[Swift-commit] r5511 - SwiftApps/SciColSim

wilde at ci.uchicago.edu wilde at ci.uchicago.edu
Mon Jan 23 12:21:23 CST 2012


Author: wilde
Date: 2012-01-23 12:21:22 -0600 (Mon, 23 Jan 2012)
New Revision: 5511

Added:
   SwiftApps/SciColSim/optimizer.protomods.cpp
Removed:
   SwiftApps/SciColSim/optimizer.cpp
Log:
Move prototype of optimizer changes for Swift to new name for archival.

Deleted: SwiftApps/SciColSim/optimizer.cpp
===================================================================
--- SwiftApps/SciColSim/optimizer.cpp	2012-01-23 18:19:35 UTC (rev 5510)
+++ SwiftApps/SciColSim/optimizer.cpp	2012-01-23 18:21:22 UTC (rev 5511)
@@ -1,1814 +0,0 @@
-//
-//  main.cpp
-//  optimizer
-//
-//  Created by Andrey Rzhetsky on 4/11/11.
-//  Copyright 2011 University of Chicago. All rights reserved.
-//
-
-#define MAXNworkers 24
-int Nworkers=MAXNworkers;
-char operation = 'n'; // n: normal; m: multi_loss; a: analyze and generate next annealing parameter set.
-
-#include <fstream>
-#include <sstream>
-#include <iostream>
-#include <stdio.h>
-#include <time.h>
-#include <ctime>    
-#include <algorithm>
-#include <string>
-
-#include <stdio.h>
-#include <sys/param.h>
-#include <sys/time.h>
-#include <sys/types.h>
-
-// #include <dispatch/dispatch.h>
-#include <fstream>
-
-
-#include <stdlib.h>
-#include <boost/numeric/ublas/io.hpp>
-#include <boost/graph/graph_traits.hpp>
-#include <boost/graph/dijkstra_shortest_paths.hpp>
-#include <boost/graph/loop_erased_random_walk.hpp>
-#include <boost/graph/random.hpp>
-#include <boost/property_map/property_map.hpp>
-#include <boost/graph/graph_concepts.hpp>
-#include <boost/graph/properties.hpp>
-
-#include <boost/graph/graph_traits.hpp>
-#include <boost/graph/adjacency_list.hpp>
-#include <boost/graph/adjacency_matrix.hpp>
-
-#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
-#define BOOST_MATH_DISCRETE_QUANTILE_POLICY real
-#include <boost/graph/random.hpp>
-#include <boost/random/geometric_distribution.hpp>
-#include <boost/random/uniform_01.hpp>
-#include <boost/random.hpp>
-#include <boost/random/linear_congruential.hpp>
-#include <boost/random/uniform_int.hpp>
-#include <boost/random/uniform_real.hpp>
-#include <boost/random/variate_generator.hpp>
-#include <boost/generator_iterator.hpp>
-#include <boost/lexical_cast.hpp>
-
-#define INT_INFINITY 2147483647
-#define NEVOPARAMS 5
-
-#define FIX_VARIABLES 1
-
-using namespace boost;
-using namespace std;
-using namespace boost::numeric::ublas;
-
-static int max_dist=0;
-
-typedef boost::adjacency_matrix<boost::directedS> Graph;
-typedef std::pair<int,int> Edge;
-typedef boost::graph_traits<Graph> GraphTraits;
-typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> prob;
-typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> pathlength;
-typedef boost::graph_traits<Graph>::vertex_descriptor vertex_descriptor;
-
-namespace std {
-	using ::time;
-}
-
-static int var_fixed[NEVOPARAMS] = {1, 0, 1, 1, 0};
-
-typedef boost::minstd_rand base_generator_type;
-typedef adjacency_list < listS, vecS, directedS,
-no_property, property < edge_weight_t, int > > graph_t;
-typedef graph_traits < graph_t >::vertex_descriptor vertex_descriptor;
-typedef graph_traits < graph_t >::edge_descriptor edge_descriptor;
-
-
-//================================================
-string strDouble(double number)
-{
-    stringstream ss;//create a stringstream
-    ss << number;//add number to the stream
-    return ss.str();//return a string with the contents of the stream
-}
-
-//================================================
-
-double gaussian(double sigma)
-{
-    double GaussNum = 0.0;
-    int NumInSum = 10;
-    for(int i = 0; i < NumInSum; i++)
-    {
-        GaussNum += ((double)rand()/(double)RAND_MAX - 0.5);
-    }
-    GaussNum = GaussNum*sqrt((double)12/(double)NumInSum);
-    
-    
-    return GaussNum;
-    
-}
-
-
-
-//=================================================
-double diffclock(clock_t clock1,clock_t clock2)
-{
-	double diffticks=clock1-clock2;
-	double diffms=(diffticks)/CLOCKS_PER_SEC;
-	return diffms;
-}
-
-//================================================
-//================================================================
-double get_new_x(double x, double dx){
-    
-    double new_x;
-    // boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-    double r = rand()/(double)(pow(2.,31)-1.);
-    
-    if (r > 0.5){            
-        new_x = x + rand()*dx/(double)(pow(2.,31)-1.);
-    } else {            
-        new_x = x - rand()*dx/(double)(pow(2.,31)-1.);
-    }
-    
-    return new_x;
-    
-}
-
-
-//===============================================   
-string string_wrap(string ins, int mode){
-    
-    std::ostringstream s;
-    
-    switch(mode){
-        case 0:
-            s << "\033[1;29m" << ins << "\033[0m";
-            break;
-        case 1:
-            s << "\033[1;34m" << ins << "\033[0m";
-            break;
-        case 2:
-            s << "\033[1;44m" << ins << "\033[0m";
-            break;
-        case 3:
-            s << "\033[1;35m" << ins << "\033[0m";
-            break;
-        case 4:
-            s << "\033[1;33;44m" << ins << "\033[0m";
-            break;
-        case 5:
-            s << "\033[1;47;34m" << ins << "\033[0m";
-            break;
-        case 6:
-            s << "\033[1;1;31m" << ins << "\033[0m";
-            break;
-        case 7:
-            s << "\033[1;1;33m" << ins << "\033[0m";
-            break;
-        case 8:
-            s << "\033[1;1;43;34m" << ins << "\033[0m";
-            break;
-        case 9:
-            s << "\033[1;1;37m" << ins << "\033[0m";
-            break;
-        case 10:
-            s << "\033[1;30;47m" << ins << "\033[0m";
-            break;
-        default:
-            s << ins;
-    }
-    
-    return s.str();
-}
-
-
-//===============================================
-string wrap_double(double val, int mode){
-    
-    std::ostringstream s;
-    s << string_wrap(strDouble(val),mode);
-    
-    return s.str();
-}
-
-
-
-//===============================================
-const     
-string i2string(int i){
-    
-    std::ostringstream s;
-    s << "worker" 
-    << lexical_cast<std::string>(i);
-    
-    return s.str();
-    
-}
-
-//===============================================
-char* i2char(int i){
-    
-    std::ostringstream s;
-    s << "worker" 
-    << lexical_cast<std::string>(i);
-    
-    char* a=new char[s.str().size()+1];
-    memcpy(a,s.str().c_str(), s.str().size());
-    
-    return a;
-}
-
-
-template <class T>
-bool from_string(T& t, 
-                 const std::string& s, 
-                 std::ios_base& (*f)(std::ios_base&))
-{
-  std::istringstream iss(s);
-  return !(iss >> f >> t).fail();
-}
-
-//================================================
-class Universe {
-	
-private:
-	
-	double alpha_i;
-	double alpha_m;
-	double beta;
-	double gamma;
-	double delta;
-	
-    double TargetNovelty;
-    double CumulativeRelativeLoss;
-    double CRLsquare;
-    string id;
-    
-    
-	int N_nodes;
-	int M_edges;
-	
-	int N_epochs;
-	int N_steps;
-	int N_repeats;
-	
-	int current_epoch;
-	double current_loss;
-	int current_repeat;
-    double current_novelty;
-	
-	int mode_identify_failed;
-    int verbose_level; // 0 is silent, higher is more
-	
-	double k_max;
-	
-	graph_t Full_g;
-	
-	double **Prob;
-	double **Tried;
-	double **Dist;
-	double **Final;
-    double **EdgeIndex;
-	double *Rank;
-	
-    base_generator_type generator;	
-    boost::uniform_real<> uni_dist;
-    boost::geometric_distribution<double> geo;
-    
-public:
-	
-    
-    
-	//======  Constructor ======
-	Universe(const std::string FileToOpen, int Epochs, int Steps, int Repeats, int identify_failed, double target, const std::string idd)
-	{
-		//typedef array_type2::index index2;
-		
-		
-		std::ifstream inFile;
-        //string line;
-        
-        //-------------------------------
-        
-        base_generator_type gene(42u);
-        generator = gene;
-        generator.seed(static_cast<unsigned int>(std::time(0)));
-        boost::uniform_real<> uni_d(0,1);
-        uni_dist = uni_d;
-        
-        //--------------------------------
-        
-		int i, k;
-        int x, y;
-		Edge* edge_array_mine;
-		int num_arcs_mine, num_nodes_mine;
-		int* weights_mine;
-		
-        TargetNovelty = target;
-        CumulativeRelativeLoss = 0.;
-        CRLsquare = 0.;
-        
-		
-		N_epochs  = Epochs;
-		N_steps   = Steps;
-		N_repeats = Repeats;
-		
-		current_epoch = 0;
-		current_loss = 0.;
-		current_repeat = 0;
-        
-        id = idd;
-        
-        verbose_level = 1;
-		
-		mode_identify_failed = identify_failed;
-        
-		
-		//-------------------------------
-		// The first pass though file with the graph
-		inFile.open(FileToOpen.c_str());
-		if (inFile.fail()) {
-			cout << "Unable to open file";
-			exit(1); // terminate with error
-		}else {
-            
-            if (verbose_level > 2){
-                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
-            }
-        }
-		
-		i=0;
-        std::string line;
-		//while (! inFile.eof() && ! inFile.fail()) {
-        while (1==1) {
-            
-            inFile >> x;
-            inFile >> y;
-            
-            if (verbose_level > 2){
-                std::cout << " x: " << x;
-                std::cout << " y: " << y << std::endl;
-            }
-            
-			if (i==0){
-				N_nodes=x;
-				M_edges=y;	
-                break;
-			}
-			i++;
-            
-			
-		}
-		inFile.close();
-        
-        if (verbose_level == 2){
-            std::cout << N_nodes <<  " nodes, " << M_edges << " edges"<<std::endl;
-        }
-		
-		// k_max is the longest distance possible
-		
-        //k_max = M_edges;
-		k_max = 70;
-        
-		//------------------------------------
-		// Get memory allocated for all class members
-		
-		Prob = allocate_2Dmatrix(N_nodes, N_nodes);
-		Tried = allocate_2Dmatrix(N_nodes, N_nodes);
-		Dist = allocate_2Dmatrix(N_nodes, N_nodes);
-		Final = allocate_2Dmatrix(N_nodes, N_nodes);
-        EdgeIndex = allocate_2Dmatrix(N_nodes, N_nodes);
-		Rank = allocate_1Dmatrix(N_nodes);
-		
-        //The second pass through file with the graph
-        
-		for(int i = 0; i < N_nodes; ++i) {
-			Rank[i]=0.;
-			for(int j = 0; j < N_nodes; ++j) {
-				Final[i][j] = 0.;
-				Prob[i][j]=0.;
-				Dist[i][j]=-1.;
-				Tried[i][j]=0.;
-                EdgeIndex[i][j]=-1;
-			}
-		}
-        
-		
-		// Fill in the final graph -- and we are ready to go!
-        
-	    inFile.open(FileToOpen.c_str());
-		if (!inFile) {
-            std::cout << "Unable to open file";
-			exit(1); // terminate with error
-		}
-		else {
-            
-            if (verbose_level > 2){
-                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
-            }
-        }
-        
-		i=0;  
-		while (inFile >> x && inFile >>y) {
-			if (i > 0) {
-				Final[x][y]=1.;
-				Final[y][x]=1.;
-                
-                
-                if (verbose_level == 2){
-                    std::cout << ".";
-                }
-			}
-			i++;
-			
-		}
-        if (verbose_level == 2){
-            std::cout << std::endl;
-        }
-		inFile.close(); 
-		
-        k=0;
-        for (int i=0; i<N_nodes-1; i++){
-            for (int j=i+1;j<N_nodes; j++){
-                if(Final[i][j] > 0.){
-                    EdgeIndex[i][j]=k;
-                    k++;
-                }
-            }
-        }
-        
-        
-		
-		//+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
-		// create graph -- hopefully, we can keep it, just modifying edge weights
-		
-		
-		edge_array_mine = new Edge[2*M_edges];
-		num_arcs_mine = 2*M_edges;
-		num_nodes_mine = N_nodes;
-		weights_mine = new int[2*M_edges];
-		for (int i=0; i<2*M_edges; i++){ weights_mine[i]=1;}
-		
-		k=0;
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				if (Final[i][j]>0.){
-					edge_array_mine[2*k]  =Edge(i,j);
-					edge_array_mine[2*k+1]=Edge(j,i);
-					k++;
-				}
-			}
-		}
-		graph_t g(edge_array_mine, edge_array_mine + num_arcs_mine, weights_mine, num_nodes_mine);
-		
-		Full_g = g;
-		delete edge_array_mine;
-		delete weights_mine;
-		
-		//===========================================================================
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-		
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0.){
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					remove_edge(u,v,Full_g);
-					remove_edge(v,u,Full_g);
-					
-				}
-			}
-		}
-		
-        
-    }
-	
-	
-	//=====================================================================
-	int sample_failed_number(double pfail){
-		
-		//boost::geometric_distribution<double> geo(pfail);
-		//boost::variate_generator<base_generator_type&, geometric_distribution<double> > geom(generator, geo);
-		
-		double r, u, g;
-        
-        r=0.;
-		for(int i=0; i<N_steps; i++){
-            
-            u=(double)rand();
-            u = 1.-u /(double)(pow(2.,31)-1.);
-            g=(int)(ceil(log(u) / log(pfail)));
-            
-			//r += geom();
-            
-            r+=g;
-		}
-        
-        if (verbose_level>=3){
-            std::cout << id << " failed " << r << std::endl;
-		}
-		return r;
-		
-	}
-    
-    //=============================================
-    double get_target(void){
-        return TargetNovelty;
-    }
-	
-    //=============================================
-    void set_target(double target){
-        TargetNovelty=target;
-    }
-	
-	//=============================================
-	int sample(){
-		
-        //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-        // double r = uni(), Summa = 0.;
-        
-        
-        
-        double r = rand(), Summa = 0.;
-        r /= (double)(pow(2.,31)-1.);
-		int result = 0;
-		int finished = 0;
-        
-        if (verbose_level==4){
-            std::cout << id << " sampled " << r << std::endl;
-        }
-		
-		for(int i=0; i<N_nodes-1 && finished==0; i++){			
-			for( int j=i+1; j<N_nodes && finished==0; j++){
-				
-				Summa += Prob[i][j];
-				
-				if (Summa > r){
-					
-					Tried[i][j]+=1.;
-					
-					if (Final[i][j] > 0.){
-						result = 1;
-					}
-					finished = 1;										
-				}
-			}
-		}
-		
-		return result;
-		
-	}
-	
-	//===============================
-	void update_current_graph(void){
-		
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-		
-		//property_map<graph_t, edge_weight_t>::type weightmap = get(edge_weight, Full_g);
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0. && Tried[i][j]>0){
-					//s = edge(i, j, Full_g);	
-					boost::graph_traits<graph_t>::edge_descriptor e1,e2;
-					bool found1, found2;
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					tie(e1, found1) = edge(u, v, Full_g);
-					tie(e2, found2) = edge(v, u, Full_g);
-					if (!found1 && !found2){
-						add_edge(u,v,1,Full_g);
-					    add_edge(v,u,1,Full_g);
-					}
-					
-				}
-			}
-			
-		}
-	}
-	
-	//===============================
-	void update_distances(void){
-		// put shortest paths to the *Dist[][]
-		std::vector<vertex_descriptor> p(num_vertices(Full_g));
-		std::vector<int> d(num_vertices(Full_g));
-		vertex_descriptor s;
-		
-		
-		// put shortest paths to the *Dist[][]
-		for (int j=0; j<num_vertices(Full_g); j++){
-			
-			if(Rank[j] > 0.){
-				s = vertex(j, Full_g);	 
-				dijkstra_shortest_paths(Full_g, s, predecessor_map(&p[0]).distance_map(&d[0]));
-				
-				//std::cout <<" Vertex "<< j << std::endl;
-				graph_traits < graph_t >::vertex_iterator vi, vend;
-				
-				for (boost::tie(vi, vend) = vertices(Full_g); vi != vend; ++vi) {
-					
-					if (p[*vi]!=*vi){
-						Dist[*vi][j]=d[*vi];
-						Dist[j][*vi]=d[*vi];
-                        
-                        if (Dist[*vi][j]>max_dist){
-                            max_dist=Dist[*vi][j];
-                        }
-                        
-                        
-					} else {
-						Dist[*vi][j]=-1.;
-						Dist[j][*vi]=-1.;
-					}
-				}
-			}
-			
-		}
-		
-		
-	}
-	
-	//======================================================
-	void update_ranks(void){
-		
-		for(int i=0; i<N_nodes; i++){
-			Rank[i]=0.;
-		}
-		
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				if (Tried[i][j]>0. && Final[i][j] >0.){
-					Rank[i]++;
-					Rank[j]++;
-				}
-			}
-		}
-		
-	}
-	
-	//====================================================================
-	void set_world(double a_i, double a_m, double b, double g, double d){
-		
-		alpha_i=a_i;
-		alpha_m=a_m;
-		gamma=g;
-		beta=b;
-		delta=d;
-		
-	}
-	
-	//====================================================================
-	void reset_world(){
-		
-        //====================================================
-		std::vector<edge_descriptor> p(num_edges(Full_g));
-		std::vector<int> d(num_edges(Full_g));
-		edge_descriptor s;
-		boost::graph_traits<graph_t>::vertex_descriptor u, v;
-        
-		
-		for (int i=0; i<N_nodes-1; i++){
-			for (int j=i+1; j<N_nodes; j++){
-				if (Final[i][j] > 0. && Tried[i][j] > 0){
-					u = vertex(i, Full_g);
-					v = vertex(j, Full_g);
-					remove_edge(u,v,Full_g);
-					remove_edge(v,u,Full_g);
-					
-				}
-			}
-		}
-        
-        //==================================================
-        
-		current_loss=0;
-		current_epoch=0;
-		current_repeat++;
-        current_novelty=0;
-		
-		for(int i = 0; i < N_nodes; ++i) {
-			Rank[i]=0.;
-			for(int j = 0; j < N_nodes; ++j) {
-				Prob[i][j]=0.;
-				Dist[i][j]=-1.;
-				Tried[i][j]=0.;
-			}
-		}
-	}
-	
-	
-    //==============================================
-    void show_parameters(void){
-        
-        std::cout << "Parameters: " 
-        << alpha_i << " "
-        << alpha_m << " | "
-        << beta << " "
-        << gamma << " | "
-        << delta << std::endl;
-        
-    }
-    
-    
-    
-    //===============================================
-    string file_name(){
-        
-        std::ostringstream s;
-        s << "world_" 
-        << lexical_cast<std::string>(alpha_i) << "_" 
-        << lexical_cast<std::string>(alpha_m) << "_"
-        << lexical_cast<std::string>(beta) << "_"
-        << lexical_cast<std::string>(gamma) << "_"
-        << lexical_cast<std::string>(delta) << "_"
-        << lexical_cast<std::string>(N_epochs) << "_"
-        << lexical_cast<std::string>(N_steps) << "_"
-        << lexical_cast<std::string>(N_repeats) << ".txt";
-        
-        return s.str();
-        
-    }
-    
-    
-    
-    
-    //=================================================
-    void set_verbose(int verbose){
-        
-        verbose_level = verbose;
-    }
-    
-    
-    //=============================================================
-    void update_probabilities(void){
-        
-        
-        //=========================
-		// Compute sampling probabilities
-		// first pass: \xi_i,j
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				
-				double bg = 0.;
-				
-				Prob[i][j] = alpha_i*log(min(Rank[i]+1.,Rank[j]+1.)) + 
-                alpha_m*log(max(Rank[i]+1.,Rank[j]+1.));
-				
-                if (Dist[i][j] > 0.){
-                    
-                    double k = Dist[i][j];
-                    if (k >= k_max){
-                        k = k_max-1;
-                    }
-					
-                    bg = beta * log(k/k_max) + gamma * log(1. - k/k_max);
-					
-                } else {
-                    bg = delta;
-                }
-				
-				Prob[i][j] = exp(Prob[i][j] + bg);
-			}
-		}
-        
-		
-		// second pass: sum
-		double Summa = 0.;
-		
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				Summa += Prob[i][j];
-			}
-		}
-		
-		// third pass: normalize
-		for(int i=0; i<N_nodes-1; i++){
-			for( int j=i+1; j<N_nodes; j++){
-				Prob[i][j] /= Summa;
-			}
-		}
-        
-    }
-    
-	// Now we are ready for simulations
-	//==============================================
-	void update_world(){
-		
-		int failed = 0;
-        
-		// Given current universe compute shortest paths
-		//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-		
-		update_current_graph();
-		update_ranks();				
-		update_distances();
-		update_probabilities();
-		
-		//===============================
-		// sampling
-		int result;
-		double cost=0., novel=0.;
-		int publishable = 0;
-		
-		
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		if (mode_identify_failed == 1){
-			
-			while(publishable < N_steps){
-				
-		    	result = sample();
-			    publishable += result;
-			    failed += (1-result);
-				
-			}
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					
-					cost+=Tried[i][j];
-					
-					if (Tried[i][j]>0. && Final[i][j]>0.){
-						novel+=1.;
-					}
-				}
-			}
-			
-		}
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		else {
-			
-			double pfail=0.;
-			int n_failed;
-			//, n_check = 0;
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					if (Final[i][j] == 0.){
-						pfail += Prob[i][j];
-						Prob[i][j] = 0.;
-					}
-					
-				}
-			}
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					Prob[i][j] /= (1.-pfail);
-				}
-				//std::cout << std::endl;
-			}			
-			
-			n_failed = sample_failed_number(pfail);
-			while(publishable < N_steps){
-				
-		    	result = sample();
-			    publishable += result;					
-			}
-            
-            
-			current_loss += (n_failed + N_steps);
-			cost = current_loss;
-			
-			for(int i=0; i<N_nodes-1; i++){
-				for( int j=i+1; j<N_nodes; j++){
-					
-					if (Tried[i][j]>0. && Final[i][j]>0.){
-						novel+=1.;
-					}
-				}
-			}
-		}
-        
-        current_novelty = novel;
-        
-        
-		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-		if (verbose_level == 2){
-            std::cout << (current_repeat+1) << "  epoch=" << (current_epoch+1) 
-            
-		    << "  cost=" << cost 
-		    << " novel=" << novel 
-		    << " rel_loss=" << cost/novel
-		    << std::endl;
-        }
-        
-		current_epoch++;
-	}
-	
-	
-	//======  Destructor ======
-	~Universe(){
-		
-		delete_2Dmatrix(Final, N_nodes);
-		delete_2Dmatrix(Dist, N_nodes);
-		delete_2Dmatrix(Tried, N_nodes);
-		delete_2Dmatrix(Prob, N_nodes);
-        delete_2Dmatrix(EdgeIndex, N_nodes);
-		delete_1Dmatrix(Rank);		
-	}
-    
-    //================================================
-    // Allocate memory
-    double** allocate_2Dmatrix(int N, int M)
-    {
-        double **pointer;
-        
-        if (verbose_level == 2){
-            std::cout<< "["<<N<<"|"<<M<<"]"<<std::endl;
-        }
-        pointer = new double*[N];
-        for (int i = 0; i < N; ++i)
-            pointer[i] = new double[M];	
-        
-        return pointer;
-    }
-    //===================
-    double* allocate_1Dmatrix(int N)
-    {
-        double *pointer;
-        
-        if(N > 0){
-            
-            pointer = new double[N];
-            
-        }else {
-            
-            pointer = NULL;
-        }
-        
-        return pointer;
-        
-    }
-    
-    //==============================================
-    // De-Allocate memory to prevent memory leak
-    void delete_2Dmatrix(double **pointer, int N){
-        
-        if (pointer != NULL){
-            
-            for (int i = 0; i < N; ++i){
-                delete [] pointer[i];
-            }
-            delete [] pointer;
-        }
-    }
-    //====================
-    void delete_1Dmatrix(double *pointer){
-        
-        delete [] pointer;
-    }
-    
-    //===========================================
-    double get_rel_loss(){
-        
-        return CumulativeRelativeLoss ;
-    }
-    
-    //===========================================
-    double get_rel_loss_err(){
-        
-        return CRLsquare ;
-    }
-    
-    
-    
-    //==================================================================================
-    void evolve_to_target_and_save(int istart, int iend, double* storage, int* counters){
-        
-        double ALOT=100000000000.;
-        
-	// std::cout<<" evolve_to_target_and_save: istart=" << istart << "iend=" << iend << "\n";
-
-        reset_world();
-        
-        for (int k = istart; k < iend; k++){
-
-	  // std::cout<<" evolve: k=" << k << "\n";
-
-            
-            
-            for(int i=0; i< N_epochs &&  current_novelty < TargetNovelty; i++){
-	      // std::cout<<" evolve: k=" << k << " i=" << i << " cur=" << current_novelty << " Target=" << TargetNovelty << "\n";
-                update_world();
-            }
-            
-            storage[k]=current_loss/current_novelty;
-            counters[k]=1;
-            
-            
-            reset_world();
-        }
-        
-    }
-    //==============================================
-    int get_reruns(void){
-        return N_repeats;
-    }
-    
-    //==============================================
-    double get_parameter(int i){
-        
-        switch(i){
-            case 0:
-                return alpha_i;
-            case 1:
-                return alpha_m;
-            case 2:
-                return beta;
-            case 3:
-                return gamma;
-            case 4:
-                return delta;
-            default:
-                
-                std::cout << "Erroneous parameter id!!!!\n\n\n";
-                return 0.;
-        }
-    }
-    
-    
-    //==============================================
-    void evolve_to_target(){
-        
-        reset_world();
-        if (beta < -1. || gamma < -1.){
-            CumulativeRelativeLoss = 100000000000.;
-            CRLsquare = 0.;
-            return;
-        }
-        
-        
-        for (int k=0; k< N_repeats; k++){
-            
-            
-            for(int i=0; i<N_epochs &&  current_novelty < TargetNovelty; i++){
-                update_world();
-            }
-            
-            CumulativeRelativeLoss += current_loss/current_novelty;
-            CRLsquare += (current_loss/current_novelty)*(current_loss/current_novelty);
-            if(verbose_level==3){
-                std::cout <<  CumulativeRelativeLoss << " | " << CRLsquare << std::endl;
-            }
-            
-            if(verbose_level==1){
-                std::cout <<  "." ;
-                
-            }
-            else if(verbose_level==2){
-                std::cout <<  "**" << (k+1) <<  "**  curr loss " << current_loss << "; curr novelty " << current_novelty << std::endl;
-            }
-            
-            
-            reset_world();
-        }
-        
-        CumulativeRelativeLoss /= double(N_repeats);
-        CRLsquare /= double(N_repeats);
-        
-        if(verbose_level==1){
-            std::cout << std::endl;
-        }
-        
-        if(verbose_level==2){
-            std::cout <<  CumulativeRelativeLoss << " || " << CRLsquare << std::endl;
-        }
-        
-        CRLsquare = 2*sqrt((CRLsquare - CumulativeRelativeLoss*CumulativeRelativeLoss)/double(N_repeats));
-        
-    }
-    
-    
-    //================================================================    
-    int set_parameter(double value, int position){
-        
-        if (position < 0 || position > 4) {return 0;}
-        
-        else {
-            
-            switch(position){
-                case 0:
-                    alpha_i=value;
-                    return 1;
-                case 1:
-                    alpha_m=value;
-                    return 1;
-                case 2:
-                    beta=value;
-                    return 1;
-                case 3:
-                    gamma=value;
-                    return 1;
-                case 4:
-                    delta=value;
-                    return 1;
-            }
-            
-        }
-        
-        return 0;
-    }
-
-#ifdef notdef
-    //=================================================================
-    void try_annealing(double starting_jump, int iterations, 
-                       double temp_start, double temp_end, double target_rejection){
-        
-        double dx[5]={0.,0.,0.,0.,0};
-        double x[5]={0.,0.,0.,0.,0};
-        double rejection[5]={0., 0., 0., 0., 0.};
-        double curr_x, curr_err, x_tmp;
-        double temperature;
-        double ratio, r;
-        int cycle=10;
-        boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-        
-        // set up parameter for annealing
-        
-        x[0]=alpha_i;
-        x[1]=alpha_m;
-        x[2]=beta;
-        x[3]=gamma;
-        x[4]=delta;
-        
-        for(int i=0;i<5;i++){
-            dx[i] = starting_jump;
-        }
-        
-        // establish the current value
-        
-        //..........................................
-        evolve_to_target();        
-        std::cout << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
-        
-        curr_x   = CumulativeRelativeLoss;
-        curr_err = CRLsquare;
-        CumulativeRelativeLoss = 0;
-        CRLsquare = 0;
-        //...........................................
-        
-        // optimization cycle
-        for(int i=0; i<iterations; i++){
-            
-            temperature = temp_start*exp( i*(log(temp_end)-log(temp_start))/(double)iterations);
-            std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
-            
-            if (i % cycle == 0 && i > 0){
-                
-                for (int k=0; k<5; k++){
-                    
-                    rejection[k]/=(double)cycle;
-                    if (rejection[k] > 0){
-                        dx[k] = dx[k]/(rejection[k]/target_rejection);
-                        rejection[k]=0.;
-                    }
-                    else{
-                        dx[k]*=2.;
-                    }
-                    std::cout  << dx[k] << " ";
-                }
-                std::cout  << std::endl;
-            }
-            
-            
-            for (int j=0; j<5; j++){
-                
-                // get new value of x[j]
-                x_tmp = get_new_x(x[j],dx[j]);
-                
-                
-                
-                //.............................................
-                set_parameter(x_tmp, j);
-                
-                
-                evolve_to_target(); 
-                
-                std::cout  << std::endl << "......... " << std::endl;
-                std::cout << "Trying... " << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
-                
-                ratio = min(1.,exp(-(CumulativeRelativeLoss-curr_x)/temperature));
-                r = uni();
-                std::cout << r << " vs " << ratio << std::endl;
-                
-                if (r > ratio){
-                    
-                    std::cout << string_wrap(id, 4) <<" "<< (i+1) << ","<< (j) 
-                    <<" "<< (i+1) << " Did not accept " 
-                    << x_tmp << "(" << j << ")" << std::endl;
-                    std::cout << alpha_i << " "<< alpha_m << " "
-                    << beta << " " << gamma << " " 
-                    << delta << " " << std::endl;
-                    set_parameter(x[j], j);
-                    CumulativeRelativeLoss = 0;
-                    CRLsquare = 0;
-                    
-                    rejection[j]+=1.;
-                }
-                
-                else {
-                    
-                    curr_x   = CumulativeRelativeLoss;
-                    curr_err = CRLsquare;
-                    x[j] = x_tmp;
-                    CumulativeRelativeLoss = 0;
-                    CRLsquare = 0;
-                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
-                    << wrap_double(rejection[0],2) 
-                    << " "<< wrap_double(rejection[1], 7) << " "
-                    << wrap_double(rejection[2],5) << " " << wrap_double(rejection[2],9) << " " 
-                    << wrap_double(rejection[4],6) << " " 
-                    << std::endl << std::endl;
-                    
-                    std::cout << string_wrap(id, 4) <<" "<< (i+1) <<","<< (j) 
-                    <<" "
-                    << string_wrap((string) "***** Did accept! ", 3) 
-                    << wrap_double(alpha_i,2) 
-                    << " "<< wrap_double(alpha_m, 7) << " "
-                    << wrap_double(beta,5) << " " 
-                    << wrap_double(gamma,9) << " " 
-                    << wrap_double(delta,6) << " " 
-                    << std::endl << std::endl;
-                    
-                }
-                //........................................................ 
-                
-            }
-            
-        }
-        
-    }
-    
-#endif // notdef
-
-	
-};
-
-
-//============================================================
-
-std::pair<double,double> multi_loss( // dispatch_group_t group, 
-                                    Universe* un[], 
-                                    // dispatch_queue_t* CustomQueues,
-                                    double* Results,
-                                    int*    Counters,
-                                    double* params){
-    
-    int N = un[0]->get_reruns();
-    int step = (int)(double)N/(double)(Nworkers);
-    int istart=0;
-    int iend = istart+step;
-    
-    double Loss=0., LossSquare=0.;
-    
-    timeval startTime, endTime;
-    double elapsedTime;
-    // start timer
-    gettimeofday(&startTime, NULL);
-
-    //err:    for(int i=0; i<Nworkers; i++){
-
-    std::cout << "Entry to multi_loss, params: ";
-    for(int i=0; i<Nworkers; i++){
-        for(int j=0; j<NEVOPARAMS; j++){
-	  std::cout << "[" << un[i]->get_parameter(j) << "," << params[j] << "] ";
-            un[i]->set_parameter(params[j],j);
-        }
-    }
-    std::cout << "\n";
-    int i;
-    #pragma omp parallel for private (i)
-    for(i=0; i<Nworkers; i++){
-
-      // dispatch_group_async(group, CustomQueues[i], ^{
-      std::cout<<"multi_loss: Calling evolve_to_target_and_save i=" << i << " N=" << N << " step=" << step << " istart=" << i*step << " iend=" << (i+1)*step << "\n";
-      //un[i]->evolve_to_target_and_save(istart, iend, Results, Counters);
-      un[i]->evolve_to_target_and_save(i*step, min((i+1)*step,N), Results, Counters);
-      //});
-
-      // std::cout<<"multi_loss: Returned from evolve_to_target_and_save " << i << "\n";
-      
-
-      //istart += step;
-      //iend = min(istart+step,N);
-            
-    }
-    // err    }
-    // dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
-    //dispatch_release(group);
-    
-
-    for (int i=0; i<N; i++){
-        
-        Loss+=Results[i]/(double)N;
-        LossSquare+=Results[i]*Results[i]/(double)N;
-        
-        std::cout<<i<<":"<< Results[i] << " ";
-    }
-    
-    std::cout<<" \n\n\n";
-    double two_std = ((LossSquare - Loss*Loss)/(double)N);
-    
-    two_std = 2.*sqrt(two_std);
-    std::pair<double,double> Res;
-    Res.first=Loss;
-    Res.second=two_std;
-
-    gettimeofday(&endTime, NULL);
-    elapsedTime = (endTime.tv_sec - startTime.tv_sec) * 1000.0;      // sec to ms
-    elapsedTime += (endTime.tv_usec - startTime.tv_usec) / 1000.0;   // us to ms
-    elapsedTime /= 1000.;
-    cout << "multi_loss(N=" << N << ") elapsed time: " << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
-    
-    return Res;
-}
-//============================================================
-
-
-//============================================================
-void multi_annealing( // dispatch_group_t group, 
-                     Universe* un[], 
-                     // dispatch_queue_t* CustomQueues, 
-                     double T_start, double T_end, 
-                     double Target_rejection, 
-                     int Annealing_repeats, 
-                     double starting_jump,
-                     double* Results,
-                     int*    Counters,
-                     double* params0,
-                     double annealing_cycles){
-    //.................................
-    // re-implement annealing
-    
-    double dx[NEVOPARAMS]={0.,0.,0.,0.,0};
-    double x[NEVOPARAMS]={0.,0.,0.,0.,0};
-    double rejection[NEVOPARAMS]={0., 0., 0., 0., 0.};
-    double curr_x, curr_err, x_tmp;
-    double temperature;
-    double ratio, r;
-    int cycle=10;
-    //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
-    
-    // set up parameter for annealing
-
-    for(int i=0;i<NEVOPARAMS;i++){
-        x[i]=params0[i];
-        dx[i] = starting_jump;
-	for(int w=0; w<Nworkers; w++){
-	  un[w]->set_parameter(x[i], i);
-	}
-    }
-    
-    // establish the current value
-    std::pair<double,double>Res;
-    
-    if ( operation == 'm' ) {
-      // Nworkers = 1;
-    }
-    else if (operation == 'g') {
-      // generate params
-    }
-    else if (operation == 'a') {
-      // analyze multi_loss() results
-
-      string line;
-      ifstream mlossdata ("multi_loss.data");
-      double d, Loss, LossSquare, two_std;
-      bool b;
-      int n=0;
-      if (mlossdata.is_open()) {
-	while ( getline (mlossdata,line) ) {
-	  b = from_string<double>(d, std::string(line), std::dec);
-	  cout << line << " d=" << d << endl;
-	  Loss += d;
-          LossSquare += (d*d);
-          n++;
-	}
-        Loss /= double(n);
-	LossSquare /= double(n);
-	two_std = ((LossSquare - Loss*Loss)/(double)n);
-	two_std = 2.*sqrt(two_std);
-	std::cout<<"n="<<n<<" Loss="<<Loss<<" LossSquare="<<LossSquare<<" two_std="<<two_std<<"\n\n\n";
-	mlossdata.close();
-	FILE *f=fopen("multi_loss_stats.txt","w");
-	fprintf(f,"%d n\n",n);
-	fprintf(f,"%.20e Loss\n",Loss);
-	fprintf(f,"%.20e LossSquare\n",LossSquare);
-	fprintf(f,"%.20e two_std\n",two_std);
-        fclose(f);
-	exit(0);
-      }
-      else {
-	cout << "Unable to open file multi_loss.data"; 
-	exit(1);
-      }
-    }
-
-    std::cout << "Calling initial multi_loss:\n";
-    Res = multi_loss( /* group,*/ un, /* CustomQueues,*/ Results, Counters, x);
-    std::cout << "Ret from initial multi_loss:\n";
-    std::cout << Res.first << " +- " << Res.second << std::endl;
-
-    if ( operation == 'm' ) {
-      FILE *f;
-      int N = un[0]->get_reruns();
-
-      f = fopen("multi_loss.data","w");
-      for(int i=0; i<N; i++) {
-	fprintf(f,"%.20e\n",Results[i]);
-      }
-      fclose(f);
-      exit(0);
-    }
-    
-    curr_x   = Res.first;
-    curr_err = Res.second;
-    
-    // optimization cycle
-    
-    for(int i=0; i<annealing_cycles; i++){
-        
-        temperature = T_start*exp( i*(log(T_end)-log(T_start))/(double)annealing_cycles);
-        std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
-        
-        if (i % cycle == 0 && i > 0){
-            
-            for (int k=0; k<NEVOPARAMS; k++){
-                rejection[k]/=(double)cycle;
-                
-                if (rejection[k] > 0){
-                    dx[k] = dx[k]/(rejection[k]/Target_rejection);
-                    rejection[k]=0.;
-                }
-                else{
-                    dx[k]*=2.;
-                }
-                std::cout  << dx[k] << " ";
-            }
-            std::cout  << std::endl;
-        }
-
-        for (int j=0; j<NEVOPARAMS; j++){
-            
-            ///////////////////////////////
-            if (FIX_VARIABLES==0 || var_fixed[j]==0){
-                
-                
-                
-                // get new value of x[j]
-                double x_hold=x[j];
-                x_tmp = get_new_x(x[j],dx[j]);
-                x[j]=x_tmp;
-                
-                std::cout << wrap_double(x_tmp,10) << " " << wrap_double(j,9) << "\n\n"; 
-                //=======================================
-                //.............................................
-                for(int w=0; w<Nworkers; w++){
-                    un[w]->set_parameter(x_tmp, j);
-                }
-                
-
-		// WRITE OUT PARAMS HERE; then exit.
-
-		std::cout << "Calling multi_loss: i=" << i << " j=" << j << "\n";
-                Res = multi_loss(/* group, */ un, /* CustomQueues, */ Results, Counters, x);
-		std::cout << "Ret from multi_loss: i=" << i << " j=" << j << "\n";
-                std::cout << Res.first << " +- " << Res.second << std::endl;
-                
-                ratio = min(1.,exp(-(Res.first-curr_x)/temperature));
-                r = rand()/(double)(pow(2.,31)-1.);
-                std::cout << r << " vs " << ratio << std::endl;
-                
-                double ALOT=100000000000.;
-                
-                if (Res.first < ALOT)
-                {
-                    ofstream filestr;
-                    
-                    filestr.open ("best_opt_some.txt", ofstream::app);
-                    
-                    // >> i/o operations here <<
-                    filestr << un[0]->get_target() << "," 
-                    << Res.first 
-                    << "," << un[0]->get_parameter(0) 
-                    << "," << un[0]->get_parameter(1) 
-                    << "," << un[0]->get_parameter(2) 
-                    << "," << un[0]->get_parameter(3) 
-                    << "," << un[0]->get_parameter(4) << "," << Res.second << ",\n";
-                    
-                    filestr.close();
-                    
-                    
-                    filestr.open ("max_dist.txt", ofstream::app);
-                    
-                    // >> i/o operations here <<
-                    filestr << max_dist << ",\n";
-                    
-                    filestr.close();
-                    
-                }
-                
-                
-                if (r > ratio){
-                    
-                    std::cout << " "<< (i+1) << ","<< (j) 
-                    <<" "<< (i+1) << " Did not accept " 
-                    << x_tmp << "(" << j << ")" << std::endl;
-                    std::cout << un[0]->get_parameter(0) 
-                    << " " << un[0]->get_parameter(1) 
-                    << " " << un[0]->get_parameter(2) 
-                    << " " << un[0]->get_parameter(3) 
-                    << " " << un[0]->get_parameter(4) << " " << std::endl;
-                    
-                    x[j]=x_hold;
-                    for(int w=0; w<Nworkers; w++){
-                        un[w]->set_parameter(x[j], j);
-                    }
-                    
-                    
-                    //set_parameter(x[j], j);     
-                    rejection[j]+=1.;
-                }
-                
-                else {
-                    
-                    curr_x   = Res.first;
-                    curr_err = Res.second;
-                    x[j] = x_tmp;
-                    
-                    for(int w=0; w<Nworkers; w++){
-                        un[w]->set_parameter(x[j], j);
-                    }
-                    
-                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
-                    << wrap_double(rejection[0],2) << " " 
-                    << wrap_double(rejection[1],7) << " "
-                    << wrap_double(rejection[2],5) << " " 
-                    << wrap_double(rejection[3],9) << " " 
-                    << wrap_double(rejection[4],6) << " " 
-                    << std::endl << std::endl;
-                    
-                    std::cout << " "<< (i+1) <<","<< (j) 
-                    <<" "
-                    << string_wrap((string) "***** Did accept! ", 3) 
-                    << wrap_double(un[0]->get_parameter(0),2) << " "
-                    << wrap_double(un[0]->get_parameter(1),7) << " "
-                    << wrap_double(un[0]->get_parameter(2),5) << " " 
-                    << wrap_double(un[0]->get_parameter(3),9) << " " 
-                    << wrap_double(un[0]->get_parameter(4),6) << " " 
-                    << std::endl << std::endl;
-                    
-                    
-                    
-                }
-                //........................................................ 
-                
-            }
-        }
-        
-    }
-    
-}
-
-
-//================================================
-int
-main(int argc, char* argv[])
-{
-    
-    double params0[6] = {0., 0., 0., 0., 0., 0.2}, target=50., range;
-    string par_names0[6] = {"alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
-    string par_names1[4] = {"n_epochs", "n_steps", "n_reruns", "range"};
-    string par_names2[5] = {"T_start", "T_end", "Annealing_steps","Target_rejection","Starting_jump"};
-    string par_names3[5] = {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
-    string par_names4[2] = {"Operation", "Nworkers"};    
-    int params1[4] = {300, 50, 1000, 10};
-    int params3[5] = { 0, 0, 0, 0, 0};
-    
-    //          temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
-    double params2[5] = {1,             0.001,               100,              0.3,           1.5};
-    
-    int verbose_level = 2;
-    const std::string one="one", two="two";
-    static Universe* un[MAXNworkers];
-    // static dispatch_queue_t CustomQueues[MAXNworkers];
-    
-    static double* Results;
-    static int*    Counters;
-    
-    timeval t1, t2;
-    double elapsedTime;
-    // start timer
-    gettimeofday(&t1, NULL);
-    
-    
-    if (argc < 8) {
-        std::cout << "Usage: super_optimizer alpha_i alpha_m beta gamma delta target_innov [n_epochs n_steps n_reruns] [range] [verbose_level]\n"; 
-        std::cout << "         [T_start T_end Annealing_steps Target_rejection Starting_jump]\n"; 
-        std::cout << "         [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta]\n"; 
-        
-        system("pwd");
-        
-        
-        return(1);
-    }
-    else {
-      std::cout << "argc=" << argc << std::endl;
-
-        for (int nArg=0; nArg < argc; nArg++){
-            //std::cout << nArg << " " << argv[nArg] << std::endl;
-            if (nArg > 0 && nArg < 7){
-                params0[nArg-1]= atof(argv[nArg]);
-                std::cout << par_names0[nArg-1] << ": " << params0[nArg-1] <<  std::endl;
-            }
-            if (nArg > 6 && nArg < 11){
-                params1[nArg-7]= atoi(argv[nArg]);
-                std::cout << par_names1[nArg-7] << ": " << params1[nArg-7] <<  std::endl;
-            }
-            if (nArg == 11){
-                verbose_level = atoi(argv[nArg]);
-                std::cout << "verbose level: " << verbose_level <<  std::endl;
-            }
-            if (nArg > 11 && nArg < 17){
-                params2[nArg-12]= atof(argv[nArg]);
-                std::cout << par_names2[nArg-12] << ": " << params2[nArg-12] <<  std::endl;
-            }
-            if (nArg > 16 && nArg < 22){
-                params3[nArg-17]= atof(argv[nArg]);
-                var_fixed[nArg-17]= atof(argv[nArg]);
-                std::cout << par_names3[nArg-17] << ": " << var_fixed[nArg-17] <<  std::endl;
-            }
-            if (nArg == 22 ){
-                operation = *argv[nArg];
-		std::cout << par_names4[0] << ": " << operation <<  std::endl;
-            }
-            if (nArg == 23 ){
-	        Nworkers = atoi(argv[nArg]);
-		std::cout << par_names4[1] << ": " << Nworkers <<  std::endl;
-            }
-        }
-    }
-    
-    /*
-    for target in range(58,1009,50):
-	s = ("%d" % target)
-	print s
-
-	for i in range(15):
-          # Param groups separated by "|" below for documentation. NOTE that | is not used on command line!
-	  os.system("./supe_duper_optimizer |0 0 4 50 -1 "+s+" | 40000 20 1000 2 | 1 | 2. 0.01 100 0.3 2.3 | 1 1 0 0 0")
-    */
-
-    /*   Parameters, re-iterated:
-
-                        "alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
-    double params0[6] = {0.,        0.,        0.,     0.,      0.,      0.2}, target=50., range;
-
-                    {"n_epochs", "n_steps", "n_reruns", "range"};
-    int params1[4] = {300,        50,       1000,        10};
-
-                       {"T_start",          "T_end",          "Annealing_steps","Target_rejection","Starting_jump"};
-                         temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
-    double params2[5] = {1,                  0.001,            100,            0.3,              1.5};
-
-                     {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
-    int params3[5] = { 0,                0,                0,             0,              0};
-
-
-    */
-
-    for (int j=0; j<NEVOPARAMS; j++){
-        
-        cout << j << " | " << var_fixed[j] << " (fixed) \n";
-    }
-	
-    target=params0[NEVOPARAMS];
-    range = (double)params1[3];
-	int identify_failed = 0;
-	char* filename= (char *)"movie_graph.txt";
- 	int n_ep=params1[0], n_st=params1[1], n_rep=params1[2];
-    
-    //...............................
-    
-    for(int i=0; i<Nworkers; i++){
-        un[i] = new Universe((char *)filename,n_ep,n_st,
-                             (int)n_rep,
-                             identify_failed, target, i2string(i));
-        // CustomQueues[i] = dispatch_queue_create(i2char(i), NULL);
-    }
-    
-    //...............................
-    // mw: n_rep == n_reruns = # times evolve is done within multi_loss, spread across Nworkers.
-    if(n_rep > 0){
-        
-        Results = new double[n_rep];
-        Counters = new int[n_rep];
-        
-    }else {
-        
-        Results =  NULL;
-        Counters = NULL;
-        std::cout << " Number of reruns should be positive! " <<  std::endl;
-        return 0;
-        
-    }
-    //...............................
-
-    //srand(time(0));
-    //srandomdev();
-    {
-      timeval t; 
-      gettimeofday(&t, NULL);
-      srand(t.tv_usec);
-    }
-    
-    {
-        double r=0;
-        for (int j=0; j<100; j++){
-            
-            
-            
-            r = rand()/(double)(pow(2.,31)-1.);
-            std::cout << r << " ";
-        }
-        std::cout << "\n ";
-    }
-  	//random initiation of starting parameters
-    
-    if (range > 0.){
-        
-        for (int i=0; i < 5; i++){
-            
-            if (params0[i]==-100.){
-                
-                double r1 = (rand()/(double)(pow(2.,31)-1.));
-                double r2 = (rand()/(double)(pow(2.,31)-1.));
-                double sign = 1.;
-                
-                if(r1 > 0.5){
-                    sign=-1.;
-                }
-                
-                params0[i] = sign*r2*range;
-                
-                std::cout << par_names0[i] << ": " << params0[i] <<  std::endl;
-            }
-        }
-        
-    }
-    
-    
-    double T_start=params2[0], T_end=params2[1], Target_rejection=params2[3], starting_jump=params2[4];
-    int Annealing_repeats = (int) params2[2];
-    
-    
-    // dispatch_group_t group = dispatch_group_create();
-    
-    //.............................
-    multi_annealing( /* group, */ un, /* CustomQueues, */ T_start, T_end, Target_rejection, Annealing_repeats, 
-		     starting_jump, Results, Counters, params0, Annealing_repeats);
-    
-    //dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
-    // dispatch_release(group);
-    //.............................
-    
-    
-    // stop timer
-    gettimeofday(&t2, NULL);
-    
-    // compute and print the elapsed time in millisec
-    elapsedTime = (t2.tv_sec - t1.tv_sec) * 1000.0;      // sec to ms
-    elapsedTime += (t2.tv_usec - t1.tv_usec) / 1000.0;   // us to ms
-    elapsedTime /= 1000.;
-    cout << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
-    
-    //.....................
-    
-    for(int i=0; i<Nworkers; i++){
-        delete un[i];
-    }
-    
-    //....................
-    if(n_rep > 0){
-        
-        delete [] Results;
-        delete [] Counters;
-        
-    }
-    
-    return 0;
-	
-	
-	
-}
-

Copied: SwiftApps/SciColSim/optimizer.protomods.cpp (from rev 5504, SwiftApps/SciColSim/optimizer.cpp)
===================================================================
--- SwiftApps/SciColSim/optimizer.protomods.cpp	                        (rev 0)
+++ SwiftApps/SciColSim/optimizer.protomods.cpp	2012-01-23 18:21:22 UTC (rev 5511)
@@ -0,0 +1,1818 @@
+//
+//  main.cpp
+//  optimizer
+//
+//  Created by Andrey Rzhetsky on 4/11/11.
+//  Copyright 2011 University of Chicago. All rights reserved.
+//
+
+#define MAXNworkers 24
+int Nworkers=MAXNworkers;
+
+// Add operation code to enable existing code to be used at lower level from Swift scripts:
+
+char operation = 'n'; // n: normal; m: do one multi_loss (with n_reruns).
+                      // Not used: a: analyze and generate next annealing parameter set. g: tbd
+
+#include <fstream>
+#include <sstream>
+#include <iostream>
+#include <stdio.h>
+#include <time.h>
+#include <ctime>    
+#include <algorithm>
+#include <string>
+
+#include <stdio.h>
+#include <sys/param.h>
+#include <sys/time.h>
+#include <sys/types.h>
+
+// #include <dispatch/dispatch.h>
+#include <fstream>
+
+
+#include <stdlib.h>
+#include <boost/numeric/ublas/io.hpp>
+#include <boost/graph/graph_traits.hpp>
+#include <boost/graph/dijkstra_shortest_paths.hpp>
+#include <boost/graph/loop_erased_random_walk.hpp>
+#include <boost/graph/random.hpp>
+#include <boost/property_map/property_map.hpp>
+#include <boost/graph/graph_concepts.hpp>
+#include <boost/graph/properties.hpp>
+
+#include <boost/graph/graph_traits.hpp>
+#include <boost/graph/adjacency_list.hpp>
+#include <boost/graph/adjacency_matrix.hpp>
+
+#define BOOST_MATH_OVERFLOW_ERROR_POLICY ignore_error
+#define BOOST_MATH_DISCRETE_QUANTILE_POLICY real
+#include <boost/graph/random.hpp>
+#include <boost/random/geometric_distribution.hpp>
+#include <boost/random/uniform_01.hpp>
+#include <boost/random.hpp>
+#include <boost/random/linear_congruential.hpp>
+#include <boost/random/uniform_int.hpp>
+#include <boost/random/uniform_real.hpp>
+#include <boost/random/variate_generator.hpp>
+#include <boost/generator_iterator.hpp>
+#include <boost/lexical_cast.hpp>
+
+#define INT_INFINITY 2147483647
+#define NEVOPARAMS 5
+
+#define FIX_VARIABLES 1
+
+using namespace boost;
+using namespace std;
+using namespace boost::numeric::ublas;
+
+static int max_dist=0;
+
+typedef boost::adjacency_matrix<boost::directedS> Graph;
+typedef std::pair<int,int> Edge;
+typedef boost::graph_traits<Graph> GraphTraits;
+typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> prob;
+typedef boost::numeric::ublas::triangular_matrix<double, boost::numeric::ublas::strict_upper> pathlength;
+typedef boost::graph_traits<Graph>::vertex_descriptor vertex_descriptor;
+
+namespace std {
+	using ::time;
+}
+
+static int var_fixed[NEVOPARAMS] = {1, 0, 1, 1, 0};
+
+typedef boost::minstd_rand base_generator_type;
+typedef adjacency_list < listS, vecS, directedS,
+no_property, property < edge_weight_t, int > > graph_t;
+typedef graph_traits < graph_t >::vertex_descriptor vertex_descriptor;
+typedef graph_traits < graph_t >::edge_descriptor edge_descriptor;
+
+
+//================================================
+string strDouble(double number)
+{
+    stringstream ss;//create a stringstream
+    ss << number;//add number to the stream
+    return ss.str();//return a string with the contents of the stream
+}
+
+//================================================
+
+double gaussian(double sigma)
+{
+    double GaussNum = 0.0;
+    int NumInSum = 10;
+    for(int i = 0; i < NumInSum; i++)
+    {
+        GaussNum += ((double)rand()/(double)RAND_MAX - 0.5);
+    }
+    GaussNum = GaussNum*sqrt((double)12/(double)NumInSum);
+    
+    
+    return GaussNum;
+    
+}
+
+
+
+//=================================================
+double diffclock(clock_t clock1,clock_t clock2)
+{
+	double diffticks=clock1-clock2;
+	double diffms=(diffticks)/CLOCKS_PER_SEC;
+	return diffms;
+}
+
+//================================================
+//================================================================
+double get_new_x(double x, double dx){
+    
+    double new_x;
+    // boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+    double r = rand()/(double)(pow(2.,31)-1.);
+    
+    if (r > 0.5){            
+        new_x = x + rand()*dx/(double)(pow(2.,31)-1.);
+    } else {            
+        new_x = x - rand()*dx/(double)(pow(2.,31)-1.);
+    }
+    
+    return new_x;
+    
+}
+
+
+//===============================================   
+string string_wrap(string ins, int mode){
+    
+    std::ostringstream s;
+    
+    switch(mode){
+        case 0:
+            s << "\033[1;29m" << ins << "\033[0m";
+            break;
+        case 1:
+            s << "\033[1;34m" << ins << "\033[0m";
+            break;
+        case 2:
+            s << "\033[1;44m" << ins << "\033[0m";
+            break;
+        case 3:
+            s << "\033[1;35m" << ins << "\033[0m";
+            break;
+        case 4:
+            s << "\033[1;33;44m" << ins << "\033[0m";
+            break;
+        case 5:
+            s << "\033[1;47;34m" << ins << "\033[0m";
+            break;
+        case 6:
+            s << "\033[1;1;31m" << ins << "\033[0m";
+            break;
+        case 7:
+            s << "\033[1;1;33m" << ins << "\033[0m";
+            break;
+        case 8:
+            s << "\033[1;1;43;34m" << ins << "\033[0m";
+            break;
+        case 9:
+            s << "\033[1;1;37m" << ins << "\033[0m";
+            break;
+        case 10:
+            s << "\033[1;30;47m" << ins << "\033[0m";
+            break;
+        default:
+            s << ins;
+    }
+    
+    return s.str();
+}
+
+
+//===============================================
+string wrap_double(double val, int mode){
+    
+    std::ostringstream s;
+    s << string_wrap(strDouble(val),mode);
+    
+    return s.str();
+}
+
+
+
+//===============================================
+const     
+string i2string(int i){
+    
+    std::ostringstream s;
+    s << "worker" 
+    << lexical_cast<std::string>(i);
+    
+    return s.str();
+    
+}
+
+//===============================================
+char* i2char(int i){
+    
+    std::ostringstream s;
+    s << "worker" 
+    << lexical_cast<std::string>(i);
+    
+    char* a=new char[s.str().size()+1];
+    memcpy(a,s.str().c_str(), s.str().size());
+    
+    return a;
+}
+
+
+template <class T>
+bool from_string(T& t, 
+                 const std::string& s, 
+                 std::ios_base& (*f)(std::ios_base&))
+{
+  std::istringstream iss(s);
+  return !(iss >> f >> t).fail();
+}
+
+//================================================
+class Universe {
+	
+private:
+	
+	double alpha_i;
+	double alpha_m;
+	double beta;
+	double gamma;
+	double delta;
+	
+    double TargetNovelty;
+    double CumulativeRelativeLoss;
+    double CRLsquare;
+    string id;
+    
+    
+	int N_nodes;
+	int M_edges;
+	
+	int N_epochs;
+	int N_steps;
+	int N_repeats;
+	
+	int current_epoch;
+	double current_loss;
+	int current_repeat;
+    double current_novelty;
+	
+	int mode_identify_failed;
+    int verbose_level; // 0 is silent, higher is more
+	
+	double k_max;
+	
+	graph_t Full_g;
+	
+	double **Prob;
+	double **Tried;
+	double **Dist;
+	double **Final;
+    double **EdgeIndex;
+	double *Rank;
+	
+    base_generator_type generator;	
+    boost::uniform_real<> uni_dist;
+    boost::geometric_distribution<double> geo;
+    
+public:
+	
+    
+    
+	//======  Constructor ======
+	Universe(const std::string FileToOpen, int Epochs, int Steps, int Repeats, int identify_failed, double target, const std::string idd)
+	{
+		//typedef array_type2::index index2;
+		
+		
+		std::ifstream inFile;
+        //string line;
+        
+        //-------------------------------
+        
+        base_generator_type gene(42u);
+        generator = gene;
+        generator.seed(static_cast<unsigned int>(std::time(0)));
+        boost::uniform_real<> uni_d(0,1);
+        uni_dist = uni_d;
+        
+        //--------------------------------
+        
+		int i, k;
+        int x, y;
+		Edge* edge_array_mine;
+		int num_arcs_mine, num_nodes_mine;
+		int* weights_mine;
+		
+        TargetNovelty = target;
+        CumulativeRelativeLoss = 0.;
+        CRLsquare = 0.;
+        
+		
+		N_epochs  = Epochs;
+		N_steps   = Steps;
+		N_repeats = Repeats;
+		
+		current_epoch = 0;
+		current_loss = 0.;
+		current_repeat = 0;
+        
+        id = idd;
+        
+        verbose_level = 1;
+		
+		mode_identify_failed = identify_failed;
+        
+		
+		//-------------------------------
+		// The first pass though file with the graph
+		inFile.open(FileToOpen.c_str());
+		if (inFile.fail()) {
+			cout << "Unable to open file";
+			exit(1); // terminate with error
+		}else {
+            
+            if (verbose_level > 2){
+                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
+            }
+        }
+		
+		i=0;
+        std::string line;
+		//while (! inFile.eof() && ! inFile.fail()) {
+        while (1==1) {
+            
+            inFile >> x;
+            inFile >> y;
+            
+            if (verbose_level > 2){
+                std::cout << " x: " << x;
+                std::cout << " y: " << y << std::endl;
+            }
+            
+			if (i==0){
+				N_nodes=x;
+				M_edges=y;	
+                break;
+			}
+			i++;
+            
+			
+		}
+		inFile.close();
+        
+        if (verbose_level == 2){
+            std::cout << N_nodes <<  " nodes, " << M_edges << " edges"<<std::endl;
+        }
+		
+		// k_max is the longest distance possible
+		
+        //k_max = M_edges;
+		k_max = 70;
+        
+		//------------------------------------
+		// Get memory allocated for all class members
+		
+		Prob = allocate_2Dmatrix(N_nodes, N_nodes);
+		Tried = allocate_2Dmatrix(N_nodes, N_nodes);
+		Dist = allocate_2Dmatrix(N_nodes, N_nodes);
+		Final = allocate_2Dmatrix(N_nodes, N_nodes);
+        EdgeIndex = allocate_2Dmatrix(N_nodes, N_nodes);
+		Rank = allocate_1Dmatrix(N_nodes);
+		
+        //The second pass through file with the graph
+        
+		for(int i = 0; i < N_nodes; ++i) {
+			Rank[i]=0.;
+			for(int j = 0; j < N_nodes; ++j) {
+				Final[i][j] = 0.;
+				Prob[i][j]=0.;
+				Dist[i][j]=-1.;
+				Tried[i][j]=0.;
+                EdgeIndex[i][j]=-1;
+			}
+		}
+        
+		
+		// Fill in the final graph -- and we are ready to go!
+        
+	    inFile.open(FileToOpen.c_str());
+		if (!inFile) {
+            std::cout << "Unable to open file";
+			exit(1); // terminate with error
+		}
+		else {
+            
+            if (verbose_level > 2){
+                std::cout <<  " Opened <" << FileToOpen << ">"<<std::endl;
+            }
+        }
+        
+		i=0;  
+		while (inFile >> x && inFile >>y) {
+			if (i > 0) {
+				Final[x][y]=1.;
+				Final[y][x]=1.;
+                
+                
+                if (verbose_level == 2){
+                    std::cout << ".";
+                }
+			}
+			i++;
+			
+		}
+        if (verbose_level == 2){
+            std::cout << std::endl;
+        }
+		inFile.close(); 
+		
+        k=0;
+        for (int i=0; i<N_nodes-1; i++){
+            for (int j=i+1;j<N_nodes; j++){
+                if(Final[i][j] > 0.){
+                    EdgeIndex[i][j]=k;
+                    k++;
+                }
+            }
+        }
+        
+        
+		
+		//+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+		// create graph -- hopefully, we can keep it, just modifying edge weights
+		
+		
+		edge_array_mine = new Edge[2*M_edges];
+		num_arcs_mine = 2*M_edges;
+		num_nodes_mine = N_nodes;
+		weights_mine = new int[2*M_edges];
+		for (int i=0; i<2*M_edges; i++){ weights_mine[i]=1;}
+		
+		k=0;
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				if (Final[i][j]>0.){
+					edge_array_mine[2*k]  =Edge(i,j);
+					edge_array_mine[2*k+1]=Edge(j,i);
+					k++;
+				}
+			}
+		}
+		graph_t g(edge_array_mine, edge_array_mine + num_arcs_mine, weights_mine, num_nodes_mine);
+		
+		Full_g = g;
+		delete edge_array_mine;
+		delete weights_mine;
+		
+		//===========================================================================
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+		
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0.){
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					remove_edge(u,v,Full_g);
+					remove_edge(v,u,Full_g);
+					
+				}
+			}
+		}
+		
+        
+    }
+	
+	
+	//=====================================================================
+	int sample_failed_number(double pfail){
+		
+		//boost::geometric_distribution<double> geo(pfail);
+		//boost::variate_generator<base_generator_type&, geometric_distribution<double> > geom(generator, geo);
+		
+		double r, u, g;
+        
+        r=0.;
+		for(int i=0; i<N_steps; i++){
+            
+            u=(double)rand();
+            u = 1.-u /(double)(pow(2.,31)-1.);
+            g=(int)(ceil(log(u) / log(pfail)));
+            
+			//r += geom();
+            
+            r+=g;
+		}
+        
+        if (verbose_level>=3){
+            std::cout << id << " failed " << r << std::endl;
+		}
+		return r;
+		
+	}
+    
+    //=============================================
+    double get_target(void){
+        return TargetNovelty;
+    }
+	
+    //=============================================
+    void set_target(double target){
+        TargetNovelty=target;
+    }
+	
+	//=============================================
+	int sample(){
+		
+        //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+        // double r = uni(), Summa = 0.;
+        
+        
+        
+        double r = rand(), Summa = 0.;
+        r /= (double)(pow(2.,31)-1.);
+		int result = 0;
+		int finished = 0;
+        
+        if (verbose_level==4){
+            std::cout << id << " sampled " << r << std::endl;
+        }
+		
+		for(int i=0; i<N_nodes-1 && finished==0; i++){			
+			for( int j=i+1; j<N_nodes && finished==0; j++){
+				
+				Summa += Prob[i][j];
+				
+				if (Summa > r){
+					
+					Tried[i][j]+=1.;
+					
+					if (Final[i][j] > 0.){
+						result = 1;
+					}
+					finished = 1;										
+				}
+			}
+		}
+		
+		return result;
+		
+	}
+	
+	//===============================
+	void update_current_graph(void){
+		
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+		
+		//property_map<graph_t, edge_weight_t>::type weightmap = get(edge_weight, Full_g);
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0. && Tried[i][j]>0){
+					//s = edge(i, j, Full_g);	
+					boost::graph_traits<graph_t>::edge_descriptor e1,e2;
+					bool found1, found2;
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					tie(e1, found1) = edge(u, v, Full_g);
+					tie(e2, found2) = edge(v, u, Full_g);
+					if (!found1 && !found2){
+						add_edge(u,v,1,Full_g);
+					    add_edge(v,u,1,Full_g);
+					}
+					
+				}
+			}
+			
+		}
+	}
+	
+	//===============================
+	void update_distances(void){
+		// put shortest paths to the *Dist[][]
+		std::vector<vertex_descriptor> p(num_vertices(Full_g));
+		std::vector<int> d(num_vertices(Full_g));
+		vertex_descriptor s;
+		
+		
+		// put shortest paths to the *Dist[][]
+		for (int j=0; j<num_vertices(Full_g); j++){
+			
+			if(Rank[j] > 0.){
+				s = vertex(j, Full_g);	 
+				dijkstra_shortest_paths(Full_g, s, predecessor_map(&p[0]).distance_map(&d[0]));
+				
+				//std::cout <<" Vertex "<< j << std::endl;
+				graph_traits < graph_t >::vertex_iterator vi, vend;
+				
+				for (boost::tie(vi, vend) = vertices(Full_g); vi != vend; ++vi) {
+					
+					if (p[*vi]!=*vi){
+						Dist[*vi][j]=d[*vi];
+						Dist[j][*vi]=d[*vi];
+                        
+                        if (Dist[*vi][j]>max_dist){
+                            max_dist=Dist[*vi][j];
+                        }
+                        
+                        
+					} else {
+						Dist[*vi][j]=-1.;
+						Dist[j][*vi]=-1.;
+					}
+				}
+			}
+			
+		}
+		
+		
+	}
+	
+	//======================================================
+	void update_ranks(void){
+		
+		for(int i=0; i<N_nodes; i++){
+			Rank[i]=0.;
+		}
+		
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				if (Tried[i][j]>0. && Final[i][j] >0.){
+					Rank[i]++;
+					Rank[j]++;
+				}
+			}
+		}
+		
+	}
+	
+	//====================================================================
+	void set_world(double a_i, double a_m, double b, double g, double d){
+		
+		alpha_i=a_i;
+		alpha_m=a_m;
+		gamma=g;
+		beta=b;
+		delta=d;
+		
+	}
+	
+	//====================================================================
+	void reset_world(){
+		
+        //====================================================
+		std::vector<edge_descriptor> p(num_edges(Full_g));
+		std::vector<int> d(num_edges(Full_g));
+		edge_descriptor s;
+		boost::graph_traits<graph_t>::vertex_descriptor u, v;
+        
+		
+		for (int i=0; i<N_nodes-1; i++){
+			for (int j=i+1; j<N_nodes; j++){
+				if (Final[i][j] > 0. && Tried[i][j] > 0){
+					u = vertex(i, Full_g);
+					v = vertex(j, Full_g);
+					remove_edge(u,v,Full_g);
+					remove_edge(v,u,Full_g);
+					
+				}
+			}
+		}
+        
+        //==================================================
+        
+		current_loss=0;
+		current_epoch=0;
+		current_repeat++;
+        current_novelty=0;
+		
+		for(int i = 0; i < N_nodes; ++i) {
+			Rank[i]=0.;
+			for(int j = 0; j < N_nodes; ++j) {
+				Prob[i][j]=0.;
+				Dist[i][j]=-1.;
+				Tried[i][j]=0.;
+			}
+		}
+	}
+	
+	
+    //==============================================
+    void show_parameters(void){
+        
+        std::cout << "Parameters: " 
+        << alpha_i << " "
+        << alpha_m << " | "
+        << beta << " "
+        << gamma << " | "
+        << delta << std::endl;
+        
+    }
+    
+    
+    
+    //===============================================
+    string file_name(){
+        
+        std::ostringstream s;
+        s << "world_" 
+        << lexical_cast<std::string>(alpha_i) << "_" 
+        << lexical_cast<std::string>(alpha_m) << "_"
+        << lexical_cast<std::string>(beta) << "_"
+        << lexical_cast<std::string>(gamma) << "_"
+        << lexical_cast<std::string>(delta) << "_"
+        << lexical_cast<std::string>(N_epochs) << "_"
+        << lexical_cast<std::string>(N_steps) << "_"
+        << lexical_cast<std::string>(N_repeats) << ".txt";
+        
+        return s.str();
+        
+    }
+    
+    
+    
+    
+    //=================================================
+    void set_verbose(int verbose){
+        
+        verbose_level = verbose;
+    }
+    
+    
+    //=============================================================
+    void update_probabilities(void){
+        
+        
+        //=========================
+		// Compute sampling probabilities
+		// first pass: \xi_i,j
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				
+				double bg = 0.;
+				
+				Prob[i][j] = alpha_i*log(min(Rank[i]+1.,Rank[j]+1.)) + 
+                alpha_m*log(max(Rank[i]+1.,Rank[j]+1.));
+				
+                if (Dist[i][j] > 0.){
+                    
+                    double k = Dist[i][j];
+                    if (k >= k_max){
+                        k = k_max-1;
+                    }
+					
+                    bg = beta * log(k/k_max) + gamma * log(1. - k/k_max);
+					
+                } else {
+                    bg = delta;
+                }
+				
+				Prob[i][j] = exp(Prob[i][j] + bg);
+			}
+		}
+        
+		
+		// second pass: sum
+		double Summa = 0.;
+		
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				Summa += Prob[i][j];
+			}
+		}
+		
+		// third pass: normalize
+		for(int i=0; i<N_nodes-1; i++){
+			for( int j=i+1; j<N_nodes; j++){
+				Prob[i][j] /= Summa;
+			}
+		}
+        
+    }
+    
+	// Now we are ready for simulations
+	//==============================================
+	void update_world(){
+		
+		int failed = 0;
+        
+		// Given current universe compute shortest paths
+		//~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+		
+		update_current_graph();
+		update_ranks();				
+		update_distances();
+		update_probabilities();
+		
+		//===============================
+		// sampling
+		int result;
+		double cost=0., novel=0.;
+		int publishable = 0;
+		
+		
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		if (mode_identify_failed == 1){
+			
+			while(publishable < N_steps){
+				
+		    	result = sample();
+			    publishable += result;
+			    failed += (1-result);
+				
+			}
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					
+					cost+=Tried[i][j];
+					
+					if (Tried[i][j]>0. && Final[i][j]>0.){
+						novel+=1.;
+					}
+				}
+			}
+			
+		}
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		else {
+			
+			double pfail=0.;
+			int n_failed;
+			//, n_check = 0;
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					if (Final[i][j] == 0.){
+						pfail += Prob[i][j];
+						Prob[i][j] = 0.;
+					}
+					
+				}
+			}
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					Prob[i][j] /= (1.-pfail);
+				}
+				//std::cout << std::endl;
+			}			
+			
+			n_failed = sample_failed_number(pfail);
+			while(publishable < N_steps){
+				
+		    	result = sample();
+			    publishable += result;					
+			}
+            
+            
+			current_loss += (n_failed + N_steps);
+			cost = current_loss;
+			
+			for(int i=0; i<N_nodes-1; i++){
+				for( int j=i+1; j<N_nodes; j++){
+					
+					if (Tried[i][j]>0. && Final[i][j]>0.){
+						novel+=1.;
+					}
+				}
+			}
+		}
+        
+        current_novelty = novel;
+        
+        
+		//^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+		if (verbose_level == 2){
+            std::cout << (current_repeat+1) << "  epoch=" << (current_epoch+1) 
+            
+		    << "  cost=" << cost 
+		    << " novel=" << novel 
+		    << " rel_loss=" << cost/novel
+		    << std::endl;
+        }
+        
+		current_epoch++;
+	}
+	
+	
+	//======  Destructor ======
+	~Universe(){
+		
+		delete_2Dmatrix(Final, N_nodes);
+		delete_2Dmatrix(Dist, N_nodes);
+		delete_2Dmatrix(Tried, N_nodes);
+		delete_2Dmatrix(Prob, N_nodes);
+        delete_2Dmatrix(EdgeIndex, N_nodes);
+		delete_1Dmatrix(Rank);		
+	}
+    
+    //================================================
+    // Allocate memory
+    double** allocate_2Dmatrix(int N, int M)
+    {
+        double **pointer;
+        
+        if (verbose_level == 2){
+            std::cout<< "["<<N<<"|"<<M<<"]"<<std::endl;
+        }
+        pointer = new double*[N];
+        for (int i = 0; i < N; ++i)
+            pointer[i] = new double[M];	
+        
+        return pointer;
+    }
+    //===================
+    double* allocate_1Dmatrix(int N)
+    {
+        double *pointer;
+        
+        if(N > 0){
+            
+            pointer = new double[N];
+            
+        }else {
+            
+            pointer = NULL;
+        }
+        
+        return pointer;
+        
+    }
+    
+    //==============================================
+    // De-Allocate memory to prevent memory leak
+    void delete_2Dmatrix(double **pointer, int N){
+        
+        if (pointer != NULL){
+            
+            for (int i = 0; i < N; ++i){
+                delete [] pointer[i];
+            }
+            delete [] pointer;
+        }
+    }
+    //====================
+    void delete_1Dmatrix(double *pointer){
+        
+        delete [] pointer;
+    }
+    
+    //===========================================
+    double get_rel_loss(){
+        
+        return CumulativeRelativeLoss ;
+    }
+    
+    //===========================================
+    double get_rel_loss_err(){
+        
+        return CRLsquare ;
+    }
+    
+    
+    
+    //==================================================================================
+    void evolve_to_target_and_save(int istart, int iend, double* storage, int* counters){
+        
+        double ALOT=100000000000.;
+        
+	// std::cout<<" evolve_to_target_and_save: istart=" << istart << "iend=" << iend << "\n";
+
+        reset_world();
+        
+        for (int k = istart; k < iend; k++){
+
+	  // std::cout<<" evolve: k=" << k << "\n";
+
+            
+            
+            for(int i=0; i< N_epochs &&  current_novelty < TargetNovelty; i++){
+	      // std::cout<<" evolve: k=" << k << " i=" << i << " cur=" << current_novelty << " Target=" << TargetNovelty << "\n";
+                update_world();
+            }
+            
+            storage[k]=current_loss/current_novelty;
+            counters[k]=1;
+            
+            
+            reset_world();
+        }
+        
+    }
+    //==============================================
+    int get_reruns(void){
+        return N_repeats;
+    }
+    
+    //==============================================
+    double get_parameter(int i){
+        
+        switch(i){
+            case 0:
+                return alpha_i;
+            case 1:
+                return alpha_m;
+            case 2:
+                return beta;
+            case 3:
+                return gamma;
+            case 4:
+                return delta;
+            default:
+                
+                std::cout << "Erroneous parameter id!!!!\n\n\n";
+                return 0.;
+        }
+    }
+    
+    
+    //==============================================
+    void evolve_to_target(){
+        
+        reset_world();
+        if (beta < -1. || gamma < -1.){
+            CumulativeRelativeLoss = 100000000000.;
+            CRLsquare = 0.;
+            return;
+        }
+        
+        
+        for (int k=0; k< N_repeats; k++){
+            
+            
+            for(int i=0; i<N_epochs &&  current_novelty < TargetNovelty; i++){
+                update_world();
+            }
+            
+            CumulativeRelativeLoss += current_loss/current_novelty;
+            CRLsquare += (current_loss/current_novelty)*(current_loss/current_novelty);
+            if(verbose_level==3){
+                std::cout <<  CumulativeRelativeLoss << " | " << CRLsquare << std::endl;
+            }
+            
+            if(verbose_level==1){
+                std::cout <<  "." ;
+                
+            }
+            else if(verbose_level==2){
+                std::cout <<  "**" << (k+1) <<  "**  curr loss " << current_loss << "; curr novelty " << current_novelty << std::endl;
+            }
+            
+            
+            reset_world();
+        }
+        
+        CumulativeRelativeLoss /= double(N_repeats);
+        CRLsquare /= double(N_repeats);
+        
+        if(verbose_level==1){
+            std::cout << std::endl;
+        }
+        
+        if(verbose_level==2){
+            std::cout <<  CumulativeRelativeLoss << " || " << CRLsquare << std::endl;
+        }
+        
+        CRLsquare = 2*sqrt((CRLsquare - CumulativeRelativeLoss*CumulativeRelativeLoss)/double(N_repeats));
+        
+    }
+    
+    
+    //================================================================    
+    int set_parameter(double value, int position){
+        
+        if (position < 0 || position > 4) {return 0;}
+        
+        else {
+            
+            switch(position){
+                case 0:
+                    alpha_i=value;
+                    return 1;
+                case 1:
+                    alpha_m=value;
+                    return 1;
+                case 2:
+                    beta=value;
+                    return 1;
+                case 3:
+                    gamma=value;
+                    return 1;
+                case 4:
+                    delta=value;
+                    return 1;
+            }
+            
+        }
+        
+        return 0;
+    }
+
+#ifdef notdef
+    //=================================================================
+    void try_annealing(double starting_jump, int iterations, 
+                       double temp_start, double temp_end, double target_rejection){
+        
+        double dx[5]={0.,0.,0.,0.,0};
+        double x[5]={0.,0.,0.,0.,0};
+        double rejection[5]={0., 0., 0., 0., 0.};
+        double curr_x, curr_err, x_tmp;
+        double temperature;
+        double ratio, r;
+        int cycle=10;
+        boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+        
+        // set up parameter for annealing
+        
+        x[0]=alpha_i;
+        x[1]=alpha_m;
+        x[2]=beta;
+        x[3]=gamma;
+        x[4]=delta;
+        
+        for(int i=0;i<5;i++){
+            dx[i] = starting_jump;
+        }
+        
+        // establish the current value
+        
+        //..........................................
+        evolve_to_target();        
+        std::cout << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
+        
+        curr_x   = CumulativeRelativeLoss;
+        curr_err = CRLsquare;
+        CumulativeRelativeLoss = 0;
+        CRLsquare = 0;
+        //...........................................
+        
+        // optimization cycle
+        for(int i=0; i<iterations; i++){
+            
+            temperature = temp_start*exp( i*(log(temp_end)-log(temp_start))/(double)iterations);
+            std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
+            
+            if (i % cycle == 0 && i > 0){
+                
+                for (int k=0; k<5; k++){
+                    
+                    rejection[k]/=(double)cycle;
+                    if (rejection[k] > 0){
+                        dx[k] = dx[k]/(rejection[k]/target_rejection);
+                        rejection[k]=0.;
+                    }
+                    else{
+                        dx[k]*=2.;
+                    }
+                    std::cout  << dx[k] << " ";
+                }
+                std::cout  << std::endl;
+            }
+            
+            
+            for (int j=0; j<5; j++){
+                
+                // get new value of x[j]
+                x_tmp = get_new_x(x[j],dx[j]);
+                
+                
+                
+                //.............................................
+                set_parameter(x_tmp, j);
+                
+                
+                evolve_to_target(); 
+                
+                std::cout  << std::endl << "......... " << std::endl;
+                std::cout << "Trying... " << CumulativeRelativeLoss << " +- " << CRLsquare << std::endl;
+                
+                ratio = min(1.,exp(-(CumulativeRelativeLoss-curr_x)/temperature));
+                r = uni();
+                std::cout << r << " vs " << ratio << std::endl;
+                
+                if (r > ratio){
+                    
+                    std::cout << string_wrap(id, 4) <<" "<< (i+1) << ","<< (j) 
+                    <<" "<< (i+1) << " Did not accept " 
+                    << x_tmp << "(" << j << ")" << std::endl;
+                    std::cout << alpha_i << " "<< alpha_m << " "
+                    << beta << " " << gamma << " " 
+                    << delta << " " << std::endl;
+                    set_parameter(x[j], j);
+                    CumulativeRelativeLoss = 0;
+                    CRLsquare = 0;
+                    
+                    rejection[j]+=1.;
+                }
+                
+                else {
+                    
+                    curr_x   = CumulativeRelativeLoss;
+                    curr_err = CRLsquare;
+                    x[j] = x_tmp;
+                    CumulativeRelativeLoss = 0;
+                    CRLsquare = 0;
+                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
+                    << wrap_double(rejection[0],2) 
+                    << " "<< wrap_double(rejection[1], 7) << " "
+                    << wrap_double(rejection[2],5) << " " << wrap_double(rejection[2],9) << " " 
+                    << wrap_double(rejection[4],6) << " " 
+                    << std::endl << std::endl;
+                    
+                    std::cout << string_wrap(id, 4) <<" "<< (i+1) <<","<< (j) 
+                    <<" "
+                    << string_wrap((string) "***** Did accept! ", 3) 
+                    << wrap_double(alpha_i,2) 
+                    << " "<< wrap_double(alpha_m, 7) << " "
+                    << wrap_double(beta,5) << " " 
+                    << wrap_double(gamma,9) << " " 
+                    << wrap_double(delta,6) << " " 
+                    << std::endl << std::endl;
+                    
+                }
+                //........................................................ 
+                
+            }
+            
+        }
+        
+    }
+    
+#endif // notdef
+
+	
+};
+
+
+//============================================================
+
+std::pair<double,double> multi_loss( // dispatch_group_t group, 
+                                    Universe* un[], 
+                                    // dispatch_queue_t* CustomQueues,
+                                    double* Results,
+                                    int*    Counters,
+                                    double* params){
+    
+    int N = un[0]->get_reruns();
+    int step = (int)(double)N/(double)(Nworkers);
+    int istart=0;
+    int iend = istart+step;
+    
+    double Loss=0., LossSquare=0.;
+    
+    timeval startTime, endTime;
+    double elapsedTime;
+    // start timer
+    gettimeofday(&startTime, NULL);
+
+    //err:    for(int i=0; i<Nworkers; i++){
+
+    std::cout << "Entry to multi_loss, params: ";
+    for(int i=0; i<Nworkers; i++){
+        for(int j=0; j<NEVOPARAMS; j++){
+	  std::cout << "[" << un[i]->get_parameter(j) << "," << params[j] << "] ";
+            un[i]->set_parameter(params[j],j);
+        }
+    }
+    std::cout << "\n";
+    int i;
+    #pragma omp parallel for private (i)
+    for(i=0; i<Nworkers; i++){
+
+      // dispatch_group_async(group, CustomQueues[i], ^{
+      std::cout<<"multi_loss: Calling evolve_to_target_and_save i=" << i << " N=" << N << " step=" << step << " istart=" << i*step << " iend=" << (i+1)*step << "\n";
+      //un[i]->evolve_to_target_and_save(istart, iend, Results, Counters);
+      un[i]->evolve_to_target_and_save(i*step, min((i+1)*step,N), Results, Counters);
+      //});
+
+      // std::cout<<"multi_loss: Returned from evolve_to_target_and_save " << i << "\n";
+      
+
+      //istart += step;
+      //iend = min(istart+step,N);
+            
+    }
+    // err    }
+    // dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
+    //dispatch_release(group);
+    
+
+    for (int i=0; i<N; i++){
+        
+        Loss+=Results[i]/(double)N;
+        LossSquare+=Results[i]*Results[i]/(double)N;
+        
+        std::cout<<i<<":"<< Results[i] << " ";
+    }
+    
+    std::cout<<" \n\n\n";
+    double two_std = ((LossSquare - Loss*Loss)/(double)N);
+    
+    two_std = 2.*sqrt(two_std);
+    std::pair<double,double> Res;
+    Res.first=Loss;
+    Res.second=two_std;
+
+    gettimeofday(&endTime, NULL);
+    elapsedTime = (endTime.tv_sec - startTime.tv_sec) * 1000.0;      // sec to ms
+    elapsedTime += (endTime.tv_usec - startTime.tv_usec) / 1000.0;   // us to ms
+    elapsedTime /= 1000.;
+    cout << "multi_loss(N=" << N << ") elapsed time: " << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
+    
+    return Res;
+}
+//============================================================
+
+
+//============================================================
+void multi_annealing( // dispatch_group_t group, 
+                     Universe* un[], 
+                     // dispatch_queue_t* CustomQueues, 
+                     double T_start, double T_end, 
+                     double Target_rejection, 
+                     int Annealing_repeats, 
+                     double starting_jump,
+                     double* Results,
+                     int*    Counters,
+                     double* params0,
+                     double annealing_cycles){
+    //.................................
+    // re-implement annealing
+    
+    double dx[NEVOPARAMS]={0.,0.,0.,0.,0};
+    double x[NEVOPARAMS]={0.,0.,0.,0.,0};
+    double rejection[NEVOPARAMS]={0., 0., 0., 0., 0.};
+    double curr_x, curr_err, x_tmp;
+    double temperature;
+    double ratio, r;
+    int cycle=10;
+    //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
+    
+    // set up parameter for annealing
+
+    for(int i=0;i<NEVOPARAMS;i++){
+        x[i]=params0[i];
+        dx[i] = starting_jump;
+	for(int w=0; w<Nworkers; w++){
+	  un[w]->set_parameter(x[i], i);
+	}
+    }
+    
+    // establish the current value
+    std::pair<double,double>Res;
+    
+    if ( operation == 'm' ) {
+      // Nworkers = 1;
+    }
+    else if (operation == 'g') {
+      // generate params: not yet implemented - to be deprecated
+    }
+    else if (operation == 'a') {
+      // analyze multi_loss() results: not tested or used - to be deprecated
+
+      string line;
+      ifstream mlossdata ("multi_loss.data");
+      double d, Loss, LossSquare, two_std;
+      bool b;
+      int n=0;
+      if (mlossdata.is_open()) {
+	while ( getline (mlossdata,line) ) {
+	  b = from_string<double>(d, std::string(line), std::dec);
+	  cout << line << " d=" << d << endl;
+	  Loss += d;
+          LossSquare += (d*d);
+          n++;
+	}
+        Loss /= double(n);
+	LossSquare /= double(n);
+	two_std = ((LossSquare - Loss*Loss)/(double)n);
+	two_std = 2.*sqrt(two_std);
+	std::cout<<"n="<<n<<" Loss="<<Loss<<" LossSquare="<<LossSquare<<" two_std="<<two_std<<"\n\n\n";
+	mlossdata.close();
+	FILE *f=fopen("multi_loss_stats.txt","w");
+	fprintf(f,"%d n\n",n);
+	fprintf(f,"%.20e Loss\n",Loss);
+	fprintf(f,"%.20e LossSquare\n",LossSquare);
+	fprintf(f,"%.20e two_std\n",two_std);
+        fclose(f);
+	exit(0);
+      }
+      else {
+	cout << "Unable to open file multi_loss.data"; 
+	exit(1);
+      }
+    }
+
+    std::cout << "Calling initial multi_loss:\n";
+    Res = multi_loss( /* group,*/ un, /* CustomQueues,*/ Results, Counters, x);
+    std::cout << "Ret from initial multi_loss:\n";
+    std::cout << Res.first << " +- " << Res.second << std::endl;
+
+    if ( operation == 'm' ) {
+      FILE *f;
+      int N = un[0]->get_reruns();
+
+      f = fopen("multi_loss.data","w");
+      for(int i=0; i<N; i++) {
+	fprintf(f,"%.20e\n",Results[i]);
+      }
+      fclose(f);
+      exit(0);
+    }
+    
+    curr_x   = Res.first;
+    curr_err = Res.second;
+    
+    // optimization cycle
+    
+    for(int i=0; i<annealing_cycles; i++){
+        
+        temperature = T_start*exp( i*(log(T_end)-log(T_start))/(double)annealing_cycles);
+        std::cout  << std::endl << "....T = " << wrap_double(temperature,3) << std::endl << std::endl;
+        
+        if (i % cycle == 0 && i > 0){
+            
+            for (int k=0; k<NEVOPARAMS; k++){
+                rejection[k]/=(double)cycle;
+                
+                if (rejection[k] > 0){
+                    dx[k] = dx[k]/(rejection[k]/Target_rejection);
+                    rejection[k]=0.;
+                }
+                else{
+                    dx[k]*=2.;
+                }
+                std::cout  << dx[k] << " ";
+            }
+            std::cout  << std::endl;
+        }
+
+        for (int j=0; j<NEVOPARAMS; j++){
+            
+            ///////////////////////////////
+            if (FIX_VARIABLES==0 || var_fixed[j]==0){
+                
+                
+                
+                // get new value of x[j]
+                double x_hold=x[j];
+                x_tmp = get_new_x(x[j],dx[j]);
+                x[j]=x_tmp;
+                
+                std::cout << wrap_double(x_tmp,10) << " " << wrap_double(j,9) << "\n\n"; 
+                //=======================================
+                //.............................................
+                for(int w=0; w<Nworkers; w++){
+                    un[w]->set_parameter(x_tmp, j);
+                }
+                
+
+		// WRITE OUT PARAMS HERE; then exit.
+
+		std::cout << "Calling multi_loss: i=" << i << " j=" << j << "\n";
+                Res = multi_loss(/* group, */ un, /* CustomQueues, */ Results, Counters, x);
+		std::cout << "Ret from multi_loss: i=" << i << " j=" << j << "\n";
+                std::cout << Res.first << " +- " << Res.second << std::endl;
+                
+                ratio = min(1.,exp(-(Res.first-curr_x)/temperature));
+                r = rand()/(double)(pow(2.,31)-1.);
+                std::cout << r << " vs " << ratio << std::endl;
+                
+                double ALOT=100000000000.;
+                
+                if (Res.first < ALOT)
+                {
+                    ofstream filestr;
+                    
+                    filestr.open ("best_opt_some.txt", ofstream::app);
+                    
+                    // >> i/o operations here <<
+                    filestr << un[0]->get_target() << "," 
+                    << Res.first 
+                    << "," << un[0]->get_parameter(0) 
+                    << "," << un[0]->get_parameter(1) 
+                    << "," << un[0]->get_parameter(2) 
+                    << "," << un[0]->get_parameter(3) 
+                    << "," << un[0]->get_parameter(4) << "," << Res.second << ",\n";
+                    
+                    filestr.close();
+                    
+                    
+                    filestr.open ("max_dist.txt", ofstream::app);
+                    
+                    // >> i/o operations here <<
+                    filestr << max_dist << ",\n";
+                    
+                    filestr.close();
+                    
+                }
+                
+                
+                if (r > ratio){
+                    
+                    std::cout << " "<< (i+1) << ","<< (j) 
+                    <<" "<< (i+1) << " Did not accept " 
+                    << x_tmp << "(" << j << ")" << std::endl;
+                    std::cout << un[0]->get_parameter(0) 
+                    << " " << un[0]->get_parameter(1) 
+                    << " " << un[0]->get_parameter(2) 
+                    << " " << un[0]->get_parameter(3) 
+                    << " " << un[0]->get_parameter(4) << " " << std::endl;
+                    
+                    x[j]=x_hold;
+                    for(int w=0; w<Nworkers; w++){
+                        un[w]->set_parameter(x[j], j);
+                    }
+                    
+                    
+                    //set_parameter(x[j], j);     
+                    rejection[j]+=1.;
+                }
+                
+                else {
+                    
+                    curr_x   = Res.first;
+                    curr_err = Res.second;
+                    x[j] = x_tmp;
+                    
+                    for(int w=0; w<Nworkers; w++){
+                        un[w]->set_parameter(x[j], j);
+                    }
+                    
+                    std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8) 
+                    << wrap_double(rejection[0],2) << " " 
+                    << wrap_double(rejection[1],7) << " "
+                    << wrap_double(rejection[2],5) << " " 
+                    << wrap_double(rejection[3],9) << " " 
+                    << wrap_double(rejection[4],6) << " " 
+                    << std::endl << std::endl;
+                    
+                    std::cout << " "<< (i+1) <<","<< (j) 
+                    <<" "
+                    << string_wrap((string) "***** Did accept! ", 3) 
+                    << wrap_double(un[0]->get_parameter(0),2) << " "
+                    << wrap_double(un[0]->get_parameter(1),7) << " "
+                    << wrap_double(un[0]->get_parameter(2),5) << " " 
+                    << wrap_double(un[0]->get_parameter(3),9) << " " 
+                    << wrap_double(un[0]->get_parameter(4),6) << " " 
+                    << std::endl << std::endl;
+                    
+                    
+                    
+                }
+                //........................................................ 
+                
+            }
+        }
+        
+    }
+    
+}
+
+
+//================================================
+int
+main(int argc, char* argv[])
+{
+    
+    double params0[6] = {0., 0., 0., 0., 0., 0.2}, target=50., range;
+    string par_names0[6] = {"alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
+    string par_names1[4] = {"n_epochs", "n_steps", "n_reruns", "range"};
+    string par_names2[5] = {"T_start", "T_end", "Annealing_steps","Target_rejection","Starting_jump"};
+    string par_names3[5] = {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
+    string par_names4[2] = {"Operation", "Nworkers"};    
+    int params1[4] = {300, 50, 1000, 10};
+    int params3[5] = { 0, 0, 0, 0, 0};
+    
+    //          temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
+    double params2[5] = {1,             0.001,               100,              0.3,           1.5};
+    
+    int verbose_level = 2;
+    const std::string one="one", two="two";
+    static Universe* un[MAXNworkers];
+    // static dispatch_queue_t CustomQueues[MAXNworkers];
+    
+    static double* Results;
+    static int*    Counters;
+    
+    timeval t1, t2;
+    double elapsedTime;
+    // start timer
+    gettimeofday(&t1, NULL);
+    
+    
+    if (argc < 8) {
+        std::cout << "Usage: super_optimizer alpha_i alpha_m beta gamma delta target_innov [n_epochs n_steps n_reruns] [range] [verbose_level]\n"; 
+        std::cout << "         [T_start T_end Annealing_steps Target_rejection Starting_jump]\n"; 
+        std::cout << "         [FREEZE_alpha_i FREEZE_alpha_m FREEZE_beta FREEZE_gamma FREEZE_delta]\n"; 
+        
+        system("pwd");
+        
+        
+        return(1);
+    }
+    else {
+      std::cout << "argc=" << argc << std::endl;
+
+        for (int nArg=0; nArg < argc; nArg++){
+            //std::cout << nArg << " " << argv[nArg] << std::endl;
+            if (nArg > 0 && nArg < 7){
+                params0[nArg-1]= atof(argv[nArg]);
+                std::cout << par_names0[nArg-1] << ": " << params0[nArg-1] <<  std::endl;
+            }
+            if (nArg > 6 && nArg < 11){
+                params1[nArg-7]= atoi(argv[nArg]);
+                std::cout << par_names1[nArg-7] << ": " << params1[nArg-7] <<  std::endl;
+            }
+            if (nArg == 11){
+                verbose_level = atoi(argv[nArg]);
+                std::cout << "verbose level: " << verbose_level <<  std::endl;
+            }
+            if (nArg > 11 && nArg < 17){
+                params2[nArg-12]= atof(argv[nArg]);
+                std::cout << par_names2[nArg-12] << ": " << params2[nArg-12] <<  std::endl;
+            }
+            if (nArg > 16 && nArg < 22){
+                params3[nArg-17]= atof(argv[nArg]);
+                var_fixed[nArg-17]= atof(argv[nArg]);
+                std::cout << par_names3[nArg-17] << ": " << var_fixed[nArg-17] <<  std::endl;
+            }
+            if (nArg == 22 ){
+                operation = *argv[nArg];
+		std::cout << par_names4[0] << ": " << operation <<  std::endl;
+            }
+            if (nArg == 23 ){
+	        Nworkers = atoi(argv[nArg]);
+		std::cout << par_names4[1] << ": " << Nworkers <<  std::endl;
+            }
+        }
+    }
+    
+    /*
+    for target in range(58,1009,50):
+	s = ("%d" % target)
+	print s
+
+	for i in range(15):
+          # Param groups separated by "|" below for documentation. NOTE that | is not used on command line!
+	  os.system("./supe_duper_optimizer |0 0 4 50 -1 "+s+" | 40000 20 1000 2 | 1 | 2. 0.01 100 0.3 2.3 | 1 1 0 0 0")
+    */
+
+    /*   Parameters, re-iterated:
+
+                        "alpha_i", "alpha_m", "beta", "gamma", "delta", "target"};
+    double params0[6] = {0.,        0.,        0.,     0.,      0.,      0.2}, target=50., range;
+
+                    {"n_epochs", "n_steps", "n_reruns", "range"};
+    int params1[4] = {300,        50,       1000,        10};
+
+                       {"T_start",          "T_end",          "Annealing_steps","Target_rejection","Starting_jump"};
+                         temperature_start,  temperature_end,  annealing_steps target_rejection  Starting_jump
+    double params2[5] = {1,                  0.001,            100,            0.3,              1.5};
+
+                     {"FREEZE_alpha_i", "FREEZE_alpha_m", "FREEZE_beta", "FREEZE_gamma", "FREEZE_delta"};    
+    int params3[5] = { 0,                0,                0,             0,              0};
+
+
+    */
+
+    for (int j=0; j<NEVOPARAMS; j++){
+        
+        cout << j << " | " << var_fixed[j] << " (fixed) \n";
+    }
+	
+    target=params0[NEVOPARAMS];
+    range = (double)params1[3];
+	int identify_failed = 0;
+	char* filename= (char *)"movie_graph.txt";
+ 	int n_ep=params1[0], n_st=params1[1], n_rep=params1[2];
+    
+    //...............................
+    
+    for(int i=0; i<Nworkers; i++){
+        un[i] = new Universe((char *)filename,n_ep,n_st,
+                             (int)n_rep,
+                             identify_failed, target, i2string(i));
+        // CustomQueues[i] = dispatch_queue_create(i2char(i), NULL);
+    }
+    
+    //...............................
+    // mw: n_rep == n_reruns = # times evolve is done within multi_loss, spread across Nworkers.
+    if(n_rep > 0){
+        
+        Results = new double[n_rep];
+        Counters = new int[n_rep];
+        
+    }else {
+        
+        Results =  NULL;
+        Counters = NULL;
+        std::cout << " Number of reruns should be positive! " <<  std::endl;
+        return 0;
+        
+    }
+    //...............................
+
+    //srand(time(0));
+    //srandomdev();
+    {
+      timeval t; 
+      gettimeofday(&t, NULL);
+      srand(t.tv_usec);
+    }
+    
+    {
+        double r=0;
+        for (int j=0; j<100; j++){
+            
+            
+            
+            r = rand()/(double)(pow(2.,31)-1.);
+            std::cout << r << " ";
+        }
+        std::cout << "\n ";
+    }
+  	//random initiation of starting parameters
+    
+    if (range > 0.){
+        
+        for (int i=0; i < 5; i++){
+            
+            if (params0[i]==-100.){
+                
+                double r1 = (rand()/(double)(pow(2.,31)-1.));
+                double r2 = (rand()/(double)(pow(2.,31)-1.));
+                double sign = 1.;
+                
+                if(r1 > 0.5){
+                    sign=-1.;
+                }
+                
+                params0[i] = sign*r2*range;
+                
+                std::cout << par_names0[i] << ": " << params0[i] <<  std::endl;
+            }
+        }
+        
+    }
+    
+    
+    double T_start=params2[0], T_end=params2[1], Target_rejection=params2[3], starting_jump=params2[4];
+    int Annealing_repeats = (int) params2[2];
+    
+    
+    // dispatch_group_t group = dispatch_group_create();
+    
+    //.............................
+    multi_annealing( /* group, */ un, /* CustomQueues, */ T_start, T_end, Target_rejection, Annealing_repeats, 
+		     starting_jump, Results, Counters, params0, Annealing_repeats);
+    
+    //dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
+    // dispatch_release(group);
+    //.............................
+    
+    
+    // stop timer
+    gettimeofday(&t2, NULL);
+    
+    // compute and print the elapsed time in millisec
+    elapsedTime = (t2.tv_sec - t1.tv_sec) * 1000.0;      // sec to ms
+    elapsedTime += (t2.tv_usec - t1.tv_usec) / 1000.0;   // us to ms
+    elapsedTime /= 1000.;
+    cout << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
+    
+    //.....................
+    
+    for(int i=0; i<Nworkers; i++){
+        delete un[i];
+    }
+    
+    //....................
+    if(n_rep > 0){
+        
+        delete [] Results;
+        delete [] Counters;
+        
+    }
+    
+    return 0;
+	
+	
+	
+}
+




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