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NormalBayesClassifier Class Reference
[Machine Learning]
Bayes classifier for normally distributed data. More...
#include <ml.hpp>
Inherits cv::ml::StatModel.
Public Types | |
| enum | Flags { , RAW_OUTPUT = 1 } |
Predict options. More... | |
Public Member Functions | |
| virtual CV_WRAP float | predictProb (InputArray inputs, OutputArray outputs, OutputArray outputProbs, int flags=0) const =0 |
| Predicts the response for sample(s). | |
| virtual CV_WRAP int | getVarCount () const =0 |
| Returns the number of variables in training samples. | |
| virtual CV_WRAP bool | empty () const |
| Returns true if the Algorithm is empty (e.g. | |
| virtual CV_WRAP bool | isTrained () const =0 |
| Returns true if the model is trained. | |
| virtual CV_WRAP bool | isClassifier () const =0 |
| Returns true if the model is classifier. | |
| virtual CV_WRAP bool | train (const Ptr< TrainData > &trainData, int flags=0) |
| Trains the statistical model. | |
| virtual CV_WRAP bool | train (InputArray samples, int layout, InputArray responses) |
| Trains the statistical model. | |
| virtual CV_WRAP float | calcError (const Ptr< TrainData > &data, bool test, OutputArray resp) const |
| Computes error on the training or test dataset. | |
| virtual CV_WRAP float | predict (InputArray samples, OutputArray results=noArray(), int flags=0) const =0 |
| Predicts response(s) for the provided sample(s) | |
| virtual CV_WRAP void | clear () |
| Clears the algorithm state. | |
| virtual void | write (FileStorage &fs) const |
| Stores algorithm parameters in a file storage. | |
| virtual void | read (const FileNode &fn) |
| Reads algorithm parameters from a file storage. | |
| virtual CV_WRAP void | save (const String &filename) const |
| Saves the algorithm to a file. | |
| virtual CV_WRAP String | getDefaultName () const |
| Returns the algorithm string identifier. | |
Static Public Member Functions | |
| static CV_WRAP Ptr < NormalBayesClassifier > | create () |
| Creates empty model Use StatModel::train to train the model after creation. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | train (const Ptr< TrainData > &data, int flags=0) |
| Create and train model with default parameters. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | read (const FileNode &fn) |
| Reads algorithm from the file node. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| Loads algorithm from the file. | |
| template<typename _Tp > | |
| static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| Loads algorithm from a String. | |
Detailed Description
Bayes classifier for normally distributed data.
- See also:
- ml_intro_bayes
Definition at line 379 of file ml.hpp.
Member Enumeration Documentation
enum Flags [inherited] |
Member Function Documentation
| virtual CV_WRAP float calcError | ( | const Ptr< TrainData > & | data, |
| bool | test, | ||
| OutputArray | resp | ||
| ) | const [virtual, inherited] |
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%).
| virtual CV_WRAP void clear | ( | ) | [virtual, inherited] |
Clears the algorithm state.
Reimplemented in DescriptorMatcher, and FlannBasedMatcher.
| static CV_WRAP Ptr<NormalBayesClassifier> create | ( | ) | [static] |
Creates empty model Use StatModel::train to train the model after creation.
| virtual CV_WRAP bool empty | ( | ) | const [virtual, inherited] |
| virtual CV_WRAP String getDefaultName | ( | ) | const [virtual, inherited] |
Returns the algorithm string identifier.
This string is used as top level xml/yml node tag when the object is saved to a file or string.
| virtual CV_WRAP int getVarCount | ( | ) | const [pure virtual, inherited] |
Returns the number of variables in training samples.
| virtual CV_WRAP bool isClassifier | ( | ) | const [pure virtual, inherited] |
Returns true if the model is classifier.
| virtual CV_WRAP bool isTrained | ( | ) | const [pure virtual, inherited] |
Returns true if the model is trained.
| static Ptr<_Tp> load | ( | const String & | filename, |
| const String & | objname = String() |
||
| ) | [static, inherited] |
Loads algorithm from the file.
- Parameters:
-
filename Name of the file to read. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).
| static Ptr<_Tp> loadFromString | ( | const String & | strModel, |
| const String & | objname = String() |
||
| ) | [static, inherited] |
Loads algorithm from a String.
- Parameters:
-
strModel The string variable containing the model you want to load. objname The optional name of the node to read (if empty, the first top-level node will be used)
This is static template method of Algorithm. It's usage is following (in the case of SVM):
Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);
| virtual CV_WRAP float predict | ( | InputArray | samples, |
| OutputArray | results = noArray(), |
||
| int | flags = 0 |
||
| ) | const [pure virtual, inherited] |
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 LogisticRegression.
| virtual CV_WRAP float predictProb | ( | InputArray | inputs, |
| OutputArray | outputs, | ||
| OutputArray | outputProbs, | ||
| int | flags = 0 |
||
| ) | const [pure virtual] |
Predicts the response for sample(s).
The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix inputs. In case of multiple input vectors, there should be one output vector outputs. The predicted class for a single input vector is returned by the method. The vector outputProbs contains the output probabilities corresponding to each element of result.
Reads algorithm from the file node.
This is static template method of Algorithm. It's usage is following (in the case of SVM):
cv::FileStorage fsRead("example.xml", FileStorage::READ); Ptr<SVM> svm = Algorithm::read<SVM>(fsRead.root());
In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)
Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.
| virtual void read | ( | const FileNode & | fn ) | [virtual, inherited] |
Reads algorithm parameters from a file storage.
Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.
| virtual CV_WRAP void save | ( | const String & | filename ) | const [virtual, inherited] |
Saves the algorithm to a file.
In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).
| virtual CV_WRAP bool train | ( | const Ptr< TrainData > & | trainData, |
| int | flags = 0 |
||
| ) | [virtual, inherited] |
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).
| virtual CV_WRAP bool train | ( | InputArray | samples, |
| int | layout, | ||
| InputArray | responses | ||
| ) | [virtual, inherited] |
Trains the statistical model.
- Parameters:
-
samples training samples layout See ml::SampleTypes. responses vector of responses associated with the training samples.
| virtual void write | ( | FileStorage & | fs ) | const [virtual, inherited] |
Stores algorithm parameters in a file storage.
Reimplemented in Feature2D, DescriptorMatcher, and FlannBasedMatcher.
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