Classes |
class | RandomForest< LabelType, PreprocessorTag > |
class | RandomForestOptions |
| Options object for the random forest. More...
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Namespaces |
namespace | vigra::detail |
Learning |
Following functions differ in the degree of customization allowed
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template<class U , class C1 , class U2 , class C2 , class Split_t , class Stop_t , class Visitor_t , class Random_t > |
double | onlineLearn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &response, int new_start_index, Visitor_t visitor_, Split_t split_, Stop_t stop_, Random_t &random, bool adjust_thresholds=false) |
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) |
template<class U , class C1 , class U2 , class C2 , class Split_t , class Stop_t , class Visitor_t , class Random_t > |
double | 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
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template<class U , class C1 , class U2 , class C2 , class Split_t , class Stop_t , class Visitor_t > |
double | 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 > |
double | onlineLearn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &labels, int new_start_index, bool adjust_thresholds=false) |
template<class U , class C1 , class U2 , class C2 > |
void | reLearnTree (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &labels, int treeId) |
template<class U , class C1 , class U2 , class C2 , class Visitor_t > |
double | 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 > |
double | 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 > |
double | learn (MultiArrayView< 2, U, C1 > const &features, MultiArrayView< 2, U2, C2 > const &labels) |
| learn on data with default configuration
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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.
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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
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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
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template<class U , class C > |
LabelType | predictLabel (MultiArrayView< 2, U, C >const &features) |
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
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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
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This module provides classification algorithms that map features to labels or label probablities.