Class LogisticRegression

Implements Logistic Regression classifier.

Inheritance
System.Object
DisposableObject
DisposableCvObject
Algorithm
StatModel
LogisticRegression
Implements
Inherited Members
Namespace: OpenCvSharp.ML
Assembly: OpenCvSharp.dll
Syntax
public class LogisticRegression : StatModel, ICvPtrHolder

Constructors

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LogisticRegression(IntPtr)

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

Declaration
protected LogisticRegression(IntPtr p)
Parameters
Type Name Description
IntPtr p

Properties

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Iterations

Number of iterations.

Declaration
public int Iterations { get; set; }
Property Value
Type Description
System.Int32
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LearningRate

Learning rate

Declaration
public double LearningRate { get; set; }
Property Value
Type Description
System.Double
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MiniBatchSize

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.

Declaration
public int MiniBatchSize { get; set; }
Property Value
Type Description
System.Int32
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Regularization

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

Declaration
public LogisticRegression.RegKinds Regularization { get; set; }
Property Value
Type Description
LogisticRegression.RegKinds
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TermCriteria

Termination criteria of the training algorithm.

Declaration
public TermCriteria TermCriteria { get; set; }
Property Value
Type Description
OpenCvSharp.TermCriteria
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TrainMethod

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

Declaration
public LogisticRegression.Methods TrainMethod { get; set; }
Property Value
Type Description
LogisticRegression.Methods

Methods

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Create()

Creates the empty model.

Declaration
public static LogisticRegression Create()
Returns
Type Description
LogisticRegression
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DisposeManaged()

Releases managed resources

Declaration
protected override void DisposeManaged()
Overrides
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GetLearntThetas()

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.

Declaration
public Mat GetLearntThetas()
Returns
Type Description
Mat
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Load(String)

Loads and creates a serialized model from a file.

Declaration
public static LogisticRegression Load(string filePath)
Parameters
Type Name Description
System.String filePath
Returns
Type Description
LogisticRegression
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LoadFromString(String)

Loads algorithm from a String.

Declaration
public static LogisticRegression LoadFromString(string strModel)
Parameters
Type Name Description
System.String strModel

he string variable containing the model you want to load.
Returns
Type Description
LogisticRegression
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Predict(InputArray, OutputArray, Int32)

Predicts responses for input samples and returns a float type.

Declaration
public float Predict(InputArray samples, OutputArray results = null, int flags = 0)
Parameters
Type Name Description
InputArray samples

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.
OutputArray results

Predicted labels as a column matrix of type CV_32S.
System.Int32 flags

Not used.
Returns
Type Description
System.Single

Implements