SimpleCV splits data into two sets - training and validation sets - and then runs training on the training set and evaluates performance on the validation set. More...

Public Member Functions
template < typename MatInType , typename PredictionsInType >
	SimpleCV (const double validationSize, MatInType &&xs, PredictionsInType &&ys)
	This constructor can be used for regression algorithms and for binary classification algorithms. More...

template < typename MatInType , typename PredictionsInType >
	SimpleCV (const double validationSize, MatInType &&xs, PredictionsInType &&ys, const size_t numClasses)
	This constructor can be used for multiclass classification algorithms. More...

template < typename MatInType , typename PredictionsInType >
	SimpleCV (const double validationSize, MatInType &&xs, const data::DatasetInfo &datasetInfo, PredictionsInType &&ys, const size_t numClasses)
	This constructor can be used for multiclass classification algorithms that can take a data::DatasetInfo parameter. More...

template < typename MatInType , typename PredictionsInType , typename WeightsInType >
	SimpleCV (const double validationSize, MatInType &&xs, PredictionsInType &&ys, WeightsInType &&weights)
	This constructor can be used for regression and binary classification algorithms that support weighted learning. More...

template < typename MatInType , typename PredictionsInType , typename WeightsInType >
	SimpleCV (const double validationSize, MatInType &&xs, PredictionsInType &&ys, const size_t numClasses, WeightsInType &&weights)
	This constructor can be used for multiclass classification algorithms that support weighted learning. More...

template < typename MatInType , typename PredictionsInType , typename WeightsInType >
	SimpleCV (const double validationSize, MatInType &&xs, const data::DatasetInfo &datasetInfo, PredictionsInType &&ys, const size_t numClasses, WeightsInType &&weights)
	This constructor can be used for multiclass classification algorithms that can take a data::DatasetInfo parameter and support weighted learning. More...

template<typename... MLAlgorithmArgs>
double	Evaluate (const MLAlgorithmArgs &... args)
	Train on the training set and assess performance on the validation set by using the class Metric. More...

MLAlgorithm &	Model ()
	Access and modify the last trained model. More...

Detailed Description

template<typename MLAlgorithm, typename Metric, typename MatType = arma::mat, typename PredictionsType = typename MetaInfoExtractor<MLAlgorithm, MatType>::PredictionsType, typename WeightsType = typename MetaInfoExtractor<MLAlgorithm, MatType, PredictionsType>::WeightsType>
class mlpack::cv::SimpleCV< MLAlgorithm, Metric, MatType, PredictionsType, WeightsType >

SimpleCV splits data into two sets - training and validation sets - and then runs training on the training set and evaluates performance on the validation set.

To construct a SimpleCV object you need to pass the validationSize parameter and arguments that specify data. For example, SoftmaxRegression can be validated in the following way.

// 100-point 5-dimensional random dataset.
arma::mat data = arma::randu<arma::mat>(5, 100);
// Random labels in the [0, 4] interval.
arma::Row<size_t> labels =
    arma::randi<arma::Row<size_t>>(100, arma::distr_param(0, 4));
size_t numClasses = 5;
double validationSize = 0.2;
SimpleCV<SoftmaxRegression<>, Accuracy> cv(validationSize, data, labels,
    numClasses);
double lambda = 0.1;
double softmaxAccuracy = cv.Evaluate(lambda);

In the example above, 80% of the passed dataset will be used for training, and remaining 20% will be used for calculating the accuracy metric.

Template Parameters

MLAlgorithm	A machine learning algorithm.
Metric	A metric to assess the quality of a trained model.
MatType	The type of data.
PredictionsType	The type of predictions (should be passed when the predictions type is a template parameter in Train methods of the given MLAlgorithm; arma::Row<size_t> will be used otherwise).
WeightsType	The type of weights (should be passed when weighted learning is supported, and the weights type is a template parameter in Train methods of the given MLAlgorithm; arma::vec will be used otherwise).

Definition at line 68 of file simple_cv.hpp.

Constructor & Destructor Documentation

◆ SimpleCV() [1/6]

SimpleCV	(	const double	validationSize,
		MatInType &&	xs,
		PredictionsInType &&	ys
	)

This constructor can be used for regression algorithms and for binary classification algorithms.

Parameters

validationSize	A proportion (between 0 and 1) of data used as a validation set.
xs	Data points to cross-validate on.
ys	Predictions (labels for classification algorithms and responses for regression algorithms) for each data point.

Template Parameters

MatInType	A type that can be converted to MatType.
PredictionsInType	A type that can be converted to PredictionsType.

◆ SimpleCV() [2/6]

SimpleCV	(	const double	validationSize,
		MatInType &&	xs,
		PredictionsInType &&	ys,
		const size_t	numClasses
	)

This constructor can be used for multiclass classification algorithms.

Parameters

validationSize	A proportion (between 0 and 1) of data used as a validation set.
xs	Data points to cross-validate on.
ys	Labels for each data point.
numClasses	Number of classes in the dataset.

Template Parameters

MatInType	A type that can be converted to MatType.
PredictionsInType	A type that can be converted to PredictionsType.

◆ SimpleCV() [3/6]

SimpleCV	(	const double	validationSize,
		MatInType &&	xs,
		const data::DatasetInfo &	datasetInfo,
		PredictionsInType &&	ys,
		const size_t	numClasses
	)

This constructor can be used for multiclass classification algorithms that can take a data::DatasetInfo parameter.

Parameters

validationSize	A proportion (between 0 and 1) of data used as a validation set.
xs	Data points to cross-validate on.
datasetInfo	Type information for each dimension of the dataset.
ys	Labels for each data point.
numClasses	Number of classes in the dataset.

Template Parameters

MatInType	A type that can be converted to MatType.
PredictionsInType	A type that can be converted to PredictionsType.

◆ SimpleCV() [4/6]

SimpleCV	(	const double	validationSize,
		MatInType &&	xs,
		PredictionsInType &&	ys,
		WeightsInType &&	weights
	)

This constructor can be used for regression and binary classification algorithms that support weighted learning.

Parameters

validationSize	A proportion (between 0 and 1) of data used as a validation set.
xs	Data points to cross-validate on.
ys	Predictions (labels for classification algorithms and responses for regression algorithms) for each data point.
weights	Observation weights (for boosting).

Template Parameters

MatInType	A type that can be converted to MatType.
PredictionsInType	A type that can be converted to PredictionsType.
WeightsInType	A type that can be converted to WeightsType.

◆ SimpleCV() [5/6]

SimpleCV	(	const double	validationSize,
		MatInType &&	xs,
		PredictionsInType &&	ys,
		const size_t	numClasses,
		WeightsInType &&	weights
	)

This constructor can be used for multiclass classification algorithms that support weighted learning.

Parameters

validationSize	A proportion (between 0 and 1) of data used as a validation set.
xs	Data points to cross-validate on.
ys	Labels for each data point.
numClasses	Number of classes in the dataset.
weights	Observation weights (for boosting).

Template Parameters

MatInType	A type that can be converted to MatType.
PredictionsInType	A type that can be converted to PredictionsType.
WeightsInType	A type that can be converted to WeightsType.

◆ SimpleCV() [6/6]

SimpleCV	(	const double	validationSize,
		MatInType &&	xs,
		const data::DatasetInfo &	datasetInfo,
		PredictionsInType &&	ys,
		const size_t	numClasses,
		WeightsInType &&	weights
	)

This constructor can be used for multiclass classification algorithms that can take a data::DatasetInfo parameter and support weighted learning.

Parameters

validationSize	A proportion (between 0 and 1) of data used as a validation set.
xs	Data points to cross-validate on.
datasetInfo	Type information for each dimension of the dataset.
ys	Labels for each data point.
numClasses	Number of classes in the dataset.
weights	Observation weights (for boosting).

Template Parameters

MatInType	A type that can be converted to MatType.
PredictionsInType	A type that can be converted to PredictionsType.
WeightsInType	A type that can be converted to WeightsType.

Member Function Documentation

◆ Evaluate()

double Evaluate ( const MLAlgorithmArgs &... args )

Train on the training set and assess performance on the validation set by using the class Metric.

Parameters

args	Arguments for the given MLAlgorithm taken by its constructor (in addition to the passed ones in the SimpleCV constructor).

◆ Model()

MLAlgorithm& Model ( )

Access and modify the last trained model.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/core/cv/simple_cv.hpp

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ SimpleCV() [1/6]

◆ SimpleCV() [2/6]

◆ SimpleCV() [3/6]

◆ SimpleCV() [4/6]

◆ SimpleCV() [5/6]

◆ SimpleCV() [6/6]

Member Function Documentation

◆ Evaluate()

◆ Model()