void ActiveConnect(ArgListType &arg) //AT_FUN
{
// process the function arguments
static string FunctionName = "ActiveConnect";
static IntArg StartNeuron("-Nstart", "start neuron", 1);
static IntArg EndNeuron("-Nend", "end neuron{-1 gives last neuron}", -1);
static IntArg StartPat("-Pstart", "start pattern(or vector)", 1);
static IntArg EndPat("-Pend", "end pattern(or vector) {-1 gives end time}",
-1);
static StrArg SeqName("-name", "sequence name");
static StrArg ResetSeq("-reset", "reset sequence", "{zeros}");
static StrArg External("-ext", "external sequence", "{zeros}");
static FlagArg DoSummary("-sum", "-nosum", "print a summary", 1);
static int argunset = true;
static CommandLine ComL(FunctionName);
if (argunset) {
ComL.HelpSet("@ActiveConnect() \n"
"\tGenerates the data about conditional probabilities of firing\n"
"\tand saves the results into the ActiveConBuffer.\n"
"\tThe reset sequence is used for the 0 time step and\n"
"\tif externals are given then they are ignored.\n"
"\tActiveConBuffer is a matrix with 6 Vectors:\n"
"\t\t1: TimeStep(Pattern Number)\n"
"\t\t2: Absolute Activity Last Time\n"
"\t\t3: Absolute Activity This Time - Externals\n"
"\t\t4: # of Conns between CoActive Neurons\n"
"\t\t5: 4/3 = Ave # of Active Conns for Active Neurons\n"
"\t\t6: 4/(2*3) = Prob cij=1 given zi=1,zj=1\n"
"\tActiveConnect also updates the variable Pczz which\n"
"\tholds the Prob(cij=1 | zi=1,zj=1) for the whole sequence\n");
ComL.IntSet(4, &StartNeuron, &EndNeuron, &StartPat, &EndPat);
ComL.StrSet(3, &SeqName, &ResetSeq, &External);
ComL.FlagSet(1, &DoSummary);
argunset = false;
}
ComL.Process(arg, Output::Err());
if (!program::Main().getNetworkCreated()) {
CALL_ERROR << "Error: You must call @CreateNetwork() before you can "
"call @ActiveConnect." << ERR_WHERE;
exit(EXIT_FAILURE);
}
UIPtnSequence Seq = SystemVar::getSequence(SeqName, FunctionName, ComL);
int SeqSize = Seq.size();
UIPtnSequence ReSeq;
if (ResetSeq.getValue() != "{zeros}") {
ReSeq = SystemVar::getSequence(ResetSeq, FunctionName, ComL);
} else {
ReSeq.resize(SeqSize, UIVector(0));
}
int lastNeuron = 0;
for (UIPtnSequenceCIt it = Seq.begin(); it != Seq.end(); it++) {
updateMax(lastNeuron, it->back());
}
if (EndNeuron.getValue() == -1) {
EndNeuron.setValue(lastNeuron);
}
if (EndPat.getValue() == -1) {
EndPat.setValue(SeqSize);
}
if (StartNeuron.getValue() < 1 ||
StartNeuron.getValue() > EndNeuron.getValue() ||
EndNeuron.getValue() > static_cast<int>(ni)) {
CALL_ERROR << "Error in " << FunctionName << ": Neuron range of "
<< StartNeuron.getValue() << "..." << EndNeuron.getValue()
<< " is invalid." << ERR_WHERE;
ComL.DisplayHelp(Output::Err());
}
if (EndPat.getValue() < 1 || EndPat.getValue() > SeqSize ||
StartPat.getValue() < 1 || StartPat.getValue() > EndPat.getValue()) {
CALL_ERROR << "Error in " << FunctionName << ": Pattern range of "
<< StartPat.getValue() << "..." << EndPat.getValue() << " is invalid."
<< ERR_WHERE;
ComL.DisplayHelp(Output::Err());
}
// get externals
UIMatrix ExtSeq;
if (External.getValue() != "{zeros}") {
ExtSeq = SystemVar::getSequence(External, FunctionName, ComL);
} else {
ExtSeq.resize(SeqSize, UIVector(0));
}
// Do the loops
int NumPats = EndPat.getValue() - StartPat.getValue() + 1;
DataList Ave(NumPats);
DataList Sum(NumPats);
DataList Pats(NumPats);
DataList jNumFired(NumPats);
DataList iNumFired(NumPats);
DataList PczzV(NumPats);
int index = 0;
UIPtnSequenceCIt SeqConstIterator = Seq.begin();
UIPtnSequenceCIt ExtConstIterator = ExtSeq.begin();
UIVector pat_b4;
if (StartPat.getValue() == 1) {
pat_b4 = ReSeq.front();
} else {
for (int inc = 0; inc < StartPat.getValue() - 2; ++inc) ++SeqConstIterator;
pat_b4 = *(SeqConstIterator++);
}
for (int t = StartPat.getValue(); t <= EndPat.getValue(); ++t, ++index, ++SeqConstIterator, ++ExtConstIterator) {
Pats.at(index) = static_cast<float>(t);
Pattern pat = UIVectorToPtn(*SeqConstIterator, lastNeuron);
Pattern pat_ext = UIVectorToPtn(*ExtConstIterator, lastNeuron);
for (int j = StartNeuron.getValue(); j <= EndNeuron.getValue(); j++) {
// count the number on
if (pat_b4.at(j-1)) ++iNumFired.at(index);
// do the calculation for non-externals and active
if (pat.at(j-1) && (!(pat_ext.at(j-1)))) {
++jNumFired.at(index);
for (unsigned int c = 0; c < FanInCon.at(j-1); c++) {
if (pat_b4.at(inMatrix[j-1][c].getSrcNeuron()))
++Sum.at(index);
}
}
}
pat_b4 = pat;
}
// Output the data
Output::Out() << "\nSummed Active Connections in sequence " << SeqName.getValue()
<< " using neurons " << StartNeuron.getValue() << "..."
<< EndNeuron.getValue() << " and patterns " << StartPat.getValue()
<< "..." << EndPat.getValue() << "\n\tignoring externals given by "
<< External.getValue();
if (DoSummary.getValue()) {
Output::Out() << "\n" << std::setw(11) << "1:TimeStep"
<< std::setw(9) << "2:#z(t-1)"
<< std::setw(9) << "3:#z(t)"
<< std::setw(15) << "4:#cz(t-1)z(t)"
<< std::setw(9) << "4/3" << std::setw(9) << "4/(2*3)" << "\n";
}
float TotalSumC = 0.0f;
float TotalSumZ = 0.0f;
for (int i = 0; i < index; i++) {
if (fabs(Sum.at(i)) < verySmallFloat) {
Ave.at(i) = 0.0f;
PczzV.at(i) = 0.0f;
} else {
Ave.at(i) = Sum.at(i) / jNumFired.at(i);
PczzV.at(i) = Ave.at(i) / iNumFired.at(i);
}
TotalSumC += Sum.at(i);
TotalSumZ += iNumFired.at(i) * jNumFired.at(i);
if (DoSummary.getValue()) {
Output::Out() << "\n" << std::setw(11) << static_cast<int>(Pats.at(i))
<< std::setw(9) << static_cast<int>(iNumFired.at(i))
<< std::setw(9) << static_cast<int>(jNumFired.at(i))
<< std::setw(15) << static_cast<int>(Sum.at(i))
<< std::setw(9) << Ave.at(i)
<< std::setw(9) << PczzV.at(i);
}
}
Output::Out() << "\n";
float Pczz;
if (fabs(TotalSumC) < verySmallFloat) {
Pczz = 0.0f;
} else {
Pczz = TotalSumC / TotalSumZ;
}
SystemVar::SetFloatVar("Pczz", Pczz);
if (DoSummary.getValue()) {
Output::Out() << "Average: " << Pczz << "\n";
}
// Save the vector to the ActiveConBuffer
DataMatrix ActiveConMat;
ActiveConMat.push_back(Pats);
ActiveConMat.push_back(iNumFired);
ActiveConMat.push_back(jNumFired);
ActiveConMat.push_back(Sum);
ActiveConMat.push_back(Ave);
ActiveConMat.push_back(PczzV);
SystemVar::insertData("ActiveConBuffer", ActiveConMat, DLT_matrix);
return;
}
