X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=bayesian.cpp;h=49be4af57ff66f46ac04912535d7918518cc75d2;hb=a9dbc22713bfc056a797361dd757b1a5c98e1c01;hp=54a123c5a7835d7c1a2c48d36fe3ec7070462027;hpb=91a27e0483827c06c21c4fe89558923bbfe86573;p=mothur.git

diff --git a/bayesian.cpp b/bayesian.cpp
index 54a123c..49be4af 100644
--- 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; }
 			}