Merge branch 'master' of ssh+git://easea-dev@atia/home/easea-dev/src

Conflicts:

	examples/weierstrass_gpu/weierstrass.ez
	examples/weierstrass_std/weierstrass.ez

examples/weierstrass_gpu/weierstrass.ez

-/*_________________________________________________________
-
-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
-     child1.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

@@ -118,7 +118,7 @@ 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
 
@@ -165,8 +165,7 @@ CPPFLAGS+=
   Final reduce operator: Tournament 2
 
   Elitism: Strong			//Weak or Strong
-  Elite: 0
-
+  Elite: 1
   Print stats:1				//Default: 1
   Generate cvs stats file:0			
   Generate gnuplot script:0

libeasea/CEvolutionaryAlgorithm.cpp

@@ -238,7 +238,8 @@ void CEvolutionaryAlgorithm::showPopulationStats(struct timeval beginTime){
   if((this->params->plotStats && this->gnuplot->valid) || this->params->generateGnuplotScript){
  	FILE *f;
 	string fichier (params->outputFilename);
- 	f = fopen(params->outputFilename,"a"); //ajouter .dat
+	fichier.append(".dat");
+ 	f = fopen(fichier.c_str(),"a"); //ajouter .dat
 	if(f!=NULL){
 	  if(currentGeneration==0)
 		fprintf(f,"#GEN\tTIME\t\tEVAL\tBEST\t\tAVG\t\tSTDEV\n\n");
@@ -262,7 +263,7 @@ void CEvolutionaryAlgorithm::showPopulationStats(struct timeval beginTime){
   #ifdef __linux__
   if(this->params->plotStats && this->gnuplot->valid){
 	if(currentGeneration==0)
-		fprintf(this->gnuplot->fWrit,"plot \'%s\' using 3:4 t \'Best Fitness\' w lines, \'%s\' using 3:5 t  \'Average\' w lines, \'%s\' using 3:6 t \'StdDev\' w lines\n", params->outputFilename,params->outputFilename,params->outputFilename);
+		fprintf(this->gnuplot->fWrit,"plot \'%s.dat\' using 3:4 t \'Best Fitness\' w lines, \'%s.dat\' using 3:5 t  \'Average\' w lines, \'%s.dat\' using 3:6 t \'StdDev\' w lines\n", params->outputFilename,params->outputFilename,params->outputFilename);
 	else
 		fprintf(this->gnuplot->fWrit,"replot\n");
 	fflush(this->gnuplot->fWrit);

libeasea/COptionParser.cpp

@@ -137,7 +137,7 @@ void parseArguments(const char* parametersFileName, int ac, char** av,
 	("inputfile",po::value<string>(),"Set an input file for the initial population (default : none)")
 	("printStats",po::value<int>(),"Print the Stats (default : 1)")
 	("plotStats",po::value<int>(),"Plot the Stats with gnuplot (default : 0)")
-	("generateCVSFile",po::value<int>(),"Print the Stats to a CVS File (Filename: ProjectName.dat) (default : 0)")
+	("generateCSV",po::value<int>(),"Print the Stats to a CSV File (Filename: ProjectName.csv) (default : 0)")
 	("generateGnuplotScript",po::value<int>(),"Generates a Gnuplot script to plat the Stats (Filename: ProjectName.plot) (default : 0)")
 	("generateRScript",po::value<int>(),"Generates a R script to plat the Stats (Filename: ProjectName.r) (default : 0)")
 //	("printStatsFile",po::value<int>(),"Print the Stats to a File (Filename: ProjectName.dat) (default : 0)")

tpl/CMAES.tpl

@@ -287,7 +287,7 @@ void ParametersImpl::setDefaultParameters(int argc, char** argv){
         this->printInitialPopulation = setVariable("printInitialPopulation",0);
         this->printFinalPopulation = setVariable("printFinalPopulation",0);
 
-        this->outputFilename = (char*)"EASEA.dat";
+        this->outputFilename = (char*)"EASEA";
         this->plotOutputFilename = (char*)"EASEA.png";
 }