BAG& BAG::operator=(const BAG &rhs){
if( this != &rhs ){
//Clear any previous ensemble
clearEnsemble();
//Copy the weights
this->weights = rhs.weights;
//Deep copy each classifier in the ensemble
for(UINT i=0; i<rhs.getEnsembleSize(); i++){
addClassifierToEnsemble( *(rhs.ensemble[i]) );
}
//Copy the base classifier variables
copyBaseVariables( (Classifier*)&rhs );
}
return *this;
}
// One can imagine that the band of operation is just selected
// by the "ini" file, it is pretty unlikely that one changes
// the band during operation
void RadioInterface::set_bandSelect (QString s) {
if (running) {
running = false;
inputDevice -> stopReader ();
inputDevice -> resetBuffer ();
soundOut -> stop ();
usleep (100);
clearEnsemble ();
}
if (s == "BAND III")
dabBand = BAND_III;
else
dabBand = L_BAND;
//setupChannels (channelSelector, dabBand);
}
/**
* \brief setStart is a function that is called after pushing
* the start button.
* if "autoStart" == true, then the initializer will start
*
*/
void RadioInterface::setStart (void) {
bool r = 0;
if (running) // only listen when not running yet
return;
//
r = inputDevice -> restartReader ();
qDebug ("Starting %d\n", r);
if (!r) {
qDebug ("Opening input stream failed\n");
return;
}
//
// Of course, starting the machine will generate a new instance
// of the ensemble, so the listing - if any - should be cleared
clearEnsemble (); // the display
//
/// this does not hurt
soundOut -> restart ();
running = true;
}
bool BAG::deepCopyFrom(const Classifier *classifier){
if( classifier == NULL ) return false;
if( this->getClassifierType() == classifier->getClassifierType() ){
BAG *ptr = (BAG*)classifier;
//Clear any previous ensemble
clearEnsemble();
//Copy the weights
this->weights = ptr->weights;
//Deep copy each classifier in the ensemble
for(UINT i=0; i<ptr->getEnsembleSize(); i++){
addClassifierToEnsemble( *(ptr->ensemble[i]) );
}
//Copy the base classifier variables
return copyBaseVariables( classifier );
}
return false;
}
BAG::~BAG(void)
{
clearEnsemble();
}
bool BAG::loadModelFromFile(fstream &file){
clear();
UINT ensembleSize = 0;
if(!file.is_open())
{
errorLog << "loadModelFromFile(string filename) - Could not open file to load model" << endl;
return false;
}
std::string word;
file >> word;
//Check to see if we should load a legacy file
if( word == "GRT_BAG_MODEL_FILE_V1.0" ){
return loadLegacyModelFromFile( file );
}
//Find the file type header
if(word != "GRT_BAG_MODEL_FILE_V2.0"){
errorLog << "loadModelFromFile(string filename) - Could not find Model File Header" << endl;
return false;
}
//Load the base settings from the file
if( !Classifier::loadBaseSettingsFromFile(file) ){
errorLog << "loadModelFromFile(string filename) - Failed to load base settings from file!" << endl;
return false;
}
if( trained ){
//Load the ensemble size
file >> word;
if(word != "EnsembleSize:"){
errorLog << "loadModelFromFile(string filename) - Could not find the EnsembleSize!" << endl;
return false;
}
file >> ensembleSize;
//Load the weights
weights.resize( ensembleSize );
file >> word;
if(word != "Weights:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Weights!" << endl;
return false;
}
for(UINT i=0; i<ensembleSize; i++){
file >> weights[i];
}
//Load the classifier types
vector< string > classifierTypes( ensembleSize );
file >> word;
if(word != "ClassifierTypes:"){
errorLog << "loadModelFromFile(string filename) - Could not find the ClassifierTypes!" << endl;
return false;
}
for(UINT i=0; i<ensembleSize; i++){
file >> classifierTypes[i];
}
//Load the ensemble
file >> word;
if(word != "Ensemble:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Ensemble!" << endl;
return false;
}
ensemble.resize(ensembleSize,NULL);
for(UINT i=0; i<ensembleSize; i++){
ensemble[i] = createInstanceFromString( classifierTypes[i] );
if( ensemble[i] == NULL ){
errorLog << "loadModelFromFile(string filename) - Could not create a new classifier instance from the classifierType: " << classifierTypes[i] << endl;
clearEnsemble();
return false;
}
if( !ensemble[i]->loadModelFromFile( file ) ){
errorLog << "loadModelFromFile(string filename) - Failed to load ensemble classifier: " << i << endl;
clearEnsemble();
return false;
}
}
//Recompute the null rejection thresholds
recomputeNullRejectionThresholds();
//Resize the prediction results to make sure it is setup for realtime prediction
maxLikelihood = DEFAULT_NULL_LIKELIHOOD_VALUE;
bestDistance = DEFAULT_NULL_DISTANCE_VALUE;
classLikelihoods.resize(numClasses,DEFAULT_NULL_LIKELIHOOD_VALUE);
classDistances.resize(numClasses,DEFAULT_NULL_DISTANCE_VALUE);
}
return true;
}
bool BAG::loadModelFromFile(fstream &file){
trained = false;
numFeatures = 0;
numClasses = 0;
classLabels.clear();
clearEnsemble();
UINT ensembleSize = 0;
if(!file.is_open())
{
errorLog << "loadModelFromFile(string filename) - Could not open file to load model" << endl;
return false;
}
std::string word;
//Find the file type header
file >> word;
if(word != "GRT_BAG_MODEL_FILE_V1.0"){
errorLog << "loadModelFromFile(string filename) - Could not find Model File Header" << endl;
return false;
}
file >> word;
if(word != "Trained:"){
errorLog << "loadModelFromFile(string filename) - Could not find Trained Header" << endl;
return false;
}
file >> trained;
file >> word;
if(word != "NumFeatures:"){
errorLog << "loadModelFromFile(string filename) - Could not find NumFeatures!" << endl;
return false;
}
file >> numFeatures;
file >> word;
if(word != "NumClasses:"){
errorLog << "loadModelFromFile(string filename) - Could not find NumClasses!" << endl;
return false;
}
file >> numClasses;
file >> word;
if(word != "UseScaling:"){
errorLog << "loadModelFromFile(string filename) - Could not find UseScaling!" << endl;
return false;
}
file >> useScaling;
file >> word;
if(word != "UseNullRejection:"){
errorLog << "loadModelFromFile(string filename) - Could not find UseNullRejection!" << endl;
return false;
}
file >> useNullRejection;
///Read the ranges if needed
if( useScaling ){
//Resize the ranges buffer
ranges.resize(numFeatures);
file >> word;
if(word != "Ranges:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Ranges!" << endl;
return false;
}
for(UINT n=0; n<ranges.size(); n++){
file >> ranges[n].minValue;
file >> ranges[n].maxValue;
}
}
//Resize the buffer
classLabels.resize(numClasses);
//Load the class labels
file >> word;
if(word != "ClassLabels:"){
errorLog << "loadModelFromFile(string filename) - Could not find the ClassLabels!" << endl;
return false;
}
for(UINT i=0; i<numClasses; i++){
file >> classLabels[i];
}
//Load the ensemble size
file >> word;
if(word != "EnsembleSize:"){
errorLog << "loadModelFromFile(string filename) - Could not find the EnsembleSize!" << endl;
return false;
}
file >> ensembleSize;
if( ensembleSize > 0 ){
//Load the weights
weights.resize( ensembleSize );
file >> word;
if(word != "Weights:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Weights!" << endl;
return false;
}
for(UINT i=0; i<ensembleSize; i++){
file >> weights[i];
}
//Load the classifier types
vector< string > classifierTypes( ensembleSize );
file >> word;
if(word != "ClassifierTypes:"){
errorLog << "loadModelFromFile(string filename) - Could not find the ClassifierTypes!" << endl;
return false;
}
for(UINT i=0; i<ensembleSize; i++){
file >> classifierTypes[i];
}
//Load the ensemble
file >> word;
if(word != "Ensemble:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Ensemble!" << endl;
return false;
}
ensemble.resize(ensembleSize,NULL);
for(UINT i=0; i<ensembleSize; i++){
ensemble[i] = createInstanceFromString( classifierTypes[i] );
if( ensemble[i] == NULL ){
errorLog << "loadModelFromFile(string filename) - Could not create a new classifier instance from the classifierType: " << classifierTypes[i] << endl;
clearEnsemble();
return false;
}
if( !ensemble[i]->loadModelFromFile( file ) ){
errorLog << "loadModelFromFile(string filename) - Failed to load ensemble classifier: " << i << endl;
clearEnsemble();
return false;
}
}
}