Un-broken INSTEAD_EVAL CUDA.tpl

Easea.h

@@ -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

@@ -1828,7 +1828,7 @@ 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"))){
@@ -1842,6 +1842,8 @@ 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

@@ -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

-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

@@ -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/cmaes_cuda_test/cigtabGPU.ez

+*_________________________________________________________
+
+Test functions
+log normal adaptive mutation
+Selection operator: Tournament
+__________________________________________________________*/
+
+\User declarations :
+#define SIZE 22
+#define X_MIN -1.
+#define X_MAX 1.
+#define ITER 50      
+#define SIGMA  1.                     /*  mutation parameter */
+#define PI 3.141592654
+
+ 
+float pMutPerGene=0.1;
+
+struct gpuOptions initOptions;
+
+\end
+
+\User functions:
+//fitness function
+#include <math.h>
+
+__device__ __host__ float SQR(float d)
+{
+  return (d*d);
+}
+
+__device__ __host__ inline float rosenbrock( float const *x, int size)
+{
+  float qualitaet;
+  int i;
+  int DIM = size;
+        qualitaet = 0.0;
+
+        for( i = DIM-2; i >= 0; --i)
+          qualitaet += 100.*SQR(SQR(x[i])-x[i+1]) + SQR(1.-x[i]);
+        return ( qualitaet);
+} /* f_rosenbrock() */
+
+
+__device__ __host__ inline float cigtab(float *x, int size)
+{
+  int i;
+  float sum = 0.;
+
+  sum = x[0]*x[0] + 1e8*x[size-1]*x[size-1];
+  for (i = 1; i < size-1; ++i)
+    sum += 1e4*x[i]*x[i];
+  return sum;
+}
+\end
+
+\GenomeClass::display:
+	for(int i=0; i<SIZE; i++){
+		cout << Genome.x[i] << "|";
+	}
+\end
+
+\Before everything else function:
+\end
+
+\After everything else function:
+\end
+
+\At the beginning of each generation function:
+\end
+
+\At the end of each generation function:
+\end
+
+\At each generation before reduce function:
+\end
+
+\User classes :
+GenomeClass { 
+  float x[SIZE];
+}
+\end
+
+\GenomeClass::initialiser : // "initializer" is also accepted
+\end
+
+\GenomeClass::display:
+\end
+
+\GenomeClass::crossover :
+\end
+
+\GenomeClass::mutator : // Must return the number of mutations
+  int NbMut=0;
+return NbMut;
+\end
+
+\GenomeClass::evaluator : // Returns the score
+  float Score= 0.0;
+  Score= rosenbrock(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 deactive
+  Population size : 1000     // POP_SIZE
+  Offspring size : 2000
+  Final reduce operator:Deterministic //Deterministic, Tournament, Random, Roulette(only Maximise)
+  Elitism: Strong                       //Weak or Strong
+  Elite: 5
+  Print stats:1                         //Default: 1
+  Print stats to file:0
+  Plot stats:0                          //Default: 0
+\end
+	

examples/cmaes_tests/cigtab.ez

+*_________________________________________________________
+
+Test functions
+__________________________________________________________*/
+
+\User declarations :
+#define SIZE 22
+#define X_MIN -1.
+#define X_MAX 1.
+#define ITER 50      
+#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;
+
+
+
+\end
+
+\User functions:
+//fitness function
+#include <math.h>
+
+static double SQR(double d)
+{
+  return (d*d);
+}
+
+double rosenbrock( double const *x)
+/*
+	Rosenbrock's Function, generalized.
+*/
+{
+  double qualitaet;
+  int i;
+  int DIM = SIZE;
+	qualitaet = 0.0;
+
+	for( i = DIM-2; i >= 0; --i)
+	  qualitaet += 100.*SQR(SQR(x[i])-x[i+1]) + SQR(1.-x[i]);
+	return ( qualitaet);
+} /* f_rosenbrock() */
+
+double cigtab(double *x)
+{
+  int i;
+  double sum = 0.;
+
+  sum = x[0]*x[0] + 1e8*x[SIZE-1]*x[SIZE-1];
+  for (i = 1; i < SIZE-1; ++i)
+    sum += 1e4*x[i]*x[i];
+  return sum;
+}
+\end
+
+\GenomeClass::display:
+	for(int i=0; i<SIZE; i++){
+		cout << Genome.x[i] << "|";
+	}
+\end
+
+\Before everything else function:
+\end
+
+\After everything else function:
+\end
+
+\At the beginning of each generation function:
+\end
+
+\At the end of each generation function:
+\end
+
+\At each generation before reduce function:
+\end
+
+\User classes :
+GenomeClass { 
+  double x[SIZE];
+}
+\end
+
+\GenomeClass::display:
+\end
+
+\GenomeClass::initialiser : // "initializer" is also accepted
+\end
+
+\GenomeClass::crossover :
+\end
+
+\GenomeClass::mutator : // Must return the number of mutations
+  int NbMut=0;
+return NbMut;
+\end
+
+\GenomeClass::evaluator : // Returns the score
+  float Score= 0.0;
+  Score= cigtab(Genome.x);
+  //Score= rosenbrock(Genome.x);         
+  return Score;
+\end
+
+\User Makefile options: 
+CXXFLAGS+=
+LIBS+=
+\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 deactive
+  Population size : 7     // POP_SIZE
+  Offspring size : 13
+  Final reduce operator:Deterministic //Deterministic, Tournament, Random, Roulette(only Maximise)
+  Elitism: Strong                       //Weak or Strong
+  Elite: 5
+  Print stats:1                         //Default: 1
+  Print stats to file:0
+  Plot stats:0                          //Default: 0
+\end
+	

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
+	

examples/weierstrass_std/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;
+
+
+\end
+
+\User functions:
+//fitness function
+#include <math.h>
+
+__device__ __host__ inline float Weierstrass(float x[SIZE], 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
+
+
+
+\Before everything else function:
+  //cout<<"Before everything else function called "<<endl;
+\end
+
+\After everything else function:
+  //cout << "After everything else function called" << endl;
+\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::display:
+/* 	 for( size_t i=0 ; i<SIZE ; i++){ */
+/* 	      //     cout << Genome.x[i] << ":" << Genome.sigma[i] << "|"; */
+/* 	      printf("%.02f:%.02f|",Genome.x[i],Genome.sigma[i]); */
+/* 	 }	       */
+\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