[Swift-commit] r5515 - SwiftApps/SciColSim
wilde at ci.uchicago.edu
wilde at ci.uchicago.edu
Mon Jan 23 15:05:03 CST 2012
Author: wilde
Date: 2012-01-23 15:05:03 -0600 (Mon, 23 Jan 2012)
New Revision: 5515
Added:
SwiftApps/SciColSim/optimizer.orig-mac.cpp
Log:
Add cpp file for orginal Mac Grand Central version with only necessary logic fixes beyond original.
Added: SwiftApps/SciColSim/optimizer.orig-mac.cpp
===================================================================
--- SwiftApps/SciColSim/optimizer.orig-mac.cpp (rev 0)
+++ SwiftApps/SciColSim/optimizer.orig-mac.cpp 2012-01-23 21:05:03 UTC (rev 5515)
@@ -0,0 +1,1673 @@
+//
+// main.cpp
+// optimizer
+//
+// Created by Andrey Rzhetsky on 4/11/11.
+// Copyright 2011 University of Chicago. All rights reserved.
+//
+
+#define Nworkers 24
+
+#include <fstream>
+#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 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[5] = {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;
+}
+
+//================================================
+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.;
+
+ reset_world();
+
+ for (int k = istart; k < iend; k++){
+
+
+ for(int i=0; i< N_epochs && current_novelty < TargetNovelty; i++){
+ 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;
+ }
+
+
+ //=================================================================
+ 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;
+
+ }
+ //........................................................
+
+ }
+
+ }
+
+ }
+
+
+};
+
+//============================================================
+
+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.;
+
+ for(int i=0; i<Nworkers; i++){
+ for(int j=0; j<5; j++){
+ un[i]->set_parameter(params[j],j);
+ }
+ }
+
+
+ for(int i=0; i<Nworkers; i++){
+
+ dispatch_group_async(group, CustomQueues[i], ^{
+
+ un[i]->evolve_to_target_and_save(istart, iend, Results, Counters);
+ });
+
+ std::cout << "queued: i=" << i << " N=" << N << " istart=" << istart << " iend=" << iend << "\n";
+ istart += step;
+ iend = min(istart+step,N);
+
+ }
+ 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<<" " << 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;
+
+ 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[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]=params0[0];
+ x[1]=params0[1];
+ x[2]=params0[2];
+ x[3]=params0[3];
+ x[4]=params0[4];
+
+ for(int i=0;i<5;i++){
+ dx[i] = starting_jump;
+ }
+
+ // establish the current value
+ std::pair<double,double>Res;
+
+ Res = multi_loss(group, un, CustomQueues, Results, Counters, x);
+ std::cout << Res.first << " +- " << Res.second << std::endl;
+
+ 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<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++){
+
+ ///////////////////////////////
+ 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);
+ }
+
+
+ Res = multi_loss(group, un, CustomQueues, Results, Counters, x);
+ 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"};
+ 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[Nworkers];
+ static dispatch_queue_t CustomQueues[Nworkers];
+
+ 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 {
+ 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;
+ }
+
+
+ }
+
+ }
+
+ for (int j=0; j<5; j++){
+
+ cout << j << " | " << var_fixed[j] << " (fixed) \n";
+ }
+
+ target=params0[5];
+ 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);
+ }
+
+ //...............................
+ 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();
+
+ {
+ 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 \n .....(" << 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;
+
+
+
+}
+
Property changes on: SwiftApps/SciColSim/optimizer.orig-mac.cpp
___________________________________________________________________
Added: svn:executable
+ *
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