OpenCV  3.3.1-dev
Open Source Computer Vision
Public Types | Public Member Functions | Static Public Member Functions | List of all members
cv::ml::StatModel Class Referenceabstract

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

#include "ml.hpp"

Inheritance diagram for cv::ml::StatModel:
cv::Algorithm cv::ml::ANN_MLP cv::ml::DTrees cv::ml::EM cv::ml::KNearest cv::ml::LogisticRegression cv::ml::NormalBayesClassifier cv::ml::SVM cv::ml::SVMSGD

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

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
datathe training data
testif 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.
respthe 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
samplesThe input samples, floating-point matrix
resultsThe optional output matrix of results.
flagsThe 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
trainDatatraining data that can be loaded from file using TrainData::loadFromCSV or created with TrainData::create.
flagsoptional 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
samplestraining samples
layoutSee ml::SampleTypes.
responsesvector 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 
)
inlinestatic
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: