#include <iclStochasticOptimizer.h> #include <algorithm> #include <numeric> #include <vector> #include <functional> #include <iclMacros.h> namespace icl{ template<class T> StochasticOptimizerResult<T>::StochasticOptimizerResult(const T *data,T error, T startError, int steps): data(data),error(error),startError(startError),steps(steps){ } template<class T> StochasticOptimizer<T>::StochasticOptimizer(int dataDim): m_dataDim(dataDim){ } template<class T> void StochasticOptimizer<T>::notifyProgress(int,int,int, int, int,const T *, int){} template<class T> StochasticOptimizerResult<T> StochasticOptimizer<T>::optimize(T minError, int maxSteps){ reinitialize(); T *data = getData(); T error = getError(data); T startError = error; std::vector<T> noisedData(m_dataDim); int t = 0; notifyProgress(t,maxSteps,startError,error,error,data,m_dataDim); do{ std::transform(data,data+m_dataDim,getNoise(t,maxSteps), noisedData.data(),std::plus<T>()); T currError = getError(noisedData.data()); notifyProgress(t,maxSteps,startError,error,currError,data,m_dataDim); if(currError < error){ error = currError; std::copy(noisedData.begin(),noisedData.end(),data); } if(minError>0 && currError <= minError){ return Result(data,error,startError,maxSteps-t); } ++t; }while(t<maxSteps); return Result(data,error,startError,maxSteps); } template class StochasticOptimizer<float>; template class StochasticOptimizer<double>; }