~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> >&);
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;
void ludcmp(vector<vector<float> >&, vector<int>&, float&);
void lubksb(vector<vector<float> >&, vector<int>&, vector<float>&);
-
-
};
#endif