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The MlInvoke type exposes the following members.

Methods

  NameDescription
Public methodStatic memberCvANN_MLPCreate
Create a neural network using the specific parameters
Public methodStatic memberCvANN_MLPGetLayerCount
Get the number of layers in the ANN_MLP
Public methodStatic memberCvANN_MLPPredict
Given the model, predit the outputs response of the inputs samples
Public methodStatic memberCvANN_MLPRelease
Release the ANN_MLP model
Public methodStatic memberCvANN_MLPTrain
Train the ANN_MLP model with the specific paramters
Public methodStatic memberCvBoostCreate
Create a default boost classicfier
Public methodStatic memberCvBoostParamsCreate
Create default parameters for CvBoost
Public methodStatic memberCvBoostParamsRelease
Release the CvBoostParams
Public methodStatic memberCvBoostPredict
Runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting
Public methodStatic memberCvBoostRelease
Release the boost classicfier
Public methodStatic memberCvBoostTrain
Train the boost tree using the specific traning data
Public methodStatic memberCvDTreeCreate
Create a default decision tree
Public methodStatic memberCvDTreeParamsCreate
Create default parameters for CvDTreeParams
Public methodStatic memberCvDTreeParamsRelease
Release the CvDTreeParams
Public methodStatic memberCvDTreePredict
The method takes the feature vector and the optional missing measurement mask on input, traverses the decision tree and returns the reached leaf node on output. The prediction result, either the class label or the estimated function value, may be retrieved as value field of the CvDTreeNode structure
Public methodStatic memberCvDTreeRelease
Release the decision tree model
Public methodStatic memberCvDTreeTrain
Train the decision tree using the specific training data
Public methodStatic memberCvEMDefaultCreate
Create a default EM model
Public methodStatic memberCvEMLegacyDefaultCreate
Create a default EM model
Public methodStatic memberCvEMLegacyGetCovs
Get the covariance matrices of the clusters from the EM model
Public methodStatic memberCvEMLegacyGetMeans
Get the means of the clusters from the EM model
Public methodStatic memberCvEMLegacyGetNclusters
Get the number of clusters from the EM model
Public methodStatic memberCvEMLegacyGetProbs
Get the probabilities from the EM model
Public methodStatic memberCvEMLegacyGetWeights
Get the weights of the clusters from the EM model
Public methodStatic memberCvEMLegacyPredict
Given the EM model, predit the probability of the samples
Public methodStatic memberCvEMLegacyRelease
Release the EM model
Public methodStatic memberCvEMLegacyTrain
Train the EM model using the specific training data
Public methodStatic memberCvEMPredict
Given the EM model, predit the probability of the samples
Public methodStatic memberCvEMRelease
Release the EM model
Public methodStatic memberCvEMTrain
Starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm.
Public methodStatic memberCvERTreesCreate
Create a default extreme random tree
Public methodStatic memberCvERTreesRelease
Release the extreme random tree model
Public methodStatic memberCvGBTreesCreate
Create a default Gradient Boosting Trees (GBT)
Public methodStatic memberCvGBTreesPredict
Runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting
Public methodStatic memberCvGBTreesRelease
Release the Gradient Boosting Trees (GBT)
Public methodStatic memberCvGBTreesTrain
Train the boost tree using the specific traning data
Public methodStatic memberCvKNearestCreate
Create the KNearest classifier using the specific traing data.
Public methodStatic memberCvKNearestDefaultCreate
Create a KNearest classifier
Public methodStatic memberCvKNearestFindNearest
For each input vector (which are rows of the matrix samples) the method finds k <= get_max_k() nearest neighbor. In case of regression, the predicted result will be a mean value of the particular vector's neighbor responses. In case of classification the class is determined by voting.
Public methodStatic memberCvKNearestRelease
Release the KNearest classifer
Public methodStatic memberCvKNearestTrain
Update the KNearest classifier using the specific traing data.
Public methodStatic memberCvNormalBayesClassifierCreate
Create a normal Bayes classifier using the specific training data
Public methodStatic memberCvNormalBayesClassifierDefaultCreate
Create a normal bayes classifier
Public methodStatic memberCvNormalBayesClassifierPredict
Given the NormalBayesClassifier model, predit the probability of the samples
Public methodStatic memberCvNormalBayesClassifierRelease
Release the memory associated with the bayes classifier
Public methodStatic memberCvNormalBayesClassifierTrain
Train the classifier using the specific data
Public methodStatic memberCvRTParamsCreate
Create default parameters for CvRTParams
Public methodStatic memberCvRTParamsRelease
Release the CvRTParams
Public methodStatic memberCvRTreesCreate
Create a default random tree
Public methodStatic memberCvRTreesGetTreeCount
Get the number of Trees in the Random tree
Public methodStatic memberCvRTreesGetVarImportance
Get the variable importance
Public methodStatic memberCvRTreesPredict
The method takes the feature vector and the optional missing measurement mask on input, traverses the random tree and returns the cumulative result from all the trees in the forest (the class that receives the majority of voices, or the mean of the regression function estimates)
Public methodStatic memberCvRTreesRelease
Release the random tree model
Public methodStatic memberCvRTreesTrain
Train the random tree using the specific traning data
Public methodStatic memberCvSVMDefaultCreate
Create a default SVM model
Public methodStatic memberCvSVMGetDefaultGrid
Get the default parameter grid for the specific SVM type
Public methodStatic memberCvSVMGetParameters
Get the parameters of the SVM model
Public methodStatic memberCvSVMGetSupportVector
The method retrieves a given support vector
Public methodStatic memberCvSVMGetSupportVectorCount
The method retrieves the number of support vectors
Public methodStatic memberCvSVMGetVarCount
The method retrieves the number of vars
Public methodStatic memberCvSVMPredict
Predicts response for the input sample.
Public methodStatic memberCvSVMRelease
Release the SVM model and all the memory associated to ir
Public methodStatic memberCvSVMTrain
Train the SVM model with the specific paramters
Public methodStatic memberCvSVMTrainAuto
The method trains the SVM model automatically by choosing the optimal parameters C, gamma, p, nu, coef0, degree from CvSVMParams. By the optimality one mean that the cross-validation estimate of the test set error is minimal.
Public methodEquals
Determines whether the specified Object is equal to the current Object.
(Inherited from Object.)
Protected methodFinalize
Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as a hash function for a particular type.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodStatic memberStatModelClear
Clear the statistic model
Public methodStatic memberStatModelLoad
Load the statistic model from the specific file
Public methodStatic memberStatModelSave
Save the statistic model to the specific file
Public methodToString
Returns a String that represents the current Object.
(Inherited from Object.)

See Also