Class LogisticRegression

Implements Logistic Regression classifier.

Inheritance

System.Object

DisposableObject

DisposableCvObject

Algorithm

StatModel

LogisticRegression

Implements

ICvPtrHolder

Inherited Members

StatModel.GetVarCount()

StatModel.Empty()

StatModel.IsTrained()

StatModel.IsClassifier()

StatModel.Train(TrainData, Int32)

StatModel.Train(InputArray, SampleTypes, InputArray)

StatModel.CalcError(TrainData, Boolean, OutputArray)

StatModel.Predict(InputArray, OutputArray, StatModel.Flags)

Algorithm.Write(FileStorage)

Algorithm.Read(FileNode)

Algorithm.Save(String)

Algorithm.GetDefaultName()

DisposableCvObject.ptr

DisposableCvObject.CvPtr

DisposableObject.DataHandle

DisposableObject.IsDisposed

DisposableObject.IsEnabledDispose

DisposableObject.AllocatedMemory

DisposableObject.AllocatedMemorySize

DisposableObject.Dispose()

DisposableObject.Dispose(Boolean)

DisposableObject.AllocGCHandle(Object)

DisposableObject.AllocMemory(Int32)

DisposableObject.NotifyMemoryPressure(Int64)

DisposableObject.ThrowIfDisposed()

Namespace: OpenCvSharp.ML

Assembly: OpenCvSharp.dll

Syntax

public class LogisticRegression : StatModel, ICvPtrHolder

Constructors

LogisticRegression(IntPtr)

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

Declaration

protected LogisticRegression(IntPtr p)

Parameters

Type	Name	Description
IntPtr	p

Properties

Iterations

Number of iterations.

Declaration

public int Iterations { get; set; }

Property Value

Type	Description
System.Int32

LearningRate

Learning rate

Declaration

public double LearningRate { get; set; }

Property Value

Type	Description
System.Double

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

Regularization

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

Declaration

public LogisticRegression.RegKinds Regularization { get; set; }

Property Value

Type	Description
LogisticRegression.RegKinds

TermCriteria

Termination criteria of the training algorithm.

Declaration

public TermCriteria TermCriteria { get; set; }

Property Value

Type	Description
OpenCvSharp.TermCriteria

TrainMethod

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

Declaration

public LogisticRegression.Methods TrainMethod { get; set; }

Property Value

Type	Description
LogisticRegression.Methods

Methods

Create()

Creates the empty model.

Declaration

public static LogisticRegression Create()

Returns

Type	Description
LogisticRegression

DisposeManaged()

Releases managed resources

Declaration

protected override void DisposeManaged()

Overrides

DisposableObject.DisposeManaged()

DisposeUnmanaged()

Declaration

protected override void DisposeUnmanaged()

Overrides

DisposableCvObject.DisposeUnmanaged()

Get()

Declaration

public override IntPtr Get()

Returns

Type	Description
IntPtr

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

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

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

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

ICvPtrHolder