org.bytedeco.javacpp

Class opencv_ml.StatModel

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.opencv_core.Algorithm

org.bytedeco.javacpp.helper.opencv_ml.AbstractStatModel

org.bytedeco.javacpp.opencv_ml.StatModel

All Implemented Interfaces:

AutoCloseable

Direct Known Subclasses:

opencv_ml.ANN_MLP, opencv_ml.DTrees, opencv_ml.EM, opencv_ml.KNearest, opencv_ml.LogisticRegression, opencv_ml.NormalBayesClassifier, opencv_ml.SVM

Enclosing class:

opencv_ml

@Namespace(value="cv::ml")
public static class opencv_ml.StatModel
extends opencv_ml.AbstractStatModel

\brief Base class for statistical models in OpenCV ML.

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator

Field Summary

Fields

Modifier and Type	Field and Description
static int	COMPRESSED_INPUT enum cv::ml::StatModel::Flags
static int	PREPROCESSED_INPUT enum cv::ml::StatModel::Flags
static int	RAW_OUTPUT enum cv::ml::StatModel::Flags
static int	UPDATE_MODEL enum cv::ml::StatModel::Flags

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
opencv_ml.StatModel(Pointer p) Pointer cast constructor.

Method Summary

Methods

Modifier and Type	Method and Description
float	calcError(opencv_ml.TrainData data, boolean test, opencv_core.Mat resp) \brief Computes error on the training or test dataset
boolean	empty() \brief Returns true if the Algorithm is empty (e.g.
int	getVarCount() \brief Returns the number of variables in training samples
boolean	isClassifier() \brief Returns true if the model is classifier
boolean	isTrained() \brief Returns true if the model is trained
float	predict(opencv_core.Mat samples)
float	predict(opencv_core.Mat samples, opencv_core.Mat results, int flags) \brief Predicts response(s) for the provided sample(s)
boolean	train(opencv_core.Mat samples, int layout, opencv_core.Mat responses) \brief Trains the statistical model
boolean	train(opencv_ml.TrainData trainData)
boolean	train(opencv_ml.TrainData trainData, int flags) \brief Trains the statistical model

Methods inherited from class org.bytedeco.javacpp.helper.opencv_ml.AbstractStatModel

loadANN_MLP, loadANN_MLP, loadBoost, loadBoost, loadDTrees, loadDTrees, loadEM, loadEM, loadKNearest, loadKNearest, loadLogisticRegression, loadLogisticRegression, loadNormalBayesClassifier, loadNormalBayesClassifier, loadRTrees, loadRTrees, loadSVM, loadSVM

Methods inherited from class org.bytedeco.javacpp.opencv_core.Algorithm

clear, getDefaultName, position, read, save, save, write

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, capacity, capacity, close, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, hashCode, isNull, limit, limit, maxBytes, memchr, memcmp, memcpy, memmove, memset, offsetof, position, put, setNull, sizeof, toString, totalBytes, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Field Detail

UPDATE_MODEL

public static final int UPDATE_MODEL

enum cv::ml::StatModel::Flags

See Also:

Constant Field Values

RAW_OUTPUT

public static final int RAW_OUTPUT

enum cv::ml::StatModel::Flags

See Also:

Constant Field Values

COMPRESSED_INPUT

public static final int COMPRESSED_INPUT

enum cv::ml::StatModel::Flags

See Also:

Constant Field Values

PREPROCESSED_INPUT

public static final int PREPROCESSED_INPUT

enum cv::ml::StatModel::Flags

See Also:

Constant Field Values

Constructor Detail

opencv_ml.StatModel

public opencv_ml.StatModel(Pointer p)

Pointer cast constructor. Invokes Pointer.Pointer(Pointer).

Method Detail

getVarCount

public int getVarCount()

\brief Returns the number of variables in training samples

empty

@Cast(value="bool")
public boolean empty()

Description copied from class: opencv_core.Algorithm

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

Overrides:

empty in class opencv_core.Algorithm

isTrained

@Cast(value="bool")
public boolean isTrained()

\brief Returns true if the model is trained

isClassifier

@Cast(value="bool")
public boolean isClassifier()

\brief Returns true if the model is classifier

train

@Cast(value="bool")
public boolean train(@opencv_core.Ptr
                      opencv_ml.TrainData trainData,
                      int flags)

\brief 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).

train

@Cast(value="bool")
public boolean train(@opencv_core.Ptr
                      opencv_ml.TrainData trainData)

train

@Cast(value="bool")
public boolean train(@ByVal
                      opencv_core.Mat samples,
                      int layout,
                      @ByVal
                      opencv_core.Mat responses)

\brief Trains the statistical model

Parameters:

samples - training samples

layout - See ml::SampleTypes.

responses - vector of responses associated with the training samples.

calcError

public float calcError(@opencv_core.Ptr
              opencv_ml.TrainData data,
              @Cast(value="bool")
              boolean test,
              @ByVal
              opencv_core.Mat resp)

\brief 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%).

predict

public float predict(@ByVal
            opencv_core.Mat samples,
            @ByVal(nullValue="cv::noArray()")
            opencv_core.Mat results,
            int flags)

\brief 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.

predict

public float predict(@ByVal
            opencv_core.Mat samples)