Merge remote-tracking branch 'origin/master'

[mothur.git] / classifyseqscommand.h

diff --git a/classifyseqscommand.h b/classifyseqscommand.h
index 6d43dcb9dd2067bab91f46d42ff8afa238a4a191..87d4f51cf78230f6ab301e1d72462a461b9f9da9 100644 (file)
--- a/classifyseqscommand.h
+++ b/classifyseqscommand.h
@@ -19,9 +19,11 @@
 #include "phylotree.h"
 #include "phylosummary.h"
 #include "knn.h"
+#include "kmertree.h"
+#include "aligntree.h"
 
 
-//KNN and Bayesian methods modeled from algorithms in
+//KNN and Wang methods modeled from algorithms in
 //Naı¨ve Bayesian Classifier for Rapid Assignment of rRNA Sequences 
 //into the New Bacterial Taxonomy􏰎† 
 //Qiong Wang,1 George M. Garrity,1,2 James M. Tiedje,1,2 and James R. Cole1* 
@@ -74,7 +76,7 @@ private:
        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;
+       bool abort, probs, save, flip, hasName, hasCount, writeShortcuts;
        
        int driver(linePair*, string, string, string, string);
        int createProcesses(string, string, string, string); 
@@ -100,10 +102,10 @@ struct classifyData {
        MothurOut* m;
        float match, misMatch, gapOpen, gapExtend;
        int count, kmerSize, threadID, cutoff, iters, numWanted;
-       bool probs, flip;
+       bool probs, flip, writeShortcuts;
         
        classifyData(){}
-       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) {
+       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, bool wsh) {
                accnos = acc;
                taxonomyFileName = tx;
                templateFileName = te;
@@ -127,6 +129,7 @@ struct classifyData {
                probs = p;
                count = 0;
                flip = fli;
+        writeShortcuts = wsh;
        }
 };
 
@@ -163,12 +166,18 @@ 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, pDataArray->flip);         }
+        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);             }
                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;
+            if (pDataArray->search == "kmer") {   myclassify = new KmerTree(pDataArray->templateFileName, pDataArray->taxonomyFileName, pDataArray->kmerSize, pDataArray->cutoff); }
+            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->mothurOutEndLine();
-                       myclassify = new Bayesian(pDataArray->taxonomyFileName, pDataArray->templateFileName, pDataArray->search, pDataArray->kmerSize, pDataArray->cutoff, pDataArray->iters, pDataArray->threadID, pDataArray->flip); 
+                       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; }