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Commit 5e121a27 authored by kruger's avatar kruger
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Merge branch 'master' of git+ssh://easea-dev@atia/home/easea-dev/src

parents 1da585d6 f4256389
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Easea.h
View file @ 5e121a27
......@@ -27,6 +27,8 @@ Centre de Math
#define CUDA_FLAVOR_SO 0
#define CUDA_FLAVOR_MO 1
#define CUDA_FLAVOR_CMAES 2
#define CUDA_FLAVOR_GP 3
#define UNIX 1
#define WINDOWS 2
......
EaseaLex.l
View file @ 5e121a27
......@@ -1786,7 +1786,7 @@ int CEASEALexer::create(CEASEAParser* pParser, CSymbolTable* pSymTable)
strcat(sTemp,"CUDA.tpl");
else if(TARGET_FLAVOR == CUDA_FLAVOR_CMAES )
strcat(sTemp,"CMAES_CUDA.tpl");
else
else
strcat(sTemp,"CUDA_MO.tpl");
if (!(yyin = fpTemplateFile = fopen(sTemp, "r"))){
......@@ -1800,6 +1800,8 @@ int CEASEALexer::create(CEASEAParser* pParser, CSymbolTable* pSymTable)
strcat(sTemp,"STD.tpl");
else if (TARGET_FLAVOR == STD_FLAVOR_CMAES)
strcat(sTemp,"CMAES.tpl");
else if( TARGET_FLAVOR == CUDA_FLAVOR_GP )
strcat(sTemp,"CUDA_GP.tpl");
else
strcat(sTemp,"STD_MO.tpl");
if (!(yyin = fpTemplateFile = fopen(sTemp, "r"))){
......
EaseaParse.y
View file @ 5e121a27
......@@ -657,6 +657,10 @@ int main(int argc, char *argv[]){
TARGET=CUDA;
TARGET_FLAVOR = CUDA_FLAVOR_MO;
}
else if( !mystricmp(sTemp,"cuda_gp") ){
TARGET=STD;
TARGET_FLAVOR = CUDA_FLAVOR_GP;
}
else if (!mystricmp(sTemp,"std")) {
TARGET=STD;
......@@ -675,8 +679,12 @@ int main(int argc, char *argv[]){
TARGET_FLAVOR = CUDA_FLAVOR_CMAES;
}
else if (!mystricmp(sTemp,"v")) bVERBOSE=true;
else if (!mystricmp(sTemp,"tl")) bLINE_NUM_EZ_FILE=false;
else if (!mystricmp(sTemp,"tl")){
printf("yopyop true line\n");
bLINE_NUM_EZ_FILE=false;
}
else if (!mystricmp(sTemp,"path")) {
if (argv[++nParamNb][0]=='"') {
strcpy(sEZ_PATH,&(argv[nParamNb][1]));
......
dev/tgp_regression/data.csv deleted 100644 → 0
View file @ 1da585d6
77
0.05,0.61,0.3,5,27.2
0.05,0.61,0.3,15,44.9
0.05,0.61,0.3,30,59.3
0.05,0.61,0.3,45,68.7
0.05,0.61,0.3,75,80.1
0.05,0.61,0.3,120,87.2
0.05,0.61,0.3,180,94.2
0.05,0.86,0.61,5,27.2
0.05,0.86,0.61,15,41.6
0.05,0.86,0.61,30,56.4
0.05,0.86,0.61,45,64.7
0.05,0.86,0.61,75,74
0.05,0.86,0.61,120,81.4
0.05,0.86,0.61,180,86.2
0.05,0.76,0.76,5,8.7
0.05,0.76,0.76,15,21.3
0.05,0.76,0.76,30,33.9
0.05,0.76,0.76,45,42.9
0.05,0.76,0.76,75,57.3
0.05,0.76,0.76,120,65.5
0.05,0.76,0.76,180,69.2
0.06,0.86,0.61,5,17.7
0.06,0.86,0.61,15,30.1
0.06,0.86,0.61,30,43.9
0.06,0.86,0.61,45,52
0.06,0.86,0.61,75,64.7
0.06,0.86,0.61,120,74.6
0.06,0.86,0.61,180,82
0.25,0.86,0.61,5,8.5
0.25,0.86,0.61,15,20.9
0.25,0.86,0.61,30,33.6
0.25,0.86,0.61,45,44.6
0.25,0.86,0.61,75,56.6
0.25,0.86,0.61,120,68.2
0.25,0.86,0.61,180,76
0.35,0.76,0.76,5,5
0.35,0.76,0.76,15,14.9
0.35,0.76,0.76,30,28.6
0.35,0.76,0.76,45,37.3
0.35,0.76,0.76,75,51.3
0.35,0.76,0.76,120,63.6
0.35,0.76,0.76,180,72.1
0.5,0.86,0.61,5,5
0.5,0.86,0.61,15,14.5
0.5,0.86,0.61,30,27.6
0.5,0.86,0.61,45,36.4
0.5,0.86,0.61,75,52.3
0.5,0.86,0.61,120,64.3
0.5,0.86,0.61,180,72
0.5,1.52,0.76,5,10.5
0.5,1.52,0.76,15,20.4
0.5,1.52,0.76,30,34
0.5,1.52,0.76,45,46.4
0.5,1.52,0.76,75,61.2
0.5,1.52,0.76,120,76.2
0.5,1.52,0.76,180,80
0.5,1.07,0.76,5,10.5
0.5,1.07,0.76,15,19.7
0.5,1.07,0.76,30,32.3
0.5,1.07,0.76,45,42.3
0.5,1.07,0.76,75,57.1
0.5,1.07,0.76,120,70.8
0.5,1.07,0.76,180,76
0.5,0.94,0.76,5,9.1
0.5,0.94,0.76,15,18.4
0.5,0.94,0.76,30,31.7
0.5,0.94,0.76,45,40.3
0.5,0.94,0.76,75,54.1
0.5,0.94,0.76,120,67.8
0.5,0.94,0.76,180,74
0.5,0.76,0.76,5,9.5
0.5,0.76,0.76,15,17.3
0.5,0.76,0.76,30,28.3
0.5,0.76,0.76,45,37.2
0.5,0.76,0.76,75,49
0.5,0.76,0.76,120,61.7
0.5,0.76,0.76,180,66
dev/tgp_regression/tgp_regression.ez
View file @ 5e121a27
......@@ -10,6 +10,11 @@ __________________________________________________________*/
#include <errno.h>
#include <sstream>
#include <math.h>
#include <cutil.h>
#define OPERAND 0
......@@ -191,7 +196,7 @@ void toDotFile(GPNode* root, const char* baseFileName, int treeId){
std::ostringstream oss;
oss << baseFileName << "-" << treeId << ".gv";
FILE* outputFile = fopen(oss.str().c_str(),"w");
FILE* outputFile = fopen(oss.str().c_str(),"a");
if( !outputFile ){
perror("Opening file for outputing dot representation ");
exit(-1);
......@@ -356,9 +361,15 @@ GPNode* selectNode( GPNode* root, int* childId, int* depth){
fitnessCasesSetLength = load_data(&inputs,&outputs,"quadra_reg_data.csv");
cout << "number of point in fitness cases set : " << fitnessCasesSetLength << endl;
INSTEAD_EVAL_STEP=true;
// Adding another stopping, as we are minimizing, the goal is 0
CGoalCriterion* gc = new CGoalCriterion(0,true);
EA->stoppingCriteria.push_back(gc);
// Here starts the CUDA parts
cudaSetDevice(1); // on GTX295 ;) we want to use the second card for computation
}
\end
......@@ -393,7 +404,11 @@ GPNode* selectNode( GPNode* root, int* childId, int* depth){
\end
\Instead evaluation function:
cout << "yopyop" << endl;
{
for( int i=0 ; i<popSize ; i++ ){
cout << population[i] << endl;
}
}
\end
\User classes :
......@@ -627,7 +642,6 @@ GenomeClass {
\end
\User Makefile options:
CPPFLAGS+=
\end
\Default run parameters : // Please let the parameters appear in this order
......
examples/weierstrass_gpu/weierstrass.ez
View file @ 5e121a27
/*_________________________________________________________
Test functions
log normal adaptive mutation
Selection operator: Tournament
__________________________________________________________*/
\User declarations :
#define SIZE 100
#define X_MIN -1.
#define X_MAX 1.
#define ITER 120
#define Abs(x) ((x) < 0 ? -(x) : (x))
#define MAX(x,y) ((x)>(y)?(x):(y))
#define MIN(x,y) ((x)<(y)?(x):(y))
#define SIGMA 1. /* mutation parameter */
#define PI 3.141592654
float pMutPerGene=0.1;
struct gpuOptions;
\end
\User functions:
//fitness function
#include <math.h>
__device__ __host__ inline float Weierstrass(float *x, int n) // Weierstrass multimidmensionnel h = 0.25
{
float res = 0.;
float val[SIZE];
float b=2.;
float h = 0.25;
for (int i = 0;i<n; i++) {
val[i] = 0.;
for (int k=0;k<ITER;k++)
val[i] += pow(b,-(float)k*h) * sin(pow(b,(float)k)*x[i]);
res += Abs(val[i]);
}
return (res);
}
float gauss()
/* Generates a normally distributed random value with variance 1 and 0 mean.
Algorithm based on "gasdev" from Numerical recipes' pg. 203. */
{
int iset = 0;
float gset = 0.0;
float v1 = 0.0, v2 = 0.0, r = 0.0;
float factor = 0.0;
if (iset) {
iset = 0;
return gset;
}
else {
do {
v1 = (float)random(0.,1.) * 2.0 - 1.0;
v2 = (float)random(0.,1.) * 2.0 - 1.0;
r = v1 * v1 + v2 * v2;
}
while (r > 1.0);
factor = sqrt (-2.0 * log (r) / r);
gset = v1 * factor;
iset = 1;
return (v2 * factor);
}
}
\end
\GenomeClass::display:
for(int i=0; i<SIZE; i++){
cout << Genome.x[i] << ":" << Genome.sigma[i] << "|";
}
\end
\Before everything else function:
\end
\After everything else function:
\end
\At the beginning of each generation function:
//cout << "At the beginning of each generation function called" << endl;
\end
\At the end of each generation function:
//cout << "At the end of each generation function called" << endl;
\end
\At each generation before reduce function:
//cout << "At each generation before replacement function called" << endl;
\end
\User classes :
GenomeClass {
float x[SIZE];
float sigma[SIZE]; // auto-adaptative mutation parameter
}
\end
\GenomeClass::initialiser : // "initializer" is also accepted
for(int i=0; i<SIZE; i++ ) {
Genome.x[i] = (float)random(X_MIN,X_MAX);
Genome.sigma[i]=(float)random(0.,0.5);
}
\end
\GenomeClass::crossover :
for (int i=0; i<SIZE; i++)
{
float alpha = (float)random(0.,1.); // barycentric crossover
child.x[i] = alpha*parent1.x[i] + (1.-alpha)*parent2.x[i];
}
\end
\GenomeClass::mutator : // Must return the number of mutations
int NbMut=0;
float pond = 1./sqrt((float)SIZE);
for (int i=0; i<SIZE; i++)
if (tossCoin(pMutPerGene)){
NbMut++;
Genome.sigma[i] = Genome.sigma[i] * exp(SIGMA*pond*(float)gauss());
Genome.sigma[i] = MIN(0.5,Genome.sigma[0]);
Genome.sigma[i] = MAX(0.,Genome.sigma[0]);
Genome.x[i] += Genome.sigma[i]*(float)gauss();
Genome.x[i] = MIN(X_MAX,Genome.x[i]); // pour eviter les depassements
Genome.x[i] = MAX(X_MIN,Genome.x[i]);
}
return NbMut;
\end
\GenomeClass::evaluator : // Returns the score
float Score= 0.0;
Score= Weierstrass(Genome.x, SIZE);
return Score;
\end
\User Makefile options:
CPPFLAGS+=
\end
\Default run parameters : // Please let the parameters appear in this order
Number of generations : 100 // NB_GEN
Time limit: 0 // In seconds, 0 to deactivate
Population size : 3840 //POP_SIZE
Offspring size : 3840 // 40%
Mutation probability : 1 // MUT_PROB
Crossover probability : 1 // XOVER_PROB
Evaluator goal : minimise // Maximise
Selection operator: Tournament 2.0
Surviving parents: 3840//percentage or absolute
Surviving offspring: 3840
Reduce parents operator: Tournament 2.0
Reduce offspring operator: Tournament 2
Final reduce operator: Tournament 2
Elitism: Strong //Weak or Strong
Elite: 5
Print stats:1 //Default: 1
Plot stats:0 //Default: 0
Generate cvs stats file:0
Generate gnuplot script:0
Generate R script:0
\end
/*_________________________________________________________
Test functions
log normal adaptive mutation
Selection operator: Tournament
__________________________________________________________*/
\User declarations :
#define SIZE 100
#define X_MIN -1.
#define X_MAX 1.
#define ITER 120
#define Abs(x) ((x) < 0 ? -(x) : (x))
#define MAX(x,y) ((x)>(y)?(x):(y))
#define MIN(x,y) ((x)<(y)?(x):(y))
#define SIGMA 1. /* mutation parameter */
#define PI 3.141592654
float pMutPerGene=0.1;
struct gpuOptions;
\end
\User functions:
//fitness function
#include <math.h>
__device__ __host__ inline float Weierstrass(float *x, int n) // Weierstrass multimidmensionnel h = 0.25
{
float res = 0.;
float val[SIZE];
float b=2.;
float h = 0.25;
for (int i = 0;i<n; i++) {
val[i] = 0.;
for (int k=0;k<ITER;k++)
val[i] += pow(b,-(float)k*h) * sin(pow(b,(float)k)*x[i]);
res += Abs(val[i]);
}
return (res);
}
float gauss()
/* Generates a normally distributed random value with variance 1 and 0 mean.
Algorithm based on "gasdev" from Numerical recipes' pg. 203. */
{
int iset = 0;
float gset = 0.0;
float v1 = 0.0, v2 = 0.0, r = 0.0;
float factor = 0.0;
if (iset) {
iset = 0;
return gset;
}
else {
do {
v1 = (float)random(0.,1.) * 2.0 - 1.0;
v2 = (float)random(0.,1.) * 2.0 - 1.0;
r = v1 * v1 + v2 * v2;
}
while (r > 1.0);
factor = sqrt (-2.0 * log (r) / r);
gset = v1 * factor;
iset = 1;
return (v2 * factor);
}
}
\end
\GenomeClass::display:
for(int i=0; i<SIZE; i++){
cout << Genome.x[i] << ":" << Genome.sigma[i] << "|";
}
\end
\Before everything else function:
\end
\After everything else function:
\end
\At the beginning of each generation function:
//cout << "At the beginning of each generation function called" << endl;
\end
\At the end of each generation function:
//cout << "At the end of each generation function called" << endl;
\end
\At each generation before reduce function:
//cout << "At each generation before replacement function called" << endl;
\end
\User classes :
GenomeClass {
float x[SIZE];
float sigma[SIZE]; // auto-adaptative mutation parameter
}
\end
\GenomeClass::initialiser : // "initializer" is also accepted
for(int i=0; i<SIZE; i++ ) {
Genome.x[i] = (float)random(X_MIN,X_MAX);
Genome.sigma[i]=(float)random(0.,0.5);
}
\end
\GenomeClass::crossover :
for (int i=0; i<SIZE; i++)
{
float alpha = (float)random(0.,1.); // barycentric crossover
child.x[i] = alpha*parent1.x[i] + (1.-alpha)*parent2.x[i];
}
\end
\GenomeClass::mutator : // Must return the number of mutations
int NbMut=0;
float pond = 1./sqrt((float)SIZE);
for (int i=0; i<SIZE; i++)
if (tossCoin(pMutPerGene)){
NbMut++;
Genome.sigma[i] = Genome.sigma[i] * exp(SIGMA*pond*(float)gauss());
Genome.sigma[i] = MIN(0.5,Genome.sigma[0]);
Genome.sigma[i] = MAX(0.,Genome.sigma[0]);
Genome.x[i] += Genome.sigma[i]*(float)gauss();
Genome.x[i] = MIN(X_MAX,Genome.x[i]); // pour eviter les depassements
Genome.x[i] = MAX(X_MIN,Genome.x[i]);
}
return NbMut;
\end
\GenomeClass::evaluator : // Returns the score
float Score= 0.0;
Score= Weierstrass(Genome.x, SIZE);
return Score;
\end
\User Makefile options:
CPPFLAGS+=
\end
\Default run parameters : // Please let the parameters appear in this order
Number of generations : 100 // NB_GEN
Time limit: 0 // In seconds, 0 to deactivate
Population size : 3840 //POP_SIZE
Offspring size : 3840 // 40%
Mutation probability : 1 // MUT_PROB
Crossover probability : 1 // XOVER_PROB
Evaluator goal : minimise // Maximise
Selection operator: Tournament 2.0
Surviving parents: 3840//percentage or absolute
Surviving offspring: 3840
Reduce parents operator: Tournament 2.0
Reduce offspring operator: Tournament 2
Final reduce operator: Tournament 2
Elitism: Strong //Weak or Strong
Elite: 5
Print stats:1 //Default: 1
Plot stats:0 //Default: 0
Generate cvs stats file:0
Generate gnuplot script:0
Generate R script:0
\end
examples/weierstrass_std/weierstrass.ez
View file @ 5e121a27
......@@ -118,9 +118,8 @@ GenomeClass {
for (int i=0; i<SIZE; i++)
{
float alpha = (float)random(0.,1.); // barycentric crossover
child1.x[i] = alpha*parent1.x[i] + (1.-alpha)*parent2.x[i];
child.x[i] = alpha*parent1.x[i] + (1.-alpha)*parent2.x[i];
}
\end
\GenomeClass::mutator : // Must return the number of mutations
......@@ -167,11 +166,10 @@ CPPFLAGS+=
Elitism: Strong //Weak or Strong
Elite: 1
Print stats:1 //Default: 1
Generate cvs stats file:0
Generate gnuplot script:0
Generate R script:1
Plot stats:1 //Default: 0
Generate R script:0
Plot stats:0 //Default: 0
\end
tpl/CUDA.tpl
View file @ 5e121a27
......@@ -79,7 +79,7 @@ int main(int argc, char** argv){
using namespace std;
#include "EASEAIndividual.hpp"
bool INSTEAD_EVAL_STEP = false;
CRandomGenerator* globalRandomGenerator;
......@@ -126,6 +126,11 @@ void cudaPreliminaryProcess(size_t populationSize, dim3* dimBlock, dim3* dimGrid
\INSERT_BOUND_CHECKING
void evale_pop_chunk(CIndividual** population, int popSize){
printf("evalPopChunk\n");
\INSTEAD_EVAL_FUNCTION
}
void EASEAInit(int argc, char** argv){
\INSERT_INIT_FCT_CALL
}
......
tpl/CUDA_GP.tpl 0 → 100755
View file @ 5e121a27
\TEMPLATE_START
#ifdef WIN32
#define _CRT_SECURE_NO_WARNINGS
#pragma comment(lib, "libEasea.lib")
#endif
/**
This is program entry for TreeGP template for EASEA
*/
\ANALYSE_PARAMETERS
#include <stdlib.h>
#include <iostream>
#include <time.h>
#include "COptionParser.h"
#include "CRandomGenerator.h"
#include "CEvolutionaryAlgorithm.h"
#include "global.h"
#include "EASEAIndividual.hpp"
using namespace std;
/** Global variables for the whole algorithm */
CIndividual** pPopulation = NULL;
CIndividual* bBest = NULL;
float* pEZ_MUT_PROB = NULL;
float* pEZ_XOVER_PROB = NULL;
size_t *EZ_NB_GEN;
size_t *EZ_current_generation;
CEvolutionaryAlgorithm* EA;
int main(int argc, char** argv){
parseArguments("EASEA.prm",argc,argv);
ParametersImpl p;
p.setDefaultParameters(argc,argv);
CEvolutionaryAlgorithm* ea = p.newEvolutionaryAlgorithm();
EA = ea;
EASEAInit(argc,argv);
CPopulation* pop = ea->getPopulation();
ea->runEvolutionaryLoop();
EASEAFinal(pop);
delete pop;
#ifdef WIN32
system("pause");
#endif
return 0;
}
\START_CUDA_GENOME_CU_TPL
#ifdef WIN32
#define _CRT_SECURE_NO_WARNINGS
#pragma comment(lib, "libEasea.lib")
#endif
#include <string.h>
#include <fstream>