int ncols = thisLookUp.size();//groups
double initscore = 0.0;
- vector<int> initcolumntotal; initcolumntotal.resize(ncols, 0);
- vector<int> initrowtotal; initrowtotal.resize(nrows, 0);
+ vector<int> columntotal; columntotal.resize(ncols, 0);
+ vector<int> rowtotal; rowtotal.resize(nrows, 0);
vector<double> stats;
+ double probabilityMatrix[ncols * nrows];
+ vector<vector<int> > nullmatrix(nrows, vector<int>(ncols, 0));
TrialSwap2 trial;
- initcolumntotal = rowtotal;
- initrowtotal = columntotal;
- trial.update_row_col_totals(co_matrix, rowtotal, columntotal);
+ n = accumulate( columntotal.begin(), columntotal.end(), 0 );
- if (metric == "cscore") { initscore = trial.calc_c_score(co_matrix, rowtotal); }
- else if (metric == "checker") { initscore = trial.calc_checker(co_matrix, rowtotal); }
- else if (metric == "vratio") { initscore = trial.calc_vratio(rowtotal, columntotal); }
- else if (metric == "combo") { initscore = trial.calc_combo(co_matrix); }
+ //============================================================
+
+ //generate a probability matrix. Only do this once.
+ float start = 0.0;
+
+ if (matrix == "sim1") {
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + 1/double(nrows*ncols);
+ start = start + 1/double(nrows*ncols);
+ }
+ }
+ }
+ else if (matrix == "sim2") {
+ for(int i=0;i<nrows;i++) {
+ start = 0.0;
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + 1/double(ncols);
+ start = start + 1/double(ncols);
+ }
+ }
+ }
+
+ else if (matrix == "sim3") {
+ for(int j=0;j<ncols;j++) {
+ start = 0.0;
+ for(int i=0;i<nrows;i++) {
+ probabilityMatrix[ncols * i + j] = start + 1/double(nrows);
+ start = start + 1/double(nrows);
+ }
+ }
+ }
+
+ else if (matrix == "sim4") {
+ for(int i=0;i<nrows;i++) {
+ start = 0.0;
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + columntotal[j]/double(n);
+ start = start + columntotal[j]/double(n);
+ }
+ }
+ }
+
+ else if (matrix == "sim5") {
+ for(int j=0;j<ncols;j++) {
+ start = 0.0;
+ for(int i=0;i<nrows;i++) {
+ probabilityMatrix[ncols * i + j] = start + rowtotal[i]/double(n);
+ start = start + rowtotal[i]/double(n);
+ }
+ }
+ }
+
+ else if (matrix == "sim6") {
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + columntotal[j]/double(n*nrows);
+ start = start + columntotal[j]/double(n*nrows);
+ }
+ }
+ }
+
+
+ else if (matrix == "sim7") {
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + rowtotal[i]/double(n*ncols);
+ start = start + rowtotal[i]/double(n*ncols);
+ }
+ }
+ }
+
+ else if (matrix == "sim8") {
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ probabilityMatrix[ncols * i + j] = start + (rowtotal[i]*columntotal[j])/double(n*n);
+ start = start + (rowtotal[i]*columntotal[j])/double(n*n);
+ }
+ }
+ }
+
+ else {
+ if(sim != 9) {
+ m->mothurOut("[ERROR]: No model selected! \n");
+ m->control_pressed = true;
+ }
+ }
+
+
+
+ if (metric == "cscore") { initscore = trial.calc_c_score(initmatrix, rowtotal, ncols, nrows); }
+ else if (metric == "checker") { initscore = trial.calc_checker(initmatrix, rowtotal, ncols, nrows); }
+ else if (metric == "vratio") { initscore = trial.calc_vratio(nrows, ncols, rowtotal, columntotal); }
+ else if (metric == "combo") { initscore = trial.calc_combo(nrows, ncols, initmatrix); }
else { m->mothurOut("[ERROR]: No metric selected!\n"); m->control_pressed = true; return 1; }
m->mothurOut("Initial c score: " + toString(initscore)); m->mothurOutEndLine();
- //nullmatrix burn in
- for(int i=0;i<10000;i++) {
- if (m->control_pressed) { return 0; }
- if (matrix == "sim1") {
- trial.sim1(co_matrix);
- }else if (matrix == "sim2") {
- trial.sim2(co_matrix);
- }else if (matrix == "sim3") {
- trial.sim3(initmatrix);
- co_matrix = initmatrix;
- }else if (matrix == "sim4") {
- trial.sim4(columntotal, rowtotal, co_matrix);
- }else if (matrix == "sim5") {
- trial.sim5(initcolumntotal, initrowtotal, initmatrix);
- trial.transpose_matrix(initmatrix,co_matrix);
- }else if (matrix == "sim6") {
- trial.sim6(columntotal, co_matrix);
- }else if (matrix == "sim7") {
- trial.sim7(initcolumntotal, initmatrix);
- co_matrix = initmatrix;
- }else if (matrix == "sim8") {
- trial.sim8(columntotal, rowtotal, co_matrix);
- }else if (matrix == "sim9") {
- trial.swap_checkerboards (co_matrix);
- }else{
- m->mothurOut("[ERROR]: No model selected! \n");
- m->control_pressed = true;
+ double previous;
+ double current;
+ double randnum;
+ int count;
+
+ //burn-in
+ for(int i=0;i<10000;i++){
+ nullmatrix.clear();
+ //zero-fill the null matrix
+ nullmatrix.assign(nrows, vector<int>(ncols, 0));
+
+ if(matrix == "sim1" || matrix == "sim6" || matrix == "sim8" || matrix == "sim7") {
+ count = 0;
+ while(count < n) {
+ nextnum:
+ previous = 0.0;
+ randnum = rand() / double(RAND_MAX);
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous) {
+ nullmatrix[i][j] = 1;
+ count++;
+ if (count > n) break;
+ else
+ goto nextnum;
+ }
+ previous = current;
+ }
+ }
+ }
+ }
+
+ else if(matrix == "sim2" || matrix == "sim4") {
+ for(int i=0;i<nrows;i++) {
+ previous = 0.0;
+ count = 0;
+ while(count < rowtotal[i]) {
+ randnum = rand() / double(RAND_MAX);
+ for(int j=0;j<ncols;j++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous && nullmatrix[i][j] != 1) {
+ nullmatrix[i][j] = 1;
+ count++;
+ previous = 0.0;
+ break;
+ }
+ previous = current;
+ }
+ }
+ }
}
+
+ else if(matrix == "sim3" || matrix == "sim5") {
+ //columns
+ for(int j=0;j<ncols;j++) {
+ count = 0;
+ while(count < columntotal[j]) {
+ randnum = rand() / double(RAND_MAX);
+ for(int i=0;i<nrows;i++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous && nullmatrix[i][j] != 1) {
+ nullmatrix[i][j] = 1;
+ count++;
+ previous = 0.0;
+ break;
+ }
+ previous = current;
+ }
+ }
+ }
+ }
+
}
-
- //run
- for(int i=0;i<runs;i++) {
- if (m->control_pressed) { return 0; }
- //calc metric of nullmatrix
- if (matrix == "sim1") {
- trial.sim1(co_matrix);
- }else if (matrix == "sim2") {
- trial.sim2(co_matrix);
- }else if (matrix == "sim3") {
- trial.sim3(initmatrix);
- co_matrix = initmatrix;
- }else if (matrix == "sim4") {
- trial.sim4(columntotal, rowtotal, co_matrix);
- }else if (matrix == "sim5") {
- trial.sim5(initcolumntotal, initrowtotal, initmatrix);
- trial.transpose_matrix(initmatrix,co_matrix);
- }else if (matrix == "sim6") {
- trial.sim6(columntotal, co_matrix);
- }else if (matrix == "sim7") {
- trial.sim7(initcolumntotal, initmatrix);
- co_matrix = initmatrix;
- }else if (matrix == "sim8") {
- trial.sim8(columntotal, rowtotal, co_matrix);
- }else if (matrix == "sim9") {
- trial.swap_checkerboards (co_matrix);
- }else{
- m->mothurOut("[ERROR]: No model selected! \n");
- m->control_pressed = true;
+
+ //populate null matrix from probability matrix, do this a lot.
+ for(int i=0;i<runs;i++){
+ nullmatrix.clear();
+ //zero-fill the null matrix
+ nullmatrix.assign(nrows, vector<int>(ncols, 0));
+
+ if(matrix == "sim1" || matrix == "sim6" || matrix == "sim8" || matrix == "sim7") {
+ count = 0;
+ while(count < n) {
+ nextnum:
+ previous = 0.0;
+ randnum = rand() / double(RAND_MAX);
+ for(int i=0;i<nrows;i++) {
+ for(int j=0;j<ncols;j++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous) {
+ nullmatrix[i][j] = 1;
+ count++;
+ if (count > n) break;
+ else
+ goto nextnum;
+ }
+ previous = current;
+ }
+ }
+ }
+ }
+
+ else if(matrix == "sim2" || matrix == "sim4") {
+ for(int i=0;i<nrows;i++) {
+ previous = 0.0;
+ count = 0;
+ while(count < rowtotal[i]) {
+ randnum = rand() / double(RAND_MAX);
+ for(int j=0;j<ncols;j++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous && nullmatrix[i][j] != 1) {
+ nullmatrix[i][j] = 1;
+ count++;
+ previous = 0.0;
+ break;
+ }
+ previous = current;
+ }
+ }
+ }
+ }
+
+ else if(matrix == "sim3" || matrix == "sim5") {
+ //columns
+ for(int j=0;j<ncols;j++) {
+ count = 0;
+ while(count < columntotal[j]) {
+ randnum = rand() / double(RAND_MAX);
+ for(int i=0;i<nrows;i++) {
+ current = probabilityMatrix[ncols * i + j];
+ if(randnum <= current && randnum > previous && nullmatrix[i][j] != 1) {
+ nullmatrix[i][j] = 1;
+ count++;
+ previous = 0.0;
+ break;
+ }
+ previous = current;
+ }
+ }
+ }
}
- //
- //
- //trial.update_row_col_totals(co_matrix, rowtotal, columntotal);
- if (metric == "cscore") {
- stats.push_back(trial.calc_c_score(co_matrix, rowtotal));
- }else if (metric == "checker") {
- stats.push_back(trial.calc_checker(co_matrix, rowtotal));
- }else if (metric == "vratio") {
- stats.push_back(trial.calc_vratio(rowtotal, columntotal));
- }else if (metric == "combo") {
- stats.push_back(trial.calc_combo(co_matrix));
- }else {
- m->mothurOut("[ERROR]: No metric selected!\n");
- m->control_pressed = true;
+ //swap_checkerboards takes the original matrix and swaps checkerboards
+ else if(matrix == "sim9") {
+ trial.swap_checkerboards (initmatrix, rowtotal, columntotal, ncols, nrows);
+ }
+ else {
+ cout << "[ERROR]: No null model selected!\n" << endl;
return 1;
}
-
+
+ //run metric on null matrix and add score to the stats vector
+ if (metric == "cscore"){
+ stats.push_back(trial.calc_c_score(nullmatrix, rowtotal, ncols, nrows));
+ }
+ else if (metric == "checker") {
+ stats.push_back(trial.calc_checker(nullmatrix, rowtotal, ncols, nrows));
+ }
+ else if (metric == "vratio") {
+ stats.push_back(trial.calc_vratio(nrows, ncols, rowtotal, columntotal));
+ }
+ else if (metric == "combo") {
+ stats.push_back(trial.calc_combo(nrows, ncols, nullmatrix));
+ }
+ else {
+ cout << "[ERROR]: No metric selected!\n" << endl;
+ return 1;
+ }
+
}
+
+
double total = 0.0;
for (int i=0; i<stats.size();i++) { total+=stats[i]; }
projects / mothur.git / blobdiff