Emgu CV Library Documentation
MlInvoke Class
NamespacesEmgu.CV.MLMlInvoke

www.emgu.com/wiki
This class contains functions to call into machine learning library
Declaration Syntax
C#Visual BasicVisual C++
public class MlInvoke
Public Class MlInvoke
public ref class MlInvoke
Members
All MembersConstructorsMethods



IconMemberDescription
MlInvoke()()()
Initializes a new instance of the MlInvoke class

CvANN_MLPCreate(IntPtr, ANN_MLP_ACTIVATION_FUNCTION, Double, Double)
Create a neural network using the specific parameters

CvANN_MLPPredict(IntPtr, IntPtr, IntPtr)
Given the model, predit the outputs response of the inputs samples

CvANN_MLPRelease(IntPtr)
Release the ANN_MLP model

CvANN_MLPTrain(IntPtr, IntPtr, IntPtr, IntPtr, IntPtr, MCvANN_MLP_TrainParams, ANN_MLP_TRAINING_FLAG)
Train the ANN_MLP model with the specific paramters

CvBoostCreate()()()
Create a default boost classicfier

CvBoostParamsCreate()()()
Create default parameters for CvBoost

CvBoostParamsRelease(IntPtr)
Release the CvBoostParams

CvBoostPredict(IntPtr, IntPtr, IntPtr, IntPtr, MCvSlice, Boolean)
Runs the sample through the trees in the ensemble and returns the output class label based on the weighted voting

CvBoostRelease(IntPtr)
Release the boost classicfier

CvBoostTrain(IntPtr, IntPtr, DATA_LAYOUT_TYPE, IntPtr, IntPtr, IntPtr, IntPtr, IntPtr, MCvBoostParams, Boolean)
Train the boost tree using the specific traning data

CvDTreeCreate()()()
Create a default decision tree

CvDTreeParamsCreate()()()
Create default parameters for CvDTreeParams

CvDTreeParamsRelease(IntPtr)
Release the CvDTreeParams

CvDTreePredict(IntPtr, IntPtr, IntPtr, Boolean)
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

CvDTreeRelease(IntPtr)
Release the decision tree model

CvDTreeTrain(IntPtr, IntPtr, DATA_LAYOUT_TYPE, IntPtr, IntPtr, IntPtr, IntPtr, IntPtr, MCvDTreeParams)
Train the decision tree using the specific training data

CvEMDefaultCreate()()()
Create a default EM model

CvEMGetCovs(IntPtr)
Get the covariance matrices of the clusters from the EM model

CvEMGetMeans(IntPtr)
Get the means of the clusters from the EM model

CvEMGetNclusters(IntPtr)
Get the number of clusters from the EM model

CvEMGetProbs(IntPtr)
Get the probabilities from the EM model

CvEMGetWeights(IntPtr)
Get the weights of the clusters from the EM model

CvEMPredict(IntPtr, IntPtr, IntPtr)
Given the EM model, predit the probability of the samples

CvEMRelease(IntPtr)
Release the EM model

CvEMTrain(IntPtr, IntPtr, IntPtr, MCvEMParams, IntPtr)
Train the EM model using the specific training data

CvKNearestCreate(IntPtr, IntPtr, IntPtr, Boolean, Int32)
Create the KNearest classifier using the specific traing data.

CvKNearestDefaultCreate()()()
Create a KNearest classifier

CvKNearestFindNearest(IntPtr, IntPtr, Int32, IntPtr, array<IntPtr>[]()[], IntPtr, IntPtr)
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.

CvKNearestRelease(IntPtr)
Release the KNearest classifer

CvKNearestTrain(IntPtr, IntPtr, IntPtr, IntPtr, Boolean, Int32, Boolean)
Update the KNearest classifier using the specific traing data.

CvNormalBayesClassifierCreate(IntPtr, IntPtr, IntPtr, IntPtr)
Create a normal Bayes classifier using the specific training data

CvNormalBayesClassifierDefaultCreate()()()
Create a normal bayes classifier

CvNormalBayesClassifierPredict(IntPtr, IntPtr, IntPtr)
Given the NormalBayesClassifier model, predit the probability of the samples

CvNormalBayesClassifierRelease(IntPtr)
Release the memory associated with the bayes classifier

CvNormalBayesClassifierTrain(IntPtr, IntPtr, IntPtr, IntPtr, IntPtr, Boolean)
Train the classifier using the specific data

CvRTParamsCreate()()()
Create default parameters for CvRTParams

CvRTParamsRelease(IntPtr)
Release the CvRTParams

CvRTreesCreate()()()
Create a default random tree

CvRTreesPredict(IntPtr, IntPtr, IntPtr)
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)

CvRTreesRelease(IntPtr)
Release the random tree model

CvRTreesTrain(IntPtr, IntPtr, DATA_LAYOUT_TYPE, IntPtr, IntPtr, IntPtr, IntPtr, IntPtr, MCvRTParams)
Train the random tree using the specific traning data

CvSVMDefaultCreate()()()
Create a default SVM model

CvSVMGetDefaultGrid(SVM_PARAM_TYPE, MCvParamGrid%)
Get the default parameter grid for the specific SVM type

cvSVMGetSupportVector(IntPtr, Int32)
The method retrieves a given support vector

cvSVMGetSupportVectorCount(IntPtr)
The method retrieves the number of support vectors

cvSVMGetVarCount(IntPtr)
The method retrieves the number of vars

cvSVMPredict(IntPtr, IntPtr)
Predicts response for the input sample.

CvSVMRelease(IntPtr)
Release the SVM model and all the memory associated to ir

CvSVMTrain(IntPtr, IntPtr, IntPtr, IntPtr, IntPtr, MCvSVMParams)
Train the SVM model with the specific paramters

CvSVMTrainAuto(IntPtr, IntPtr, IntPtr, IntPtr, IntPtr, MCvSVMParams, Int32, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid, MCvParamGrid)
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.

Equals(Object)
Determines whether the specified Object is equal to the current Object.
(Inherited from Object.)
Finalize()()()
Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.
(Inherited from Object.)
GetHashCode()()()
Serves as a hash function for a particular type.
(Inherited from Object.)
GetType()()()
Gets the Type of the current instance.
(Inherited from Object.)
MemberwiseClone()()()
Creates a shallow copy of the current Object.
(Inherited from Object.)
StatModelClear(IntPtr)
Clear the statistic model

StatModelLoad(IntPtr, String, IntPtr)
Load the statistic model from the specific file

StatModelSave(IntPtr, String, IntPtr)
Save the statistic model to the specific file

ToString()()()
Returns a String that represents the current Object.
(Inherited from Object.)
Inheritance Hierarchy
Object
MlInvoke

Assembly: Emgu.CV.ML (Module: Emgu.CV.ML) Version: 1.0.0.0 (1.0.0.0)