mods in testing 1.16.0

[mothur.git] / linearalgebra.cpp

diff --git a/linearalgebra.cpp b/linearalgebra.cpp
index e015b5fa1472d9e587a8f24fe008f6af4f049c84..a2ee221b9d79c0997f25f6c9baf09cdd0b752bf2 100644 (file)
--- a/linearalgebra.cpp
+++ b/linearalgebra.cpp
@@ -172,7 +172,7 @@ int LinearAlgebra::qtli(vector<double>& d, vector<double>& e, vector<vector<doub
                                        if(fabs(e[myM])+dd == dd) break;
                                }
                                if(myM != l){
-                                       if(iter++ == 30) cerr << "Too many iterations in tqli\n";
+                                       if(iter++ == 3000) cerr << "Too many iterations in tqli\n";
                                        g = (d[l+1]-d[l]) / (2.0 * e[l]);
                                        r = pythag(g, 1.0);
                                        g = d[myM] - d[l] + e[l] / (g + SIGN(r,g));
@@ -234,5 +234,155 @@ int LinearAlgebra::qtli(vector<double>& d, vector<double>& e, vector<vector<doub
        }
 }
 /*********************************************************************************************************************************/
+//groups by dimension
+vector< vector<double> > LinearAlgebra::calculateEuclidianDistance(vector< vector<double> >& axes, int dimensions){
+       try {
+               //make square matrix
+               vector< vector<double> > dists; dists.resize(axes.size());
+               for (int i = 0; i < dists.size(); i++) {  dists[i].resize(axes.size(), 0.0); }
+               
+               if (dimensions == 1) { //one dimension calc = abs(x-y)
+                       
+                       for (int i = 0; i < dists.size(); i++) {
+                               
+                               if (m->control_pressed) { return dists; }
+                               
+                               for (int j = 0; j < i; j++) {
+                                       dists[i][j] = abs(axes[i][0] - axes[j][0]);
+                                       dists[j][i] = dists[i][j];
+                               }
+                       }
+                       
+               }else if (dimensions > 1) { //two dimension calc = sqrt ((x1 - y1)^2 + (x2 - y2)^2)...
+                       
+                       for (int i = 0; i < dists.size(); i++) {
+                               
+                               if (m->control_pressed) { return dists; }
+                               
+                               for (int j = 0; j < i; j++) {
+                                       double sum = 0.0;
+                                       for (int k = 0; k < dimensions; k++) {
+                                               sum += ((axes[i][k] - axes[j][k]) * (axes[i][k] - axes[j][k]));
+                                       }
+                                       
+                                       dists[i][j] = sqrt(sum);
+                                       dists[j][i] = dists[i][j];
+                               }
+                       }
+                       
+               }
+               
+               return dists;
+       }
+       catch(exception& e) {
+               m->errorOut(e, "LinearAlgebra", "calculateEuclidianDistance");
+               exit(1);
+       }
+}
+/*********************************************************************************************************************************/
+//returns groups by dimensions from dimensions by groups
+vector< vector<double> > LinearAlgebra::calculateEuclidianDistance(vector< vector<double> >& axes){
+       try {
+               //make square matrix
+               vector< vector<double> > dists; dists.resize(axes[0].size());
+               for (int i = 0; i < dists.size(); i++) {  dists[i].resize(axes[0].size(), 0.0); }
+               
+               if (axes.size() == 1) { //one dimension calc = abs(x-y)
+                       
+                       for (int i = 0; i < dists.size(); i++) {
+                               
+                               if (m->control_pressed) { return dists; }
+                               
+                               for (int j = 0; j < i; j++) {
+                                       dists[i][j] = abs(axes[0][i] - axes[0][j]);
+                                       dists[j][i] = dists[i][j];
+                               }
+                       }
+                       
+               }else if (axes.size() > 1) { //two dimension calc = sqrt ((x1 - y1)^2 + (x2 - y2)^2)...
+                       
+                       for (int i = 0; i < dists[0].size(); i++) {
+                               
+                               if (m->control_pressed) { return dists; }
+                               
+                               for (int j = 0; j < i; j++) {
+                                       double sum = 0.0;
+                                       for (int k = 0; k < axes.size(); k++) {
+                                               sum += ((axes[k][i] - axes[k][j]) * (axes[k][i] - axes[k][j]));
+                                       }
+                                       
+                                       dists[i][j] = sqrt(sum);
+                                       dists[j][i] = dists[i][j];
+                               }
+                       }
+                       
+               }
+               
+               return dists;
+       }
+       catch(exception& e) {
+               m->errorOut(e, "LinearAlgebra", "calculateEuclidianDistance");
+               exit(1);
+       }
+}
+/*********************************************************************************************************************************/
+//assumes both matrices are square and the same size
+double LinearAlgebra::calcPearson(vector< vector<double> >& euclidDists, vector< vector<double> >& userDists){
+       try {
+               
+               //find average for - X
+               int count = 0;
+               float averageEuclid = 0.0; 
+               for (int i = 0; i < euclidDists.size(); i++) {
+                       for (int j = 0; j < i; j++) {
+                               averageEuclid += euclidDists[i][j];  
+                               count++;
+                       }
+               }
+               averageEuclid = averageEuclid / (float) count;   
+                       
+               //find average for - Y
+               count = 0;
+               float averageUser = 0.0; 
+               for (int i = 0; i < userDists.size(); i++) {
+                       for (int j = 0; j < i; j++) {
+                               averageUser += userDists[i][j]; 
+                               count++;
+                       }
+               }
+               averageUser = averageUser / (float) count;  
+
+               double numerator = 0.0;
+               double denomTerm1 = 0.0;
+               double denomTerm2 = 0.0;
+               
+               for (int i = 0; i < euclidDists.size(); i++) {
+                       
+                       for (int k = 0; k < i; k++) { //just lt dists
+                               
+                               float Yi = userDists[i][k];
+                               float Xi = euclidDists[i][k];
+                               
+                               numerator += ((Xi - averageEuclid) * (Yi - averageUser));
+                               denomTerm1 += ((Xi - averageEuclid) * (Xi - averageEuclid));
+                               denomTerm2 += ((Yi - averageUser) * (Yi - averageUser));
+                       }
+               }
+               
+               double denom = (sqrt(denomTerm1) * sqrt(denomTerm2));
+               double r = numerator / denom;
+               
+               //divide by zero error
+               if (isnan(r) || isinf(r)) { r = 0.0; }
+               
+               return r;
+               
+       }
+       catch(exception& e) {
+               m->errorOut(e, "LinearAlgebra", "calculateEuclidianDistance");
+               exit(1);
+       }
+}
+/*********************************************************************************************************************************/