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));
}
}
/*********************************************************************************************************************************/
+//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);
+ }
+}
+/*********************************************************************************************************************************/
+double LinearAlgebra::calcPearson(vector< vector<double> >& euclidDists, vector< vector<double> >& userDists){
+ try {
+
+ //find average for - X
+ vector<float> averageEuclid; averageEuclid.resize(euclidDists.size(), 0.0);
+ for (int i = 0; i < euclidDists.size(); i++) {
+ for (int j = 0; j < euclidDists[i].size(); j++) {
+ averageEuclid[i] += euclidDists[i][j];
+ }
+ }
+ for (int i = 0; i < averageEuclid.size(); i++) { averageEuclid[i] = averageEuclid[i] / (float) euclidDists.size(); }
+
+ //find average for - Y
+ vector<float> averageUser; averageUser.resize(userDists.size(), 0.0);
+ for (int i = 0; i < userDists.size(); i++) {
+ for (int j = 0; j < userDists[i].size(); j++) {
+ averageUser[i] += userDists[i][j];
+ }
+ }
+ for (int i = 0; i < averageUser.size(); i++) { averageUser[i] = averageUser[i] / (float) userDists.size(); }
+
+ 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++) {
+
+ float Yi = userDists[i][k];
+ float Xi = euclidDists[i][k];
+
+ numerator += ((Xi - averageEuclid[k]) * (Yi - averageUser[k]));
+ denomTerm1 += ((Xi - averageEuclid[k]) * (Xi - averageEuclid[k]));
+ denomTerm2 += ((Yi - averageUser[k]) * (Yi - averageUser[k]));
+ }
+ }
+
+ double denom = (sqrt(denomTerm1) * sqrt(denomTerm2));
+ double r = numerator / denom;
+
+ return r;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "calculateEuclidianDistance");
+ exit(1);
+ }
+}
+/*********************************************************************************************************************************/