//
// 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()));
}
