~LinearAlgebra() {}
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> >&);
- int tred2(vector<vector<double> >&, vector<double>&, 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;
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