X-Git-Url: https://git.donarmstrong.com/?p=mothur.git;a=blobdiff_plain;f=unifracweightedcommand.h;h=a5507cdb6cc2d95152eec0241f386acd66de1aab;hp=fead41b0e8cb5dae95eed551ff76427b8c8d85f3;hb=b206f634aae1b4ce13978d203247fb64757d5482;hpb=90708fe9701e3827e477c82fb3652539c3bf2a0d

diff --git a/unifracweightedcommand.h b/unifracweightedcommand.h
index fead41b..a5507cd 100644
--- a/unifracweightedcommand.h
+++ b/unifracweightedcommand.h
@@ -28,8 +28,9 @@ class UnifracWeightedCommand : public Command {
 		vector<string> setParameters();
 		string getCommandName()			{ return "unifrac.weighted";		}
 		string getCommandCategory()		{ return "Hypothesis Testing";		}
-		string getOutputFileNameTag(string, string);
+		
 	string getHelpString();	
+    string getOutputPattern(string);	
 		string getCitation() { return "Lozupone CA, Hamady M, Kelley ST, Knight R (2007). Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol 73: 1576-85. \nhttp://www.mothur.org/wiki/Unifrac.weighted"; }
 		string getDescription()		{ return "generic tests that describes whether two or more communities have the same structure"; }
 
@@ -53,9 +54,9 @@ class UnifracWeightedCommand : public Command {
 		int iters, numGroups, numComp, counter;
 		vector< vector<double> > rScores;  //vector<weighted scores for random trees.> each group comb has an entry
 		vector< vector<double> > uScores;  //vector<weighted scores for user trees.> each group comb has an entry
-		vector< map<float, float> > rScoreFreq;  //map <weighted score, number of random trees with that score.> -vector entry for each combination.
-		vector< map<float, float> > rCumul;  //map <weighted score, cumulative percentage of number of random trees with that score or higher.> -vector entry for each c								
-		map<float, float>  validScores;  //map contains scores from random
+		vector< map<double, double> > rScoreFreq;  //map <weighted score, number of random trees with that score.> -vector entry for each combination.
+		vector< map<double, double> > rCumul;  //map <weighted score, cumulative percentage of number of random trees with that score or higher.> -vector entry for each c								
+		map<double, double>  validScores;  //map contains scores from random
 		
 		bool abort, phylip, random, includeRoot, subsample, consensus;
 		string groups, itersString, outputForm, treefile, groupfile, namefile, countfile;
@@ -79,6 +80,77 @@ class UnifracWeightedCommand : public Command {
 		
 };
 
+/***********************************************************************/
+struct weightedRandomData {
+    int start;
+	int num;
+	MothurOut* m;
+    vector< vector<double> > scores;
+    vector< vector<string> > namesOfGroupCombos;
+    Tree* t;
+    CountTable* ct;
+    bool includeRoot;
+	
+	weightedRandomData(){}
+	weightedRandomData(MothurOut* mout, int st, int en, vector< vector<string> > ngc, Tree* tree, CountTable* count, bool ir, vector< vector<double> > sc) {
+        m = mout;
+		start = st;
+		num = en;
+        namesOfGroupCombos = ngc;
+        t = tree;
+        ct = count;
+        includeRoot = ir;
+        scores = sc;
+	}
+};
+
+/**************************************************************************************************/
+#if defined (__APPLE__) || (__MACH__) || (linux) || (__linux) || (__linux__) || (__unix__) || (__unix)
+#else
+static DWORD WINAPI MyWeightedRandomThreadFunction(LPVOID lpParam){
+	weightedRandomData* pDataArray;
+	pDataArray = (weightedRandomData*)lpParam;
+	try {
+        
+        Tree* randT = new Tree(pDataArray->ct);
+        
+        Weighted weighted(pDataArray->includeRoot);
+        
+		for (int h = pDataArray->start; h < (pDataArray->start+pDataArray->num); h++) {
+            
+			if (pDataArray->m->control_pressed) { return 0; }
+            
+			//initialize weighted score
+			string groupA = pDataArray->namesOfGroupCombos[h][0];
+			string groupB = pDataArray->namesOfGroupCombos[h][1];
+			
+			//copy T[i]'s info.
+			randT->getCopy(pDataArray->t);
+            
+			//create a random tree with same topology as T[i], but different labels
+			randT->assembleRandomUnifracTree(groupA, groupB);
+			
+			if (pDataArray->m->control_pressed) { delete randT;  return 0;  }
+            
+			//get wscore of random tree
+			EstOutput randomData = weighted.getValues(randT, groupA, groupB);
+            
+			if (pDataArray->m->control_pressed) { delete randT;  return 0;  }
+            
+			//save scores
+			pDataArray->scores[h].push_back(randomData[0]);
+		}
+        
+		delete randT;
+        
+        return 0;
+    }
+	catch(exception& e) {
+		pDataArray->m->errorOut(e, "Weighted", "MyWeightedRandomThreadFunction");
+		exit(1);
+	}
+}
+#endif
 
 
 #endif