#include <BALL/QSAR/nBModel.h>

Inheritance diagram for BALL::QSAR::NBModel:

Public Member Functions
Constructors and Destructors
	NBModel (const QSARData &q)

	~NBModel ()

Accessors
void	train ()

Eigen::VectorXd	predict (const vector< double > &substance, bool transform=1)

void	saveToFile (string filename)

void	readFromFile (string filename)

vector< double >	getParameters () const

void	setParameters (vector< double > &v)

bool	isTrained ()

vector< double >	calculateProbabilities (int activitiy_index, int feature_index, double feature_value)

int	getNoResponseVariables ()

Public Member Functions inherited from BALL::QSAR::BayesModel
	BayesModel (const QSARData &q)

Public Member Functions inherited from BALL::QSAR::ClassificationModel
	ClassificationModel (const QSARData &q)

	~ClassificationModel ()

virtual void	operator= (ClassificationModel &m)

std::vector< int >	getClassLabels ()

Public Member Functions inherited from BALL::QSAR::Model
	Model (const QSARData &q)

virtual	~Model ()

virtual void	operator= (const Model &m)

void	copyData (const Model &m)

void	copyDescriptorIDs (const Model &m)

void	readTrainingData ()

void	deleteDescriptorIDs ()

virtual bool	optimizeParameters (int, int)

bool	optimizeParameters (int k)

virtual double	calculateStdErr ()

std::multiset< unsigned int > *	getDescriptorIDs ()

void	setDataSource (const QSARData *q)

const Eigen::MatrixXd *	getDescriptorMatrix ()

const vector< string > *	getSubstanceNames ()

const vector< string > *	getDescriptorNames ()

const Eigen::MatrixXd	getDescriptorTransformations ()

const Eigen::MatrixXd	getYTransformations ()

const Eigen::MatrixXd *	getY ()

void	setDescriptorIDs (const std::multiset< unsigned int > &sl)

const string *	getType ()

void	getUnnormalizedFeatureValue (int compound, int feature, double &return_value)

void	getUnnormalizedResponseValue (int compound, int response, double &return_value)

Additional Inherited Members
Public Attributes inherited from BALL::QSAR::ClassificationModel
ClassificationValidation *	validation

Public Attributes inherited from BALL::QSAR::Model
const QSARData *	data

Validation *	model_val

Protected Member Functions inherited from BALL::QSAR::ClassificationModel
void	readClassInformationFromFile (std::ifstream &input, int no_classes)

void	saveClassInformationToFile (std::ofstream &out)

void	readLabels ()

void	equalSpaceDiscretization (unsigned int bins, Eigen::MatrixXd &discretization_information)

void	equalSpaceDiscretizationTestData (Eigen::VectorXd &compound, unsigned int bins, const Eigen::MatrixXd &discretization_information)

Protected Member Functions inherited from BALL::QSAR::Model
void	readMatrix (Eigen::MatrixXd &mat, std::ifstream &in, unsigned int lines, unsigned int col)

void	readVector (Eigen::RowVectorXd &vec, std::ifstream &in, unsigned int no_cells, bool column_vector)

void	readModelParametersFromFile (std::ifstream &in)

void	saveModelParametersToFile (std::ofstream &out)

virtual void	saveDescriptorInformationToFile (std::ofstream &out)

virtual void	readDescriptorInformationFromFile (std::ifstream &in, int no_descriptors, bool transformation)

void	readResponseTransformationFromFile (std::ifstream &in, int no_y)

void	saveResponseTransformationToFile (std::ofstream &out)

Eigen::VectorXd	getSubstanceVector (const vector< double > &substance, bool transform)

Eigen::VectorXd	getSubstanceVector (const Eigen::VectorXd &substance, bool transform)

void	backTransformPrediction (Eigen::VectorXd &pred)

void	addLambda (Eigen::MatrixXd &matrix, double &lambda)

void	readDescriptorInformation ()

Protected Attributes inherited from BALL::QSAR::BayesModel
double	min_prob_diff_

double	undef_act_class_id_

Protected Attributes inherited from BALL::QSAR::ClassificationModel
std::vector< int >	labels_

std::vector< int >	no_substances_

void(ClassificationModel::*	discretizeFeatures )(unsigned int bins, Eigen::MatrixXd &discretization_information)

void(ClassificationModel::*	discretizeTestDataFeatures )(Eigen::VectorXd &compound, unsigned int bins, const Eigen::MatrixXd &discretization_information)

Protected Attributes inherited from BALL::QSAR::Model
int	default_no_opt_steps_

Eigen::MatrixXd	descriptor_matrix_

vector< string >	substance_names_

vector< string >	descriptor_names_

Eigen::MatrixXd	descriptor_transformations_

Eigen::MatrixXd	y_transformations_

Eigen::MatrixXd	Y_

String	type_

std::multiset< unsigned int >	descriptor_IDs_

Detailed Description

class for Naive Bayes

Definition at line 27 of file nBModel.h.

Constructor & Destructor Documentation

BALL::QSAR::NBModel::NBModel ( const QSARData & q )

BALL::QSAR::NBModel::~NBModel ( )

Member Function Documentation

vector<double> BALL::QSAR::NBModel::calculateProbabilities	(	int	activitiy_index,
		int	feature_index,
		double	feature_value
	)

virtual

calculate the probability for the specified feature to assume the given value for each class

Returns: a probability for each class

Implements BALL::QSAR::BayesModel.

int BALL::QSAR::NBModel::getNoResponseVariables ( )

virtual

returns the number of response variables for which this model has been trained

Implements BALL::QSAR::BayesModel.

vector<double> BALL::QSAR::NBModel::getParameters ( ) const

virtual

Reimplemented from BALL::QSAR::Model.

bool BALL::QSAR::NBModel::isTrained ( )

virtual

Implements BALL::QSAR::BayesModel.

Eigen::VectorXd BALL::QSAR::NBModel::predict	(	const vector< double > &	substance,
		bool	transform = `1`
	)

virtual

Predicts the activities of a given substance

Parameters

substance the substance which activity is to be predicted in form of a vecor containing the values for all descriptors (if neccessary, relevant descriptors will be selected automatically)

transform determines whether the values for each descriptor of the given substance should be transformed before prediction of activity.
If (transform==1): each descriptor value is transformed according to the centering of the respective column of QSARData.descriptor_matrix used to train this model.
If the substance to be predicted is part of the same input data (e.g. same SD-file) as the training data (as is the case during cross validation), transform should therefore be set to 0.