The DTree type exposes the following methods.
Methods
 Public Protected | Instance Static | Declared Inherited | XNA Framework Only .NET Compact Framework Only |
| Member | Description | |
|---|---|---|
 | Clear()()()() |
Clear the statistic model
(Inherited from StatModel.) |
| Dispose()()()() |
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.) |
 | DisposeObject()()()() |
Release the decision tree and all the memory associate with it
(Overrides DisposableObject..::..DisposeObject()()()().) |
| Equals(Object) | (Inherited from Object.) |
| Finalize()()()() |
Destructor
(Inherited from DisposableObject.) |
| GetHashCode()()()() |
Serves as a hash function for a particular type.
(Inherited from Object.) |
| GetType()()()() |
Gets the Type of the current instance.
(Inherited from Object.) |
| Load(String) |
Load the statistic model from file
(Inherited from StatModel.) |
| MemberwiseClone()()()() |
Creates a shallow copy of the current Object.
(Inherited from Object.) |
| Predict(Matrix<(Of <<'(Single>)>>), Matrix<(Of <<'(Byte>)>>), 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
|
| ReleaseManagedResources()()()() |
Release the managed resources. This function will be called during the disposal of the current object.
override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object
(Inherited from DisposableObject.) |
| Save(String) |
Save the statistic model to file
(Inherited from StatModel.) |
| ToString()()()() | (Inherited from Object.) |
| Train(Matrix<(Of <<'(Single>)>>), DATA_LAYOUT_TYPE, Matrix<(Of <<'(Single>)>>), Matrix<(Of <<'(Byte>)>>), Matrix<(Of <<'(Byte>)>>), Matrix<(Of <<'(Byte>)>>), Matrix<(Of <<'(Byte>)>>), MCvDTreeParams) |
Train the decision tree using the specific traning data
|
See Also
