Bayesian::Bayesian(string tfile, string tempFile, string method, int ksize, int cutoff, int i) :
Classify(), kmerSize(ksize), confidenceThreshold(cutoff), iters(i) {
try {
-
+
/************calculate the probablity that each word will be in a specific taxonomy*************/
string tfileroot = tfile.substr(0,tfile.find_last_of(".")+1);
- string tempfileroot = getRootName(getSimpleName(tempFile));
+ string tempfileroot = m->getRootName(m->getSimpleName(tempFile));
string phyloTreeName = tfileroot + "tree.train";
string phyloTreeSumName = tfileroot + "tree.sum";
string probFileName = tfileroot + tempfileroot + char('0'+ kmerSize) + "mer.prob";
//initialze probabilities
wordGenusProb.resize(numKmers);
-
+ //cout << numKmers << '\t' << genusNodes.size() << endl;
for (int j = 0; j < wordGenusProb.size(); j++) { wordGenusProb[j].resize(genusNodes.size()); }
-
+ //cout << numKmers << '\t' << genusNodes.size() << endl;
ofstream out;
ofstream out2;
#endif
- openOutputFile(probFileName, out);
+ m->openOutputFile(probFileName, out);
//output mothur version
out << "#" << m->getVersion() << endl;
out << numKmers << endl;
- openOutputFile(probFileName2, out2);
+ m->openOutputFile(probFileName2, out2);
//output mothur version
out2 << "#" << m->getVersion() << endl;
/**************************************************************************************************/
Bayesian::~Bayesian() {
try {
+
delete phyloTree;
if (database != NULL) { delete database; }
}
//get words contained in query
//getKmerString returns a string where the index in the string is hte kmer number
//and the character at that index can be converted to be the number of times that kmer was seen
-
+
string queryKmerString = kmer.getKmerString(seq->getUnaligned());
vector<int> queryKmers;
if (queryKmers.size() == 0) { m->mothurOut(seq->getName() + "is bad."); m->mothurOutEndLine(); return "bad seq"; }
+
int index = getMostProbableTaxonomy(queryKmers);
if (m->control_pressed) { return tax; }
-//cout << seq->getName() << '\t' << index << endl;
+
//bootstrap - to set confidenceScore
int numToSelect = queryKmers.size() / 8;
+
tax = bootstrapResults(queryKmers, index, numToSelect);
-
+
return tax;
}
catch(exception& e) {
try {
map<int, int> confidenceScores;
+
+ //initialize confidences to 0
+ int seqIndex = tax;
+ TaxNode seq = phyloTree->get(tax);
+ confidenceScores[tax] = 0;
+
+ while (seq.level != 0) { //while you are not at the root
+ seqIndex = seq.parent;
+ confidenceScores[seqIndex] = 0;
+ seq = phyloTree->get(seq.parent);
+ }
map<int, int>::iterator itBoot;
map<int, int>::iterator itBoot2;
if (m->control_pressed) { return "control"; }
vector<int> temp;
-
for (int j = 0; j < numToSelect; j++) {
int index = int(rand() % kmers.size());
//get taxonomy
int newTax = getMostProbableTaxonomy(temp);
+ //int newTax = 1;
TaxNode taxonomyTemp = phyloTree->get(newTax);
-
+
//add to confidence results
while (taxonomyTemp.level != 0) { //while you are not at the root
-
itBoot2 = confidenceScores.find(newTax); //is this a classification we already have a count on
- if (itBoot2 == confidenceScores.end()) { //not already in confidence scores
- confidenceScores[newTax] = 1;
- }else{
- confidenceScores[newTax]++;
+ if (itBoot2 != confidenceScores.end()) { //this is a classification we need a confidence for
+ (itBoot2->second)++;
}
newTax = taxonomyTemp.parent;
int confidence = 0;
if (itBoot2 != confidenceScores.end()) { //already in confidence scores
- confidence = confidenceScores[seqTaxIndex];
+ confidence = itBoot2->second;
}
if (((confidence/(float)iters) * 100) >= confidenceThreshold) {
for (int i = 0; i < queryKmer.size(); i++) {
prob += wordGenusProb[queryKmer[i]][k];
}
-
+
//is this the taxonomy with the greatest probability?
if (prob > maxProbability) {
indexofGenus = genusNodes[k];
maxProbability = prob;
}
}
-
+// cout << phyloTree->get(indexofGenus).name << '\t' << maxProbability << endl;
return indexofGenus;
}
catch(exception& e) {
MPI_File_open(MPI_COMM_WORLD, inFileName2, MPI_MODE_RDONLY, MPI_INFO_NULL, &inMPI2); //comm, filename, mode, info, filepointer
if (pid == 0) {
- positions = setFilePosEachLine(inNumName, num);
- positions2 = setFilePosEachLine(inName, num2);
+ positions = m->setFilePosEachLine(inNumName, num);
+ positions2 = m->setFilePosEachLine(inName, num2);
for(int i = 1; i < processors; i++) {
MPI_Send(&num, 1, MPI_INT, i, tag, MPI_COMM_WORLD);
MPI_Barrier(MPI_COMM_WORLD); //make everyone wait - just in case
#else
//read version
- string line = getline(in); gobble(in);
+ string line = m->getline(in); m->gobble(in);
- in >> numKmers; gobble(in);
+ in >> numKmers; m->gobble(in);
//initialze probabilities
wordGenusProb.resize(numKmers);
vector<float> zeroCountProb; zeroCountProb.resize(numKmers);
//read version
- string line2 = getline(inNum); gobble(inNum);
+ string line2 = m->getline(inNum); m->gobble(inNum);
while (inNum) {
inNum >> zeroCountProb[count] >> num[count];
count++;
- gobble(inNum);
+ m->gobble(inNum);
}
inNum.close();
wordGenusProb[kmer][name] = prob;
}
- gobble(in);
+ m->gobble(in);
}
in.close();
bool good = true;
vector<string> lines;
- lines.push_back(getline(file1));
- lines.push_back(getline(file2));
- lines.push_back(getline(file3));
- lines.push_back(getline(file4));
+ lines.push_back(m->getline(file1));
+ lines.push_back(m->getline(file2));
+ lines.push_back(m->getline(file3));
+ lines.push_back(m->getline(file4));
//before we added this check
if ((lines[0][0] != '#') || (lines[1][0] != '#') || (lines[2][0] != '#') || (lines[3][0] != '#')) { good = false; }
string version = m->getVersion();
vector<string> versionVector;
- splitAtChar(version, versionVector, '.');
+ m->splitAtChar(version, versionVector, '.');
//check each files version
for (int i = 0; i < lines.size(); i++) {
vector<string> linesVector;
- splitAtChar(lines[i], linesVector, '.');
+ m->splitAtChar(lines[i], linesVector, '.');
if (versionVector.size() != linesVector.size()) { good = false; break; }
else {
projects / mothur.git / blobdiff