// // This file is auto-generated. Please don't modify it! // #pragma once #ifdef __cplusplus //#import "opencv.hpp" #import "opencv2/ml.hpp" #else #define CV_EXPORTS #endif #import #import "DTrees.h" @class Mat; @class TermCriteria; NS_ASSUME_NONNULL_BEGIN // C++: class RTrees /** * The class implements the random forest predictor. * * @see REF: ml_intro_rtrees * * Member of `Ml` */ CV_EXPORTS @interface RTrees : DTrees #ifdef __cplusplus @property(readonly)cv::Ptr nativePtrRTrees; #endif #ifdef __cplusplus - (instancetype)initWithNativePtr:(cv::Ptr)nativePtr; + (instancetype)fromNative:(cv::Ptr)nativePtr; #endif #pragma mark - Methods // // bool cv::ml::RTrees::getCalculateVarImportance() // /** * @see `-setCalculateVarImportance:` */ - (BOOL)getCalculateVarImportance NS_SWIFT_NAME(getCalculateVarImportance()); // // void cv::ml::RTrees::setCalculateVarImportance(bool val) // /** * getCalculateVarImportance @see `-getCalculateVarImportance:` */ - (void)setCalculateVarImportance:(BOOL)val NS_SWIFT_NAME(setCalculateVarImportance(val:)); // // int cv::ml::RTrees::getActiveVarCount() // /** * @see `-setActiveVarCount:` */ - (int)getActiveVarCount NS_SWIFT_NAME(getActiveVarCount()); // // void cv::ml::RTrees::setActiveVarCount(int val) // /** * getActiveVarCount @see `-getActiveVarCount:` */ - (void)setActiveVarCount:(int)val NS_SWIFT_NAME(setActiveVarCount(val:)); // // TermCriteria cv::ml::RTrees::getTermCriteria() // /** * @see `-setTermCriteria:` */ - (TermCriteria*)getTermCriteria NS_SWIFT_NAME(getTermCriteria()); // // void cv::ml::RTrees::setTermCriteria(TermCriteria val) // /** * getTermCriteria @see `-getTermCriteria:` */ - (void)setTermCriteria:(TermCriteria*)val NS_SWIFT_NAME(setTermCriteria(val:)); // // Mat cv::ml::RTrees::getVarImportance() // /** * Returns the variable importance array. * The method returns the variable importance vector, computed at the training stage when * CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is * returned. */ - (Mat*)getVarImportance NS_SWIFT_NAME(getVarImportance()); // // void cv::ml::RTrees::getVotes(Mat samples, Mat& results, int flags) // /** * Returns the result of each individual tree in the forest. * In case the model is a regression problem, the method will return each of the trees' * results for each of the sample cases. If the model is a classifier, it will return * a Mat with samples + 1 rows, where the first row gives the class number and the * following rows return the votes each class had for each sample. * @param samples Array containing the samples for which votes will be calculated. * @param results Array where the result of the calculation will be written. * @param flags Flags for defining the type of RTrees. */ - (void)getVotes:(Mat*)samples results:(Mat*)results flags:(int)flags NS_SWIFT_NAME(getVotes(samples:results:flags:)); // // double cv::ml::RTrees::getOOBError() // /** * Returns the OOB error value, computed at the training stage when calcOOBError is set to true. * If this flag was set to false, 0 is returned. The OOB error is also scaled by sample weighting. */ - (double)getOOBError NS_SWIFT_NAME(getOOBError()); // // static Ptr_RTrees cv::ml::RTrees::create() // /** * Creates the empty model. * Use StatModel::train to train the model, StatModel::train to create and train the model, * Algorithm::load to load the pre-trained model. */ + (RTrees*)create NS_SWIFT_NAME(create()); // // static Ptr_RTrees cv::ml::RTrees::load(String filepath, String nodeName = String()) // /** * Loads and creates a serialized RTree from a file * * Use RTree::save to serialize and store an RTree to disk. * Load the RTree from this file again, by calling this function with the path to the file. * Optionally specify the node for the file containing the classifier * * @param filepath path to serialized RTree * @param nodeName name of node containing the classifier */ + (RTrees*)load:(NSString*)filepath nodeName:(NSString*)nodeName NS_SWIFT_NAME(load(filepath:nodeName:)); /** * Loads and creates a serialized RTree from a file * * Use RTree::save to serialize and store an RTree to disk. * Load the RTree from this file again, by calling this function with the path to the file. * Optionally specify the node for the file containing the classifier * * @param filepath path to serialized RTree */ + (RTrees*)load:(NSString*)filepath NS_SWIFT_NAME(load(filepath:)); @end NS_ASSUME_NONNULL_END