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::setDecisionTreeNode( const DecisionTreeNode &node ){
if( decisionTreeNode != NULL ){
delete decisionTreeNode;
decisionTreeNode = NULL;
}
this->decisionTreeNode = node.deepCopy();
return true;
}
RandomForests::RandomForests(const DecisionTreeNode &decisionTreeNode,const UINT forestSize,const UINT numRandomSplits,const UINT minNumSamplesPerNode,const UINT maxDepth,const UINT trainingMode,const bool removeFeaturesAtEachSpilt,const bool useScaling,const Float bootstrappedDatasetWeight)
{
this->decisionTreeNode = decisionTreeNode.deepCopy();
this->forestSize = forestSize;
this->numRandomSplits = numRandomSplits;
this->minNumSamplesPerNode = minNumSamplesPerNode;
this->maxDepth = maxDepth;
this->trainingMode = trainingMode;
this->removeFeaturesAtEachSpilt = removeFeaturesAtEachSpilt;
this->useScaling = useScaling;
this->bootstrappedDatasetWeight = bootstrappedDatasetWeight;
classType = "RandomForests";
classifierType = classType;
classifierMode = STANDARD_CLASSIFIER_MODE;
useNullRejection = false;
supportsNullRejection = false;
useValidationSet = true;
validationSetSize = 20;
debugLog.setProceedingText("[DEBUG RandomForests]");
errorLog.setProceedingText("[ERROR RandomForests]");
trainingLog.setProceedingText("[TRAINING RandomForests]");
warningLog.setProceedingText("[WARNING RandomForests]");
}
