// // C++: static Ptr_ANN_MLP cv::ml::ANN_MLP::create() // //javadoc: ANN_MLP::create() public static ANN_MLP create () { #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER ANN_MLP retVal = ANN_MLP.__fromPtr__(ml_ANN_1MLP_create_10()); return retVal; #else return null; #endif }
// // C++: static Ptr_ANN_MLP cv::ml::ANN_MLP::load(String filepath) // //javadoc: ANN_MLP::load(filepath) public static ANN_MLP load (string filepath) { #if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER ANN_MLP retVal = ANN_MLP.__fromPtr__(ml_ANN_1MLP_load_10(filepath)); return retVal; #else return null; #endif }
// // C++: static Ptr_ANN_MLP cv::ml::ANN_MLP::load(String filepath) // /** * Loads and creates a serialized ANN from a file * * Use ANN::save to serialize and store an ANN to disk. * Load the ANN from this file again, by calling this function with the path to the file. * * param filepath path to serialized ANN * return automatically generated */ public static ANN_MLP load(string filepath) { return(ANN_MLP.__fromPtr__(ml_ANN_1MLP_load_10(filepath))); }
// // C++: static Ptr_ANN_MLP cv::ml::ANN_MLP::create() // /** * Creates empty model * * Use StatModel::train to train the model, Algorithm::load<ANN_MLP>(filename) to load the pre-trained model. * Note that the train method has optional flags: ANN_MLP::TrainFlags. * return automatically generated */ public static ANN_MLP create() { return(ANN_MLP.__fromPtr__(ml_ANN_1MLP_create_10())); }