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| RandomForest< LabelType, PreprocessorTag > Class Template Reference Machine Learning |

`#include <vigra/random_forest.hxx>`

List of all members.

## Public Member Functions
int | class_count () const
| return number of classes used while training.
int | column_count () const
| return number of features used while training.
int | feature_count () const
| return number of features used while training.
template<class U , class C1 , class U2 , class C2 , class Split_t , class Stop_t , class Visitor_t , class Random_t >
void | reLearnTree (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &response, int treeId, Visitor_t visitor_, Split_t split_, Stop_t stop_, Random_t &random)
int | tree_count () const
| return number of trees
Constructors
Note: No copy Constructor specified as no pointers are manipulated in this class
| RandomForest (Options_t const &options=Options_t(), ProblemSpec_t const &ext_param=ProblemSpec_t())
| default constructor
template<class TopologyIterator , class ParameterIterator >
| RandomForest (int treeCount, TopologyIterator topology_begin, ParameterIterator parameter_begin, ProblemSpec_t const &problem_spec, Options_t const &options=Options_t())
| Create RF from external source.
Data Access
data access interface - usage of member variables is deprecated
ProblemSpec_t const & | ext_param () const
| return external parameters for viewing
void | set_ext_param (ProblemSpec_t const &in)
| set external parameters
Options_t & | set_options ()
| access random forest options
Options_t const & | options () const
| access const random forest options
DecisionTree_t const & | tree (int index) const
| access const trees
DecisionTree_t & | tree (int index)
| access trees
Learning
Following functions differ in the degree of customization allowed
template<class U , class C1 , class U2 , class C2 , class Split_t , class Stop_t , class Visitor_t , class Random_t >
void | learn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &response, Visitor_t visitor, Split_t split, Stop_t stop, Random_t const &random)
| learn on data with custom config and random number generator
template<class U , class C1 , class U2 , class C2 , class Split_t , class Stop_t , class Visitor_t >
void | learn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &response, Visitor_t visitor, Split_t split, Stop_t stop)
template<class U , class C1 , class U2 , class C2 , class Visitor_t >
void | learn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &labels, Visitor_t visitor)
template<class U , class C1 , class U2 , class C2 , class Visitor_t , class Split_t >
void | learn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &labels, Visitor_t visitor, Split_t split)
template<class U , class C1 , class U2 , class C2 >
void | learn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &labels)
| learn on data with default configuration
prediction
template<class U , class C , class Stop >
LabelType | predictLabel (MultiArrayView< 2, U, C >const &features, Stop &stop) const
| predict a label given a feature.
template<class U , class C >
LabelType | predictLabel (MultiArrayView< 2, U, C >const &features)
template<class U , class C >
LabelType | predictLabel (MultiArrayView< 2, U, C > const &features, ArrayVectorView< double > prior) const
| predict a label with features and class priors
template<class U , class C1 , class T , class C2 >
void | predictLabels (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &labels) const
| predict multiple labels with given features
template<class U , class C1 , class T , class C2 , class Stop >
void | predictLabels (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &labels, Stop &stop) const
template<class U , class C1 , class T , class C2 , class Stop >
void | predictProbabilities (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &prob, Stop &stop) const
| predict the class probabilities for multiple labels
template<class T1 , class T2 , class C >
void | predictProbabilities (OnlinePredictionSet< T1 > &predictionSet, MultiArrayView< 2, T2, C > &prob)
template<class U , class C1 , class T , class C2 >
void | predictProbabilities (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &prob) const
| predict the class probabilities for multiple labels
template<class U , class C1 , class T , class C2 >
void | predictRaw (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &prob) const
## Protected Attributes
MultiArray< 2, double > | garbage_prediction_

## Detailed Description

### template<class LabelType = double, class PreprocessorTag = ClassificationTag>
class vigra::RandomForest< LabelType, PreprocessorTag >

Random Forest class

Template Parameters:

  <PrprocessorTag | = ClassificationTag> Class used to preprocess the input while learning and predicting. Currently Available: ClassificationTag and RegressionTag. It is recommended to use Splitfunctor::Preprocessor_t while using custom splitfunctors as they may need the data to be in a different format.

See also:
  Preprocessor

simple usage for classification (regression is not yet supported): look at RandomForest::learn() as well as RandomForestOptions() for additional options.

      using namespace vigra;
      using namespace rf;
      typedef xxx feature_t; \\ replace xxx with whichever type
      typedef yyy label_t;   \\ likewise

      // allocate the training data
      MultiArrayView<2, feature_t> f = get_training_features();
      MultiArrayView<2, label_t>   l = get_training_labels();

      RandomForest<> rf;

      // construct visitor to calculate out-of-bag error
      visitors::OOB_Error oob_v;

      // perform training
      rf.learn(f, l, visitors::create_visitor(oob_v));

      std::cout << "the out-of-bag error is: " << oob_v.oob_breiman << "\n";

      // get features for new data to be used for prediction
      MultiArrayView<2, feature_t> pf = get_features();

      // allocate space for the response (pf.shape(0) is the number of samples)
      MultiArrayView<2, label_t> prediction(pf.shape(0), 1);
      MultiArrayView<2, double> prob(pf.shape(0), rf.class_count());

      // perform prediction on new data
      rf.predict_labels(pf, prediction);
      rf.predict_probabilities(pf, prob);

Additional information such as Variable Importance measures are accessed via Visitors defined in rf::visitors. Have a look at rf::split for other splitting methods.

## Constructor & Destructor Documentation

RandomForest | ( | Options_t const & | options = `Options_t()`,
|  | ProblemSpec_t const & | ext_param = `ProblemSpec_t()`
| ) |  |

default constructor

Parameters:

  options | general options to the Random Forest. Must be of Type Options_t
  ext_param | problem specific values that can be supplied additionally. (class weights , labels etc)

See also:
  RandomForestOptions, ProblemSpec

RandomForest | ( | int | treeCount,
|  | TopologyIterator | topology_begin,
|  | ParameterIterator | parameter_begin,
|  | ProblemSpec_t const & | problem_spec,
|  | Options_t const & | options = `Options_t()`
| ) |  |

Create RF from external source.

Parameters:

  treeCount | Number of trees to add.
  topology_begin | Iterator to a Container where the topology_ data of the trees are stored. Iterator should support at least treeCount forward iterations. (i.e. topology_end - topology_begin >= treeCount
  parameter_begin | iterator to a Container where the parameters_ data of the trees are stored. Iterator should support at least treeCount forward iterations.
  problem_spec | Extrinsic parameters that specify the problem e.g. ClassCount, featureCount etc.
  options | (optional) specify options used to train the original Random forest. This parameter is not used anywhere during prediction and thus is optional.

## Member Function Documentation

ProblemSpec_t const& ext_param | ( |  | ) | const

return external parameters for viewing

Returns:
  ProblemSpec_t

void set_ext_param | ( | ProblemSpec_t const & | in | ) |

set external parameters

Parameters:

  in | external parameters to be set

set external parameters explicitly. If Random Forest has not been trained the preprocessor will either ignore filling values set this way or will throw an exception if values specified manually do not match the value calculated & during the preparation step.

Options_t& set_options | ( |  | ) |

access random forest options

Returns:
  random forest options

Options_t const& options | ( |  | ) | const

access const random forest options

Returns:
  const Option_t

int column_count | ( |  | ) | const

return number of features used while training.

deprecated. Use feature_count() instead.

## Member Data Documentation

MultiArray<2, double> garbage_prediction_`[mutable, protected]`

optimisation for predictLabels

The documentation for this class was generated from the following file:

- vigra/random_forest.hxx

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany | html generated using doxygen and Python vigra 1.9.0 (Tue Nov 6 2012)