Inheritance diagram for AdaBoost< WeakLearnerType, MatType >:

Public Member Functions
	AdaBoost (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, const WeakLearnerType &other, const size_t iterations=100, const double tolerance=1e-6)
	Constructor. More...

	AdaBoost (const double tolerance=1e-6)
	Create the AdaBoost object without training. More...

double	Alpha (const size_t i) const
	Get the weights for the given weak learner. More...

double &	Alpha (const size_t i)
	Modify the weight for the given weak learner (be careful!). More...

void	Classify (const MatType &test, arma::Row< size_t > &predictedLabels, arma::mat &probabilities)
	Classify the given test points. More...

void	Classify (const MatType &test, arma::Row< size_t > &predictedLabels)
	Classify the given test points. More...

size_t	NumClasses () const
	Get the number of classes this model is trained on. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the AdaBoost model. More...

double	Tolerance () const
	Get the tolerance for stopping the optimization during training. More...

double &	Tolerance ()
	Modify the tolerance for stopping the optimization during training. More...

double	Train (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, const WeakLearnerType &learner, const size_t iterations=100, const double tolerance=1e-6)
	Train AdaBoost on the given dataset. More...

const WeakLearnerType &	WeakLearner (const size_t i) const
	Get the given weak learner. More...

WeakLearnerType &	WeakLearner (const size_t i)
	Modify the given weak learner (be careful!). More...

size_t	WeakLearners () const
	Get the number of weak learners in the model. More...

Detailed Description

template<typename WeakLearnerType = mlpack::perceptron::Perceptron<>, typename MatType = arma::mat>
class mlpack::adaboost::AdaBoost< WeakLearnerType, MatType >

The AdaBoost class.

AdaBoost is a boosting algorithm, meaning that it combines an ensemble of weak learners to produce a strong learner. For more information on AdaBoost, see the following paper:

@article{schapire1999improved,
  author = {Schapire, Robert E. and Singer, Yoram},
  title = {Improved Boosting Algorithms Using Confidence-rated Predictions},
  journal = {Machine Learning},
  volume = {37},
  number = {3},
  month = dec,
  year = {1999},
  issn = {0885-6125},
  pages = {297--336},
}

This class is general, and can be used with any type of weak learner, so long as the learner implements the following functions:

// A boosting constructor, which learns using the training parameters of the
// given other WeakLearner, but uses the given instance weights for training.
WeakLearner(WeakLearner& other,
            const MatType& data,
            const arma::Row<size_t>& labels,
            const arma::rowvec& weights);
// Given the test points, classify them and output predictions into
// predictedLabels.
void Classify(const MatType& data, arma::Row<size_t>& predictedLabels);

For more information on and examples of weak learners, see perceptron::Perceptron<> and tree::ID3DecisionStump.

Template Parameters

MatType	Data matrix type (i.e. arma::mat or arma::sp_mat).
WeakLearnerType	Type of weak learner to use.

Definition at line 81 of file adaboost.hpp.

Constructor & Destructor Documentation

◆ AdaBoost() [1/2]

AdaBoost	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const WeakLearnerType &	other,
		const size_t	iterations = `100`,
		const double	tolerance = `1e-6`
	)

Constructor.

This runs the AdaBoost.MH algorithm to provide a trained boosting model. This constructor takes an already-initialized weak learner; all other weak learners will learn with the same parameters as the given weak learner.

Parameters

data	Input data.
labels	Corresponding labels.
numClasses	The number of classes.
iterations	Number of boosting rounds.
tolerance	The tolerance for change in values of rt.
other	Weak learner that has already been initialized.

◆ AdaBoost() [2/2]

AdaBoost ( const double tolerance = 1e-6 )

Create the AdaBoost object without training.

Be sure to call Train() before calling Classify()!

Member Function Documentation

◆ Alpha() [1/2]

double Alpha ( const size_t i ) const

inline

Get the weights for the given weak learner.

Definition at line 122 of file adaboost.hpp.

◆ Alpha() [2/2]

double& Alpha ( const size_t i )

inline

Modify the weight for the given weak learner (be careful!).

Definition at line 124 of file adaboost.hpp.

◆ Classify() [1/2]

void Classify	(	const MatType &	test,
		arma::Row< size_t > &	predictedLabels,
		arma::mat &	probabilities
	)

Classify the given test points.

Parameters

test	Testing data.
predictedLabels	Vector in which the predicted labels of the test set will be stored.
probabilities	matrix to store the predicted class probabilities for each point in the test set.

Referenced by AdaBoost< mlpack::tree::DecisionTree >::WeakLearner().

◆ Classify() [2/2]

void Classify	(	const MatType &	test,
		arma::Row< size_t > &	predictedLabels
	)

Classify the given test points.

Parameters

test	Testing data.
predictedLabels	Vector in which the predicted labels of the test set will be stored.

◆ NumClasses()

size_t NumClasses ( ) const

inline

Get the number of classes this model is trained on.

Definition at line 116 of file adaboost.hpp.

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the AdaBoost model.

Referenced by AdaBoost< mlpack::tree::DecisionTree >::WeakLearner().

◆ Tolerance() [1/2]

double Tolerance ( ) const

inline

Get the tolerance for stopping the optimization during training.

Definition at line 111 of file adaboost.hpp.

◆ Tolerance() [2/2]

double& Tolerance ( )

inline

Modify the tolerance for stopping the optimization during training.

Definition at line 113 of file adaboost.hpp.

◆ Train()

double Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const WeakLearnerType &	learner,
		const size_t	iterations = `100`,
		const double	tolerance = `1e-6`
	)

Train AdaBoost on the given dataset.

This method takes an initialized WeakLearnerType; the parameters for this weak learner will be used to train each of the weak learners during AdaBoost training. Note that this will completely overwrite any model that has already been trained with this object.

Parameters

data	Dataset to train on.
labels	Labels for each point in the dataset.
numClasses	The number of classes.
learner	Learner to use for training.
iterations	Number of boosting rounds.
tolerance	The tolerance for change in values of rt.

Returns: The upper bound for training error.

Referenced by AdaBoost< mlpack::tree::DecisionTree >::WeakLearner().

◆ WeakLearner() [1/2]

const WeakLearnerType& WeakLearner ( const size_t i ) const

inline

Get the given weak learner.

Definition at line 127 of file adaboost.hpp.

◆ WeakLearner() [2/2]

WeakLearnerType& WeakLearner ( const size_t i )

inline

Modify the given weak learner (be careful!).

Definition at line 129 of file adaboost.hpp.

◆ WeakLearners()

size_t WeakLearners ( ) const

inline

Get the number of weak learners in the model.

Definition at line 119 of file adaboost.hpp.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/adaboost/adaboost.hpp

Public Member Functions

Detailed Description

template<typename WeakLearnerType = mlpack::perceptron::Perceptron<>, typename MatType = arma::mat> class mlpack::adaboost::AdaBoost< WeakLearnerType, MatType >

Constructor & Destructor Documentation

◆ AdaBoost() [1/2]

◆ AdaBoost() [2/2]

Member Function Documentation

◆ Alpha() [1/2]

◆ Alpha() [2/2]

◆ Classify() [1/2]

◆ Classify() [2/2]

◆ NumClasses()

◆ serialize()

◆ Tolerance() [1/2]

◆ Tolerance() [2/2]

◆ Train()

◆ WeakLearner() [1/2]

◆ WeakLearner() [2/2]

◆ WeakLearners()

template<typename WeakLearnerType = mlpack::perceptron::Perceptron<>, typename MatType = arma::mat>
class mlpack::adaboost::AdaBoost< WeakLearnerType, MatType >