bool RandomForests::loadModelFromFile(fstream &file){
clear();
if(!file.is_open())
{
errorLog << "loadModelFromFile(string filename) - Could not open file to load model" << endl;
return false;
}
std::string word;
std::string treeNodeType;
file >> word;
//Find the file type header
if(word != "GRT_RANDOM_FOREST_MODEL_FILE_V1.0"){
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;
}
file >> word;
if(word != "DecisionTreeNodeType:"){
Classifier::errorLog << "loadModelFromFile(string filename) - Could not find the DecisionTreeNodeType!" << endl;
return false;
}
file >> treeNodeType;
if( treeNodeType != "NULL" ){
decisionTreeNode = dynamic_cast< DecisionTreeNode* >( DecisionTreeNode::createInstanceFromString( treeNodeType ) );
if( decisionTreeNode == NULL ){
Classifier::errorLog << "loadModelFromFile(string filename) - Could not create new DecisionTreeNode from type: " << treeNodeType << endl;
return false;
}
if( !decisionTreeNode->loadFromFile( file ) ){
Classifier::errorLog <<"loadModelFromFile(fstream &file) - Failed to load decisionTreeNode settings from file!" << endl;
return false;
}
}else{
Classifier::errorLog <<"loadModelFromFile(fstream &file) - Failed to load decisionTreeNode! DecisionTreeNodeType is NULL!" << endl;
return false;
}
file >> word;
if(word != "ForestSize:"){
errorLog << "loadModelFromFile(string filename) - Could not find the ForestSize!" << endl;
return false;
}
file >> forestSize;
file >> word;
if(word != "NumSplittingSteps:"){
errorLog << "loadModelFromFile(string filename) - Could not find the NumSplittingSteps!" << endl;
return false;
}
file >> numRandomSplits;
file >> word;
if(word != "MinNumSamplesPerNode:"){
errorLog << "loadModelFromFile(string filename) - Could not find the MinNumSamplesPerNode!" << endl;
return false;
}
file >> minNumSamplesPerNode;
file >> word;
if(word != "MaxDepth:"){
errorLog << "loadModelFromFile(string filename) - Could not find the MaxDepth!" << endl;
return false;
}
file >> maxDepth;
file >> word;
if(word != "RemoveFeaturesAtEachSpilt:"){
errorLog << "loadModelFromFile(string filename) - Could not find the RemoveFeaturesAtEachSpilt!" << endl;
return false;
}
file >> removeFeaturesAtEachSpilt;
file >> word;
if(word != "TrainingMode:"){
errorLog << "loadModelFromFile(string filename) - Could not find the TrainingMode!" << endl;
return false;
}
file >> trainingMode;
file >> word;
if(word != "ForestBuilt:"){
errorLog << "loadModelFromFile(string filename) - Could not find the ForestBuilt!" << endl;
return false;
}
file >> trained;
if( trained ){
//Find the forest header
file >> word;
if(word != "Forest:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Forest!" << endl;
return false;
}
//Load each tree
UINT treeIndex;
forest.reserve( forestSize );
for(UINT i=0; i<forestSize; i++){
file >> word;
if(word != "Tree:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Tree Header!" << endl;
cout << "WORD: " << word << endl;
cout << "Tree i: " << i << endl;
return false;
}
file >> treeIndex;
if( treeIndex != i+1 ){
errorLog << "loadModelFromFile(string filename) - Incorrect tree index: " << treeIndex << endl;
return false;
}
file >> word;
if(word != "TreeNodeType:"){
errorLog << "loadModelFromFile(string filename) - Could not find the TreeNodeType!" << endl;
cout << "WORD: " << word << endl;
cout << "i: " << i << endl;
return false;
}
file >> treeNodeType;
//Create a new DTree
DecisionTreeNode *tree = dynamic_cast< DecisionTreeNode* >( DecisionTreeNode::createInstanceFromString( treeNodeType ) );
if( tree == NULL ){
errorLog << "loadModelFromFile(fstream &file) - Failed to create new Tree!" << endl;
return false;
}
//Load the tree from the file
tree->setParent( NULL );
if( !tree->loadFromFile( file ) ){
errorLog << "loadModelFromFile(fstream &file) - Failed to load tree from file!" << endl;
return false;
}
//Add the tree to the forest
forest.push_back( tree );
}
}
return true;
}
bool RandomForests::loadModelFromFile(fstream &file){
clear();
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_RANDOM_FOREST_MODEL_FILE_V1.0"){
errorLog << "loadModelFromFile(string filename) - Could not find Model File Header" << endl;
return false;
}
file >> word;
if(word != "NumFeatures:"){
errorLog << "loadModelFromFile(string filename) - Could not find NumFeatures!" << endl;
return false;
}
file >> numInputDimensions;
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(numInputDimensions);
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;
}
}
file >> word;
if(word != "ForestSize:"){
errorLog << "loadModelFromFile(string filename) - Could not find the ForestSize!" << endl;
return false;
}
file >> forestSize;
file >> word;
if(word != "NumSplittingSteps:"){
errorLog << "loadModelFromFile(string filename) - Could not find the NumSplittingSteps!" << endl;
return false;
}
file >> numRandomSplits;
file >> word;
if(word != "MinNumSamplesPerNode:"){
errorLog << "loadModelFromFile(string filename) - Could not find the MinNumSamplesPerNode!" << endl;
return false;
}
file >> minNumSamplesPerNode;
file >> word;
if(word != "MaxDepth:"){
errorLog << "loadModelFromFile(string filename) - Could not find the MaxDepth!" << endl;
return false;
}
file >> maxDepth;
file >> word;
if(word != "ForestBuilt:"){
errorLog << "loadModelFromFile(string filename) - Could not find the ForestBuilt!" << endl;
return false;
}
file >> trained;
if( trained ){
file >> word;
if(word != "Forest:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Forest!" << endl;
return false;
}
UINT treeIndex;
for(UINT i=0; i<forestSize; i++){
file >> word;
if(word != "Tree:"){
errorLog << "loadModelFromFile(string filename) - Could not find the Tree Header!" << endl;
return false;
}
file >> treeIndex;
if( treeIndex != i+1 ){
errorLog << "loadModelFromFile(string filename) - Incorrect tree index: " << treeIndex << endl;
return false;
}
//Create a new DTree
DecisionTreeNode *tree = new DecisionTreeNode;
if( tree == NULL ){
errorLog << "loadModelFromFile(fstream &file) - Failed to create new Tree!" << endl;
return false;
}
tree->setParent( NULL );
if( !tree->loadFromFile( file ) ){
errorLog << "loadModelFromFile(fstream &file) - Failed to load tree from file!" << endl;
return false;
}
//Add the tree to the forest
forest.push_back( tree );
}
}
return true;
}
