fixed bug with shhh.flow from file path name in write functions, added "smart" featur...

[mothur.git] / classifyseqscommand.h

diff --git a/classifyseqscommand.h b/classifyseqscommand.h
index 0e21a203caaa0c4a8166d7ecb08a685cd36f3169..0bf4a9154412080d28fa065ab33789279832fbeb 100644 (file)
--- a/classifyseqscommand.h
+++ b/classifyseqscommand.h
@@ -72,16 +72,16 @@ private:
        string fastaFileName, templateFileName, distanceFileName, namefile, search, method, taxonomyFileName, outputDir, groupfile;
        int processors, kmerSize, numWanted, cutoff, iters;
        float match, misMatch, gapOpen, gapExtend;
-       bool abort, probs, save;
+       bool abort, probs, save, flip;
        
-       int driver(linePair*, string, string, string);
+       int driver(linePair*, string, string, string, string);
        void appendTaxFiles(string, string);
-       int createProcesses(string, string, string); 
+       int createProcesses(string, string, string, string); 
        string addUnclassifieds(string, int);
        
        int MPIReadNamesFile(string);
        #ifdef USE_MPI
-       int driverMPI(int, int, MPI_File&, MPI_File&, MPI_File&, vector<unsigned long long>&);
+       int driverMPI(int, int, MPI_File&, MPI_File&, MPI_File&, MPI_File&, vector<unsigned long long>&);
        #endif
 };
 
@@ -93,16 +93,17 @@ struct classifyData {
        string taxFName; 
        string tempTFName; 
        string filename;
-       string search, taxonomyFileName, templateFileName, method;
+       string search, taxonomyFileName, templateFileName, method, accnos;
        unsigned long long start;
        unsigned long long end;
        MothurOut* m;
        float match, misMatch, gapOpen, gapExtend;
        int count, kmerSize, threadID, cutoff, iters, numWanted;
-       bool probs;
+       bool probs, flip;
         
        classifyData(){}
-       classifyData(bool p, string me, string te, string tx, string a, string r, string f, string se, int ks, int i, int numW, MothurOut* mout, unsigned long long st, unsigned long long en, float ma, float misMa, float gapO, float gapE, int cut, int tid) {
+       classifyData(string acc, bool p, string me, string te, string tx, string a, string r, string f, string se, int ks, int i, int numW, MothurOut* mout, unsigned long long st, unsigned long long en, float ma, float misMa, float gapO, float gapE, int cut, int tid, bool fli) {
+               accnos = acc;
                taxonomyFileName = tx;
                templateFileName = te;
                taxFName = a;
@@ -124,6 +125,7 @@ struct classifyData {
                threadID = tid;
                probs = p;
                count = 0;
+               flip = fli;
        }
 };
 
@@ -141,6 +143,9 @@ static DWORD WINAPI MyClassThreadFunction(LPVOID lpParam){
                ofstream outTaxSimple;
                pDataArray->m->openOutputFile(pDataArray->tempTFName, outTaxSimple);
                
+               ofstream outAcc;
+               pDataArray->m->openOutputFile(pDataArray->accnos, outAcc);
+               
                ifstream inFASTA;
                pDataArray->m->openInputFile(pDataArray->filename, inFASTA);
                
@@ -157,12 +162,12 @@ static DWORD WINAPI MyClassThreadFunction(LPVOID lpParam){
                
                //make classify
                Classify* myclassify;
-               if(pDataArray->method == "bayesian"){   myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID);           }
-               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);                           }
+               if(pDataArray->method == "bayesian"){   myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID, pDataArray->flip);         }
+               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, pDataArray->flipThreshold);                                }
                else {
                        pDataArray->m->mothurOut(pDataArray->search + " is not a valid method option. I will run the command using bayesian.");
                        pDataArray->m->mothurOutEndLine();
-                       myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID);   
+                       myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID, pDataArray->flip); 
                }
                
                if (pDataArray->m->control_pressed) { delete myclassify; return 0; }
@@ -180,16 +185,19 @@ static DWORD WINAPI MyClassThreadFunction(LPVOID lpParam){
                                
                                if (pDataArray->m->control_pressed) { delete candidateSeq; return 0; }
                                
-                               if (taxonomy != "bad seq") {
-                                       //output confidence scores or not
-                                       if (pDataArray->probs) {
-                                               outTax << candidateSeq->getName() << '\t' << taxonomy << endl;
-                                       }else{
-                                               outTax << candidateSeq->getName() << '\t' << myclassify->getSimpleTax() << endl;
-                                       }
-                                       
-                                       outTaxSimple << candidateSeq->getName() << '\t' << myclassify->getSimpleTax() << endl;
+                               if (taxonomy == "unknown;") { pDataArray->m->mothurOut("[WARNING]: " + candidateSeq->getName() + " could not be classified. You can use the remove.lineage command with taxon=unknown; to remove such sequences."); pDataArray->m->mothurOutEndLine(); }
+
+                               //output confidence scores or not
+                               if (pDataArray->probs) {
+                                       outTax << candidateSeq->getName() << '\t' << taxonomy << endl;
+                               }else{
+                                       outTax << candidateSeq->getName() << '\t' << myclassify->getSimpleTax() << endl;
                                }
+                                       
+                               outTaxSimple << candidateSeq->getName() << '\t' << myclassify->getSimpleTax() << endl;
+                                       
+                               if (myclassify->getFlipped()) { outAcc << candidateSeq->getName() << endl; }
+                               
                                count++;
                        }
                        delete candidateSeq;