| OpenCV 4.3.0 Open Source Computer Vision

Public Types | Public Member Functions | Static Public Member Functions | List of all members
cv::ml::LogisticRegression Class Referenceabstract
Machine Learning

Implements Logistic Regression classifier. More...

`#include <opencv2/ml.hpp>`

Inheritance diagram for cv::ml::LogisticRegression:

## Public Types
enum | Methods { BATCH = 0, MINI_BATCH = 1 }
| Training methods. More...
enum | RegKinds { REG_DISABLE = -1, REG_L1 = 0, REG_L2 = 1 }
| Regularization kinds. More...
Public Types inherited from cv::ml::StatModel
enum | Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 }

## Public Member Functions
virtual Mat | get_learnt_thetas () const =0
| This function returns the trained parameters arranged across rows. More...
virtual int | getIterations () const =0
virtual double | getLearningRate () const =0
virtual int | getMiniBatchSize () const =0
virtual int | getRegularization () const =0
virtual TermCriteria | getTermCriteria () const =0
virtual int | getTrainMethod () const =0
virtual float | predict (InputArray samples, OutputArray results=noArray(), int flags=0) const CV_OVERRIDE=0
| Predicts responses for input samples and returns a float type. More...
virtual void | setIterations (int val)=0
virtual void | setLearningRate (double val)=0
virtual void | setMiniBatchSize (int val)=0
virtual void | setRegularization (int val)=0
virtual void | setTermCriteria (TermCriteria val)=0
virtual void | setTrainMethod (int val)=0
Public Member Functions inherited from cv::ml::StatModel
virtual float | calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const
| Computes error on the training or test dataset. More...
virtual bool | empty () const CV_OVERRIDE
| Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...
virtual int | getVarCount () const =0
| Returns the number of variables in training samples. More...
virtual bool | isClassifier () const =0
| Returns true if the model is classifier. More...
virtual bool | isTrained () const =0
| Returns true if the model is trained. More...
virtual bool | train (const Ptr< TrainData > &trainData, int flags=0)
| Trains the statistical model. More...
virtual bool | train (InputArray samples, int layout, InputArray responses)
| Trains the statistical model. More...
Public Member Functions inherited from cv::Algorithm
| Algorithm ()
virtual | ~Algorithm ()
virtual void | clear ()
| Clears the algorithm state. More...
virtual String | getDefaultName () const
virtual void | read (const FileNode &fn)
| Reads algorithm parameters from a file storage. More...
virtual void | save (const String &filename) const
virtual void | write (FileStorage &fs) const
| Stores algorithm parameters in a file storage. More...
void | write (const Ptr< FileStorage > &fs, const String &name=String()) const
| simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...

## Static Public Member Functions
static Ptr< LogisticRegression > | create ()
| Creates empty model. More...
static Ptr< LogisticRegression > | load (const String &filepath, const String &nodeName=String())
| Loads and creates a serialized LogisticRegression from a file. More...
Static Public Member Functions inherited from cv::ml::StatModel
template<typename _Tp >
static Ptr< _Tp > | train (const Ptr< TrainData > &data, int flags=0)
| Create and train model with default parameters. More...
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp > | load (const String &filename, const String &objname=String())
| Loads algorithm from the file. More...
template<typename _Tp >
static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String())
| Loads algorithm from a String. More...
template<typename _Tp >
static Ptr< _Tp > | read (const FileNode &fn)
| Reads algorithm from the file node. More...

## Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void | writeFormat (FileStorage &fs) const

## Detailed Description

Implements Logistic Regression classifier.

See also
  Logistic Regression

## Member Enumeration Documentation

## ◆ Methods

enum cv::ml::LogisticRegression::Methods

Training methods.

Enumerator
BATCH |
MINI_BATCH | Set MiniBatchSize to a positive integer when using this method.

## ◆ RegKinds

enum cv::ml::LogisticRegression::RegKinds

Regularization kinds.

Enumerator
REG_DISABLE | Regularization disabled.
REG_L1 | L1 norm
REG_L2 | L2 norm

## Member Function Documentation

## ◆ create()

static Ptr<LogisticRegression> cv::ml::LogisticRegression::create | ( |  | ) |
| static

Python:
| retval | = | cv.ml.LogisticRegression_create( |  | )

Creates empty model.

Creates Logistic Regression model with parameters given.

## ◆ get_learnt_thetas()

virtual Mat cv::ml::LogisticRegression::get_learnt_thetas | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_LogisticRegression.get_learnt_thetas( |  | )

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.

## ◆ getIterations()

virtual int cv::ml::LogisticRegression::getIterations | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_LogisticRegression.getIterations( |  | )

Number of iterations.

See also
  setIterations

## ◆ getLearningRate()

virtual double cv::ml::LogisticRegression::getLearningRate | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_LogisticRegression.getLearningRate( |  | )

Learning rate.

See also
  setLearningRate

## ◆ getMiniBatchSize()

virtual int cv::ml::LogisticRegression::getMiniBatchSize | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_LogisticRegression.getMiniBatchSize( |  | )

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.

See also
  setMiniBatchSize

## ◆ getRegularization()

virtual int cv::ml::LogisticRegression::getRegularization | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_LogisticRegression.getRegularization( |  | )

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

See also
  setRegularization

## ◆ getTermCriteria()

virtual TermCriteria cv::ml::LogisticRegression::getTermCriteria | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_LogisticRegression.getTermCriteria( |  | )

Termination criteria of the algorithm.

See also
  setTermCriteria

## ◆ getTrainMethod()

virtual int cv::ml::LogisticRegression::getTrainMethod | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_LogisticRegression.getTrainMethod( |  | )

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

See also
  setTrainMethod

## ◆ load()

static Ptr<LogisticRegression> cv::ml::LogisticRegression::load | ( | const String & | filepath,
|  | const String & | nodeName = `String()`
| ) |  |
| static

Python:
| retval | = | cv.ml.LogisticRegression_load( | filepath[, nodeName] | )

Loads and creates a serialized LogisticRegression from a file.

Use LogisticRegression::save to serialize and store an LogisticRegression to disk. Load the LogisticRegression from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters

  filepath | path to serialized LogisticRegression
  nodeName | name of node containing the classifier

## ◆ predict()

virtual float cv::ml::LogisticRegression::predict | ( | InputArray | samples,
|  | OutputArray | results = `noArray()`,
|  | int | flags = `0`
| ) |  | const
| pure virtual

Python:
| retval, results | = | cv.ml_LogisticRegression.predict( | samples[, results[, flags]] | )

Predicts responses for input samples and returns a float type.

Parameters

  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.
  results | Predicted labels as a column matrix of type CV_32S.
  flags | Not used.

Implements cv::ml::StatModel.

## ◆ setIterations()

virtual void cv::ml::LogisticRegression::setIterations | ( | int | val | ) |
| pure virtual

Python:
| None | = | cv.ml_LogisticRegression.setIterations( | val | )

See also
  getIterations

## ◆ setLearningRate()

virtual void cv::ml::LogisticRegression::setLearningRate | ( | double | val | ) |
| pure virtual

Python:
| None | = | cv.ml_LogisticRegression.setLearningRate( | val | )

See also
  getLearningRate

## ◆ setMiniBatchSize()

virtual void cv::ml::LogisticRegression::setMiniBatchSize | ( | int | val | ) |
| pure virtual

Python:
| None | = | cv.ml_LogisticRegression.setMiniBatchSize( | val | )

See also
  getMiniBatchSize

## ◆ setRegularization()

virtual void cv::ml::LogisticRegression::setRegularization | ( | int | val | ) |
| pure virtual

Python:
| None | = | cv.ml_LogisticRegression.setRegularization( | val | )

See also
  getRegularization

## ◆ setTermCriteria()

virtual void cv::ml::LogisticRegression::setTermCriteria | ( | TermCriteria | val | ) |
| pure virtual

Python:
| None | = | cv.ml_LogisticRegression.setTermCriteria( | val | )

See also
  getTermCriteria

## ◆ setTrainMethod()

virtual void cv::ml::LogisticRegression::setTrainMethod | ( | int | val | ) |
| pure virtual

Python:
| None | = | cv.ml_LogisticRegression.setTrainMethod( | val | )

See also
  getTrainMethod

The documentation for this class was generated from the following file:

- opencv2/ml.hpp