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; }