| OpenCV 3.4.1 Open Source Computer Vision

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

Base class for statistical models in OpenCV ML. More...

`#include "ml.hpp"`

Inheritance diagram for cv::ml::StatModel:

## Public Types
enum | Flags { UPDATE_MODEL = 1, RAW_OUTPUT =1, COMPRESSED_INPUT =2, PREPROCESSED_INPUT =4 }

## Public Member Functions
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
| 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 float | predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0
| Predicts response(s) for the provided sample(s) 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
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

Base class for statistical models in OpenCV ML.

## Member Enumeration Documentation

## § Flags

enum cv::ml::StatModel::Flags

Predict options

Enumerator
UPDATE_MODEL |
RAW_OUTPUT | makes the method return the raw results (the sum), not the class label
COMPRESSED_INPUT |
PREPROCESSED_INPUT |

## Member Function Documentation

## § calcError()

virtual float cv::ml::StatModel::calcError | ( | const Ptr< TrainData > & | data,
|  | bool | test,
|  | OutputArray | resp
| ) |  | const
| virtual

Python:
| retval, resp | = | cv.ml_StatModel.calcError( | data, test[, resp] | )

Computes error on the training or test dataset.

Parameters

  data | the training data
  test | if true, the error is computed over the test subset of the data, otherwise it's computed over the training subset of the data. Please note that if you loaded a completely different dataset to evaluate already trained classifier, you will probably want not to set the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so that the error is computed for the whole new set. Yes, this sounds a bit confusing.
  resp | the optional output responses.

The method uses StatModel::predict to compute the error. For regression models the error is computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).

## § empty()

virtual bool cv::ml::StatModel::empty | ( |  | ) | const
| virtual

Python:
| retval | = | cv.ml_StatModel.empty( |  | )

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.

Reimplemented from cv::Algorithm.

## § getVarCount()

virtual int cv::ml::StatModel::getVarCount | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_StatModel.getVarCount( |  | )

Returns the number of variables in training samples.

## § isClassifier()

virtual bool cv::ml::StatModel::isClassifier | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_StatModel.isClassifier( |  | )

Returns true if the model is classifier.

## § isTrained()

virtual bool cv::ml::StatModel::isTrained | ( |  | ) | const
| pure virtual

Python:
| retval | = | cv.ml_StatModel.isTrained( |  | )

Returns true if the model is trained.

## § predict()

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

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

Predicts response(s) for the provided sample(s)

Parameters

  samples | The input samples, floating-point matrix
  results | The optional output matrix of results.
  flags | The optional flags, model-dependent. See cv::ml::StatModel::Flags.

Implemented in cv::ml::LogisticRegression, and cv::ml::EM.

## § train() [1/3]

virtual bool cv::ml::StatModel::train | ( | const Ptr< TrainData > & | trainData,
|  | int | flags = `0`
| ) |  |
| virtual

Python:
| retval | = | cv.ml_StatModel.train( | trainData[, flags] | )
| retval | = | cv.ml_StatModel.train( | samples, layout, responses | )

Trains the statistical model.

Parameters

  trainData | training data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
  flags | optional flags, depending on the model. Some of the models can be updated with the new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).

Examples:
  train_HOG.cpp.

## § train() [2/3]

virtual bool cv::ml::StatModel::train | ( | InputArray | samples,
|  | int | layout,
|  | InputArray | responses
| ) |  |
| virtual

Python:
| retval | = | cv.ml_StatModel.train( | trainData[, flags] | )
| retval | = | cv.ml_StatModel.train( | samples, layout, responses | )

Trains the statistical model.

Parameters

  samples | training samples
  layout | See ml::SampleTypes.
  responses | vector of responses associated with the training samples.

## § train() [3/3]

template<typename _Tp >

static Ptr<_Tp> cv::ml::StatModel::train | ( | const Ptr< TrainData > & | data,
|  | int | flags = `0`
| ) |  |
| inline static

Python:
| retval | = | cv.ml_StatModel.train( | trainData[, flags] | )
| retval | = | cv.ml_StatModel.train( | samples, layout, responses | )

Create and train model with default parameters.

The class must implement static `create()` method with no parameters or with all default parameter values

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

- ml/include/opencv2/ml.hpp