linkedList* variableOrdering(graph *g,int choice){
item *currItem;
linkedList *orderedList;
vertex *v;
int i;
breakCycles(g);
resetWeights(g);
computeWeights(g);
genericSuccessorsOrdering(g, choice);
orderByWeights(g->vertices);
resetVisited(g);
orderedList= newLinkedList();
currItem = (g-> vertices)->head;
while (currItem!= NULL) {
if ((currItem->vert)->visited == 0) visit(currItem->vert,orderedList);
currItem = currItem->next;
}
return orderedList;
}
void FWSingle::FWOneStep()
{
//create an ordered version of the ParentIDs to make the weight calculation faster
vector<vector<long> > orderedPIDs=(PIDs);
vector<vector<long> >::iterator it(orderedPIDs.begin());
do
{
std::sort((*it).begin(),(*it).end());
it++;
}
while(it<orderedPIDs.end());
if (verbose>2)
app_log()<<" Done Sorting IDs"<<endl;
resetWeights();
vector<vector<long> >::iterator stepIDIterator(IDs.begin());
vector<vector<long> >::iterator stepPIDIterator(orderedPIDs.begin() + gensTransferred);
vector<vector<int> >::iterator stepWeightsIterator(Weights.begin());
//we start comparing the next generations ParentIDs with the current generations IDs
int i=0;
do
{
if (verbose>2)
app_log()<<" calculating weights for gen:"<<gensTransferred<<" step:"<<i<<"/"<<orderedPIDs.size()<<endl;
// if (verbose>2) app_log()<<"Nsamples ="<<(*stepWeightsIteratoetWeights).size()<<endl;
vector<long>::iterator IDit( (*stepIDIterator).begin() );
vector<long>::iterator PIDit( (*stepPIDIterator).begin() );
vector<int>::iterator Wit( (*stepWeightsIterator).begin() );
if (verbose>2)
app_log()<<"ID size:"<<(*stepIDIterator).size()<<" PID size:"<<(*stepPIDIterator).size()<<" Weight size:"<<(*stepWeightsIterator).size()<<endl;
do
{
if ((*PIDit)==(*IDit))
{
(*Wit)++;
PIDit++;
}
else
{
IDit++;
Wit++;
if (IDit==(*stepIDIterator).end())
{
IDit=(*stepIDIterator).begin();
Wit=(*stepWeightsIterator).begin();
}
}
}
while(PIDit<(*stepPIDIterator).end());
// if (verbose>2) { printIDs((*stepIDIterator));printIDs((*stepPIDIterator));}
// if (verbose>2) printInts((*stepWeightsIterator));
stepIDIterator++;
stepPIDIterator++;
stepWeightsIterator++;
i++;
}
while(stepPIDIterator<orderedPIDs.end());
}
int main (int argc, char* argv[])
{
if (argc == 1)
{
srand(time(NULL));
struct neural_network *network = createNetwork(3, 3, 2);
int loopCount = 0, count = 1, proceed = 0;
double out00, out01, out10, out11;
do
{
do
{
loopCount++;
setInput(1,1, network);
out11 = getOutput(network);
learn(network, 0);
setInput(0,0, network);
out00 = getOutput(network);
learn(network, 0);
setInput(0,1,network);
out01 = getOutput(network);
learn(network, 1);
setInput(1,0, network);
out10 = getOutput(network);
learn(network, 1);
printf("0xor0: %f 0xor1: %f 1xor0: %f 1xor1: %f\n",out00, out01,
out10, out11);
}while ((out00 > 0.05 || out01 < 0.95 || out10 < 0.95 ||
out11 > 0.05) && loopCount < 200000);
proceed = loopCount == 200000;
if(proceed)
{
printf("creating new network\n");
resetWeights(network);
printf("weights reset");
count++;
loopCount = 0;
}
}while(proceed);
freeNetwork(network);
printf("Network freed.\n");
printf("Loop count : %i\n",(count - 1)*200000 + loopCount);
printf("Everything went fine. Exiting...\n");
_Exit(0);
}
else if (argc == 2)
{
IplImage *img = load(argv[1]);
if(img != NULL)
{
const char* window_title = "Perfect Image";
cvNamedWindow (window_title, CV_WINDOW_AUTOSIZE);
cvShowImage (window_title, img);
cvWaitKey(0);
char path[] = "result.bmp";
cvSaveImage(&path[0], img, NULL);
cvDestroyAllWindows();
cvReleaseImage(&img);
printf("Everything went fine. Exiting...\n");
return 0;
}
else
{
printf("Error loading image.\n");
return -1;
}
}
else
{
printf("Argument issue. Exiting...\n");
return -1;
}
}
