LogisticRegression Class

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

Gets or sets a memory address allocated by AllocMemory.

Gets or sets the byte length of the allocated memory

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.

Number of iterations.

Learning rate

Specifies the number of training samples taken in each step of Mini-Batch Gradient. Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It has to take values less than the total number of training samples.

Kind of regularization to be applied. See LogisticRegression::RegKinds.

Termination criteria of the training algorithm.

Kind of training method used. See LogisticRegression::Methods.

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.

This function returns the trained parameters arranged across rows. For a two class classification problem, it returns a row matrix. It returns learnt parameters of the Logistic Regression as a matrix of type CV_32F.

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 model from a file.

Loads algorithm from a String.

Creates a shallow copy of the current Object.

Notifies the allocated size of memory.

Predicts responses for input samples and returns a float type.

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).

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