vector<int>::iterator maxPredictedOutComeIterator = max_element(predictedOutComes.begin(), predictedOutComes.end());
int majorityVotedOutcome = (int)(maxPredictedOutComeIterator - predictedOutComes.begin());
int realOutcome = dataSet[indexOfSample][numFeatures];
- cm[realOutcome][majorityVotedOutcome] = cm[realOutcome][majorityVotedOutcome] + 1;
+ cm[realOutcome][majorityVotedOutcome] = cm[realOutcome][majorityVotedOutcome] + 1;
+ }
+
+ vector<int> fw;
+ for (int w = 0; w <numGroups; w++) {
+ fw.push_back(intToTreatmentMap[w].length());
}
m->mothurOut("confusion matrix:\n\t\t");
- for (int i = 0; i < numGroups; i++) {
- m->mothurOut(intToTreatmentMap[i] + "\t");
+ for (int k = 0; k < numGroups; k++) {
+ //m->mothurOut(intToTreatmentMap[k] + "\t");
+ cout << setw(fw[k]) << intToTreatmentMap[k] << "\t";
}
for (int i = 0; i < numGroups; i++) {
+ cout << "\n" << setw(fw[i]) << intToTreatmentMap[i] << "\t";
//m->mothurOut("\n" + intToTreatmentMap[i] + "\t");
if (m->control_pressed) { return 0; }
for (int j = 0; j < numGroups; j++) {
- m->mothurOut(cm[i][j] + "\t");
+ //m->mothurOut(toString(cm[i][j]) + "\t");
+ cout << setw(fw[i]) << cm[i][j] << "\t";
}
}
- m->mothurOut("\n");
-
+ //m->mothurOut("\n");
+ cout << "\n";
+
return 0;
}