C# Класс OpenCvSharp.ML.LogisticRegression

Implements Logistic Regression classifier.

Наследование: StatModel

Открытые методы

Метод	Описание
Create ( ) : LogisticRegression	Creates the empty model.
GetLearntThetas ( ) : Mat	This function returns the trained paramters arranged across rows. For a two class classifcation problem, it returns a row matrix. It returns learnt paramters of the Logistic Regression as a matrix of type CV_32F.
Predict ( InputArray samples, OutputArray results = null, int flags ) : float	Predicts responses for input samples and returns a float type.

Защищенные методы

Метод	Описание
Dispose ( bool disposing ) : void	Clean up any resources being used.
LogisticRegression ( IntPtr p ) : System	Creates instance by raw pointer cv::ml::LogisticRegression*

Описание методов

Create() публичный статический Метод

Creates the empty model.

public static Create ( ) : LogisticRegression
Результат	LogisticRegression

Dispose() защищенный Метод

Clean up any resources being used.

protected Dispose ( bool disposing ) : void
disposing	bool	/// If disposing equals true, the method has been called directly or indirectly by a user's code. Managed and unmanaged resources can be disposed. /// If false, the method has been called by the runtime from inside the finalizer and you should not reference other objects. Only unmanaged resources can be disposed. ///
Результат	void

GetLearntThetas() публичный Метод

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

public GetLearntThetas ( ) : Mat
Результат	Mat

LogisticRegression() защищенный Метод

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

protected LogisticRegression ( IntPtr p ) : System
p	System.IntPtr
Результат	System

Predict() публичный Метод

Predicts responses for input samples and returns a float type.

public Predict ( InputArray samples, OutputArray results = null, int flags ) : float
samples	InputArray	The input data for the prediction algorithm. Matrix [m x n], /// where each row contains variables (features) of one object being classified. /// Should have data type CV_32F.
results	OutputArray	Predicted labels as a column matrix of type CV_32S.
flags	int	Not used.
Результат	float