product.resize(first_rows);
for(int i=0;i<first_rows;i++){
- product[i].resize(first_cols);
+ product[i].resize(second_cols);
}
for(int i=0;i<first_rows;i++){
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
}
}
- }else if (dimensions == 2) { //two dimension calc = sqrt ((x1 - y1)^2 + (x2 - y2)^2)
+ }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 firstDim = ((axes[i][0] - axes[j][0]) * (axes[i][0] - axes[j][0]));
- double secondDim = ((axes[i][1] - axes[j][1]) * (axes[i][1] - axes[j][1]));
+ 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((firstDim + secondDim));
+ dists[i][j] = sqrt(sum);
dists[j][i] = dists[i][j];
}
}
- }else if (dimensions == 3) { //two dimension calc = sqrt ((x1 - y1)^2 + (x2 - y2)^2 + (x3 - y3)^2)
+ }
+
+ 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++) {
- double firstDim = ((axes[i][0] - axes[j][0]) * (axes[i][0] - axes[j][0]));
- double secondDim = ((axes[i][1] - axes[j][1]) * (axes[i][1] - axes[j][1]));
- double thirdDim = ((axes[i][2] - axes[j][2]) * (axes[i][2] - axes[j][2]));
+ 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((firstDim + secondDim + thirdDim));
+ dists[i][j] = sqrt(sum);
dists[j][i] = dists[i][j];
}
}
- }else { m->mothurOut("[ERROR]: too many dimensions, aborting."); m->mothurOutEndLine(); m->control_pressed = true; }
+ }
return dists;
}
}
}
/*********************************************************************************************************************************/
+//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
- vector<float> averageEuclid; averageEuclid.resize(euclidDists.size(), 0.0);
+ int count = 0;
+ float averageEuclid = 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 j = 0; j < i; j++) {
+ averageEuclid += euclidDists[i][j];
+ count++;
}
}
- for (int i = 0; i < averageEuclid.size(); i++) { averageEuclid[i] = averageEuclid[i] / (float) euclidDists.size(); }
-
+ averageEuclid = averageEuclid / (float) count;
+
//find average for - Y
- vector<float> averageUser; averageUser.resize(userDists.size(), 0.0);
+ count = 0;
+ float averageUser = 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 j = 0; j < i; j++) {
+ averageUser += userDists[i][j];
+ count++;
}
}
- for (int i = 0; i < averageUser.size(); i++) { averageUser[i] = averageUser[i] / (float) userDists.size(); }
-
+ 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++) {
+ for (int k = 0; k < i; k++) { //just lt dists
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]));
+ numerator += ((Xi - averageEuclid) * (Yi - averageUser));
+ denomTerm1 += ((Xi - averageEuclid) * (Xi - averageEuclid));
+ denomTerm2 += ((Yi - averageUser) * (Yi - averageUser));
}
}
double r = numerator / denom;
return r;
+
}
catch(exception& e) {
m->errorOut(e, "LinearAlgebra", "calculateEuclidianDistance");
projects / mothur.git / blobdiff