The class TRndForest declares a random forest based classification engine. It actually implements multiple random forests, one for each class. The random forests are trained to distinguish one class against all other classes.




How To: Please follow these steps to train a classifier:
  1. create an instance of the class TRndForest
  2. set the basic parameters NTrees and RPar
  3. load a spectral descriptor set (SpdcSet.LoadUnchecked)
  4. load the training data using LoadTrnData
  5. call CalculateModel
  6. store the trained classifier using SaveOnDisk


How To: How to classify a dataset:
  1. load the data to be classified into a TMat4D structure (or, alternatively use the currently loaded dataset)
  2. load the trained classifier
  3. call ClassifyCubeData (Hint: for using the currently loaded dataset, set the parameter Cube to RawData and the parameter Metadata to MData).
  4. the classified data are available via ClassifiedCubeData