added sorted parameter to get.oturep, added error checking to chimera classes in...

[mothur.git] / classifyseqscommand.cpp

diff --git a/classifyseqscommand.cpp b/classifyseqscommand.cpp
index 2b76e5083d1e08d2d9dc29382688081cf462b953..eeef1cc95d87f3eb3a3c15c6b3dd9aea606795eb 100644 (file)
--- a/classifyseqscommand.cpp
+++ b/classifyseqscommand.cpp
@@ -25,7 +25,7 @@ ClassifySeqsCommand::ClassifySeqsCommand(string option){
                else {
                        
                        //valid paramters for this command
-                       string AlignArray[] =  {"template","fasta","search","ksize","method","processors","taxonomy","match","mismatch","gapopen","gapextend","numwanted","cutoff"};
+                       string AlignArray[] =  {"template","fasta","search","ksize","method","processors","taxonomy","match","mismatch","gapopen","gapextend","numwanted","cutoff","probs"};
                        vector<string> myArray (AlignArray, AlignArray+(sizeof(AlignArray)/sizeof(string)));
                        
                        OptionParser parser(option);
@@ -94,6 +94,9 @@ ClassifySeqsCommand::ClassifySeqsCommand(string option){
                        
                        temp = validParameter.validFile(parameters, "cutoff", false);           if (temp == "not found"){       temp = "0";                             }
                        convert(temp, cutoff);
+                       
+                       temp = validParameter.validFile(parameters, "probs", false);            if (temp == "not found"){       temp = "true";                  }
+                       probs = isTrue(temp);
 
                        
                        if ((method == "bayesian") && (search != "kmer"))  { 
@@ -123,7 +126,7 @@ ClassifySeqsCommand::~ClassifySeqsCommand(){
 void ClassifySeqsCommand::help(){
        try {
                mothurOut("The classify.seqs command reads a fasta file containing sequences and creates a .taxonomy file and a .tax.summary file.\n");
-               mothurOut("The classify.seqs command parameters are template, fasta, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend and numwanted.\n");
+               mothurOut("The classify.seqs command parameters are template, fasta, search, ksize, method, taxonomy, processors, match, mismatch, gapopen, gapextend, numwanted and probs.\n");
                mothurOut("The template, fasta and taxonomy parameters are required.\n");
                mothurOut("The search parameter allows you to specify the method to find most similar template.  Your options are: suffix, kmer and blast. The default is kmer.\n");
                mothurOut("The method parameter allows you to specify classification method to use.  Your options are: bayesian and knn. The default is bayesian.\n");
@@ -135,6 +138,7 @@ void ClassifySeqsCommand::help(){
                mothurOut("The gapextend parameter allows you to specify the penalty for extending a gap in an alignment.  The default is -2.0.\n");
                mothurOut("The numwanted parameter allows you to specify the number of sequence matches you want with the knn method.  The default is 10.\n");
                mothurOut("The cutoff parameter allows you to specify a bootstrap confidence threshold for your taxonomy.  The default is 0.\n");
+               mothurOut("The probs parameter shut off the bootstrapping results for the bayesian method. The default is true, meaning you want the bootstrapping to be run.\n");
                mothurOut("The classify.seqs command should be in the following format: \n");
                mothurOut("classify.seqs(template=yourTemplateFile, fasta=yourFastaFile, method=yourClassificationMethod, search=yourSearchmethod, ksize=yourKmerSize, taxonomy=yourTaxonomyFile, processors=yourProcessors) \n");
                mothurOut("Example classify.seqs(fasta=amazon.fasta, template=core.filtered, method=knn, search=gotoh, ksize=8, processors=2)\n");
@@ -155,14 +159,12 @@ int ClassifySeqsCommand::execute(){
        try {
                if (abort == true) {    return 0;       }
                
-               
-               
-               if(method == "bayesian")                        {       classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff);          }
+               if(method == "bayesian")                        {       classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, probs);           }
                else if(method == "knn")                        {       classify = new Knn(taxonomyFileName, templateFileName, search, kmerSize, gapOpen, gapExtend, match, misMatch, numWanted);                               }
                else {
                        mothurOut(search + " is not a valid method option. I will run the command using bayesian.");
                        mothurOutEndLine();
-                       classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff);  
+                       classify = new Bayesian(taxonomyFileName, templateFileName, search, kmerSize, cutoff, probs);   
                }
 
                int numFastaSeqs = 0;