changing command name classify.shared to classifyrf.shared

[mothur.git] / classifysharedcommand.cpp

diff --git a/classifysharedcommand.cpp b/classifysharedcommand.cpp
index d9864b94e6eb43f0cba337175907edc65db63271..c7eb6cd0daa18f5627824a94d297270a1ff32147 100755 (executable)
--- a/classifysharedcommand.cpp
+++ b/classifysharedcommand.cpp
@@ -22,9 +22,9 @@ vector<string> ClassifySharedCommand::setParameters(){
                CommandParameter pnumtrees("numtrees", "Number", "", "100", "", "", "","",false,false); parameters.push_back(pnumtrees);
         
             // parameters related to pruning
-        CommandParameter pdopruning("prune", "Boolean", "", "F", "", "", "", "", false, false); parameters.push_back(pdopruning);
+        CommandParameter pdopruning("prune", "Boolean", "", "T", "", "", "", "", false, false); parameters.push_back(pdopruning);
         CommandParameter ppruneaggrns("pruneaggressiveness", "Number", "", "0.9", "", "", "", "", false, false); parameters.push_back(ppruneaggrns);
-        CommandParameter pdiscardhetrees("discarderrortrees", "Boolean", "", "F", "", "", "", "", false, false); parameters.push_back(pdiscardhetrees);
+        CommandParameter pdiscardhetrees("discarderrortrees", "Boolean", "", "T", "", "", "", "", false, false); parameters.push_back(pdiscardhetrees);
         CommandParameter phetdiscardthreshold("errorthreshold", "Number", "", "0.4", "", "", "", "", false, false); parameters.push_back(phetdiscardthreshold);
         CommandParameter psdthreshold("stdthreshold", "Number", "", "0.0", "", "", "", "", false, false); parameters.push_back(psdthreshold);
             // pruning params end
@@ -359,8 +359,11 @@ void ClassifySharedCommand::processSharedAndDesignData(vector<SharedRAbundVector
         
         vector< vector<int> > dataSet(numRows, vector<int>(numColumns, 0));
         
+        vector<string> names;
+        
         for (int i = 0; i < lookup.size(); i++) {
             string sharedGroupName = lookup[i]->getGroup();
+            names.push_back(sharedGroupName);
             string treatmentName = designMap.getGroup(sharedGroupName);
             
             int j = 0;
@@ -375,15 +378,24 @@ void ClassifySharedCommand::processSharedAndDesignData(vector<SharedRAbundVector
         
         randomForest.populateDecisionTrees();
         randomForest.calcForrestErrorRate();
+        randomForest.printConfusionMatrix(intToTreatmentMap);
         
         map<string, string> variables; 
-        variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile));
+        variables["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile)) + "RF.";
         variables["[distance]"] = lookup[0]->getLabel();
         string filename = getOutputFileName("summary", variables);
         outputNames.push_back(filename); outputTypes["summary"].push_back(filename);
-        
         randomForest.calcForrestVariableImportance(filename);
         
+        //
+        map<string, string> variable; 
+        variable["[filename]"] = outputDir + m->getRootName(m->getSimpleName(sharedfile)) + "misclassifications.";
+        variable["[distance]"] = lookup[0]->getLabel();
+        string mc_filename = getOutputFileName("summary", variable);
+        outputNames.push_back(mc_filename); outputTypes["summary"].push_back(mc_filename);
+        randomForest.getMissclassifications(mc_filename, intToTreatmentMap, names);
+        //
+        
         m->mothurOutEndLine();
     }
     catch(exception& e) {