int AbstractDecisionTree::createBootStrappedSamples(){
try {
vector<bool> isInTrainingSamples(numSamples, false);
Utils util;
for (int i = 0; i < numSamples; i++) {
if (m->getControl_pressed()) { return 0; }
int randomIndex = util.getRandomIndex(numSamples-1);
bootstrappedTrainingSamples.push_back(baseDataSet[randomIndex]);
isInTrainingSamples[randomIndex] = true;
}
for (int i = 0; i < numSamples; i++) {
if (m->getControl_pressed()) { return 0; }
if (isInTrainingSamples[i]){ bootstrappedTrainingSampleIndices.push_back(i); }
else{
bootstrappedTestSamples.push_back(baseDataSet[i]);
bootstrappedTestSampleIndices.push_back(i);
}
}
// do the transpose of Test Samples
for (int i = 0; i < bootstrappedTestSamples[0].size(); i++) {
if (m->getControl_pressed()) { return 0; }
vector<int> tmpFeatureVector(bootstrappedTestSamples.size(), 0);
for (int j = 0; j < bootstrappedTestSamples.size(); j++) {
if (m->getControl_pressed()) { return 0; }
tmpFeatureVector[j] = bootstrappedTestSamples[j][i];
}
testSampleFeatureVectors.push_back(tmpFeatureVector);
}
return 0;
}
catch(exception& e) {
m->errorOut(e, "AbstractDecisionTree", "createBootStrappedSamples");
exit(1);
}
}
int AbstractDecisionTree::createBootStrappedSamples(){
try {
vector<bool> isInTrainingSamples(numSamples, false);
for (int i = 0; i < numSamples; i++) {
if (m->control_pressed) { return 0; }
// TODO: optimize the rand() function call + double check if it's working properly
int randomIndex = rand() % numSamples;
bootstrappedTrainingSamples.push_back(baseDataSet[randomIndex]);
isInTrainingSamples[randomIndex] = true;
}
for (int i = 0; i < numSamples; i++) {
if (m->control_pressed) { return 0; }
if (isInTrainingSamples[i]){ bootstrappedTrainingSampleIndices.push_back(i); }
else{
bootstrappedTestSamples.push_back(baseDataSet[i]);
bootstrappedTestSampleIndices.push_back(i);
}
}
// do the transpose of Test Samples
for (int i = 0; i < bootstrappedTestSamples[0].size(); i++) {
if (m->control_pressed) { return 0; }
vector<int> tmpFeatureVector(bootstrappedTestSamples.size(), 0);
for (int j = 0; j < bootstrappedTestSamples.size(); j++) {
if (m->control_pressed) { return 0; }
tmpFeatureVector[j] = bootstrappedTestSamples[j][i];
}
testSampleFeatureVectors.push_back(tmpFeatureVector);
}
return 0;
}
catch(exception& e) {
m->errorOut(e, "AbstractDecisionTree", "createBootStrappedSamples");
exit(1);
}
}
