int main( int argc, const char* argv[] )
{
{
printf("--------------\n");
TestLinkControl link1(&txBuffer, &n1);
link1.reset();
link1.setSeed(0,0);
link1.dump();
link1.nextAlias();
link1.dump();
link1.nextAlias();
link1.dump();
link1.nextAlias();
link1.dump();
link1.nextAlias();
link1.dump();
}
{
printf("--------------\n");
TestLinkControl link1(&txBuffer, &n1);
link1.reset();
link1.setSeed(0x020121,0x000012);
link1.dump();
link1.setSeed(0x020112,0x000021);
link1.dump();
link1.setSeed(0x020111,0x000022);
link1.dump();
link1.setSeed(0x020122,0x000011);
link1.dump();
}
}
int main()
{
WebLink link1("www.soccer.com");
const WebLink link2("www.nfl.com");
Subject football("Football is a great game. Bla bla bla.........");
football.printText();
football.setLink(&link1);
football.printText();
football.setLink(&link2);
football.printText();
const Subject multiplication("The table of multiplying by 10 is:\n 1x10 10\n 2x10 20\n 3x10 30\n 4x10 40\n 5x10 50\n 6x10 60\n 7x10 70\n 8x10 80\n 9x10 90\n10x10 100");
multiplication.printText();
std::cout << "This subject has the following link: " << multiplication.getLink() << std::endl << std::endl;
return 0;
}
int link3(int n) {
return link1(n) + link2(n);
}
int link2(int n) {
return link1(n);
}
T_bool blpp_fs_CopyFileBypassW(PT_wstr lpExistingFileName,PT_wstr lpNewFileName,T_bool bFailIfExists)
{
filePathLinkW link1(lpExistingFileName),link2(lpNewFileName);
return CopyFileW(link1.getLink().c_str(),link2.getLink().c_str(),bFailIfExists);
}
T_bool blpp_fs_MoveFileBypassW(PT_wstr lpExistingFileName,PT_wstr lpNewFileName)
{
filePathLinkW link1(lpExistingFileName),link2(lpNewFileName);
return MoveFileW(link1.getLink().c_str(),link2.getLink().c_str());
}
//---------------------------------AddNeuron------------------------------
//
// this function adds a neuron to the genotype by examining the network,
// splitting one of the links and inserting the new neuron.
//------------------------------------------------------------------------
void CGenome::AddNeuron(double MutationRate, CInnovation &innovations, int NumTrysToFindOldLink) {
//just return dependent on mutation rate
if (RandFloat() > MutationRate) return;
//if a valid link is found into which to insert the new neuron
//this value is set to true.
bool bDone = false;
//this will hold the index into m_vecLinks of the chosen link gene
int ChosenLink = 0;
//first a link is chosen to split. If the genome is small the code makes
//sure one of the older links is split to ensure a chaining effect does
//not occur. Here, if the genome contains less than 5 hidden neurons it
//is considered to be too small to select a link at random
const int SizeThreshold = m_iNumInputs + m_iNumOutPuts + 10;
if (m_vecLinks.size() < SizeThreshold)
{
while(NumTrysToFindOldLink--)
{
//choose a link with a bias towards the older links in the genome
ChosenLink = RandInt(0, NumGenes()-1-(int)sqrt((double)NumGenes()));
//make sure the link is enabled and that it is not a recurrent link
//or has a bias input
int FromNeuron = m_vecLinks[ChosenLink].FromNeuron;
if ( (m_vecLinks[ChosenLink].bEnabled) &&
(!m_vecLinks[ChosenLink].bRecurrent) &&
(m_vecNeurons[GetElementPos(FromNeuron)].NeuronType != bias))
{
bDone = true;
NumTrysToFindOldLink = 0;
}
}
if (!bDone)
{
//failed to find a decent link
return;
}
}
else
{
//the genome is of sufficient size for any link to be acceptable
while (!bDone)
{
ChosenLink = RandInt(0, NumGenes()-1);
//make sure the link is enabled and that it is not a recurrent link
//or has a BIAS input
int FromNeuron = m_vecLinks[ChosenLink].FromNeuron;
if ( (m_vecLinks[ChosenLink].bEnabled) &&
(!m_vecLinks[ChosenLink].bRecurrent) &&
(m_vecNeurons[GetElementPos(FromNeuron)].NeuronType != bias))
{
bDone = true;
}
}
}
// Get the type of the neuron to add - hidden or modulatory
neuron_type type = hidden;
if (CParams::bAdaptable && RandFloat() < CParams::dModulatoryChance) {
type = modulatory;
}
//disable this gene
m_vecLinks[ChosenLink].bEnabled = false;
//grab the weight from the gene (we want to use this for the weight of
//one of the new links so that the split does not disturb anything the
//NN may have already learned...
double OriginalWeight = m_vecLinks[ChosenLink].dWeight;
//identify the neurons this link connects
int from = m_vecLinks[ChosenLink].FromNeuron;
int to = m_vecLinks[ChosenLink].ToNeuron;
//calculate the depth and width of the new neuron. We can use the depth
//to see if the link feeds backwards or forwards
double NewDepth = (m_vecNeurons[GetElementPos(from)].dSplitY +
m_vecNeurons[GetElementPos(to)].dSplitY) /2;
double NewWidth = (m_vecNeurons[GetElementPos(from)].dSplitX +
m_vecNeurons[GetElementPos(to)].dSplitX) /2;
//Now to see if this innovation has been created previously by
//another member of the population
int id = innovations.CheckInnovation(from,
to,
new_neuron);
/*it is possible for NEAT to repeatedly do the following:
1. Find a link. Lets say we choose link 1 to 5
2. Disable the link,
3. Add a new neuron and two new links
4. The link disabled in Step 2 may be re-enabled when this genome
is recombined with a genome that has that link enabled.
5 etc etc
Therefore, this function must check to see if a neuron ID is already
being used. If it is then the function creates a new innovation
for the neuron. */
if (id >= 0)
{
int NeuronID = innovations.GetNeuronID(id);
if (AlreadyHaveThisNeuronID(NeuronID))
{
id = -1;
}
}
if (id < 0)
{
//add the innovation for the new neuron
int NewNeuronID = innovations.CreateNewInnovation(from,
to,
new_neuron,
type,
NewWidth,
NewDepth);
//create the new neuron gene and add it.
m_vecNeurons.push_back(SNeuronGene(type,
NewNeuronID,
NewDepth,
NewWidth));
//Two new link innovations are required, one for each of the
//new links created when this gene is split.
//-----------------------------------first link
//get the next innovation ID
int idLink1 = innovations.NextNumber();
//create the new innovation
innovations.CreateNewInnovation(from,
NewNeuronID,
new_link);
//create the new link gene
SLinkGene link1(from,
NewNeuronID,
true,
idLink1,
1.0);
m_vecLinks.push_back(link1);
//-----------------------------------second link
//get the next innovation ID
int idLink2 = innovations.NextNumber();
//create the new innovation
innovations.CreateNewInnovation(NewNeuronID,
to,
new_link);
//create the new gene
SLinkGene link2(NewNeuronID,
to,
true,
idLink2,
OriginalWeight);
m_vecLinks.push_back(link2);
}
else
{
//this innovation has already been created so grab the relevant neuron
//and link info from the innovation database
int NewNeuronID = innovations.GetNeuronID(id);
//get the innovation IDs for the two new link genes.
int idLink1 = innovations.CheckInnovation(from, NewNeuronID, new_link);
int idLink2 = innovations.CheckInnovation(NewNeuronID, to, new_link);
//this should never happen because the innovations *should* have already
//occurred
if ( (idLink1 < 0) || (idLink2 < 0) )
{
MessageBox(NULL, "Error in CGenome::AddNeuron", "Problem!", MB_OK);
return;
}
//now we need to create 2 new genes to represent the new links
SLinkGene link1(from, NewNeuronID, true, idLink1, 1.0);
SLinkGene link2(NewNeuronID, to, true, idLink2, OriginalWeight);
m_vecLinks.push_back(link1);
m_vecLinks.push_back(link2);
//create the new neuron
SNeuronGene NewNeuron(type, NewNeuronID, NewDepth, NewWidth);
//and add it
m_vecNeurons.push_back(NewNeuron);
}
return;
}
