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

`#include <vigra/random_forest.hxx>`

## 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. More...
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 More...
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. More...
Data Access
data access interface - usage of member variables is deprecated
ProblemSpec_t const & | ext_param () const
| return external parameters for viewing More...
void | set_ext_param (ProblemSpec_t const &in)
| set external parameters More...
Options_t & | set_options ()
| access random forest options More...
Options_t const & | options () const
| access const random forest options More...
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 More...
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 More...
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. More...
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 More...
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 More...
template<class U , class C1 , class T , class C2 >
void | predictLabels (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &labels, LabelType nanLabel) const
| predict multiple labels with given features More...
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
| predict multiple labels with given features More...
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 More...
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 More...
template<class U , class C1 , class T , class C2 >
void | predictRaw (MultiArrayView< 2, U, C1 >const &features, MultiArrayView< 2, T, C2 > &prob) const

## Detailed Description

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

Random Forest class

Template Parameters

  <LabelType | = double> Type used for predicted labels.
  <PreprocessorTag | = 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<label_t> 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.predictLabels(pf, prediction);
rf.predictProbabilities(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() [1/2]

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() [2/2]

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

## ◆ ext_param()

ProblemSpec_t const& ext_param | ( |  | ) | const

return external parameters for viewing

Returns
  ProblemSpec_t

## ◆ set_ext_param()

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.

## ◆ set_options()

Options_t& set_options | ( |  | ) |

access random forest options

Returns
  random forest options

## ◆ options()

Options_t const& options | ( |  | ) | const

access const random forest options

Returns
  const Option_t

## ◆ column_count()

int column_count | ( |  | ) | const

return number of features used while training.

deprecated. Use feature_count() instead.

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 1.11.0