added sets to amova and homova commands. added oligos to make.contigs. added metadat...

[mothur.git] / bayesian.cpp

diff --git a/bayesian.cpp b/bayesian.cpp
index 54a123c5a7835d7c1a2c48d36fe3ec7070462027..49be4af57ff66f46ac04912535d7918518cc75d2 100644 (file)
--- a/bayesian.cpp
+++ b/bayesian.cpp
@@ -12,13 +12,14 @@
 #include "phylosummary.h"
 #include "referencedb.h"
 /**************************************************************************************************/
-Bayesian::Bayesian(string tfile, string tempFile, string method, int ksize, int cutoff, int i, int tid, bool f) : 
+Bayesian::Bayesian(string tfile, string tempFile, string method, int ksize, int cutoff, int i, int tid, bool f, bool sh) : 
 Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
        try {
                ReferenceDB* rdb = ReferenceDB::getInstance();
                
                threadID = tid;
                flip = f;
+        shortcuts = sh;
                string baseName = tempFile;
                        
                if (baseName == "saved") { baseName = rdb->getSavedReference(); }
@@ -27,7 +28,7 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                if (baseTName == "saved") { baseTName = rdb->getSavedTaxonomy(); }
                
                /************calculate the probablity that each word will be in a specific taxonomy*************/
-               string tfileroot = baseTName.substr(0,baseTName.find_last_of(".")+1);
+               string tfileroot = m->getFullPathName(baseTName.substr(0,baseTName.find_last_of(".")+1));
                string tempfileroot = m->getRootName(m->getSimpleName(baseName));
                string phyloTreeName = tfileroot + "tree.train";
                string phyloTreeSumName = tfileroot + "tree.sum";
@@ -63,7 +64,7 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                        }
                        saveIn.close();                 
                }
-               
+
                if(probFileTest && probFileTest2 && phyloTreeTest && probFileTest3 && FilesGood){       
                        if (tempFile == "saved") { m->mothurOutEndLine();  m->mothurOut("Using sequences from " + rdb->getSavedReference() + " that are saved in memory.");     m->mothurOutEndLine(); }
                        
@@ -113,7 +114,7 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                WordPairDiffArr.resize(numKmers);
                        
                                for (int j = 0; j < wordGenusProb.size(); j++) {        wordGenusProb[j].resize(genusNodes.size());             }
-                    ofstream out;
+                ofstream out;
                                ofstream out2;
                                
                                #ifdef USE_MPI
@@ -124,23 +125,24 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                #endif
 
                                
-                               m->openOutputFile(probFileName, out);
+                if (shortcuts) { 
+                    m->openOutputFile(probFileName, out); 
                                
-                               //output mothur version
-                               out << "#" << m->getVersion() << endl;
+                    //output mothur version
+                    out << "#" << m->getVersion() << endl;
                                
-                               out << numKmers << endl;
+                    out << numKmers << endl;
                                
-                               m->openOutputFile(probFileName2, out2);
+                    m->openOutputFile(probFileName2, out2);
                                
-                               //output mothur version
-                               out2 << "#" << m->getVersion() << endl;
+                    //output mothur version
+                    out2 << "#" << m->getVersion() << endl;
+                }
                                
                                #ifdef USE_MPI
                                        }
                                #endif
 
-                               
                                //for each word
                                for (int i = 0; i < numKmers; i++) {
                                        if (m->control_pressed) {  break; }
@@ -151,7 +153,7 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                                if (pid == 0) {  
                                        #endif
 
-                                       out << i << '\t';
+                    if (shortcuts) {  out << i << '\t'; }
                                        
                                        #ifdef USE_MPI
                                                }
@@ -159,12 +161,10 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                        
                                        vector<int> seqsWithWordi = database->getSequencesWithKmer(i);
                                        
-                                       map<int, int> count;
-                                       for (int k = 0; k < genusNodes.size(); k++) {  count[genusNodes[k]] = 0;  }                     
-                                                       
                                        //for each sequence with that word
+                    vector<int> count; count.resize(genusNodes.size(), 0);
                                        for (int j = 0; j < seqsWithWordi.size(); j++) {
-                                               int temp = phyloTree->getIndex(names[seqsWithWordi[j]]);
+                                               int temp = phyloTree->getGenusIndex(names[seqsWithWordi[j]]);
                                                count[temp]++;  //increment count of seq in this genus who have this word
                                        }
                                        
@@ -178,9 +178,9 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                                //probabilityInThisTaxonomy = (# of seqs with that word in this taxonomy + probabilityInTemplate) / (total number of seqs in this taxonomy + 1);
                                                
                                                
-                                               wordGenusProb[i][k] = log((count[genusNodes[k]] + probabilityInTemplate) / (float) (genusTotals[k] + 1));  
+                                               wordGenusProb[i][k] = log((count[k] + probabilityInTemplate) / (float) (genusTotals[k] + 1));  
                                                                        
-                                               if (count[genusNodes[k]] != 0) { 
+                                               if (count[k] != 0) { 
                                                        #ifdef USE_MPI
                                                                int pid;
                                                                MPI_Comm_rank(MPI_COMM_WORLD, &pid); //find out who we are
@@ -188,7 +188,7 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                                                if (pid == 0) {  
                                                        #endif
 
-                                                       out << k << '\t' << wordGenusProb[i][k] << '\t' ; 
+                            if (shortcuts) { out << k << '\t' << wordGenusProb[i][k] << '\t' ; }
                                                        
                                                        #ifdef USE_MPI
                                                                }
@@ -204,8 +204,10 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                                if (pid == 0) {  
                                        #endif
                                        
-                                       out << endl;
-                                       out2 << probabilityInTemplate << '\t' << numNotZero << '\t' << log(probabilityInTemplate) << endl;
+                            if (shortcuts) { 
+                                out << endl;
+                                out2 << probabilityInTemplate << '\t' << numNotZero << '\t' << log(probabilityInTemplate) << endl;
+                            }
                                        
                                        #ifdef USE_MPI
                                                }
@@ -218,9 +220,10 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                        if (pid == 0) {  
                                #endif
                                
-                               out.close();
-                               out2.close();
-                               
+                        if (shortcuts) { 
+                            out.close();
+                            out2.close();
+                        }
                                #ifdef USE_MPI
                                        }
                                #endif
@@ -230,7 +233,7 @@ Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
                                delete phyloTree;
                                
                                phyloTree = new PhyloTree(phyloTreeTest, phyloTreeName);
-                               
+                
                                //save probabilities
                                if (rdb->save) { rdb->wordGenusProb = wordGenusProb; rdb->WordPairDiffArr = WordPairDiffArr; }
                        }