string fastaFileName, templateFileName, countfile, distanceFileName, namefile, search, method, taxonomyFileName, outputDir, groupfile;
int processors, kmerSize, numWanted, cutoff, iters;
float match, misMatch, gapOpen, gapExtend;
- bool abort, probs, save, flip, hasName, hasCount, writeShortcuts;
+ bool abort, probs, save, flip, hasName, hasCount, writeShortcuts, relabund;
int driver(linePair*, string, string, string, string);
int createProcesses(string, string, string, string);
inFASTA.seekg(pDataArray->start-1); pDataArray->m->gobble(inFASTA);
}
- pDataArray->count = pDataArray->end;
-
//make classify
Classify* myclassify;
string outputMethodTag = pDataArray->method + ".";
- if(pDataArray->method == "bayesian"){ myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID, pDataArray->flip, pDataArray->writeShortcuts); }
+ if(pDataArray->method == "wang"){ myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID, pDataArray->flip, pDataArray->writeShortcuts); }
else if(pDataArray->method == "knn"){ myclassify = new Knn(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->gapOpen, pDataArray->gapExtend, pDataArray->match, pDataArray->misMatch, pDataArray->numWanted, pDataArray->threadID); }
else if(pDataArray->method == "zap"){
outputMethodTag = pDataArray->search + "_" + outputMethodTag;
else { myclassify = new AlignTree(pDataArray->templateFileName, pDataArray->taxonomyFileName, pDataArray->cutoff); }
}
else {
- pDataArray->m->mothurOut(pDataArray->search + " is not a valid method option. I will run the command using bayesian.");
+ pDataArray->m->mothurOut(pDataArray->method + " is not a valid method option. I will run the command using wang.");
pDataArray->m->mothurOutEndLine();
myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID, pDataArray->flip, pDataArray->writeShortcuts);
}
if (pDataArray->m->control_pressed) { delete myclassify; return 0; }
- int count = 0;
+ pDataArray->count = 0;
for(int i = 0; i < pDataArray->end; i++){ //end is the number of sequences to process
if (pDataArray->m->control_pressed) { delete myclassify; return 0; }
if (myclassify->getFlipped()) { outAcc << candidateSeq->getName() << endl; }
- count++;
+ pDataArray->count++;
}
delete candidateSeq;
//report progress
- if((count) % 100 == 0){ pDataArray->m->mothurOut("Processing sequence: " + toString(count)); pDataArray->m->mothurOutEndLine(); }
+ if((pDataArray->count) % 100 == 0){ pDataArray->m->mothurOutJustToScreen("Processing sequence: " + toString(pDataArray->count)+"\n"); }
}
//report progress
- if((count) % 100 != 0){ pDataArray->m->mothurOut("Processing sequence: " + toString(count)); pDataArray->m->mothurOutEndLine(); }
+ if((pDataArray->count) % 100 != 0){ pDataArray->m->mothurOutJustToScreen("Processing sequence: " + toString(pDataArray->count)+"\n"); }
delete myclassify;
inFASTA.close();