OpenCV for Unity 2.6.3
Enox Software / Please refer to OpenCV official document ( http://docs.opencv.org/4.10.0/index.html ) for the details of the argument of the method.
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Bayes classifier for normally distributed data. More...
Public Member Functions | |
float | predictProb (Mat inputs, Mat outputs, Mat outputProbs, int flags) |
Predicts the response for sample(s). | |
float | predictProb (Mat inputs, Mat outputs, Mat outputProbs) |
Predicts the response for sample(s). | |
Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel | |
int | getVarCount () |
Returns the number of variables in training samples. | |
override bool | empty () |
Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. | |
bool | isTrained () |
Returns true if the model is trained. | |
bool | isClassifier () |
Returns true if the model is classifier. | |
bool | train (TrainData trainData, int flags) |
Trains the statistical model. | |
bool | train (TrainData trainData) |
Trains the statistical model. | |
bool | train (Mat samples, int layout, Mat responses) |
Trains the statistical model. | |
float | calcError (TrainData data, bool test, Mat resp) |
Computes error on the training or test dataset. | |
virtual float | predict (Mat samples, Mat results, int flags) |
Predicts response(s) for the provided sample(s) | |
virtual float | predict (Mat samples, Mat results) |
Predicts response(s) for the provided sample(s) | |
virtual float | predict (Mat samples) |
Predicts response(s) for the provided sample(s) | |
Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm | |
IntPtr | getNativeObjAddr () |
virtual void | clear () |
Clears the algorithm state. | |
void | save (string filename) |
virtual string | getDefaultName () |
Public Member Functions inherited from OpenCVForUnity.DisposableObject | |
void | Dispose () |
void | ThrowIfDisposed () |
Static Public Member Functions | |
static new NormalBayesClassifier | __fromPtr__ (IntPtr addr) |
static NormalBayesClassifier | create () |
static NormalBayesClassifier | load (string filepath, string nodeName) |
Loads and creates a serialized NormalBayesClassifier from a file. | |
static NormalBayesClassifier | load (string filepath) |
Loads and creates a serialized NormalBayesClassifier from a file. | |
Static Public Member Functions inherited from OpenCVForUnity.MlModule.StatModel | |
static new StatModel | __fromPtr__ (IntPtr addr) |
Static Public Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm | |
static Algorithm | __fromPtr__ (IntPtr addr) |
Static Public Member Functions inherited from OpenCVForUnity.DisposableObject | |
static IntPtr | ThrowIfNullIntPtr (IntPtr ptr) |
Protected Member Functions | |
override void | Dispose (bool disposing) |
Protected Member Functions inherited from OpenCVForUnity.MlModule.StatModel | |
override void | Dispose (bool disposing) |
Protected Member Functions inherited from OpenCVForUnity.CoreModule.Algorithm | |
Protected Member Functions inherited from OpenCVForUnity.DisposableOpenCVObject | |
DisposableOpenCVObject () | |
DisposableOpenCVObject (IntPtr ptr) | |
DisposableOpenCVObject (bool isEnabledDispose) | |
DisposableOpenCVObject (IntPtr ptr, bool isEnabledDispose) | |
Protected Member Functions inherited from OpenCVForUnity.DisposableObject | |
DisposableObject () | |
DisposableObject (bool isEnabledDispose) | |
Additional Inherited Members | |
Static Public Attributes inherited from OpenCVForUnity.MlModule.StatModel | |
const int | UPDATE_MODEL = 1 |
const int | RAW_OUTPUT = 1 |
const int | COMPRESSED_INPUT = 2 |
const int | PREPROCESSED_INPUT = 4 |
Package Functions inherited from OpenCVForUnity.MlModule.StatModel | |
Package Functions inherited from OpenCVForUnity.CoreModule.Algorithm | |
Package Attributes inherited from OpenCVForUnity.DisposableOpenCVObject | |
Properties inherited from OpenCVForUnity.DisposableObject | |
bool | IsDisposed [get, protected set] |
bool | IsEnabledDispose [get, set] |
Bayes classifier for normally distributed data.
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static |
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Creates empty model Use StatModel.train to train the model after creation.
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protectedvirtual |
Reimplemented from OpenCVForUnity.CoreModule.Algorithm.
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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
filepath | path to serialized NormalBayesClassifier |
nodeName | name of node containing the classifier |
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static |
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
filepath | path to serialized NormalBayesClassifier |
nodeName | name of node containing the classifier |
float OpenCVForUnity.MlModule.NormalBayesClassifier.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.
float OpenCVForUnity.MlModule.NormalBayesClassifier.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.