int main(void){
int i;
struct dataSet *data = NULL;
double inputs[NUMSAMPLES][NUMINPUTS];
double outputs[NUMSAMPLES][NUMOUTPUTS];
double inputTemp;
double outputTemp;
for(i=0; i<NUMSAMPLES; i++){
inputTemp = (i * (INPUTRANGE/(NUMSAMPLES-1))) - INPUTRANGE/2;
outputTemp = symbolicEq1(inputTemp);
inputs[i][0] = inputTemp;
outputs[i][0] = outputTemp;
}
data = initialiseDataSetFromArrays(NUMINPUTS, NUMOUTPUTS, NUMSAMPLES, inputs[0], outputs[0]);
saveDataSet(data, "symbolic.data");
freeDataSet(data);
return 0;
}
int main(void){
struct parameters *params = NULL;
struct chromosome *chromoA = NULL;
struct chromosome *chromoB = NULL;
struct chromosome *chromoC = NULL;
struct dataSet *trainingData = NULL;
double testInputs[NUMINPUTS];
params = initialiseParameters(NUMINPUTS, NUMNODES, NUMOUTPUTS, ARITY);
addNodeFunction(params, "add,sub,mul,sq,cube,sin");
trainingData = initialiseDataSetFromFile("./dataSets/symbolic.data");
chromoA = initialiseChromosome(params);
chromoB = initialiseChromosome(params);
setChromosomeFitness(params, chromoA, trainingData);
mutateChromosome(params,chromoA);
copyChromosome(chromoB, chromoA);
removeInactiveNodes(chromoB);
printf("chromoA with inactive nodes.\n");
printChromosome(chromoA, 0);
printf("chromoB without inactive nodes.\n");
printChromosome(chromoB, 1);
saveChromosome(chromoB, "chromoB.chromo");
chromoC = initialiseChromosomeFromFile("chromoB.chromo");
testInputs[0] = 3;
executeChromosome(chromoC, testInputs);
printf("Applied input: %f\n", testInputs[0]);
printf("Generated output: %f\n", getChromosomeOutput(chromoC, 0));
freeChromosome(chromoA);
freeChromosome(chromoB);
freeChromosome(chromoC);
freeDataSet(trainingData);
freeParameters(params);
return 0;
}
int main(void){
int i, gen;
struct parameters *params = NULL;
struct chromosome *population[POPULATIONSIZE];
struct chromosome *fittestChromosome = NULL;
struct dataSet *trainingData = NULL;
double targetFitness = 0;
int maxGens = 10000;
params = initialiseParameters(NUMINPUTS, NUMNODES, NUMOUTPUTS, ARITY);
addNodeFunction(params, "or,nor,and,nand");
/*setTargetFitness(params, targetFitness);*/
setMutationType(params, "probabilistic");
setMutationRate(params, 0.08);
trainingData = initialiseDataSetFromFile("./examples/parity3bit.data");
for(i=0; i<POPULATIONSIZE; i++){
population[i] = initialiseChromosome(params);
}
fittestChromosome = initialiseChromosome(params);
/* for the number of allowed generations*/
for(gen=0; gen<maxGens; gen++){
/* set the fitnesses of the population of chromosomes*/
for(i=0; i<POPULATIONSIZE; i++){
setChromosomeFitness(params, population[i], trainingData);
}
/* copy over the last chromosome to fittestChromosome*/
copyChromosome(fittestChromosome, population[POPULATIONSIZE - 1]);
/* for all chromosomes except the last*/
for(i=0; i<POPULATIONSIZE-1; i++){
/* copy ith chromosome to fittestChromosome if fitter*/
if(getChromosomeFitness(population[i]) < getChromosomeFitness(fittestChromosome)){
copyChromosome(fittestChromosome, population[i]);
}
}
/* termination condition*/
if(getChromosomeFitness(fittestChromosome) <= targetFitness){
break;
}
/* set the first member of the population to be the fittest chromosome*/
copyChromosome(population[0], fittestChromosome);
/* set remaining member of the population to be mutations of the
fittest chromosome*/
for(i=1; i<POPULATIONSIZE; i++){
copyChromosome(population[i], fittestChromosome);
mutateChromosome(params, population[i]);
}
}
printf("gen\tfitness\n");
printf("%d\t%f\n", gen, getChromosomeFitness(fittestChromosome));
for(i=0; i<POPULATIONSIZE; i++){
freeChromosome(population[i]);
}
freeChromosome(fittestChromosome);
freeDataSet(trainingData);
freeParameters(params);
return 0;
}
MySqlDataSet::~MySqlDataSet()
{
freeDataSet();
}
