // if continuous, we just get the value | ||
| if (v.second.vtype == VariableType::continuous) { | ||
| v.second.current_value = point[i]; | ||
| } else { | ||
| // otherwise, we scale the value from [0:maxlen] to our discrete index | ||
| double tmp = point[i] * v.second.maxlen; | ||
| v.second.current_index = std::min((size_t)(std::trunc(tmp)),(v.second.maxlen-1)); | ||
| } | ||
| v.second.set_current_value(); | ||
| i++; | ||
| } | ||
| } | ||
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| void NelderMead::evaluate(double new_cost) { | ||
| // report the result | ||
| searcher->report(new_cost); | ||
| if (new_cost < cost) { | ||
| if (new_cost < best_cost) { | ||
| best_cost = new_cost; | ||
| std::cout << "New best! " << new_cost << " k: " << k << std::endl; | ||
| for (auto& v : vars) { v.second.save_best(); } | ||
| for (auto& v : vars) { std::cout << ", " << v.first << ": " << v.second.toString(); } | ||
| std::cout << std::endl; | ||
| } | ||
| cost = new_cost; | ||
| } | ||
| k++; | ||
| return; | ||
| } | ||
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| } // genetic | ||
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| } // apex | ||
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