/*********************************************************************************************************************************/
+void LinearAlgebra::recenter(double offset, vector<vector<double> > D, vector<vector<double> >& G){
+ try {
+ int rank = D.size();
+
+ vector<vector<double> > A(rank);
+ vector<vector<double> > C(rank);
+ for(int i=0;i<rank;i++){
+ A[i].resize(rank);
+ C[i].resize(rank);
+ }
+
+ double scale = -1.0000 / (double) rank;
+
+ for(int i=0;i<rank;i++){
+ A[i][i] = 0.0000;
+ C[i][i] = 1.0000 + scale;
+ for(int j=i+1;j<rank;j++){
+ A[i][j] = A[j][i] = -0.5 * D[i][j] * D[i][j] + offset;
+ C[i][j] = C[j][i] = scale;
+ }
+ }
+
+ A = matrix_mult(C,A);
+ G = matrix_mult(A,C);
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "recenter");
+ exit(1);
+ }
+
+}
+/*********************************************************************************************************************************/
+
// This function is taken from Numerical Recipes in C++ by Press et al., 2nd edition, pg. 479
int LinearAlgebra::tred2(vector<vector<double> >& a, vector<double>& d, vector<double>& e){
}
}
/*********************************************************************************************************************************/
+//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 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");
+ m->errorOut(e, "LinearAlgebra", "calcPearson");
+ exit(1);
+ }
+}
+/*********************************************************************************************************************************/
+//assumes both matrices are square and the same size
+double LinearAlgebra::calcSpearman(vector< vector<double> >& euclidDists, vector< vector<double> >& userDists){
+ try {
+ double r;
+
+ //format data
+ map<float, int> tableX;
+ map<float, int>::iterator itTable;
+ vector<spearmanRank> scores;
+
+ for (int i = 0; i < euclidDists.size(); i++) {
+ for (int j = 0; j < i; j++) {
+ spearmanRank member(toString(scores.size()), euclidDists[i][j]);
+ scores.push_back(member);
+
+ //count number of repeats
+ itTable = tableX.find(euclidDists[i][j]);
+ if (itTable == tableX.end()) {
+ tableX[euclidDists[i][j]] = 1;
+ }else {
+ tableX[euclidDists[i][j]]++;
+ }
+ }
+ }
+
+ //sort scores
+ sort(scores.begin(), scores.end(), compareSpearman);
+
+ //calc LX
+ double Lx = 0.0;
+ for (itTable = tableX.begin(); itTable != tableX.end(); itTable++) {
+ double tx = (double) itTable->second;
+ Lx += ((pow(tx, 3.0) - tx) / 12.0);
+ }
+
+ //find ranks of xi
+ map<string, float> rankEuclid;
+ vector<spearmanRank> ties;
+ int rankTotal = 0;
+ for (int j = 0; j < scores.size(); j++) {
+ rankTotal += (j+1);
+ ties.push_back(scores[j]);
+
+ if (j != (scores.size()-1)) { // you are not the last so you can look ahead
+ if (scores[j].score != scores[j+1].score) { // you are done with ties, rank them and continue
+
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ rankEuclid[ties[k].name] = thisrank;
+ }
+ ties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ rankEuclid[ties[k].name] = thisrank;
+ }
+ }
+ }
+
+
+ //format data
+ map<float, int> tableY;
+ scores.clear();
+
+ for (int i = 0; i < userDists.size(); i++) {
+ for (int j = 0; j < i; j++) {
+ spearmanRank member(toString(scores.size()), userDists[i][j]);
+ scores.push_back(member);
+
+ //count number of repeats
+ itTable = tableY.find(userDists[i][j]);
+ if (itTable == tableY.end()) {
+ tableY[userDists[i][j]] = 1;
+ }else {
+ tableY[userDists[i][j]]++;
+ }
+ }
+ }
+
+ //sort scores
+ sort(scores.begin(), scores.end(), compareSpearman);
+
+ //calc LX
+ double Ly = 0.0;
+ for (itTable = tableY.begin(); itTable != tableY.end(); itTable++) {
+ double ty = (double) itTable->second;
+ Ly += ((pow(ty, 3.0) - ty) / 12.0);
+ }
+
+ //find ranks of yi
+ map<string, float> rankUser;
+ ties.clear();
+ rankTotal = 0;
+ for (int j = 0; j < scores.size(); j++) {
+ rankTotal += (j+1);
+ ties.push_back(scores[j]);
+
+ if (j != (scores.size()-1)) { // you are not the last so you can look ahead
+ if (scores[j].score != scores[j+1].score) { // you are done with ties, rank them and continue
+
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ rankUser[ties[k].name] = thisrank;
+ }
+ ties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ rankUser[ties[k].name] = thisrank;
+ }
+ }
+ }
+
+
+ double di = 0.0;
+ int count = 0;
+ for (int i = 0; i < userDists.size(); i++) {
+ for (int j = 0; j < i; j++) {
+
+ float xi = rankEuclid[toString(count)];
+ float yi = rankUser[toString(count)];
+
+ di += ((xi - yi) * (xi - yi));
+
+ count++;
+ }
+ }
+
+ double n = (double) count;
+
+ double SX2 = ((pow(n, 3.0) - n) / 12.0) - Lx;
+ double SY2 = ((pow(n, 3.0) - n) / 12.0) - Ly;
+
+ r = (SX2 + SY2 - di) / (2.0 * sqrt((SX2*SY2)));
+
+ //divide by zero error
+ if (isnan(r) || isinf(r)) { r = 0.0; }
+
+ return r;
+
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "calcSpearman");
+ exit(1);
+ }
+}
+
+/*********************************************************************************************************************************/
+//assumes both matrices are square and the same size
+double LinearAlgebra::calcKendall(vector< vector<double> >& euclidDists, vector< vector<double> >& userDists){
+ try {
+ double r;
+
+ //format data
+ vector<spearmanRank> scores;
+ for (int i = 0; i < euclidDists.size(); i++) {
+ for (int j = 0; j < i; j++) {
+ spearmanRank member(toString(scores.size()), euclidDists[i][j]);
+ scores.push_back(member);
+ }
+ }
+
+ //sort scores
+ sort(scores.begin(), scores.end(), compareSpearman);
+
+ //find ranks of xi
+ map<string, float> rankEuclid;
+ vector<spearmanRank> ties;
+ int rankTotal = 0;
+ for (int j = 0; j < scores.size(); j++) {
+ rankTotal += (j+1);
+ ties.push_back(scores[j]);
+
+ if (j != (scores.size()-1)) { // you are not the last so you can look ahead
+ if (scores[j].score != scores[j+1].score) { // you are done with ties, rank them and continue
+
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ rankEuclid[ties[k].name] = thisrank;
+ }
+ ties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ rankEuclid[ties[k].name] = thisrank;
+ }
+ }
+ }
+
+ vector<spearmanRank> scoresUser;
+ for (int i = 0; i < userDists.size(); i++) {
+ for (int j = 0; j < i; j++) {
+ spearmanRank member(toString(scoresUser.size()), userDists[i][j]);
+ scoresUser.push_back(member);
+ }
+ }
+
+ //sort scores
+ sort(scoresUser.begin(), scoresUser.end(), compareSpearman);
+
+ //find ranks of yi
+ map<string, float> rankUser;
+ ties.clear();
+ rankTotal = 0;
+ for (int j = 0; j < scoresUser.size(); j++) {
+ rankTotal += (j+1);
+ ties.push_back(scoresUser[j]);
+
+ if (j != (scoresUser.size()-1)) { // you are not the last so you can look ahead
+ if (scoresUser[j].score != scoresUser[j+1].score) { // you are done with ties, rank them and continue
+
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ rankUser[ties[k].name] = thisrank;
+ }
+ ties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ rankUser[ties[k].name] = thisrank;
+ }
+ }
+ }
+
+ int numCoor = 0;
+ int numDisCoor = 0;
+
+ //order user ranks
+ vector<spearmanRank> user;
+ for (int l = 0; l < scores.size(); l++) {
+ spearmanRank member(scores[l].name, rankUser[scores[l].name]);
+ user.push_back(member);
+ }
+
+ int count = 0;
+ for (int l = 0; l < scores.size(); l++) {
+
+ int numWithHigherRank = 0;
+ int numWithLowerRank = 0;
+ float thisrank = user[l].score;
+
+ for (int u = l+1; u < scores.size(); u++) {
+ if (user[u].score > thisrank) { numWithHigherRank++; }
+ else if (user[u].score < thisrank) { numWithLowerRank++; }
+ count++;
+ }
+
+ numCoor += numWithHigherRank;
+ numDisCoor += numWithLowerRank;
+ }
+
+ r = (numCoor - numDisCoor) / (float) count;
+
+ //divide by zero error
+ if (isnan(r) || isinf(r)) { r = 0.0; }
+
+ return r;
+
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "calcKendall");
exit(1);
}
}
+
/*********************************************************************************************************************************/
+vector<vector<double> > LinearAlgebra::getObservedEuclideanDistance(vector<vector<double> >& relAbundData){
+ int numSamples = relAbundData.size();
+ int numOTUs = relAbundData[0].size();
+
+ vector<vector<double> > dMatrix(numSamples);
+ for(int i=0;i<numSamples;i++){
+ dMatrix[i].resize(numSamples);
+ }
+
+ for(int i=0;i<numSamples;i++){
+ for(int j=0;j<numSamples;j++){
+
+ double d = 0;
+ for(int k=0;k<numOTUs;k++){
+ d += pow((relAbundData[i][k] - relAbundData[j][k]), 2.0000);
+ }
+ dMatrix[i][j] = pow(d, 0.50000);
+ dMatrix[j][i] = dMatrix[i][j];
+
+ }
+ }
+ return dMatrix;
+
+}
+
+/*********************************************************************************************************************************/
projects / mothur.git / blobdiff