[Swift-commit] r6110 - SwiftApps/SciColSim
wilde at ci.uchicago.edu
wilde at ci.uchicago.edu
Sat Dec 22 22:33:23 CST 2012
Author: wilde
Date: 2012-12-22 22:33:21 -0600 (Sat, 22 Dec 2012)
New Revision: 6110
Added:
SwiftApps/SciColSim/maxloss.cpp
SwiftApps/SciColSim/maxloss.py
SwiftApps/SciColSim/maxloss_ar.py
Modified:
SwiftApps/SciColSim/Makefile
SwiftApps/SciColSim/TODO
SwiftApps/SciColSim/graphsim.cpp
SwiftApps/SciColSim/samplegraph.sh
SwiftApps/SciColSim/testgraph.py
Log:
Development snapshot. Test programs for dynamic max-loss; fine-grained OpenMP of graphsim; revised graph sampler.
Modified: SwiftApps/SciColSim/Makefile
===================================================================
--- SwiftApps/SciColSim/Makefile 2012-12-19 14:06:35 UTC (rev 6109)
+++ SwiftApps/SciColSim/Makefile 2012-12-23 04:33:21 UTC (rev 6110)
@@ -7,8 +7,11 @@
openmp-optimizer: optimizer.cpp
g++ -DP_OPENMP -static -O -fopenmp -I boost_1_47_0 -o openmp-optimizer optimizer.cpp
+#t: t.cpp
+# g++ -DP_OPENMP -static -O -fopenmp -I boost_1_47_0 -o t t.cpp
+
graphsim: graphsim.cpp
- g++ -DP_OPENMP -static -pg -O -fopenmp -I boost_1_47_0 -o graphsim graphsim.cpp
+ g++ -DP_OPENMP -static -pg -O2 -fopenmp -I boost_1_47_0 -o graphsim graphsim.cpp
openmptest: openmptest.cpp
g++ -DP_OPENMP -static -O -fopenmp -o openmptest openmptest.cpp
Modified: SwiftApps/SciColSim/TODO
===================================================================
--- SwiftApps/SciColSim/TODO 2012-12-19 14:06:35 UTC (rev 6109)
+++ SwiftApps/SciColSim/TODO 2012-12-23 04:33:21 UTC (rev 6110)
@@ -1,6 +1,28 @@
+--- Questions and Open Issues ---
+When sampling the graph, we compress the node ids. Is this OK?
+Same 40K epoch count? Is this really feasible?
+
+What values for otehr loop parameters?
+
+What sampling?
+
+Limits?
+-- placed correctly?
+-- size
+-- anomalous value for tn 758
+-- adjust all for new graph?
+
+What is range of TI to sweep over?
+
+Algorithmic improvements?
+
+Graph partitioning? => for greater total parallelism?
+
+
+
x = done, - = in progress
Modified: SwiftApps/SciColSim/graphsim.cpp
===================================================================
--- SwiftApps/SciColSim/graphsim.cpp 2012-12-19 14:06:35 UTC (rev 6109)
+++ SwiftApps/SciColSim/graphsim.cpp 2012-12-23 04:33:21 UTC (rev 6110)
@@ -6,8 +6,6 @@
// Copyright 2011 University of Chicago. All rights reserved.
//
-// Select OpenMP or Grand Central Dispatch for multithreading:
-
#define MAXNworkers 1
int Nworkers=MAXNworkers;
@@ -34,13 +32,8 @@
#include <sys/types.h>
#include "unistd.h"
-#ifdef P_DISPATCH
-#include <dispatch/dispatch.h>
-#endif
-
#include <fstream>
-
#include <stdlib.h>
#include <boost/numeric/ublas/io.hpp>
#include <boost/graph/graph_traits.hpp>
@@ -88,157 +81,122 @@
typedef boost::graph_traits<Graph>::vertex_descriptor vertex_descriptor;
namespace std {
- using ::time;
+ 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;
+ 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
+ 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 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 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.);
- 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;
-
+ 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();
+ 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();
+ std::ostringstream s;
+ s << string_wrap(strDouble(val),mode);
+ return s.str();
}
-
-
-//===============================================
const
string i2string(int i){
-
- std::ostringstream s;
- s << "worker"
+ std::ostringstream s;
+ s << "worker"
<< lexical_cast<std::string>(i);
-
- return s.str();
-
+ return s.str();
}
-//===============================================
char* i2char(int i){
-
- std::ostringstream s;
- s << "worker"
+ 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;
+ char* a=new char[s.str().size()+1];
+ memcpy(a,s.str().c_str(), s.str().size());
+ return a;
}
-//================================================
-
typedef struct {
unsigned long size,resident,share,text,lib,data,dt;
} statm_t;
@@ -253,13 +211,12 @@
perror(statm_path);
exit(99);
}
-
if(7 != fscanf(f,"%ld %ld %ld %ld %ld %ld %ld",
&sp->size,&sp->resident,&sp->share,&sp->text,&sp->lib,&sp->data,&sp->dt))
- {
- perror(statm_path);
- exit(99);
- }
+ {
+ perror(statm_path);
+ exit(99);
+ }
fclose(f);
}
@@ -277,529 +234,522 @@
MB(size), MB(resident), MB(share), MB(text), MB(lib), MB(data), MB(dt) );
}
+timeval startTime, endTime;
+double elapsedTime;
+char timenow[100];
+
+char *now()
+{
+ timeval endTime;
+ double elapsedTime;
+
+ 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.;
+ sprintf(timenow, "%10.3f", elapsedTime);
+ return(timenow);
+}
+
//================================================
class Universe {
private:
- double alpha_i;
- double alpha_m;
- double beta;
- double gamma;
- double delta;
+ double alpha_i;
+ double alpha_m;
+ double beta;
+ double gamma;
+ double delta;
- double TargetNovelty;
- double CumulativeRelativeLoss;
- double CRLsquare;
- string id;
+ double TargetNovelty;
+ double CumulativeRelativeLoss;
+ double CRLsquare;
+ string id;
- int N_nodes;
- int M_edges;
+ int N_nodes;
+ int M_edges;
- int N_epochs;
- int N_steps;
- int N_repeats;
+ int N_epochs;
+ int N_steps;
+ int N_repeats;
- int current_epoch;
- double current_loss;
- int current_repeat;
- double current_novelty;
+ 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
+ int mode_identify_failed;
+ int verbose_level; // 0 is silent, higher is more
- double k_max;
+ double k_max;
- graph_t Full_g;
+ graph_t Full_g;
#define GraphRes float // was double
- GraphRes **Prob;
- GraphRes **Tried;
- GraphRes **Dist;
- GraphRes **Final;
- GraphRes **EdgeIndex;
- GraphRes *Rank;
+ GraphRes **Prob;
+ GraphRes **Tried;
+ GraphRes **Dist;
+ GraphRes **Final;
+ GraphRes **EdgeIndex;
+ GraphRes *Rank;
- base_generator_type generator;
- boost::uniform_real<> uni_dist;
- boost::geometric_distribution<double> geo;
+ double max_loss;
+ 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;
- //====== 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;
+ 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;
- std::ifstream inFile;
- //string line;
+ TargetNovelty = target;
+ CumulativeRelativeLoss = 0.;
+ CRLsquare = 0.;
- //-------------------------------
+ N_epochs = Epochs;
+ N_steps = Steps;
+ N_repeats = Repeats;
- 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;
+ current_epoch = 0;
+ current_loss = 0.;
+ current_repeat = 0;
- //--------------------------------
+ id = idd;
- int i, k;
- int x, y;
- Edge* edge_array_mine;
- int num_arcs_mine, num_nodes_mine;
- int* weights_mine;
+ verbose_level = 1;
+ mode_identify_failed = identify_failed;
- TargetNovelty = target;
- CumulativeRelativeLoss = 0.;
- CRLsquare = 0.;
+ // 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;
+ }
+ }
- N_epochs = Epochs;
- N_steps = Steps;
- N_repeats = Repeats;
+ i=0;
+ std::string line;
+ //while (! inFile.eof() && ! inFile.fail()) {
+ while (1==1) {
- current_epoch = 0;
- current_loss = 0.;
- current_repeat = 0;
+ inFile >> x;
+ inFile >> y;
- id = idd;
+ if (verbose_level > 2){
+ std::cout << " x: " << x;
+ std::cout << " y: " << y << std::endl;
+ }
- verbose_level = 1;
+ if (i==0){
+ N_nodes=x;
+ M_edges=y;
+ break;
+ }
+ i++;
+ }
+ inFile.close();
- mode_identify_failed = identify_failed;
+ if (verbose_level == 2){
+ std::cout << N_nodes << " nodes, " << M_edges << " edges"<<std::endl;
+ }
+ // k_max is the longest distance possible
- //-------------------------------
- // 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 {
+ //k_max = M_edges;
+ k_max = 70;
- if (verbose_level > 2){
- std::cout << " Opened <" << FileToOpen << ">"<<std::endl;
- }
- }
+ //------------------------------------
+ // Get memory allocated for all class members
- i=0;
- std::string line;
- //while (! inFile.eof() && ! inFile.fail()) {
- while (1==1) {
+ 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);
- inFile >> x;
- inFile >> y;
+ //The second pass through file with the graph
- if (verbose_level > 2){
- std::cout << " x: " << x;
- std::cout << " y: " << y << std::endl;
- }
+ 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;
+ }
+ }
- if (i==0){
- N_nodes=x;
- M_edges=y;
- break;
- }
- i++;
+ // 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++;
+ }
+ }
+ }
- }
- inFile.close();
+ // create graph -- hopefully, we can keep it, just modifying edge weights
- if (verbose_level == 2){
- std::cout << N_nodes << " nodes, " << M_edges << " edges"<<std::endl;
- }
+ 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_max is the longest distance possible
+ 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);
- //k_max = M_edges;
- k_max = 70;
+ Full_g = g;
+ delete edge_array_mine;
+ delete weights_mine;
- //------------------------------------
- // Get memory allocated for all class members
+ 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;
- 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);
+ 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);
- //The second pass through file with the graph
+ }
+ }
+ }
+ set_max_loss();
+ }
- 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;
- }
- }
+ void set_max_loss() {
+ // Based on formulas from random_analytical.pdf
+ // (9): E[RLt] = (1/t) * SUM(i=0 to T of: (V*(V-1) / (2 * (E-i))
+ // (12) Var[RLt] = 1 / (T**2) * SUM( i=0 to T of: ((V*(V-1))/2) - E + 1 ) / ((E-i)**2)
+ // (9) + 3sigma = E[RLt] + 3 * SQRT(Var)
+ int E = M_edges;
+ int V = N_nodes;
+ int T = TargetNovelty;
+ double sum, ERLt, VarRLt;
- // Fill in the final graph -- and we are ready to go!
+ sum = 0;
+ for(int i=0;i<=T;i++) {
+ sum += (V*(V-1)) / (2*(E-i));
+ }
+ ERLt = (1.0/T) * sum;
- 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);
-
- }
- }
- }
-
-
+ sum = 0;
+ for(int i=0;i<=T;i++) {
+ sum += (((V*(V-1))/2.0) - E + 1.0 ) / ((E-i)*(E-i));
}
+ VarRLt = (1.0 / (T*T)) * sum;
+ max_loss = ERLt + (3.0 * sqrt(VarRLt));
+ cout << "set_max_loss: V=" << V << " E=" << E << " T=" << T << " ERLt=" << ERLt << " VarRLt=" << VarRLt << " max_loss=" << max_loss << endl;
+ }
+ int sample_failed_number(double pfail) {
- //=====================================================================
- int sample_failed_number(double pfail){
+ //boost::geometric_distribution<double> geo(pfail);
+ //boost::variate_generator<base_generator_type&, geometric_distribution<double> > geom(generator, geo);
- //boost::geometric_distribution<double> geo(pfail);
- //boost::variate_generator<base_generator_type&, geometric_distribution<double> > geom(generator, geo);
+ double r, u, g;
- double r, u, g;
+ r=0.;
+ for(int i=0; i<N_steps; i++){
- 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)));
- u=(double)rand();
- u = 1.-u /(double)(pow(2.,31)-1.);
- g=(int)(ceil(log(u) / log(pfail)));
+ //r += geom();
- //r += geom();
-
- r+=g;
- }
-
- if (verbose_level>=3){
- std::cout << id << " failed " << r << std::endl;
- }
- return (int) round(r); // FIXME: Andrey: please verify that round() is correct.
-
- }
-
- //=============================================
- double get_target(void){
- return TargetNovelty;
+ r+=g;
}
- //=============================================
- void set_target(double target){
- TargetNovelty=target;
+ if (verbose_level>=3){
+ std::cout << id << " failed " << r << std::endl;
}
+ return (int) round(r); // FIXME: Andrey: please verify that round() is correct.
- //=============================================
- int sample(){
+ }
- //boost::variate_generator<base_generator_type&, boost::uniform_real<> > uni(generator, uni_dist);
- // double r = uni(), Summa = 0.;
+ double get_target(void){
+ return TargetNovelty;
+ }
+ void set_target(double target){
+ TargetNovelty=target;
+ }
+ int sample(){
- double r = rand(), Summa = 0.;
- r /= (double)(pow(2.,31)-1.);
- int result = 0;
- int finished = 0;
+ //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;
- }
+ 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;
-
+ 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;
+ void update_current_graph(void){
- //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);
- }
+ 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;
+ }
+ }
+ 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 ( (int)round(Dist[*vi][j]>max_dist)) {
- // FIXME: Andrey: please verify that (int) cast is correct. Do we need to round()?
- // also, the indent on this iff statement was way off -
- // perhaps due to space v. tab?
- max_dist=(int)round(Dist[*vi][j]);
- }
-
-
- } else {
- Dist[*vi][j]=-1.;
- Dist[j][*vi]=-1.;
- }
- }
- }
-
- }
-
-
+ // 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 ( (int)round(Dist[*vi][j]>max_dist)) {
+ // FIXME: Andrey: please verify that (int) cast is correct. Do we need to round()?
+ // also, the indent on this iff statement was way off -
+ // perhaps due to space v. tab?
+ max_dist=(int)round(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.;
- }
+ void update_ranks(void){
- 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]++;
- }
- }
- }
+ 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 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;
+ //====================================================================
+ 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);
- }
- }
- }
+ 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.;
- }
- }
- }
+ 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;
+ //==============================================
+ 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";
+ //===============================================
+ string file_name(){
- return s.str();
+ 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 set_verbose(int verbose){
+ verbose_level = verbose;
+ }
- //=============================================================
+
+ //=============================================================
void update_probabilities(void){
@@ -807,13 +757,11 @@
// Compute sampling probabilities
// first pass: \xi_i,j
int i, j;
- //#pragma omp parallel for private (j)
- //#pragma omp parallel for
- // #pragma omp parallel for default(none) shared( Prob, alpha_i, alpha_m, beta, k_max, gamma, delta, Rank, Dist) private(i,j)
- #pragma omp parallel for private(i,j)
+ std::cout << now() << " pass1 update_probabilities" << std::endl;
for( i=0; i<N_nodes-1; i++){
+#pragma omp parallel for private(j)
for( j=i+1; j<N_nodes; j++){
double bg = 0.;
@@ -838,686 +786,441 @@
}
}
+ std::cout << now() << " pass2 update_probabilities" << std::endl;
// second pass: sum
double Summa = 0.;
- #pragma omp parallel
+// pragma omp parallel
for(i=0; i<N_nodes-1; i++){
for(j=i+1; j<N_nodes; j++){
Summa += Prob[i][j];
}
}
+ std::cout << now() << " pass3 update_probabilities" << std::endl;
+
// third pass: normalize
- #pragma omp parallel
for( i=0; i<N_nodes-1; i++){
- for( j=i+1; j<N_nodes; j++){
+#pragma omp parallel for private(j)
+ for( j=i+1; j<N_nodes; j++){
Prob[i][j] /= Summa;
}
}
+ std::cout << now() << " exit update_probabilities" << std::endl;
}
- // Now we are ready for simulations
- //==============================================
- void update_world(){
+ // Now we are ready for simulations
+ //==============================================
+ void update_world(){
- int failed = 0;
+ int failed = 0;
- // Given current universe compute shortest paths
- //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ // Given current universe compute shortest paths
+ //~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-std::cout << "Before updates" << std::endl;
- update_current_graph();
-std::cout << "after current" << std::endl;
- update_ranks();
-std::cout << "after ranks" << std::endl;
- update_distances();
-std::cout << "after distances" << std::endl;
- update_probabilities();
-std::cout << "after probs" << std::endl;
+ std::cout << now() << " Before updates" << std::endl;
+ update_current_graph();
+ std::cout << now() << " after current" << std::endl;
+ update_ranks();
+ std::cout << now() << " after ranks" << std::endl;
+ update_distances();
+ std::cout << now() << " after distances" << std::endl;
+ update_probabilities();
+ std::cout << now() << " after probs" << std::endl;
- //===============================
- // sampling
- int result;
- double cost=0., novel=0.;
- int publishable = 0;
+ // sampling
+ int result;
+ double cost=0., novel=0.;
+ int publishable = 0;
+ if (mode_identify_failed == 1){
+ while(publishable < N_steps){
+ std::cout << now() << " failed:1 before sample\n";
+ result = sample();
+ std::cout << now() << " failed:1 after sample\n";
+ publishable += result;
+ failed += (1-result);
+ }
- //^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- if (mode_identify_failed == 1){
+ std::cout << now() << " failed:1 before for\n";
+ for(int i=0; i<N_nodes-1; i++){
+ int j;
+ #pragma omp parallel for private(j)
+ for( j=i+1; j<N_nodes; j++){
- while(publishable < N_steps){
+ cost+=Tried[i][j];
-std::cout << "failed:1 before sample\n";
- result = sample();
-std::cout << "failed:1 after sample\n";
- publishable += result;
- failed += (1-result);
+ if (Tried[i][j]>0. && Final[i][j]>0.){
+ novel+=1.;
+ std::cout << now() << " failed:1 novel: " << novel << std::endl;
+ }
+ }
+ }
+ std::cout << now() << " failed:1 after for\n";
+ }
+ else {
+ double pfail=0.;
+ int n_failed;
+ //, n_check = 0;
- }
-
-std::cout << "failed:1 before for\n";
- 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.;
-std::cout << "failed:1 novel: " << novel << std::endl;
- }
- }
- }
-std::cout << "failed:1 after for\n";
-
- }
- //^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
- else {
-
- double pfail=0.;
- int n_failed;
- //, n_check = 0;
-
-std::cout << "failed:0 before for\n";
- 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;
- }
-
-std::cout << "failed:0 after for\n";
- n_failed = sample_failed_number(pfail);
-
-
-std::cout << "failed:0 before while\n";
- while(publishable < N_steps){
-
- result = sample();
- publishable += result;
- }
-std::cout << "failed:0 after while\n";
-
-
- 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.;
-std::cout << "failed:0 novel: " << novel << std::endl;
-
- }
- }
- }
- }
-
- current_novelty = novel;
-
-
- //^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-verbose_level = 2;
- 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++;
+ std::cout << now() << " failed:0 before for\n";
+ for (int i=0; i<N_nodes-1; i++) {
+ int j;
+ // #pragma omp parallel for private(j)
+ for (int j=i+1; j<N_nodes; j++) {
+ if (Final[i][j] == 0.) {
+ pfail += Prob[i][j];
+ Prob[i][j] = 0.;
+ }
}
+ }
-
- //====== 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);
+ for(int i=0; i<N_nodes-1; i++){
+ int j;
+ // #pragma omp parallel for private(j)
+ for(j=i+1; j<N_nodes; j++){
+ Prob[i][j] /= (1.-pfail);
}
+ //std::cout << std::endl;
+ }
- //================================================
- // Allocate memory
- double** allocate_2Dmatrix_double(int N, int M)
- {
- double **pointer;
+ std::cout << now() << " failed:0 after for\n";
+ n_failed = sample_failed_number(pfail);
+ std::cout << now() << " failed:0 before while\n";
+ while(publishable < N_steps){
+ result = sample();
+ publishable += result;
+ }
+ std::cout << now() << " failed:0 after while\n";
- 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];
+ current_loss += (n_failed + N_steps);
+ cost = current_loss;
- return pointer;
+ 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.;
+ std::cout << now() << " failed:0 novel: " << novel << std::endl;
+ }
+ }
+ }
}
- // Allocate memory
- GraphRes** allocate_2Dmatrix(int N, int M)
- {
- GraphRes **pointer;
+ current_novelty = novel;
- if (verbose_level == 2){
- std::cout<< "["<<N<<"|"<<M<<"]"<<std::endl;
- }
- pointer = new GraphRes*[N];
- for (int i = 0; i < N; ++i)
- pointer[i] = new GraphRes[M];
-
- return pointer;
+ verbose_level = 2;
+ if (verbose_level == 2) {
+ std::cout << (current_repeat+1) << " epoch=" << (current_epoch+1)
+ << " cost=" << cost
+ << " novel=" << novel
+ << " rel_loss=" << cost/novel
+ << std::endl;
}
- //===================
- GraphRes* allocate_1Dmatrix(int N)
- {
- GraphRes *pointer;
+ current_epoch++;
+ }
- if(N > 0){
+ //====== 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);
+ }
- pointer = new GraphRes[N];
-
- }else {
-
- pointer = NULL;
- }
-
- return pointer;
-
+ // Allocate memory
+ double** allocate_2Dmatrix_double(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;
+ }
+
+ // Allocate memory
+ GraphRes** allocate_2Dmatrix(int N, int M) {
+ GraphRes **pointer;
- //==============================================
- // De-Allocate memory to prevent memory leak
- void delete_2Dmatrix_double(double **pointer, int N){
-
- if (pointer != NULL){
-
- for (int i = 0; i < N; ++i){
- delete [] pointer[i];
- }
- delete [] pointer;
- }
+ if (verbose_level == 2){
+ std::cout<< "["<<N<<"|"<<M<<"]"<<std::endl;
}
- void delete_2Dmatrix(GraphRes **pointer, int N){
+ pointer = new GraphRes*[N];
+ for (int i = 0; i < N; ++i)
+ pointer[i] = new GraphRes[M];
- if (pointer != NULL){
+ return pointer;
+ }
- for (int i = 0; i < N; ++i){
- delete [] pointer[i];
- }
- delete [] pointer;
- }
+ GraphRes* allocate_1Dmatrix(int N) {
+ GraphRes *pointer;
+ if(N > 0){
+ pointer = new GraphRes[N];
+ }else {
+ pointer = NULL;
}
- //====================
- void delete_1Dmatrix(GraphRes *pointer){
+ return pointer;
+ }
- delete [] pointer;
- }
+ // De-Allocate memory to prevent memory leak
- //===========================================
- double get_rel_loss(){
-
- return CumulativeRelativeLoss ;
+ void delete_2Dmatrix_double(double **pointer, int N) {
+ if (pointer != NULL){
+ for (int i = 0; i < N; ++i){
+ delete [] pointer[i];
+ }
+ delete [] pointer;
}
+ }
- //===========================================
- double get_rel_loss_err(){
+ void delete_2Dmatrix(GraphRes **pointer, int N) {
+ if (pointer != NULL){
- return CRLsquare ;
+ for (int i = 0; i < N; ++i){
+ delete [] pointer[i];
+ }
+ delete [] pointer;
}
+ }
+ void delete_1Dmatrix(GraphRes *pointer) {
+ delete [] pointer;
+ }
+ double get_rel_loss() {
+ return CumulativeRelativeLoss ;
+ }
- //==================================================================================
- void evolve_to_target_and_save(int istart, int iend, double* storage, int* counters){
+ double get_rel_loss_err() {
+ return CRLsquare ;
+ }
- double ALOT=100000000000.;
- double ratio = 0.0;
- int check = 0;
+ void evolve_to_target_and_save(int istart, int iend, double* storage, int* counters) {
+ double ALOT=100000000000.;
+ double ratio = 0.0;
- reset_world();
+ reset_world();
+ for (int k = istart; k < iend; k++){
+ for(int i=0; i< N_epochs && current_novelty < TargetNovelty; i++){
+ if(ratio < max_loss) {
+ update_world();
+ }
+ else{
+ break;
+ }
+ ratio = current_loss/current_novelty;
+ }
+ if (ratio < max_loss) {
+ storage[k]=current_loss/current_novelty;
+ }
+ else{
+ storage[k]=ALOT;
+ }
+ counters[k]=1;
+ reset_world();
+ }
+ }
- for (int k = istart; k < iend; k++){
+ double Omax_loss_value(int tn) {
+ if( tn == 58 ) return 128.286;
+ else if( tn == 108 ) return 131.866;
+ else if( tn == 158 ) return 135.551;
+ else if( tn == 208 ) return 139.694;
+ else if( tn == 258 ) return 144.163;
+ else if( tn == 308 ) return 148.967;
+ else if( tn == 358 ) return 154.201;
+ else if( tn == 408 ) return 159.962;
+ else if( tn == 458 ) return 166.441;
+ else if( tn == 508 ) return 173.655;
+ else if( tn == 558 ) return 181.921;
+ else if( tn == 608 ) return 191.246;
+ else if( tn == 658 ) return 202.150;
+ else if( tn == 708 ) return 215.197;
+ else if( tn == 758 ) return 202.150; // Verify with Andrey. Same as TargetNovelty of 658
+ else if( tn == 808 ) return 251.698;
+ else if( tn == 858 ) return 279.201;
+ else if( tn == 908 ) return 320.112;
+ else if( tn == 958 ) return 394.774;
+ else if( tn == 1008 ) return 1052.38; // Huge number here. Why?
+ else return 10000.; /* Pray that this does not occur. Ask Andrey what to do */
+ }
- for(int i=0; i< N_epochs && current_novelty < TargetNovelty; i++){
-
- if(ratio < max_loss_value((int)TargetNovelty)){
- update_world();
- }
- else{
- check = 1;
- break;
- }
- ratio = current_loss/current_novelty;
- }
- if( check ){
- storage[k]=ALOT;
- }
- else{
- storage[k]=current_loss/current_novelty;
- }
- counters[k]=1;
-
- reset_world();
- }
-
+ double max_loss_value(int tn){
+ switch(tn) {
+ case 58: return 128.286;
+ case 108: return 131.866;
+ case 158: return 135.551;
+ case 208: return 139.694;
+ case 258: return 144.163;
+ case 308: return 148.967;
+ case 358: return 154.201;
+ case 408: return 159.962;
+ case 458: return 166.441;
+ case 508: return 173.655;
+ case 558: return 181.921;
+ case 608: return 191.246;
+ case 658: return 202.150;
+ case 708: return 215.197;
+ case 758: return 202.150; // Verify with Andrey. Same as TargetNovelty of 658
+ case 808: return 251.698;
+ case 858: return 279.201;
+ case 908: return 320.112;
+ case 958: return 394.774;
+ case 1008: return 1052.38; // Huge number here. Why?
+ default: return 10000.; /* Pray that this does not occur. Ask Andrey what to do */
}
+ }
- //==============================================
- double Omax_loss_value(int tn){
+ int get_reruns(void) {
+ return N_repeats;
+ }
- if( tn == 58 ) return 128.286;
- else if( tn == 108 ) return 131.866;
- else if( tn == 158 ) return 135.551;
- else if( tn == 208 ) return 139.694;
- else if( tn == 258 ) return 144.163;
- else if( tn == 308 ) return 148.967;
- else if( tn == 358 ) return 154.201;
- else if( tn == 408 ) return 159.962;
- else if( tn == 458 ) return 166.441;
- else if( tn == 508 ) return 173.655;
- else if( tn == 558 ) return 181.921;
- else if( tn == 608 ) return 191.246;
- else if( tn == 658 ) return 202.150;
- else if( tn == 708 ) return 215.197;
- else if( tn == 758 ) return 202.150; // Verify with Andrey. Same as TargetNovelty of 658
- else if( tn == 808 ) return 251.698;
- else if( tn == 858 ) return 279.201;
- else if( tn == 908 ) return 320.112;
- else if( tn == 958 ) return 394.774;
- else if( tn == 1008 ) return 1052.38; // Huge number here. Why?
- else return 10000.; /* Pray that this does not occur. Ask Andrey what to do */
+ 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.;
}
- double max_loss_value(int tn){
+ }
- switch(tn) {
- case 58: return 128.286;
- case 108: return 131.866;
- case 158: return 135.551;
- case 208: return 139.694;
- case 258: return 144.163;
- case 308: return 148.967;
- case 358: return 154.201;
- case 408: return 159.962;
- case 458: return 166.441;
- case 508: return 173.655;
- case 558: return 181.921;
- case 608: return 191.246;
- case 658: return 202.150;
- case 708: return 215.197;
- case 758: return 202.150; // Verify with Andrey. Same as TargetNovelty of 658
- case 808: return 251.698;
- case 858: return 279.201;
- case 908: return 320.112;
- case 958: return 394.774;
- case 1008: return 1052.38; // Huge number here. Why?
- default: return 10000.; /* Pray that this does not occur. Ask Andrey what to do */
+ 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);
- //==============================================
- int get_reruns(void){
- return N_repeats;
+ if(verbose_level==1){
+ std::cout << std::endl;
}
-
- //==============================================
- 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.;
- }
+ 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){
- //==============================================
- void evolve_to_target(){
+ if (position < 0 || position > 4) {return 0;}
- reset_world();
- if (beta < -1. || gamma < -1.){
- CumulativeRelativeLoss = 100000000000.;
- CRLsquare = 0.;
- return;
- }
+ else {
-
- 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));
-
+ 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;
+ }
}
-
-
- //================================================================
- 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
-
+ return 0;
+ }
};
-//============================================================
-
-#ifdef P_DISPATCH
-std::pair<double,double> multi_loss(dispatch_group_t group,
- Universe* un[],
- dispatch_queue_t* CustomQueues,
- double* Results,
- int* Counters,
- double* params){
-#else
std::pair<double,double> multi_loss(Universe* un[],
double* Results,
int* Counters,
double* params){
-#endif
+ int N = un[0]->get_reruns();
+ int step = (int)floor((double)N/(double)(Nworkers)); // FIXME: Andrey: please check change in cast grouping and use of floor
+ int istart=0;
+ int iend = istart+step;
- int N = un[0]->get_reruns();
- int step = (int)floor((double)N/(double)(Nworkers)); // FIXME: Andrey: please check change in cast grouping and use of floor
- int istart=0;
- int iend = istart+step;
+ double Loss=0., LossSquare=0.;
- double Loss=0., LossSquare=0.;
+ // timeval startTime, endTime;
+ // double elapsedTime;
+ gettimeofday(&startTime, NULL);
- timeval startTime, endTime;
- double elapsedTime;
- gettimeofday(&startTime, NULL);
+ if ( N > NUniverse ) {
+ std::cerr << "Error: Number of reruns=" << N << " is greater than max NUniverse=" << NUniverse << "\n";
+ exit(1);
+ }
- if ( N > NUniverse ) {
- std::cerr << "Error: Number of reruns=" << N << " is greater than max NUniverse=" << NUniverse << "\n";
- exit(1);
+ for(int i=0; i<N; i++){
+ for(int j=0; j<5; j++){
+ un[i]->set_parameter(params[j],j);
}
+ }
- for(int i=0; i<N; i++){
- for(int j=0; j<5; j++){
- un[i]->set_parameter(params[j],j);
- }
- }
+ int i;
-#ifdef P_DISPATCH
- for(int i=0; i<Nworkers; i++){
+ // Execute rerun loop in parallel
- dispatch_group_async(group, CustomQueues[i], ^{
+ //#pragma omp parallel for private (i)
+ for(i=0; i<N; i++){
+ un[i]->evolve_to_target_and_save(i, i+1, Results, Counters);
+ }
- // 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);
- });
+ for (int i=0; i<N; i++){
+ Loss+=Results[i]/(double)N;
+ LossSquare+=Results[i]*Results[i]/(double)N;
+ }
- std::cout << "queued: i=" << i << " N=" << N << " istart=" << istart << " iend=" << iend << "\n";
- // istart += step;
- // iend = min(istart+step,N);
+ double two_std = ((LossSquare - Loss*Loss)/(double)N);
- }
- dispatch_group_wait(group, DISPATCH_TIME_FOREVER);
- //dispatch_release(group);
-#else
- int i;
+ two_std = 2.*sqrt(two_std);
+ std::pair<double,double> Res;
+ Res.first=Loss;
+ Res.second=two_std;
- // Execute rerun loop in parallel
+ 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 << ", target=" << un[0]->get_target() << ") elapsed time: " << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
- #pragma omp parallel for private (i)
- for(i=0; i<N; i++){
- un[i]->evolve_to_target_and_save(i, i+1, Results, Counters);
- }
-#endif
-
- for (int i=0; i<N; i++){
- Loss+=Results[i]/(double)N;
- LossSquare+=Results[i]*Results[i]/(double)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 << ", target=" << un[0]->get_target() << ") elapsed time: " << elapsedTime << " seconds " << elapsedTime/60. << " minutes\n\n";
-
- return Res;
+ return Res;
}
-//============================================================
-
-//============================================================
-#ifdef P_DISPATCH
-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){
-#else
void multi_annealing(Universe* un[],
double T_start, double T_end,
double Target_rejection,
@@ -1527,190 +1230,173 @@
int* Counters,
double* params0,
double annealing_cycles){
-#endif
- //.................................
- // re-implement annealing
+ //.................................
+ // 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);
+ 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
+ // 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];
+ 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;
- }
+ for(int i=0;i<5;i++){
+ dx[i] = starting_jump;
+ }
- // establish the current value
- std::pair<double,double>Res;
+ // establish the current value
+ std::pair<double,double>Res;
-#ifdef P_DISPATCH
- Res = multi_loss(group, un, CustomQueues, Results, Counters, x);
-#else
- Res = multi_loss( un, Results, Counters, x);
-#endif
- std::cout << "Returned from initial multi_loss:" << std::endl;
- std::cout << Res.first << " +- " << Res.second << std::endl;
+ Res = multi_loss( un, Results, Counters, x);
- if ( operation == 'm' ) {
- FILE *f;
- int N = un[0]->get_reruns();
+ std::cout << "Returned from initial multi_loss:" << std::endl;
+ std::cout << Res.first << " +- " << Res.second << std::endl;
- f = fopen("multi_loss.data","w");
- for(int i=0; i<N; i++) {
- fprintf(f,"%.20e\n",Results[i]);
- }
- fclose(f);
- exit(0);
+ 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;
+ curr_x = Res.first;
+ curr_err = Res.second;
- // optimization cycle
+ // optimization cycle
- for(int i=0; i<annealing_cycles; i++){
+ 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;
+ 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){
+ 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 k=0; k<5; k++){
- rejection[k]/=(double)cycle;
+ 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;
- 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;
- }
+ 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(un, Results, Counters, x);
+ std::cout << Res.first << " +- " << Res.second << std::endl;
- for (int j=0; j<5; j++){
+ ratio = min(1.,exp(-(Res.first-curr_x)/temperature));
+ r = rand()/(double)(pow(2.,31)-1.);
+ std::cout << r << " vs " << ratio << std::endl;
- ///////////////////////////////
- if (FIX_VARIABLES==0 || var_fixed[j]==0){
+ double ALOT=100000000000.;
+ if (Res.first < ALOT)
+ {
+ ofstream filestr;
+ filestr.open ("best_opt_some.txt", ofstream::app);
- // get new value of x[j]
- double x_hold=x[j];
- x_tmp = get_new_x(x[j],dx[j]);
- x[j]=x_tmp;
+ // >> i/o operations here <<
+ filestr
- 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);
- }
+ << "N, " << i << ", " << j << ", " << dx[j] << ", " << rejection[j] << ", |, " // FIXME: MW-DEBUGGING
-#ifdef P_DISPATCH
- Res = multi_loss(group, un, CustomQueues, Results, Counters, x);
-#else
- Res = multi_loss( un, Results, Counters, x);
-#endif
- std::cout << Res.first << " +- " << Res.second << std::endl;
+ << 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";
- ratio = min(1.,exp(-(Res.first-curr_x)/temperature));
- r = rand()/(double)(pow(2.,31)-1.);
- std::cout << r << " vs " << ratio << std::endl;
+ filestr.close();
- double ALOT=100000000000.;
- if (Res.first < ALOT)
- {
- ofstream filestr;
+ filestr.open ("max_dist.txt", ofstream::app);
- filestr.open ("best_opt_some.txt", ofstream::app);
+ // >> i/o operations here <<
+ filestr << max_dist << ",\n";
- // >> i/o operations here <<
- filestr
+ filestr.close();
- << "N, " << i << ", " << j << ", " << dx[j] << ", " << rejection[j] << ", |, " // FIXME: MW-DEBUGGING
+ FILE *bf;
+ bf = fopen("bestdb.txt","a");
+ fprintf(bf, "N %2d %2d %10.5f %5.2f | %5.2f %10.5f [ %5.2f %5.2f %10.5f %10.5f %10.5f ] %10.5f\n", i, j, dx[j], rejection[j],
+ 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);
+ fclose(bf);
+ }
- << 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";
+ if (r > ratio){
- filestr.close();
-
-
- filestr.open ("max_dist.txt", ofstream::app);
-
- // >> i/o operations here <<
- filestr << max_dist << ",\n";
-
- filestr.close();
-
- FILE *bf;
- bf = fopen("bestdb.txt","a");
- fprintf(bf, "N %2d %2d %10.5f %5.2f | %5.2f %10.5f [ %5.2f %5.2f %10.5f %10.5f %10.5f ] %10.5f\n", i, j, dx[j], rejection[j],
- 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);
- fclose(bf);
- }
-
-
- if (r > ratio){
-
- std::cout << " "<< (i+1) << ","<< (j)
+ std::cout << " "<< (i+1) << ","<< (j)
<<" "<< (i+1) << " Did not accept "
<< x_tmp << "(" << j << ")" << std::endl;
- std::cout << un[0]->get_parameter(0)
+ 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);
- }
+ 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.;
- }
+ //set_parameter(x[j], j);
+ rejection[j]+=1.;
+ }
- else {
+ else {
- curr_x = Res.first;
- curr_err = Res.second;
- x[j] = x_tmp;
+ 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);
- }
+ for(int w=0; w<Nworkers; w++){
+ un[w]->set_parameter(x[j], j);
+ }
- std::cout << (i+1) << string_wrap((string) " Rejection counts: ", 8)
+ 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) << " "
@@ -1718,7 +1404,7 @@
<< wrap_double(rejection[4],6) << " "
<< std::endl << std::endl;
- std::cout << " "<< (i+1) <<","<< (j)
+ std::cout << " "<< (i+1) <<","<< (j)
<<" "
<< string_wrap((string) "***** Did accept! ", 3)
<< wrap_double(un[0]->get_parameter(0),2) << " "
@@ -1727,238 +1413,176 @@
<< 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[3] = {"Operation", "Nworkers", "initSeed"};
- 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[3] = {"Operation", "Nworkers", "initSeed"};
+ int params1[4] = {300, 50, 1000, 10};
+ int params3[5] = { 0, 0, 0, 0, 0};
- 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};
- // 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[NUniverse];
+ static double* Results;
+ static int* Counters;
- int verbose_level = 2;
- const std::string one="one", two="two";
- static Universe* un[NUniverse];
-#ifdef P_DISPATCH
- static dispatch_queue_t CustomQueues[MAXNworkers];
-#endif
- static double* Results;
- static int* Counters;
+ timeval t1, t2;
+ double elapsedTime;
- timeval t1, t2;
- double elapsedTime;
- // start timer
- gettimeofday(&t1, NULL);
+ gettimeofday(&t1, NULL); // start timer
-
- 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);
+ 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]= (int)round(atof(argv[nArg])); // FIXME: Andrey: please verify that round() is correct.
+ var_fixed[nArg-17]= (int)round(atof(argv[nArg])); // FIXME: ditto
+ 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;
+ }
+ if (nArg == 24 ){
+ initSeed = atoi(argv[nArg]);
+ std::cout << par_names4[2] << ": " << initSeed << std::endl;
+ }
}
- 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]= (int)round(atof(argv[nArg])); // FIXME: Andrey: please verify that round() is correct.
- var_fixed[nArg-17]= (int)round(atof(argv[nArg])); // FIXME: ditto
- 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;
- }
- if (nArg == 24 ){
- initSeed = atoi(argv[nArg]);
- std::cout << par_names4[2] << ": " << initSeed << 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<NUniverse; i++){
+ un[i] = new Universe((char *)filename,n_ep,n_st,
+ (int)n_rep,
+ identify_failed, target, i2string(i));
+ }
- for (int j=0; j<5; j++){
+ 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;
+ }
- cout << j << " | " << var_fixed[j] << " (fixed) \n";
- }
+ //srand(time(0));
+ //srandomdev();
- 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];
+ if ( initSeed != 0.0 ) {
+ srand(initSeed);
+ }
+ else {
+ timeval t;
+ gettimeofday(&t, NULL);
+ srand(t.tv_usec);
+ }
- //...............................
-
- for(int i=0; i<NUniverse; i++){
- un[i] = new Universe((char *)filename,n_ep,n_st,
- (int)n_rep,
- identify_failed, target, i2string(i));
-#ifdef P_DISPATCH
- CustomQueues[i] = dispatch_queue_create(i2char(i), NULL);
-#endif
+ {
+ double r=0;
+ for (int j=0; j<100; j++){
+ r = rand()/(double)(pow(2.,31)-1.);
+ std::cout << r << " ";
}
+ std::cout << "\n";
+ }
- //...............................
- if(n_rep > 0){
+ //random initiation of starting parameters
- 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;
-
+ 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;
+ }
}
- //...............................
- //srand(time(0));
- //srandomdev();
+ }
- if ( initSeed != 0.0 ) {
- srand(initSeed);
- }
- else {
- timeval t;
- gettimeofday(&t, NULL);
- srand(t.tv_usec);
- }
+ double T_start=params2[0], T_end=params2[1], Target_rejection=params2[3], starting_jump=params2[4];
+ int Annealing_repeats = (int) params2[2];
- {
- double r=0;
- for (int j=0; j<100; j++){
+ // print_memory_status();
+ multi_annealing(un, T_start, T_end, Target_rejection, Annealing_repeats,
+ starting_jump, Results, Counters, params0, Annealing_repeats);
+ // stop timer
- r = rand()/(double)(pow(2.,31)-1.);
- std::cout << r << " ";
- }
- std::cout << "\n";
- }
- //random initiation of starting parameters
+ gettimeofday(&t2, NULL);
- if (range > 0.){
+ // compute and print the elapsed time in millisec
- for (int i=0; i < 5; i++){
+ 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 << "\n*** optimizer completed, elapsed time=" << elapsedTime << " seconds " << elapsedTime/60. << " minutes)\n\n";
- if (params0[i]==-100.){
+ for(int i=0; i<Nworkers; i++){
+ delete un[i];
+ }
- 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];
-
- // print_memory_status();
-
-#ifdef P_DISPATCH
- 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);
- //.............................
-#else
- multi_annealing( un, T_start, T_end, Target_rejection, Annealing_repeats,
- starting_jump, Results, Counters, params0, Annealing_repeats);
-#endif
-
-
- // 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 << "\n*** optimizer completed, elapsed time=" << 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;
-
-
-
+ if(n_rep > 0){
+ delete [] Results;
+ delete [] Counters;
+ }
+ return 0;
}
Added: SwiftApps/SciColSim/maxloss.cpp
===================================================================
--- SwiftApps/SciColSim/maxloss.cpp (rev 0)
+++ SwiftApps/SciColSim/maxloss.cpp 2012-12-23 04:33:21 UTC (rev 6110)
@@ -0,0 +1,101 @@
+#include <fstream>
+#include <iostream>
+#include <stdio.h>
+#include <time.h>
+#include <ctime>
+#include <algorithm>
+#include <string>
+
+#include <sys/param.h>
+#include <sys/time.h>
+#include <sys/types.h>
+#include <unistd.h>
+#include <stdlib.h>
+
+#include <math.h>
+
+using namespace std;
+
+void set_max_loss(long long v, long long e, long long t) {
+
+ // Based on formulas from random_analytical.pdf
+
+ // t-=100;
+ double E = e;
+ double V = v;
+ double T = t;
+
+ double max_loss;
+ double sum, ERLt, VarRLt, absVarRLt;
+
+ // (9): E[RLt] = (1/t) * SUM(i=0 to T of: (V*(V-1) / (2 * (E-i))
+
+ sum = 0;
+ for(int i=0;i<t;i++) {
+ sum += ((double)(v*(v-1))) / ((double)(2*(e-i)));
+ }
+ ERLt = (1.0/(double) t) * sum;
+
+ // (12): Var[RLt] = 1 / (T**2) * SUM( i=0 to T of: ((V*(V-1))/2) - E + 1 ) / ((E-i)**2)
+
+ sum = 0;
+ for(int i=0;i<t;i++) {
+ sum +=
+ ( (((double)(v*(v-1)))/ 2.0) - (double)e + (double)i )
+ /
+ (double)((e-i)*(e-i))
+ ;
+ if ( (double)((e-i)*(e-i)) == 0.0 ) {
+ printf( "zero denom: %d %d\n", e, i);
+ }
+ }
+ int tmp = t*t;
+ VarRLt = (1.0/(double)((t) * t)) * sum;
+ if (VarRLt < 0.0) absVarRLt=0.0;
+ else absVarRLt = VarRLt;
+
+ // (9) + 3sigma = E[RLt] + 3 * SQRT(Var)
+ max_loss = ERLt + (3.0 * sqrt(absVarRLt));
+ printf("%12d %12.3f %12.8e %12.3f\n", t, ERLt, VarRLt, max_loss);
+}
+
+struct Graph {
+ const char *n;
+ int v;
+ int e;
+} G[] =
+
+{
+ "movie", 500, 1008,
+ "big.001", 1857, 1312,
+ "big.002", 3094, 2683,
+ "big.005", 5405, 6776,
+ "big.01", 7765, 13421,
+ "big.10", 19281, 134594,
+ "big.20", 23258, 269133,
+ "big.30", 25484, 403401,
+ "big.40", 26821, 537106,
+ "big.50", 27774, 671179,
+ "big.60", 28482, 804774,
+ "big.70", 29055, 938061,
+ "big.80", 29487, 1072415,
+ "big.90", 29779, 1205741,
+ "big", 30060, 1338753,
+ "", 0, 0
+};
+
+int main()
+{
+ int E, V, T;
+ int i=0;
+ while (G[i].v > 0) {
+ cout << "\nGraph " << G[i].n << " V=" << G[i].v << " E=" << G[i].e << endl;
+ printf("Pct T ERLt VarRLt max_loss\n");
+ for(double p = 0.1; p<=1.0; p+=0.1) {
+ printf("%.2f",p);
+ set_max_loss(G[i].v, G[i].e, (int)((G[i].e * p)+0.5));
+ }
+ i++;
+ }
+}
+
Added: SwiftApps/SciColSim/maxloss.py
===================================================================
--- SwiftApps/SciColSim/maxloss.py (rev 0)
+++ SwiftApps/SciColSim/maxloss.py 2012-12-23 04:33:21 UTC (rev 6110)
@@ -0,0 +1,39 @@
+#! /usr/bin/env python
+
+n = 0
+v = 1
+e = 2
+
+graphs = [
+ [ "movie", 500, 1008 ],
+ [ "big.001", 1857, 1312 ],
+ [ "big.002", 3094, 2683 ],
+ [ "big.005", 5405, 6776 ],
+ [ "big.01", 7765, 13421 ],
+ [ "big.10", 19281, 134594 ],
+ [ "big.20", 23258, 269133 ],
+ [ "big.30", 25484, 403401 ],
+ [ "big.40", 26821, 537106 ],
+ [ "big.50", 27774, 671179 ],
+ [ "big.60", 28482, 804774 ],
+ [ "big.70", 29055, 938061 ],
+ [ "big.80", 29487, 1072415 ],
+ [ "big.90", 29779, 1205741 ],
+ [ "big", 30060, 1338753 ],
+ ]
+
+for G in graphs:
+ N = G[n]
+ V = G[v]
+ E = G[e]
+ print "\nGraph %s: V=%d E=%d\n" % (N,V,E)
+ for pct in range(1,11):
+ Tmax = int(E * (pct/10.0))
+ tl = 0.
+ vl = 0.
+ for t in range(Tmax):
+ tl = tl + float(V*(V-1.))/float(2.*(E-t))
+ vl = vl + float(V*(V-1.)/2. - E + t)/float((E-t)*(E-t))
+ tl /= float(Tmax)
+ vl /= float(Tmax)*float(Tmax)
+ print pct, Tmax, tl, vl
Property changes on: SwiftApps/SciColSim/maxloss.py
___________________________________________________________________
Added: svn:executable
+ *
Added: SwiftApps/SciColSim/maxloss_ar.py
===================================================================
--- SwiftApps/SciColSim/maxloss_ar.py (rev 0)
+++ SwiftApps/SciColSim/maxloss_ar.py 2012-12-23 04:33:21 UTC (rev 6110)
@@ -0,0 +1,15 @@
+V = [500]
+E = [1008]
+
+curr = 0
+
+
+for Tmax in range(101,1009,101):
+ tl = 0.
+ vl = 0.
+ for t in range(Tmax):
+ tl = tl + float(V[0]*(V[0]-1.))/float(2.*(E[0]-t))
+ vl = vl + float(V[0]*(V[0]-1.)/2. - E[0] + t)/float((E[0]-t)*(E[0]-t))
+ tl /= float(Tmax)
+ vl /= float(Tmax)*float(Tmax)
+ print Tmax, tl, vl
Modified: SwiftApps/SciColSim/samplegraph.sh
===================================================================
--- SwiftApps/SciColSim/samplegraph.sh 2012-12-19 14:06:35 UTC (rev 6109)
+++ SwiftApps/SciColSim/samplegraph.sh 2012-12-23 04:33:21 UTC (rev 6110)
@@ -2,7 +2,7 @@
fraction=${1:-1.0}
-tmp=$(mktemp -t samplegraph)
+tmp=$(mktemp -t samplegraph.XXXX)
awk '
@@ -16,6 +16,7 @@
BEGIN {
getmsec="perl -MTime::HiRes=gettimeofday -e \"print int(1000*gettimeofday()).qq(\\n);\""
+ getmsec="date +%N"
getmsec | getline msec
close(getmsec)
_cliff_seed = (msec % 10000) / 10000
Modified: SwiftApps/SciColSim/testgraph.py
===================================================================
--- SwiftApps/SciColSim/testgraph.py 2012-12-19 14:06:35 UTC (rev 6109)
+++ SwiftApps/SciColSim/testgraph.py 2012-12-23 04:33:21 UTC (rev 6110)
@@ -138,12 +138,14 @@
endTarget = startTarget+1
incrTarget = 50
optimizerRepeats = 1
+ # evolveReruns = 1
evolveReruns = 1
annealingSteps = 1
- NWorkers = "2"
+ NWorkers = "8"
openmp = "OMP_NUM_THREADS=" + NWorkers
operation = "m" # n=normal, m=manual (runs 1 multi_loss call)
seed = "" # "123456"
+ app = "./openmp-optimizer";
app = "./graphsim";
# Ensure we dont pass new parameters to original optimizer versions
@@ -158,9 +160,7 @@
for target in range(startTarget,endTarget,incrTarget):
for i in range(optimizerRepeats):
- args = "rm -f bestdb.txt; " + \
- openmp + " " + "/usr/bin/time -l " + \
- app + " 0 0 0 0 0 " + str(target) + " 40000 20 " + str(evolveReruns) + \
+ args = "rm -f bestdb.txt;" + openmp +" " + "/usr/bin/time --verbose " + app + " 0 0 0 0 0 " + str(target) + " 40000 20 " + str(evolveReruns) + \
" 2 2 2. 0.01 " + str(annealingSteps) + " 0.3 2.3 0 0 0 0 0 " + operation + " " + NWorkers + " " + seed + \
" # >& out.T"+str(target)+".i"+str(i) + "; #mv bestdb.txt best.T" + str(target) + ".i" + str(i)
print("\n**** Calling optimizer: "+args+"\n")
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