bool RandomForests::combineModels( const RandomForests &forest ){
if( !getTrained() ){
errorLog << "combineModels( const RandomForests &forest ) - This instance has not been trained!" << endl;
return false;
}
if( !forest.getTrained() ){
errorLog << "combineModels( const RandomForests &forest ) - This external forest instance has not been trained!" << endl;
return false;
}
if( this->getNumInputDimensions() != forest.getNumInputDimensions() ) {
errorLog << "combineModels( const RandomForests &forest ) - The number of input dimensions of the external forest (";
errorLog << forest.getNumInputDimensions() << ") does not match the number of input dimensions of this instance (";
errorLog << this->getNumInputDimensions() << ")!" << endl;
return false;
}
//Add the trees in the other forest to this model
DecisionTreeNode *node;
for(UINT i=0; i<forest.getForestSize(); i++){
node = forest.getTree(i);
if( node ){
this->forest.push_back( node->deepCopy() );
forestSize++;
}
}
return true;
}
bool RandomForests::deepCopyFrom(const Classifier *classifier){
if( classifier == NULL ) return false;
if( this->getClassifierType() == classifier->getClassifierType() ){
RandomForests *ptr = (RandomForests*)classifier;
//Clear this tree
this->clear();
if( ptr->getTrained() ){
//Deep copy the forest
for(UINT i=0; i<ptr->forest.size(); i++){
this->forest.push_back( ptr->forest[i]->deepCopyTree() );
}
}
this->forestSize = ptr->forestSize;
this->numRandomSplits = ptr->numRandomSplits;
this->minNumSamplesPerNode = ptr->minNumSamplesPerNode;
this->maxDepth = ptr->maxDepth;
//Copy the base classifier variables
return copyBaseVariables( classifier );
}
return false;
}
bool RandomForests::deepCopyFrom(const Classifier *classifier){
if( classifier == NULL ) return false;
if( this->getClassifierType() == classifier->getClassifierType() ){
RandomForests *ptr = (RandomForests*)classifier;
//Clear this tree
this->clear();
if( copyBaseVariables( classifier ) ){
//Deep copy the main node
if( this->decisionTreeNode != NULL ){
delete decisionTreeNode;
decisionTreeNode = NULL;
}
this->decisionTreeNode = ptr->deepCopyDecisionTreeNode();
if( ptr->getTrained() ){
//Deep copy the forest
this->forest.reserve( ptr->forest.size() );
for(size_t i=0; i<ptr->forest.size(); i++){
this->forest.push_back( ptr->forest[i]->deepCopy() );
}
}
this->forestSize = ptr->forestSize;
this->numRandomSplits = ptr->numRandomSplits;
this->minNumSamplesPerNode = ptr->minNumSamplesPerNode;
this->maxDepth = ptr->maxDepth;
this->removeFeaturesAtEachSpilt = ptr->removeFeaturesAtEachSpilt;
this->bootstrappedDatasetWeight = ptr->bootstrappedDatasetWeight;
this->trainingMode = ptr->trainingMode;
return true;
}
errorLog << "deepCopyFrom(const Classifier *classifier) - Failed to copy base variables!" << endl;
}
return false;
}
bool computeFeatureWeights( CommandLineParser &parser ){
infoLog << "Computing feature weights..." << endl;
string resultsFilename = "";
string modelFilename = "";
bool combineWeights = false;
//Get the model filename
if( !parser.get("model-filename",modelFilename) ){
errorLog << "Failed to parse filename from command line! You can set the model filename using the --model." << endl;
printUsage();
return false;
}
//Get the results filename
if( !parser.get("filename",resultsFilename) ){
errorLog << "Failed to parse results filename from command line! You can set the results filename using the -f." << endl;
printUsage();
return false;
}
//Get the results filename
parser.get("combine-weights",combineWeights);
//Load the model
GestureRecognitionPipeline pipeline;
if( !pipeline.load( modelFilename ) ){
errorLog << "Failed to load model from file: " << modelFilename << endl;
printUsage();
return false;
}
//Make sure the pipeline contains a random forest model and that it is trained
RandomForests *forest = pipeline.getClassifier< RandomForests >();
if( !forest ){
errorLog << "Model loaded, but the pipeline does not contain a RandomForests classifier!" << endl;
printUsage();
return false;
}
if( !forest->getTrained() ){
errorLog << "Model loaded, but the RandomForests classifier is not trained!" << endl;
printUsage();
return false;
}
//Compute the feature weights
if( combineWeights ){
VectorFloat weights = forest->getFeatureWeights();
if( weights.getSize() == 0 ){
errorLog << "Failed to compute feature weights!" << endl;
printUsage();
return false;
}
//Save the results to a file
fstream file;
file.open( resultsFilename.c_str(), fstream::out );
const unsigned int N = weights.getSize();
for(unsigned int i=0; i<N; i++){
file << weights[i] << endl;
}
file.close();
}else{
double norm = 0.0;
const unsigned int K = forest->getForestSize();
const unsigned int N = forest->getNumInputDimensions();
VectorFloat tmp( N, 0.0 );
MatrixDouble weights(K,N);
for(unsigned int i=0; i<K; i++){
DecisionTreeNode *tree = forest->getTree(i);
tree->computeFeatureWeights( tmp );
norm = 1.0 / Util::sum( tmp );
for(unsigned int j=0; j<N; j++){
tmp[j] *= norm;
weights[i][j] = tmp[j];
tmp[j] = 0;
}
}
//Save the results to a file
weights.save( resultsFilename );
}
return true;
}