8 * Created by westcott on 1/7/11.
9 * Copyright 2011 Schloss Lab. All rights reserved.
13 #include "mothurout.h"
19 LinearAlgebra() { m = MothurOut::getInstance(); }
22 vector<vector<double> > matrix_mult(vector<vector<double> >, vector<vector<double> >);
23 vector<vector<double> >transpose(vector<vector<double> >);
24 void recenter(double, vector<vector<double> >, vector<vector<double> >&);
26 int tred2(vector<vector<double> >&, vector<double>&, vector<double>&);
27 int qtli(vector<double>&, vector<double>&, vector<vector<double> >&);
29 vector< vector<double> > calculateEuclidianDistance(vector<vector<double> >&, int); //pass in axes and number of dimensions
30 vector< vector<double> > calculateEuclidianDistance(vector<vector<double> >&); //pass in axes
31 vector<vector<double> > getObservedEuclideanDistance(vector<vector<double> >&);
32 double calcPearson(vector<vector<double> >&, vector<vector<double> >&);
33 double calcSpearman(vector<vector<double> >&, vector<vector<double> >&);
34 double calcKendall(vector<vector<double> >&, vector<vector<double> >&);
35 double calcKruskalWallis(vector<spearmanRank>&, double&);
36 double calcWilcoxon(vector<double>&, vector<double>&, double&);
38 double calcPearson(vector<double>&, vector<double>&, double&);
39 double calcSpearman(vector<double>&, vector<double>&, double&);
40 double calcKendall(vector<double>&, vector<double>&, double&);
42 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.
43 double calcPearsonSig(double, double); //length, coeff.
44 double calcKendallSig(double, double); //length, coeff.
46 vector<double> solveEquations(vector<vector<double> >, vector<double>);
47 vector<float> solveEquations(vector<vector<float> >, vector<float>);
48 vector<vector<double> > getInverse(vector<vector<double> >);
49 double choose(double, double);
50 double normalvariate(double mu, double sigma);
51 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.
52 int svd(vector< vector<double> >& a, vector<double>& w, vector< vector<double> >& v); //Singular value decomposition
56 double pythag(double, double);
57 double betacf(const double, const double, const double);
58 double betai(const double, const double, const double);
59 double gammln(const double);
60 double gammq(const double, const double);
61 double gser(double&, const double, const double, double&);
62 double gcf(double&, const double, const double, double&);
64 double gammp(const double, const double);
65 double pnorm(double x);
67 double ran0(int&); //for testing
68 double ran1(int&); //for testing
69 double ran2(int&); //for testing
70 double ran3(int&); //for testing
71 double ran4(int&); //for testing
72 void psdes(unsigned long &, unsigned long &); //for testing
74 void ludcmp(vector<vector<double> >&, vector<int>&, double&);
75 void lubksb(vector<vector<double> >&, vector<int>&, vector<double>&);
77 void ludcmp(vector<vector<float> >&, vector<int>&, float&);
78 void lubksb(vector<vector<float> >&, vector<int>&, vector<float>&);