CvNormalBayesClassifier

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OpenCV 2.4.6

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org.opencv.ml
Class CvNormalBayesClassifier

java.lang.Object
 extended by org.opencv.ml.CvStatModel
 extended by org.opencv.ml.CvNormalBayesClassifier

public class CvNormalBayesClassifier
extends CvStatModel
extends CvStatModel

Bayes classifier for normally distributed data.

See Also:: org.opencv.ml.CvNormalBayesClassifier : public CvStatModel

Constructor Summary
`CvNormalBayesClassifier()` Default and training constructors.
`CvNormalBayesClassifier(Mat trainData, Mat responses)` Default and training constructors.
`CvNormalBayesClassifier(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx)` Default and training constructors.

Method Summary
`void`	`clear()`
`float`	`predict(Mat samples)` Predicts the response for sample(s).
`float`	`predict(Mat samples, Mat results)` Predicts the response for sample(s).
`boolean`	`train(Mat trainData, Mat responses)` Trains the model.
`boolean`	`train(Mat trainData, Mat responses, Mat varIdx, Mat sampleIdx, boolean update)` Trains the model.

Methods inherited from class org.opencv.ml.CvStatModel
`load, load, save, save`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

CvNormalBayesClassifier

public CvNormalBayesClassifier()

Default and training constructors.

The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

See Also:: org.opencv.ml.CvNormalBayesClassifier.CvNormalBayesClassifier

CvNormalBayesClassifier

public CvNormalBayesClassifier(Mat trainData,
 Mat responses)

Default and training constructors.

The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

Parameters:: trainData - a trainData; responses - a responses
See Also:: org.opencv.ml.CvNormalBayesClassifier.CvNormalBayesClassifier

CvNormalBayesClassifier

public CvNormalBayesClassifier(Mat trainData,
 Mat responses,
 Mat varIdx,
 Mat sampleIdx)

Default and training constructors.

The constructors follow conventions of "CvStatModel.CvStatModel". See "CvStatModel.train" for parameters descriptions.

Parameters:: trainData - a trainData; responses - a responses; varIdx - a varIdx; sampleIdx - a sampleIdx
See Also:: org.opencv.ml.CvNormalBayesClassifier.CvNormalBayesClassifier

Method Detail

clear

public void clear()

predict

public float predict(Mat samples)

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 samples. In case of multiple input vectors, there should be one output vector results. The predicted class for a single input vector is returned by the method.

The function is parallelized with the TBB library.

Parameters:: samples - a samples
See Also:: org.opencv.ml.CvNormalBayesClassifier.predict

predict

public float predict(Mat samples,
 Mat results)

Predicts the response for sample(s).

The function is parallelized with the TBB library.

Parameters:: samples - a samples; results - a results
See Also:: org.opencv.ml.CvNormalBayesClassifier.predict

train

public boolean train(Mat trainData,
 Mat responses)

Trains the model.

The method trains the Normal Bayes classifier. It follows the conventions of the generic "CvStatModel.train" approach with the following limitations:

Only CV_ROW_SAMPLE data layout is supported.
Input variables are all ordered.
Output variable is categorical, which means that elements of responses must be integer numbers, though the vector may have the CV_32FC1 type.
Missing measurements are not supported.

Parameters:: trainData - a trainData; responses - a responses
See Also:: org.opencv.ml.CvNormalBayesClassifier.train

train

public boolean train(Mat trainData,
 Mat responses,
 Mat varIdx,
 Mat sampleIdx,
 boolean update)

Trains the model.

The method trains the Normal Bayes classifier. It follows the conventions of the generic "CvStatModel.train" approach with the following limitations:

Only CV_ROW_SAMPLE data layout is supported.
Input variables are all ordered.
Output variable is categorical, which means that elements of responses must be integer numbers, though the vector may have the CV_32FC1 type.
Missing measurements are not supported.

Parameters:: trainData - a trainData; responses - a responses; varIdx - a varIdx; sampleIdx - a sampleIdx; update - Identifies whether the model should be trained from scratch (update=false) or should be updated using the new training data (update=true).
See Also:: org.opencv.ml.CvNormalBayesClassifier.train