X-Git-Url: https://git.donarmstrong.com/?p=mothur.git;a=blobdiff_plain;f=linearalgebra.h;h=e4c8c1588b60c3868de7f4e06353a43112ea3037;hp=ecb635f70672c8a8928090935fa8af724a5a819b;hb=cf9987b67aa49777a4c91c2d21f96e58bf17aa82;hpb=91a27e0483827c06c21c4fe89558923bbfe86573

diff --git a/linearalgebra.h b/linearalgebra.h
index ecb635f..e4c8c15 100644
--- a/linearalgebra.h
+++ b/linearalgebra.h
@@ -20,15 +20,20 @@ public:
 	~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&);
@@ -38,7 +43,13 @@ public:
     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;
 	
@@ -46,11 +57,12 @@ private:
     double betacf(const double, const double, const double);
     double betai(const double, const double, const double);
     double gammln(const double);
-    double gammp(const double, const double);
-    double gammq(const double, 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
@@ -59,6 +71,12 @@ private:
     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