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//
// optidata.hpp
// Mothur
//
// Created by Sarah Westcott on 5/10/18.
// Copyright © 2018 Schloss Lab. All rights reserved.
//
#ifndef optidata_hpp
#define optidata_hpp
#include "mothurout.h"
#include "listvector.hpp"
#include "sparsedistancematrix.h"
#include "counttable.h"
/*
#ifdef UNIT_TEST
friend class TestOptiMatrix;
friend class FakeOptiMatrix;
#endif
*/
class OptiData {
public:
OptiData(double c) { m = MothurOut::getInstance(); cutoff = c; }
virtual ~OptiData(){}
set<long long> getCloseSeqs(long long i);
bool isClose(long long, long long);
long long getNumClose(long long);
map<string, long long> getNameIndexMap();
string getName(long long); //name from nameMap index
set<string> getNames(set<long long>); //name from nameMap index
long long getNumSeqs() { return closeness.size(); }
long long getNumSingletons() { return singletons.size(); }
virtual long long getNumDists(); //number of distances under cutoff
ListVector* getListSingle();
//for mgcluster - reading blast files
virtual vector< set<long long> > getBlastOverlap() { vector< set<long long> > blank; return blank; }
virtual string getOverlapName(long long) { return ""; } //name from nameMap index
virtual void randomizeRefs(){};
virtual vector<string> getRefSingletonNames() { vector<string> temp; return temp; }
virtual vector<long long> getTranslatedBins(vector<vector<string> >&, vector< vector<long long> >&) { vector<long long> temp; return temp; }
virtual OptiData* extractRefMatrix() { OptiData* temp = nullptr; return temp; }
virtual OptiData* extractMatrixSubset(set<long long>&) { OptiData* temp = nullptr; return temp; }
virtual OptiData* extractMatrixSubset(set<string>&) { OptiData* temp = nullptr; return temp; }
virtual long long getNumFitSingletons() { return 0; } //user singletons
virtual long long getNumFitDists() { return 0; } //user distances under cutoff
virtual long long getNumRefDists() { return 0; } //ref distances under cutoff
virtual ListVector* getFitListSingle() { ListVector* list = nullptr; return list; }
virtual long long getNumFitTrueSingletons() { return 0; }
virtual vector<long long> getRefSeqs() { vector<long long> temp; return temp; }
virtual vector<long long> getFitSeqs() { vector<long long> temp; return temp; }
virtual long long getNumFitSeqs() { return 0; }
virtual long long getNumFitClose(long long) { return 0; }
virtual long long getNumRefClose(long long) { return 0; }
virtual set<long long> getCloseFitSeqs(long long i) { set<long long> temp; return temp; }
virtual set<long long> getCloseRefSeqs(long long i) { set<long long> temp; return temp; }
virtual bool isCloseFit(long long j, long long i, bool&) { return false; }
virtual long long print(ostream&);
// in the case of all distances being below the cutoff, the TN and FP will always be 0
// This is because nothing is considered "far apart". The mcc score will always be 0.
// In this case we need to select a different calculator to cluster
bool mccValidCalc();
protected:
Utils util; MothurOut* m;
vector< set<long long> > closeness; //closeness[0] contains indexes of seqs "close" to seq 0.
vector<string> singletons; //name of seqs with NO distances in matrix, if name file is given then it contains 2nd column of namefile
vector<string> nameMap; //name of seqs with distances in matrix, if name file is given then it contains 2nd column of namefile
double cutoff;
set<long long> getIndexes(set<string> seqs);
};
#endif /* optidata_hpp */
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