ANN_MLP Class

Creates instance by raw pointer cv::ml::ANN_MLP*

Gets or sets a memory address allocated by AllocMemory.

Gets or sets the byte length of the allocated memory

Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. Default value is 0.1.

Strength of the weight gradient term. The recommended value is about 0.1. Default value is 0.1.

Native pointer of OpenCV structure

Gets or sets a handle which allocates using cvSetData.

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Gets a value indicating whether this instance has been disposed.

Gets or sets a value indicating whether you permit disposing this instance.

Initial value Delta_0 of update-values Delta_{ij}. Default value is 0.1.

Update-values upper limit Delta_{max}. It must be >1. Default value is 50.

Update-values lower limit Delta_{min}. It must be positive. Default value is FLT_EPSILON.

Decrease factor eta^-. It must be \>1. Default value is 0.5.

Increase factor eta^+. It must be >1. Default value is 1.2.

Termination criteria of the training algorithm.

Pins the object to be allocated by cvSetData.

Allocates the specified size of memory.

Computes error on the training or test dataset

Creates the empty model.

Releases the resources

Releases the resources

Releases managed resources

releases unmanaged resources

(Inherited from StatModel.)

Determines whether the specified object is equal to the current object.

Destructor

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Serves as the default hash function.

Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer.

Gets the Type of the current instance.

Returns the number of variables in training samples

Returns true if the model is classifier

Returns true if the model is trained

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.

Loads algorithm from a String.

Creates a shallow copy of the current Object.

Notifies the allocated size of memory.

Predicts response(s) for the provided sample(s)

Reads algorithm parameters from a file storage

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage fs).

Integer vector specifying the number of neurons in each layer including the input and output layers. The very first element specifies the number of elements in the input layer. The last element - number of elements in the output layer.Default value is empty Mat.

If this object is disposed, then ObjectDisposedException is thrown.

Returns a string that represents the current object.

Trains the statistical model

Trains the statistical model

Stores algorithm parameters in a file storage

Data pointer