Package org.opencv.ml

Class NormalBayesClassifier

java.lang.Object

org.opencv.core.Algorithm

org.opencv.ml.StatModel

org.opencv.ml.NormalBayesClassifier

public class NormalBayesClassifier
extends StatModel

Bayes classifier for normally distributed data. SEE: REF: ml_intro_bayes

Field Summary

Fields inherited from class org.opencv.ml.StatModel

COMPRESSED_INPUT, PREPROCESSED_INPUT, RAW_OUTPUT, UPDATE_MODEL

Method Summary

Modifier and Type	Method	Description
static NormalBayesClassifier	__fromPtr__(long addr)
static NormalBayesClassifier	create()	Creates empty model Use StatModel::train to train the model after creation.
static NormalBayesClassifier	load(String filepath)	Loads and creates a serialized NormalBayesClassifier from a file Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk.
static NormalBayesClassifier	load(String filepath, String nodeName)	Loads and creates a serialized NormalBayesClassifier from a file Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk.
float	predictProb(Mat inputs, Mat outputs, Mat outputProbs)	Predicts the response for sample(s).
float	predictProb(Mat inputs, Mat outputs, Mat outputProbs, int flags)	Predicts the response for sample(s).

Methods inherited from class org.opencv.ml.StatModel

calcError, empty, getVarCount, isClassifier, isTrained, predict, predict, predict, train, train, train

Methods inherited from class org.opencv.core.Algorithm

clear, getDefaultName, getNativeObjAddr, save

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

__fromPtr__

public static NormalBayesClassifier __fromPtr__(long addr)

predictProb

public float predictProb(Mat inputs,
 Mat outputs,
 Mat outputProbs,
 int flags)

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.

Parameters:

inputs - automatically generated

outputs - automatically generated

outputProbs - automatically generated

flags - automatically generated

Returns:

automatically generated

predictProb

public float predictProb(Mat inputs,
 Mat outputs,
 Mat outputProbs)

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.

Parameters:

inputs - automatically generated

outputs - automatically generated

outputProbs - automatically generated

Returns:

automatically generated

create

public static NormalBayesClassifier create()

Creates empty model Use StatModel::train to train the model after creation.

Returns:

automatically generated

load

public static NormalBayesClassifier load(String filepath,
 String nodeName)

Loads and creates a serialized NormalBayesClassifier from a file Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk. Load the NormalBayesClassifier from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters:

filepath - path to serialized NormalBayesClassifier

nodeName - name of node containing the classifier

Returns:

automatically generated

load

public static NormalBayesClassifier load(String filepath)

Loads and creates a serialized NormalBayesClassifier from a file Use NormalBayesClassifier::save to serialize and store an NormalBayesClassifier to disk. Load the NormalBayesClassifier from this file again, by calling this function with the path to the file. Optionally specify the node for the file containing the classifier

Parameters:

filepath - path to serialized NormalBayesClassifier

Returns:

automatically generated