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#ifndef LINEARALGEBRA
#define LINEARALGEBRA
/*
* linearalgebra.h
* mothur
*
* Created by westcott on 1/7/11.
* Copyright 2011 Schloss Lab. All rights reserved.
*
*/
#include "mothurout.h"
#include "utils.hpp"
class LinearAlgebra {
public:
LinearAlgebra() { m = MothurOut::getInstance(); }
~LinearAlgebra() = default;
vector<vector<double> > matrix_mult(vector<vector<double> >, vector<vector<double> >);
vector<vector<double> >transpose(vector<vector<double> >);
void recenter(double, vector<vector<double> >, vector<vector<double> >&);
//eigenvectors
int tred2(vector<vector<double> >&, vector<double>&, vector<double>&);
int qtli(vector<double>&, vector<double>&, vector<vector<double> >&);
vector< vector<double> > calculateEuclidianDistance(vector<vector<double> >&, int); //pass in axes and number of dimensions
vector< vector<double> > calculateEuclidianDistance(vector<vector<double> >&); //pass in axes
vector<vector<double> > getObservedEuclideanDistance(vector<vector<double> >&);
double calcPearson(vector<vector<double> >&, vector<vector<double> >&);
double calcSpearman(vector<vector<double> >&, vector<vector<double> >&);
double calcKendall(vector<vector<double> >&, vector<vector<double> >&);
double calcKruskalWallis(vector<spearmanRank>&, double&);
double calcWilcoxon(vector<double>&, vector<double>&, double&);
double calcPearson(vector<double>&, vector<double>&, double&);
double calcSpearman(vector<double>&, vector<double>&, double&);
double calcKendall(vector<double>&, vector<double>&, double&);
double calcSpearmanSig(double, double, double, double); //length, f^3 - f where f is the number of ties in x, f^3 - f where f is the number of ties in y, sum of squared diffs in ranks. - designed to find the sif of one score.
double calcPearsonSig(double, double); //length, coeff.
double calcKendallSig(double, double); //length, coeff.
vector<double> solveEquations(vector<vector<double> >, vector<double>);
vector<float> solveEquations(vector<vector<float> >, vector<float>);
vector<vector<double> > getInverse(vector<vector<double> >);
double choose(double, double);
double normalvariate(double mu, double sigma);
vector< vector<double> > lda(vector< vector<double> >& a, vector<string> groups, vector< vector<double> >& means, bool&); //Linear discriminant analysis - a is [features][valuesFromGroups] groups indicates which group each sampling comes from. For example if groups = early, late, mid, early, early. a[0][0] = value for feature0 from groupEarly.
int svd(vector< vector<double> >& a, vector<double>& w, vector< vector<double> >& v); //Singular value decomposition
private:
MothurOut* m;
Utils util;
double pythag(double, double);
double betacf(const double, const double, const double);
double betai(const double, const double, const double);
double gammln(const double);
//double gammq(const double, const double);
double gser(double&, const double, const double, double&);
double gcf(double&, const double, const double, double&);
double erfcc(double);
double gammp(const double, const double);
double pnorm(double x);
double ran0(int&); //for testing
double ran1(int&); //for testing
double ran2(int&); //for testing
double ran3(int&); //for testing
double ran4(int&); //for testing
void psdes(unsigned long &, unsigned long &); //for testing
void ludcmp(vector<vector<double> >&, vector<int>&, double&);
void lubksb(vector<vector<double> >&, vector<int>&, vector<double>&);
void ludcmp(vector<vector<float> >&, vector<int>&, float&);
void lubksb(vector<vector<float> >&, vector<int>&, vector<float>&);
};
#endif
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