<commit_before>#ifndef __DYNMEANS_IMPL_HPP
template<class Vec>
DynMeans<Vec>::DynMeans(double lambda, double Q, double tau, bool verbose){
	this->verbose = verbose;
	this->lambda = lambda;
	this->Q = Q;
	this->tau = tau;
	this->ages.clear();
	this->oldprms.clear();
	this->labels.clear();
	this->observations.clear();
	this->weights.clear();
}

template<class Vec>
DynMeans<Vec>::~DynMeans(){
}

template<class Vec>
void DynMeans<Vec>::reset(){
	this->ages.clear();
	this->oldprms.clear();
	this->labels.clear();
	this->observations.clear();
	this->weights.clear();
}

//This function is used when sampling parameters - it returns a vector of the observations in the next cluster,
//along with the index of that cluster.
//If this is the last parameter to be sampled, the function returns true; otherwise, false.
template<class Vec>
std::vector<Vec> DynMeans<Vec>::getObsInCluster(int idx, std::vector<int> lbls){
	//std::cout << "Getting obs set in next cluster" << std::endl;
	std::vector<Vec> obsInCluster;
	obsInCluster.reserve(lbls.size());
	//std::cout << "Getting obs for param with idx " << idx << std::endl;
	for (int i = 0; i < lbls.size(); i++){
		if (lbls[i] == idx){
			obsInCluster.push_back(this->observations[i]);
		}
	}
	return obsInCluster;
}

template<class Vec>
void DynMeans<Vec>::updateState(std::vector<int> lbls, std::vector<int> cnts, std::vector<Vec> prms){
	this->oldprms = prms;
	this->labels = lbls;
	//update the weights/ages
	for (int i = 0; i < prms.size(); i++){
		if (i < this->weights.size() && cnts[i] > 0){
			//this is an instantiated cluster from a previous time; set age to 0 and update weights
			this->weights[i] = 1.0/(1.0/this->weights[i] + this->ages[i]*this->tau) + cnts[i];
			this->ages[i] = 0;
		} else if (i >= this->weights.size() ) {
			//new cluster
			//push back a 0 for the age, and set the weight to the number of observations
			this->ages.push_back(0);
			this->weights.push_back(cnts[i]);
		}
		//increment the age for all clusters (including old clusters with i < weights.size() && cnts = 0)
		this->ages[i]++;
	}
}

template<class Vec>
void DynMeans<Vec>::cluster(std::vector<Vec>& newobservations, int nRestarts, 
		std::vector<int>& finalLabels, std::vector<Vec>& finalParams, double& finalObj, double& tTaken){
	timeval tStart;
	gettimeofday(&tStart, NULL);


	//set the new obs
	this->observations = newobservations;

	if (newobservations.size() == 0){
		std::cout << "libdynmeans: ERROR: newobservations is empty" << std::endl;
		return -1;
	}
	if (nRestarts <= 0){
		std::cout << "libdynmeans: ERROR: Cannot have nRestarts <= 0" << std::endl;
		return -1;
	}

	//generate the orderings
	std::vector< std::vector<int> > randOrderings;
	std::vector<int> tmpindices;
	for (int i = 0; i < newobservations.size(); i++){
		tmpindices.push_back(i);
	}
	std::srand( unsigned( std::time(0) ) );
	for (int i = 0; i < nRestarts; i++){
		//cout << "Generating random order " << i+1 << "/" << nOrderings << "               \r" << flush;
		random_shuffle(tmpindices.begin(), tmpindices.end());
		randOrderings.push_back(tmpindices);
	}

	//stuff for storing best clustering (all other countries make inferior potassium) 
	finalObj =  std::numeric_limits<double>::max();<commit_msg>fixing compiler errors<commit_after>#ifndef __DYNMEANS_IMPL_HPP
template<class Vec>
DynMeans<Vec>::DynMeans(double lambda, double Q, double tau, bool verbose){
	this->verbose = verbose;
	this->lambda = lambda;
	this->Q = Q;
	this->tau = tau;
	this->ages.clear();
	this->oldprms.clear();
	this->labels.clear();
	this->observations.clear();
	this->weights.clear();
}

template<class Vec>
DynMeans<Vec>::~DynMeans(){
}

template<class Vec>
void DynMeans<Vec>::reset(){
	this->ages.clear();
	this->oldprms.clear();
	this->labels.clear();
	this->observations.clear();
	this->weights.clear();
}

//This function is used when sampling parameters - it returns a vector of the observations in the next cluster,
//along with the index of that cluster.
//If this is the last parameter to be sampled, the function returns true; otherwise, false.
template<class Vec>
std::vector<Vec> DynMeans<Vec>::getObsInCluster(int idx, std::vector<int> lbls){
	//std::cout << "Getting obs set in next cluster" << std::endl;
	std::vector<Vec> obsInCluster;
	obsInCluster.reserve(lbls.size());
	//std::cout << "Getting obs for param with idx " << idx << std::endl;
	for (int i = 0; i < lbls.size(); i++){
		if (lbls[i] == idx){
			obsInCluster.push_back(this->observations[i]);
		}
	}
	return obsInCluster;
}

template<class Vec>
void DynMeans<Vec>::updateState(std::vector<int> lbls, std::vector<int> cnts, std::vector<Vec> prms){
	this->oldprms = prms;
	this->labels = lbls;
	//update the weights/ages
	for (int i = 0; i < prms.size(); i++){
		if (i < this->weights.size() && cnts[i] > 0){
			//this is an instantiated cluster from a previous time; set age to 0 and update weights
			this->weights[i] = 1.0/(1.0/this->weights[i] + this->ages[i]*this->tau) + cnts[i];
			this->ages[i] = 0;
		} else if (i >= this->weights.size() ) {
			//new cluster
			//push back a 0 for the age, and set the weight to the number of observations
			this->ages.push_back(0);
			this->weights.push_back(cnts[i]);
		}
		//increment the age for all clusters (including old clusters with i < weights.size() && cnts = 0)
		this->ages[i]++;
	}
}

template<class Vec>
void DynMeans<Vec>::cluster(std::vector<Vec>& newobservations, int nRestarts, 
		std::vector<int>& finalLabels, std::vector<Vec>& finalParams, double& finalObj, double& tTaken){
	timeval tStart;
	gettimeofday(&tStart, NULL);


	//set the new obs
	this->observations = newobservations;

	if (newobservations.size() == 0){
		std::cout << "libdynmeans: ERROR: newobservations is empty" << std::endl;
		return;
	}
	if (nRestarts <= 0){
		std::cout << "libdynmeans: ERROR: Cannot have nRestarts <= 0" << std::endl;
		return;
	}

	//generate the orderings
	std::vector< std::vector<int> > randOrderings;
	std::vector<int> tmpindices;
	for (int i = 0; i < newobservations.size(); i++){
		tmpindices.push_back(i);
	}
	std::srand( unsigned( std::time(0) ) );
	for (int i = 0; i < nRestarts; i++){
		//cout << "Generating random order " << i+1 << "/" << nOrderings << "               \r" << flush;
		random_shuffle(tmpindices.begin(), tmpindices.end());
		randOrderings.push_back(tmpindices);
	}

	//stuff for storing best clustering (all other countries make inferior potassium) 
	finalObj =  std::numeric_limits<double>::max();
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{'dir': 'git-commits-cleaned', 'id': '6053378', 'n_tokens_mistral': 2398, 'n_tokens_neox': 2275, 'n_words': 613}