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//
//
#ifndef BALL_QSAR_CLASSIFICATIONMODEL_H
#define BALL_QSAR_CLASSIFICATIONMODEL_H
#ifndef BALL_QSAR_MODEL_H
#include <BALL/QSAR/Model.h>
#endif
#ifndef BALL_QSAR_CLASSIFICATIONVALIDATION_H
#include <BALL/QSAR/classificationValidation.h>
#endif
#ifndef BALL_QSAR_EXCEPTION_H
#include <BALL/QSAR/exception.h>
#endif
namespace BALL
{
namespace QSAR
{
class BALL_EXPORT ClassificationModel
: public Model
{
public:
/** @name Constructors and Destructors
*/
//@{
/** constructur,
@param q QSAR-wrapper object, from which the data for this model should be taken */
ClassificationModel(const QSARData& q);
~ClassificationModel();
virtual void operator=(ClassificationModel& m);
//@}
/** @name Attributes
*/
//@{
/** a ClassificationValidation object, that is used to validate this model and that will contain the results of the validations */
ClassificationValidation* validation;
std::vector<int> getClassLabels();
//@}
protected:
/** @name Accessors
*/
//@{
/** read all different class labels from matrix Y into ClassificationModel.labels */
void readLabels();
/** the number of substances in each class of the training data */
std::vector<int> no_substances_;
//@}
/** @name Input and Output. The following methods can be used to implement the functions saveToFile() and readFromFile() in final classes derived from this base-class
*/
//@{
void readClassInformationFromFile(std::ifstream& input, int no_classes);
void saveClassInformationToFile(std::ofstream& out);
//@}
/** @name Feature discretization function
*/
//@{
/** Interface function-definition for discretization of features.\n
Thus, various functions that do the actual discretization can be created as members of ClassificationModel, allowing switching of the discretization technique by only (re-)setting this function-pointer.
@param bins the number of discrete values to be created for each features
@param discretization_information a Eigen::MatrixXd into which the information about the applied discretization should be saved (into one column per feature). \n
This Eigen::MatrixXd can later be used to discretize test data according to the discretization of the training data. */
void (ClassificationModel::* discretizeFeatures)(unsigned int bins, Eigen::MatrixXd& discretization_information);
/** Discretizes test data according to the given discretization of the training data.
@param discretization_information the discretization according to which the current descriptor_matrix should be discretized. */
void (ClassificationModel::* discretizeTestDataFeatures)(Eigen::VectorXd& compound, unsigned int bins, const Eigen::MatrixXd& discretization_information);
/** discretizes each feature in such a way that each created bin has an equal range of values */
void equalSpaceDiscretization(unsigned int bins, Eigen::MatrixXd& discretization_information);
void equalSpaceDiscretizationTestData(Eigen::VectorXd& compound, unsigned int bins, const Eigen::MatrixXd& discretization_information);
//@}
/** @name Attributes
*/
//@{
/** labels for the different classes */
std::vector<int> labels_;
//@}
friend class ClassificationValidation;
};
}
}
#endif // CLASSIFICATION
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