Initializes a new instance of the MlInvoke class

Create a neural network using the specific parameters

Get the number of layers in the ANN_MLP

Given the model, predit the outputs response of the inputs samples

Release the ANN_MLP model

Train the ANN_MLP model with the specific paramters

Create a default boost classicfier

Create default parameters for CvBoost

Release the CvBoostParams

Runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting

Release the boost classicfier

Train the boost tree using the specific traning data

Create a default decision tree

Create default parameters for CvDTreeParams

Release the CvDTreeParams

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

Release the decision tree model

Train the decision tree using the specific training data

Create a default EM model

Get the covariance matrices of the clusters from the EM model

Get the means of the clusters from the EM model

Get the number of clusters from the EM model

Get the probabilities from the EM model

Get the weights of the clusters from the EM model

Given the EM model, predit the probability of the samples

Release the EM model

Train the EM model using the specific training data

Create a default extreme random tree

Release the extreme random tree model

Create the KNearest classifier using the specific traing data.

Create a KNearest classifier

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.

Release the KNearest classifer

Update the KNearest classifier using the specific traing data.

Create a normal Bayes classifier using the specific training data

Create a normal bayes classifier

Given the NormalBayesClassifier model, predit the probability of the samples

Release the memory associated with the bayes classifier

Train the classifier using the specific data

Create default parameters for CvRTParams

Release the CvRTParams

Create a default random tree

Get the number of Trees in the Random tree

Get the variable importance

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)

Release the random tree model

Train the random tree using the specific traning data

Create a default SVM model

Get the default parameter grid for the specific SVM type

Get the parameters of the SVM model

The method retrieves a given support vector

The method retrieves the number of support vectors

The method retrieves the number of vars

Predicts response for the input sample.

Release the SVM model and all the memory associated to ir

Train the SVM model with the specific paramters

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.

Determines whether the specified Object is equal to the current Object.

Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Creates a shallow copy of the current Object.

Clear the statistic model

Load the statistic model from the specific file

Save the statistic model to the specific file

Returns a String that represents the current Object.