void dslashQuda(void *h_out, void *h_in, QudaInvertParam *inv_param, QudaParity parity)
{
ColorSpinorParam cpuParam(h_in, *inv_param, gaugePrecise->X(), 1);
ColorSpinorParam cudaParam(cpuParam, *inv_param);
cpuColorSpinorField hIn(cpuParam);
cudaColorSpinorField in(hIn, cudaParam);
cudaParam.create = QUDA_NULL_FIELD_CREATE;
cudaColorSpinorField out(in, cudaParam);
if (inv_param->dirac_order == QUDA_CPS_WILSON_DIRAC_ORDER) {
if (parity == QUDA_EVEN_PARITY) {
parity = QUDA_ODD_PARITY;
} else {
parity = QUDA_EVEN_PARITY;
}
axCuda(gaugePrecise->Anisotropy(), in);
}
bool pc = true;
DiracParam diracParam;
setDiracParam(diracParam, inv_param, pc);
Dirac *dirac = Dirac::create(diracParam); // create the Dirac operator
dirac->Dslash(out, in, parity); // apply the operator
delete dirac; // clean up
cpuParam.v = h_out;
cpuColorSpinorField hOut(cpuParam);
out.saveCPUSpinorField(hOut); // since this is a reference, this won't work: hOut = out;
}
void CSourceDataGenerator::SaveCharmap(const CharMap &charmap, bool compress, enum OutputFormat format, const wxString &fileName)
{
wxFileName fileNameInfo(fileName, wxPATH_NATIVE);
wxString hFilePath, cFilePath;
if (fileNameInfo.GetExt() == "h") {
hFilePath = fileName;
fileNameInfo.SetExt("c");
if (fileNameInfo.Exists() && wxMessageBox(_("Also the file \"") + fileNameInfo.GetFullName() +
_("\" will be generated. Do you want to overwrite it?"), _("Confirm"), wxYES_NO) == wxNO) {
return;
}
cFilePath = fileNameInfo.GetFullPath();
}
else if (fileNameInfo.GetExt() == "c") {
cFilePath = fileName;
fileNameInfo.SetExt("h");
if (fileNameInfo.Exists() && wxMessageBox(_("The file \"") + fileNameInfo.GetFullName() +
_("\" will be generated. Do you want to overwrite it?"), _("Confirm"), wxYES_NO) == wxNO) {
return;
}
hFilePath = fileNameInfo.GetFullPath();
}
wxFFileOutputStream hFile(hFilePath);
wxFFileOutputStream cFile(cFilePath);
wxTextOutputStream hOut(hFile);
wxTextOutputStream cOut(cFile);
EMGL_font_t *font = LoadEMGLFont(charmap, compress, format);
hOut << GetHeaderFile(fileNameInfo.GetName());
cOut << GetSourceFile(font, fileNameInfo.GetName());
DeleteEMGLFont(font);
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void writeOutput(bool didReplace, QStringList& outLines, QString filename)
{
if(didReplace == true)
{
QFileInfo fi2(filename);
#if OVERWRITE_SOURCE_FILE
QFile hOut(filename);
#else
QString tmpPath = "/tmp/" + fi2.fileName();
QFile hOut(tmpPath);
#endif
hOut.open(QFile::WriteOnly);
QTextStream stream( &hOut );
stream << outLines.join("\n");
hOut.close();
qDebug() << "Saved File " << fi2.absoluteFilePath();
}
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void writeOutput(bool didReplace, QVector<QString>& outLines, QString filename)
{
if(didReplace == true)
{
QFileInfo fi2(filename);
#if OVERWRITE_SOURCE_FILE
QFile hOut(filename);
#else
QString tmpPath = "/tmp/" + fi2.fileName();
QFile hOut(tmpPath);
#endif
hOut.open(QFile::WriteOnly);
QTextStream stream( &hOut );
for(qint32 i = 0; i < outLines.size() - 1; i++)
{
stream << outLines[i] << "\n";
}
hOut.close();
qDebug() << "Saved File " << fi2.absoluteFilePath();
}
}
void MatDagMatQuda(void *h_out, void *h_in, QudaInvertParam *inv_param)
{
bool pc = (inv_param->solution_type == QUDA_MATPC_SOLUTION ||
inv_param->solution_type == QUDA_MATPCDAG_MATPC_SOLUTION);
ColorSpinorParam cpuParam(h_in, *inv_param, gaugePrecise->X(), pc);
ColorSpinorParam cudaParam(cpuParam, *inv_param);
cpuColorSpinorField hIn(cpuParam);
cudaColorSpinorField in(hIn, cudaParam);
cudaParam.create = QUDA_NULL_FIELD_CREATE;
cudaColorSpinorField out(in, cudaParam);
// double kappa = inv_param->kappa;
// if (inv_param->dirac_order == QUDA_CPS_WILSON_DIRAC_ORDER) kappa *= gaugePrecise->anisotropy;
DiracParam diracParam;
setDiracParam(diracParam, inv_param, pc);
Dirac *dirac = Dirac::create(diracParam); // create the Dirac operator
dirac->MdagM(out, in); // apply the operator
delete dirac; // clean up
double kappa = inv_param->kappa;
if (pc) {
if (inv_param->mass_normalization == QUDA_MASS_NORMALIZATION) {
axCuda(1.0/pow(2.0*kappa,4), out);
} else if (inv_param->mass_normalization == QUDA_ASYMMETRIC_MASS_NORMALIZATION) {
axCuda(0.25/(kappa*kappa), out);
}
} else {
if (inv_param->mass_normalization == QUDA_MASS_NORMALIZATION ||
inv_param->mass_normalization == QUDA_ASYMMETRIC_MASS_NORMALIZATION) {
axCuda(0.25/(kappa*kappa), out);
}
}
cpuParam.v = h_out;
cpuColorSpinorField hOut(cpuParam);
out.saveCPUSpinorField(hOut); // since this is a reference, this won't work: hOut = out;
}
int main(int argc, char* argv[])
{
// Read parameters from console.
CCmdLine cmdLine;
if(cmdLine.SplitLine(argc, argv) < 5)
{
cerr << "Usage: ./bbnet -s score_file -n node -b bkg -f func_depth -o output" << endl;
cerr << endl << "Additional parameter:" << endl;
cerr << "-k\tPenalty parameter(logK, Default = 5.0)" << endl;
cerr << "-c\tnumber of candidate motifs (Default=50)" << endl;
cerr << "-d\tpositive negative (for prediction using BN)" << endl;
cerr << "-l\toutput of all training samples' information." << endl;
cerr << "-t\ttranslational(transcriptional) start sites.(Default = right end)" << endl;
cerr << "-rb\tbit-string to determine which rules to include.(Default = 111110)" << endl;
cerr << "-i\tUse mutual information instead of Bayesian score" << endl;
cerr << endl << "Contact: \"Li Shen\"<*****@*****.**>" << endl;
return 1;
}
string s, n, b, f, o;
try
{
s = cmdLine.GetArgument("-s", 0); // score file.
n = cmdLine.GetArgument("-n", 0); // node gene list file.
b = cmdLine.GetArgument("-b", 0); // bkg gene list file.
f = cmdLine.GetArgument("-f", 0); // func depth folder.
o = cmdLine.GetArgument("-o", 0); // results output file.
}
catch(int)
{
cerr << "Wrong arguments!" << endl;
return 1;
}
if(cmdLine.HasSwitch("-i"))
itag = true;
//itag = true;
//string s = "../gbnet/data/Beer/scor_test.list";
//string n = "../gbnet/data/Beer/node.list";
//string b = "../gbnet/data/Beer/bkg.list";
//string f = "../gbnet/data/Beer/func";
//string k = "0.015";
//logK = atof(k.data());
//string o = "../gbnet/data/Beer/bb_res_test2.txt";
string k; // Penalty parameter; logK value.
if(!itag)
k = cmdLine.GetSafeArgument("-k", 0, "5.0");
else
k = cmdLine.GetSafeArgument("-k", 0, "0.015");
logK = atof(k.data());
string c = cmdLine.GetSafeArgument("-c", 0, "50"); // number of candidate motifs. default = 50.
motifcand = atoi(c.data());
// Use prior counts for some motifs if specified.
string p = cmdLine.GetSafeArgument("-p", 0, "0"); // prior counts.
pricnt = atoi(p.data());
if(pricnt > 0) // read preferred motifs list from file.
{
prior = 1;
string fPrim = cmdLine.GetSafeArgument("-p", 1, "primot.txt");
vector<string> primv;
if(get1stcol(fPrim, primv) < 0)
return 1;
for(size_t i = 0; i < primv.size(); i++)
primo.insert(primv[i]);
}
// File names for positive, negative and left-out testing lists.
string pos = cmdLine.GetSafeArgument("-d", 0, ""); // positive testing cases.
string neg = cmdLine.GetSafeArgument("-d", 1, ""); // negative testing cases.
string res = cmdLine.GetSafeArgument("-d", 2, ""); // left-out testing cases.
vector<string> plst, nlst, rlst; // positive, negative and left-out lists.
if(pos != "" && neg != "")
{
if(get1stcol(pos, plst) < 0)
return 1;
if(get1stcol(neg, nlst) < 0)
return 1;
}
if(res != "")
{
if(get1stcol(res, rlst) < 0)
return 1;
}
// File for output of all training samples' information.
string finfo = cmdLine.GetSafeArgument("-l", 0, "");
// File to store all genes' translational/transcriptional start sites.
string ftss = cmdLine.GetSafeArgument("-t", 0, "");
if(ftss != "")
loadtss(ftss, mtss);
// A bit-string to determine which rules to include.
rb = cmdLine.GetSafeArgument("-rb", 0, "111110");
string bp = cmdLine.GetSafeArgument("-bp", 0, ""); // Output each gene's probability like in Beer's prediction.
// Load motif Bayesian score file.
if(loadscor(mscor, s) != 0)
{
cerr << "Load motif scores eror!" << endl;
return 1;
}
else
{
#ifdef VERBOSE
cout << "Display candidate motifs that are loaded:" << endl;
dispscor(mscor);
#endif
}
vector<MotifScore> oscor = mscor; // Save an original copy of motif scores.
// Load gene list.
vector<Case> tlst, blst, genlst;
set<string> genset;
if(loadgene(tlst, blst, n, b) != 0)
{
cerr << "Load gene lists error!" << endl;
return 1;
}
else
{
genlst.insert(genlst.end(), tlst.begin(), tlst.end());
genlst.insert(genlst.end(), blst.begin(), blst.end());
#ifdef VERBOSE
cout << "Load gene list completed!" << endl;
#endif
// All training and testing gene names are put into genmap.
for(size_t i = 0; i < genlst.size(); i++)
genset.insert(genlst[i].name);
for(size_t i = 0; i < plst.size(); i++)
genset.insert(plst[i]);
for(size_t i = 0; i < nlst.size(); i++)
genset.insert(nlst[i]);
for(size_t i = 0; i < rlst.size(); i++)
genset.insert(rlst[i]);
}
// Load motif binding information of genes in genmap.
if(loadbind(allbind, mscor, genset, f) != 0)
{
cerr << "Load binding information error!" << endl;
return 1;
}
else
{
#ifdef VERBOSE
cout << "Load binding information completed!" << endl;
#endif
}
// File for output.
ofstream hOut(o.data());
if(!hOut)
{
cerr << "Can't open " << o << endl;
return 1;
}
hOut << "Number of genes in category 1: " << tlst.size() << endl;
hOut << "Number of genes in category 0: " << blst.size() << endl << endl;
#ifdef VERBOSE
cout << endl << "Running on original data." << endl;
#endif
vector<Constraint> cons;
vector<CPTRow> cpt;
clock_t start = clock();
double scor = bbnet(cons, cpt, genlst);
clock_t finish = clock();
if(outbayes(hOut, scor, cons, cpt, oscor, tlst.size(), blst.size()) != 0)
{
cerr << "Output Bayesian network results error!" << endl;
return 1;
}
if(finfo != "")
{
if(outgene(finfo, tlst, blst, cons) != 0)
cerr << "Output training samples' information error!" << endl;
return 1;
}
if(pos != "" && neg != "")
{
Pred d = predict(cons, cpt, plst, nlst);
outpred(hOut, d, n, b, pos, neg);
if(bp != "") // output each gene's probability being in this cluster if output file is specified.
{
ofstream hbp(bp.data());
if(!hbp)
{
cerr << "Can't open " << bp << endl;
return 1;
}
vector<BPred> trnbp = predict(cons, cpt, genlst, 0); // probabilities for training genes.
outpred(hbp, trnbp);
vector<string> tstlst; // probabilities for testing genes.
tstlst.insert(tstlst.end(), plst.begin(), plst.end()); // positive testings.
tstlst.insert(tstlst.end(), nlst.begin(), nlst.end()); // negative testings.
vector<BPred> tstbp = predict(cons, cpt, tstlst, 1);
outpred(hbp, tstbp);
if(res != "") // probabilities for left-out genes if the left-out file is specified.
{
vector<BPred> lefbp = predict(cons, cpt, rlst, -1);
outpred(hbp, lefbp);
}
hbp.close();
}
}
hOut << endl << "Bayesian network occupied CPU " << (double)(finish-start)/CLOCKS_PER_SEC << " seconds." << endl;
hOut.close();
return 0;
}
