X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=bayesian.cpp;h=af1fa27f2202de76086e562c775229f4f241e387;hb=30f2d98fffb579b870c8969ddcc1dfc61ccbb70a;hp=3a2d3e0156468f1114dd0ebb2d47fe87fe577789;hpb=7b3c9ca940891c1b20b3b7ec13e05d7e7b316b63;p=mothur.git

diff --git a/bayesian.cpp b/bayesian.cpp
index 3a2d3e0..af1fa27 100644
--- a/bayesian.cpp
+++ b/bayesian.cpp
@@ -11,8 +11,8 @@
 #include "kmer.hpp"
 
 /**************************************************************************************************/
-Bayesian::Bayesian(string tfile, string tempFile, string method, int ksize, int cutoff) : 
-Classify(tfile, tempFile, method, ksize, 0, 0, 0, 0), kmerSize(ksize), confidenceThreshold(cutoff)  {
+Bayesian::Bayesian(string tfile, string tempFile, string method, int ksize, int cutoff, int i) : 
+Classify(tfile, tempFile, method, ksize, 0.0, 0.0, 0.0, 0.0), kmerSize(ksize), confidenceThreshold(cutoff), iters(i)  {
 	try {
 					
 		numKmers = database->getMaxKmer() + 1;
@@ -43,10 +43,10 @@ Classify(tfile, tempFile, method, ksize, 0, 0, 0, 0), kmerSize(ksize), confidenc
 		int start = time(NULL);
 		
 		if(probFileTest && probFileTest2){	
-			mothurOut("Reading template probabilities...     "); cout.flush();
+			m->mothurOut("Reading template probabilities...     "); cout.flush();
 			readProbFile(probFileTest, probFileTest2);	
 		}else{
-			mothurOut("Calculating template probabilities...     "); cout.flush();
+			m->mothurOut("Calculating template probabilities...     "); cout.flush();
 
 			ofstream out;
 			openOutputFile(probFileName, out);
@@ -56,6 +56,7 @@ Classify(tfile, tempFile, method, ksize, 0, 0, 0, 0), kmerSize(ksize), confidenc
 			
 			//for each word
 			for (int i = 0; i < numKmers; i++) {
+				if (m->control_pressed) { break; }
 				
 				out << i << '\t';
 				
@@ -88,18 +89,18 @@ Classify(tfile, tempFile, method, ksize, 0, 0, 0, 0), kmerSize(ksize), confidenc
 		}
 		
 		
-		mothurOut("DONE."); mothurOutEndLine();
-		mothurOut("It took " + toString(time(NULL) - start) + " seconds get probabilities. "); mothurOutEndLine();
+		m->mothurOut("DONE."); m->mothurOutEndLine();
+		m->mothurOut("It took " + toString(time(NULL) - start) + " seconds get probabilities. "); m->mothurOutEndLine();
 	}
 	catch(exception& e) {
-		errorOut(e, "Bayesian", "getTaxonomy");
+		m->errorOut(e, "Bayesian", "Bayesian");
 		exit(1);
 	}
 }
 /**************************************************************************************************/
 string Bayesian::getTaxonomy(Sequence* seq) {
 	try {
-		string tax;
+		string tax = "";
 		Kmer kmer(kmerSize);
 		
 		//get words contained in query
@@ -114,15 +115,17 @@ string Bayesian::getTaxonomy(Sequence* seq) {
 		}
 	
 		int index = getMostProbableTaxonomy(queryKmers);
+		
+		if (m->control_pressed) { return tax; }
 					
 		//bootstrap - to set confidenceScore
 		int numToSelect = queryKmers.size() / 8;
 		tax = bootstrapResults(queryKmers, index, numToSelect);
-				
+						
 		return tax;	
 	}
 	catch(exception& e) {
-		errorOut(e, "Bayesian", "getTaxonomy");
+		m->errorOut(e, "Bayesian", "getTaxonomy");
 		exit(1);
 	}
 }
@@ -142,7 +145,9 @@ string Bayesian::bootstrapResults(vector<int> kmers, int tax, int numToSelect) {
 		map<string, int>::iterator itBoot2;
 		map<int, int>::iterator itConvert;
 		
-		for (int i = 0; i < 100; i++) {
+		for (int i = 0; i < iters; i++) {
+			if (m->control_pressed) { return "control"; }
+			
 			vector<int> temp;
 						
 			for (int j = 0; j < numToSelect; j++) {
@@ -185,7 +190,7 @@ string Bayesian::bootstrapResults(vector<int> kmers, int tax, int numToSelect) {
 				}
 				
 				if (confidence >= confidenceThreshold) {
-					confidenceTax = seqTax.name + "(" + toString(confidence) + ");" + confidenceTax;
+					confidenceTax = seqTax.name + "(" + toString(((confidence/(float)iters) * 100)) + ");" + confidenceTax;
 					simpleTax = seqTax.name + ";" + simpleTax;
 				}
 				
@@ -196,7 +201,7 @@ string Bayesian::bootstrapResults(vector<int> kmers, int tax, int numToSelect) {
 		
 	}
 	catch(exception& e) {
-		errorOut(e, "Bayesian", "bootstrapResults");
+		m->errorOut(e, "Bayesian", "bootstrapResults");
 		exit(1);
 	}
 }
@@ -225,7 +230,7 @@ int Bayesian::getMostProbableTaxonomy(vector<int> queryKmer) {
 		return indexofGenus;
 	}
 	catch(exception& e) {
-		errorOut(e, "Bayesian", "getMostProbableTaxonomy");
+		m->errorOut(e, "Bayesian", "getMostProbableTaxonomy");
 		exit(1);
 	}
 }
@@ -252,7 +257,7 @@ map<string, int> Bayesian::parseTaxMap(string newTax) {
 		
 	}
 	catch(exception& e) {
-		errorOut(e, "Bayesian", "parseTax");
+		m->errorOut(e, "Bayesian", "parseTax");
 		exit(1);
 	}
 }
@@ -291,7 +296,7 @@ void Bayesian::readProbFile(ifstream& in, ifstream& inNum) {
 		in.close();
 	}
 	catch(exception& e) {
-		errorOut(e, "Bayesian", "readProbFile");
+		m->errorOut(e, "Bayesian", "readProbFile");
 		exit(1);
 	}
 }