This class implements a decision stump. More...

Public Member Functions
	DecisionStump (const MatType &data, const arma::Row< size_t > &labels, const size_t classes, const size_t bucketSize=10)
	Constructor. More...

	DecisionStump (const DecisionStump<> &other, const MatType &data, const arma::Row< size_t > &labels, const arma::rowvec &weights)
	Alternate constructor which copies the parameters bucketSize and classes from an already initiated decision stump, other. More...

	DecisionStump ()
	Create a decision stump without training. More...

const arma::Col< size_t >	BinLabels () const
	Access the labels for each split bin. More...

arma::Col< size_t > &	BinLabels ()
	Modify the labels for each split bin (be careful!). More...

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

template<typename Archive >
void	Serialize (Archive &ar, const unsigned int)
	Serialize the decision stump. More...

const arma::vec &	Split () const
	Access the splitting values. More...

arma::vec &	Split ()
	Modify the splitting values (be careful!). More...

size_t	SplitDimension () const
	Access the splitting dimension. More...

size_t &	SplitDimension ()
	Modify the splitting dimension (be careful!). More...

void	Train (const MatType &data, const arma::Row< size_t > &labels, const size_t classes, const size_t bucketSize)
	Train the decision stump on the given data. More...

void	Train (const MatType &data, const arma::Row< size_t > &labels, const arma::rowvec &weights, const size_t classes, const size_t bucketSize)
	Train the decision stump on the given data, with the given weights. More...

Private Member Functions
template<bool UseWeights, typename VecType , typename WeightVecType >
double	CalculateEntropy (const VecType &labels, const WeightVecType &weights)
	Calculate the entropy of the given dimension. More...

template<typename VecType >
double	CountMostFreq (const VecType &subCols)
	Count the most frequently occurring element in subCols. More...

template<typename VecType >
int	IsDistinct (const VecType &featureRow)
	Returns 1 if all the values of featureRow are not same. More...

void	MergeRanges ()
	After the "split" matrix has been set up, merge ranges with identical class labels. More...

template<bool UseWeights, typename VecType >
double	SetupSplitDimension (const VecType &dimension, const arma::Row< size_t > &labels, const arma::rowvec &weightD)
	Sets up dimension as if it were splitting on it and finds entropy when splitting on dimension. More...

template<bool UseWeights>
void	Train (const MatType &data, const arma::Row< size_t > &labels, const arma::rowvec &weights)
	Train the decision stump on the given data and labels. More...

template<typename VecType >
void	TrainOnDim (const VecType &dimension, const arma::Row< size_t > &labels)
	After having decided the dimension on which to split, train on that dimension. More...

Private Attributes
arma::Col< size_t >	binLabels
	Stores the labels for each splitting bin. More...

size_t	bucketSize
	The minimum number of points in a bucket. More...

size_t	classes
	The number of classes (we must store this for boosting). More...

arma::vec	split
	Stores the splitting values after training. More...

size_t	splitDimension
	Stores the value of the dimension on which to split. More...

Detailed Description

template<typename MatType = arma::mat>
class mlpack::decision_stump::DecisionStump< MatType >

This class implements a decision stump.

It constructs a single level decision tree, i.e., a decision stump. It uses entropy to decide splitting ranges.

The stump is parameterized by a splitting dimension (the dimension on which points are split), a vector of bin split values, and a vector of labels for each bin. Bin i is specified by the range [split[i], split[i + 1]). The last bin has range up to (split[i + 1] does not exist in that case). Points that are below the first bin will take the label of the first bin.

Template Parameters

MatType Type of matrix that is being used (sparse or dense).

Definition at line 34 of file decision_stump.hpp.

Constructor & Destructor Documentation

template<typename MatType = arma::mat>

mlpack::decision_stump::DecisionStump< MatType >::DecisionStump	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	classes,
		const size_t	bucketSize = `10`
	)

Constructor.

Train on the provided data. Generate a decision stump from data.

Parameters

data	Input, training data.
labels	Labels of training data.
classes	Number of distinct classes in labels.
bucketSize	Minimum size of bucket when splitting.

template<typename MatType = arma::mat>

mlpack::decision_stump::DecisionStump< MatType >::DecisionStump	(	const DecisionStump<> &	other,
		const MatType &	data,
		const arma::Row< size_t > &	labels,
		const arma::rowvec &	weights
	)

Alternate constructor which copies the parameters bucketSize and classes from an already initiated decision stump, other.

It appropriately sets the weight vector.

Parameters

other	The other initiated Decision Stump object from which we copy the values.
data	The data on which to train this object on.
labels	The labels of data.
weights	Weight vector to use while training. For boosting purposes.

template<typename MatType = arma::mat>

mlpack::decision_stump::DecisionStump< MatType >::DecisionStump ( )

Create a decision stump without training.

This stump will not be useful and will always return a class of 0 for anything that is to be classified, so it would be a prudent idea to call Train() after using this constructor.

Member Function Documentation

template<typename MatType = arma::mat>

const arma::Col<size_t> mlpack::decision_stump::DecisionStump< MatType >::BinLabels ( ) const

inline

Access the labels for each split bin.

Definition at line 127 of file decision_stump.hpp.

References mlpack::decision_stump::DecisionStump< MatType >::binLabels.

template<typename MatType = arma::mat>

arma::Col<size_t>& mlpack::decision_stump::DecisionStump< MatType >::BinLabels ( )

inline

Modify the labels for each split bin (be careful!).

Definition at line 129 of file decision_stump.hpp.

References mlpack::decision_stump::DecisionStump< MatType >::binLabels, and mlpack::decision_stump::DecisionStump< MatType >::Serialize().

template<typename MatType = arma::mat>

template<bool UseWeights, typename VecType , typename WeightVecType >

double mlpack::decision_stump::DecisionStump< MatType >::CalculateEntropy	(	const VecType &	labels,
		const WeightVecType &	weights
	)

private

Calculate the entropy of the given dimension.

Parameters

labels	Corresponding labels of the dimension.
classes	Number of classes.
weights	Weights for this set of labels.

Template Parameters

UseWeights If true, the weights in the weight vector will be used (otherwise they are ignored).

template<typename MatType = arma::mat>

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

Classification function.

After training, classify test, and put the predicted classes in predictedLabels.

Parameters

test	Testing data or data to classify.
predictedLabels	Vector to store the predicted classes after classifying test data.

template<typename MatType = arma::mat>

template<typename VecType >

double mlpack::decision_stump::DecisionStump< MatType >::CountMostFreq ( const VecType & subCols )

private

Count the most frequently occurring element in subCols.

Parameters

subCols The vector in which to find the most frequently occurring element.

template<typename MatType = arma::mat>

template<typename VecType >

int mlpack::decision_stump::DecisionStump< MatType >::IsDistinct ( const VecType & featureRow )

private

Returns 1 if all the values of featureRow are not same.

Parameters

featureRow The dimension which is checked for identical values.

template<typename MatType = arma::mat>

void mlpack::decision_stump::DecisionStump< MatType >::MergeRanges ( )

private

After the "split" matrix has been set up, merge ranges with identical class labels.

template<typename MatType = arma::mat>

template<typename Archive >

void mlpack::decision_stump::DecisionStump< MatType >::Serialize	(	Archive &	ar,
		const unsigned	int
	)

Serialize the decision stump.

Referenced by mlpack::decision_stump::DecisionStump< MatType >::BinLabels().

template<typename MatType = arma::mat>

template<bool UseWeights, typename VecType >

double mlpack::decision_stump::DecisionStump< MatType >::SetupSplitDimension	(	const VecType &	dimension,
		const arma::Row< size_t > &	labels,
		const arma::rowvec &	weightD
	)

private

Sets up dimension as if it were splitting on it and finds entropy when splitting on dimension.

Parameters

dimension A row from the training data, which might be a candidate for the splitting dimension.

Template Parameters

UseWeights Whether we need to run a weighted Decision Stump.

template<typename MatType = arma::mat>

const arma::vec& mlpack::decision_stump::DecisionStump< MatType >::Split ( ) const

inline

Access the splitting values.

Definition at line 122 of file decision_stump.hpp.

References mlpack::decision_stump::DecisionStump< MatType >::split.

template<typename MatType = arma::mat>

arma::vec& mlpack::decision_stump::DecisionStump< MatType >::Split ( )

inline

Modify the splitting values (be careful!).

Definition at line 124 of file decision_stump.hpp.

References mlpack::decision_stump::DecisionStump< MatType >::split.

template<typename MatType = arma::mat>

size_t mlpack::decision_stump::DecisionStump< MatType >::SplitDimension ( ) const

inline

Access the splitting dimension.

Definition at line 117 of file decision_stump.hpp.

References mlpack::decision_stump::DecisionStump< MatType >::splitDimension.

template<typename MatType = arma::mat>

size_t& mlpack::decision_stump::DecisionStump< MatType >::SplitDimension ( )

inline

Modify the splitting dimension (be careful!).

Definition at line 119 of file decision_stump.hpp.

References mlpack::decision_stump::DecisionStump< MatType >::splitDimension.

template<typename MatType = arma::mat>

void mlpack::decision_stump::DecisionStump< MatType >::Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	classes,
		const size_t	bucketSize
	)

Train the decision stump on the given data.

This completely overwrites any previous training data, so after training the stump may be completely different.

Parameters

data	Dataset to train on.
labels	Labels for each point in the dataset.
classes	Number of classes in the dataset.
bucketSize	Minimum size of bucket when splitting.

template<typename MatType = arma::mat>

void mlpack::decision_stump::DecisionStump< MatType >::Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const arma::rowvec &	weights,
		const size_t	classes,
		const size_t	bucketSize
	)

Train the decision stump on the given data, with the given weights.

This completely overwrites any previous training data, so after training the stump may be completely different.

Parameters

data	Dataset to train on.
labels	Labels for each point in the dataset.
weights	Weights for each point in the dataset.
classes	Number of classes in the dataset.
bucketSize	Minimum size of bucket when splitting.

template<typename MatType = arma::mat>

template<bool UseWeights>

void mlpack::decision_stump::DecisionStump< MatType >::Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const arma::rowvec &	weights
	)

private

Train the decision stump on the given data and labels.

Parameters

data	Dataset to train on.
labels	Labels for dataset.
weights	Weights for this set of labels.

Template Parameters

UseWeights If true, the weights in the weight vector will be used (otherwise they are ignored).

template<typename MatType = arma::mat>

template<typename VecType >

void mlpack::decision_stump::DecisionStump< MatType >::TrainOnDim	(	const VecType &	dimension,
		const arma::Row< size_t > &	labels
	)

private

After having decided the dimension on which to split, train on that dimension.

Template Parameters

dimension dimension is the dimension decided by the constructor on which we now train the decision stump.

Member Data Documentation

template<typename MatType = arma::mat>

arma::Col<size_t> mlpack::decision_stump::DecisionStump< MatType >::binLabels

private

Stores the labels for each splitting bin.

Definition at line 146 of file decision_stump.hpp.

Referenced by mlpack::decision_stump::DecisionStump< MatType >::BinLabels().

template<typename MatType = arma::mat>

size_t mlpack::decision_stump::DecisionStump< MatType >::bucketSize

private

The minimum number of points in a bucket.

Definition at line 139 of file decision_stump.hpp.

template<typename MatType = arma::mat>

size_t mlpack::decision_stump::DecisionStump< MatType >::classes

private

The number of classes (we must store this for boosting).

Definition at line 137 of file decision_stump.hpp.

template<typename MatType = arma::mat>

arma::vec mlpack::decision_stump::DecisionStump< MatType >::split

private

Stores the splitting values after training.

Definition at line 144 of file decision_stump.hpp.

Referenced by mlpack::decision_stump::DecisionStump< MatType >::Split().

template<typename MatType = arma::mat>

size_t mlpack::decision_stump::DecisionStump< MatType >::splitDimension

private

Stores the value of the dimension on which to split.

Definition at line 142 of file decision_stump.hpp.

Referenced by mlpack::decision_stump::DecisionStump< MatType >::SplitDimension().

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

src/mlpack/methods/decision_stump/decision_stump.hpp

Public Member Functions

Private Member Functions

Private Attributes

Detailed Description

template<typename MatType = arma::mat> class mlpack::decision_stump::DecisionStump< MatType >

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation

template<typename MatType = arma::mat>
class mlpack::decision_stump::DecisionStump< MatType >