An auxiliary class for crossvalidation. More...
Public Types  
using  MIE = MetaInfoExtractor< MLAlgorithm, MatType, PredictionsType, WeightsType > 
A short alias for MetaInfoExtractor. More...  
Public Member Functions  
CVBase ()  
Assert that MLAlgorithm doesn't take any additional basic parameters like numClasses. More...  
CVBase (const size_t numClasses)  
Assert that MLAlgorithm takes the numClasses parameter and store it. More...  
CVBase (const data::DatasetInfo &datasetInfo, const size_t numClasses)  
Assert that MLAlgorithm takes the numClasses parameter and a data::DatasetInfo parameter and store them. More...  
template<typename... MLAlgorithmArgs>  
MLAlgorithm  Train (const MatType &xs, const PredictionsType &ys, const MLAlgorithmArgs &... args) 
Train MLAlgorithm with given data points, predictions, and hyperparameters depending on what CVBase constructor has been called. More...  
template<typename... MLAlgorithmArgs>  
MLAlgorithm  Train (const MatType &xs, const PredictionsType &ys, const WeightsType &weights, const MLAlgorithmArgs &... args) 
Train MLAlgorithm with given data points, predictions, weights, and hyperparameters depending on what CVBase constructor has been called. More...  
Static Public Member Functions  
static void  AssertDataConsistency (const MatType &xs, const PredictionsType &ys) 
Assert there is the equal number of data points and predictions. More...  
static void  AssertWeightsConsistency (const MatType &xs, const WeightsType &weights) 
Assert weighted learning is supported and there is the equal number of data points and weights. More...  
An auxiliary class for crossvalidation.
It serves to handle basic nondata constructor parameters of a machine learning algorithm (like datasetInfo or numClasses) and to assert that the machine learning algorithm and data satisfy certain conditions.
This class is not meant to be used directly by users. To crossvalidate rather use enduser classes like SimpleCV or KFoldCV.
MLAlgorithm  A machine learning algorithm. 
MatType  The type of data. 
PredictionsType  The type of predictions (labels/responses). 
WeightsType  The type of weights. It supposed to be void* when weights are not supported. 
Definition at line 39 of file cv_base.hpp.
using MIE = MetaInfoExtractor<MLAlgorithm, MatType, PredictionsType, WeightsType> 
A short alias for MetaInfoExtractor.
Definition at line 44 of file cv_base.hpp.
CVBase  (  ) 
Assert that MLAlgorithm doesn't take any additional basic parameters like numClasses.
CVBase  (  const size_t  numClasses  ) 
Assert that MLAlgorithm takes the numClasses parameter and store it.
numClasses  Number of classes in the dataset. 
CVBase  (  const data::DatasetInfo &  datasetInfo, 
const size_t  numClasses  
) 
Assert that MLAlgorithm takes the numClasses parameter and a data::DatasetInfo parameter and store them.
datasetInfo  Type information for each dimension of the dataset. 
numClasses  Number of classes in the dataset. 

static 
Assert there is the equal number of data points and predictions.

static 
Assert weighted learning is supported and there is the equal number of data points and weights.
MLAlgorithm Train  (  const MatType &  xs, 
const PredictionsType &  ys,  
const MLAlgorithmArgs &...  args  
) 
Train MLAlgorithm with given data points, predictions, and hyperparameters depending on what CVBase constructor has been called.
MLAlgorithm Train  (  const MatType &  xs, 
const PredictionsType &  ys,  
const WeightsType &  weights,  
const MLAlgorithmArgs &...  args  
) 
Train MLAlgorithm with given data points, predictions, weights, and hyperparameters depending on what CVBase constructor has been called.