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| NBModel (const QSARData &q) |
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| ~NBModel () |
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void | train () |
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Eigen::VectorXd | predict (const vector< double > &substance, bool transform=1) |
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void | saveToFile (string filename) |
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void | readFromFile (string filename) |
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vector< double > | getParameters () const |
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void | setParameters (vector< double > &v) |
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bool | isTrained () |
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vector< double > | calculateProbabilities (int activitiy_index, int feature_index, double feature_value) |
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int | getNoResponseVariables () |
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| BayesModel (const QSARData &q) |
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virtual bool | isTrained ()=0 |
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virtual vector< double > | calculateProbabilities (int activitiy_index, int feature_index, double feature_value)=0 |
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virtual int | getNoResponseVariables ()=0 |
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| ClassificationModel (const QSARData &q) |
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| ~ClassificationModel () |
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virtual void | operator= (ClassificationModel &m) |
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std::vector< int > | getClassLabels () |
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| Model (const QSARData &q) |
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virtual | ~Model () |
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virtual void | operator= (const Model &m) |
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void | copyData (const Model &m) |
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void | copyDescriptorIDs (const Model &m) |
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void | readTrainingData () |
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void | deleteDescriptorIDs () |
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virtual bool | optimizeParameters (int, int) |
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bool | optimizeParameters (int k) |
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virtual double | calculateStdErr () |
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std::multiset< unsigned int > * | getDescriptorIDs () |
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void | setDataSource (const QSARData *q) |
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const Eigen::MatrixXd * | getDescriptorMatrix () |
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const vector< string > * | getSubstanceNames () |
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const vector< string > * | getDescriptorNames () |
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const Eigen::MatrixXd | getDescriptorTransformations () |
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const Eigen::MatrixXd | getYTransformations () |
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const Eigen::MatrixXd * | getY () |
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void | setDescriptorIDs (const std::multiset< unsigned int > &sl) |
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const string * | getType () |
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void | getUnnormalizedFeatureValue (int compound, int feature, double &return_value) |
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void | getUnnormalizedResponseValue (int compound, int response, double &return_value) |
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ClassificationValidation * | validation |
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const QSARData * | data |
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Validation * | model_val |
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void | readClassInformationFromFile (std::ifstream &input, int no_classes) |
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void | saveClassInformationToFile (std::ofstream &out) |
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void | readLabels () |
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void | equalSpaceDiscretization (unsigned int bins, Eigen::MatrixXd &discretization_information) |
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void | equalSpaceDiscretizationTestData (Eigen::VectorXd &compound, unsigned int bins, const Eigen::MatrixXd &discretization_information) |
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void | readMatrix (Eigen::MatrixXd &mat, std::ifstream &in, unsigned int lines, unsigned int col) |
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void | readVector (Eigen::RowVectorXd &vec, std::ifstream &in, unsigned int no_cells, bool column_vector) |
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void | readModelParametersFromFile (std::ifstream &in) |
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void | saveModelParametersToFile (std::ofstream &out) |
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virtual void | saveDescriptorInformationToFile (std::ofstream &out) |
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virtual void | readDescriptorInformationFromFile (std::ifstream &in, int no_descriptors, bool transformation) |
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void | readResponseTransformationFromFile (std::ifstream &in, int no_y) |
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void | saveResponseTransformationToFile (std::ofstream &out) |
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Eigen::VectorXd | getSubstanceVector (const vector< double > &substance, bool transform) |
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Eigen::VectorXd | getSubstanceVector (const Eigen::VectorXd &substance, bool transform) |
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void | backTransformPrediction (Eigen::VectorXd &pred) |
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void | addLambda (Eigen::MatrixXd &matrix, double &lambda) |
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void | readDescriptorInformation () |
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double | min_prob_diff_ |
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double | undef_act_class_id_ |
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std::vector< int > | labels_ |
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std::vector< int > | no_substances_ |
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void(ClassificationModel::* | discretizeFeatures )(unsigned int bins, Eigen::MatrixXd &discretization_information) |
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void(ClassificationModel::* | discretizeTestDataFeatures )(Eigen::VectorXd &compound, unsigned int bins, const Eigen::MatrixXd &discretization_information) |
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int | default_no_opt_steps_ |
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Eigen::MatrixXd | descriptor_matrix_ |
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vector< string > | substance_names_ |
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vector< string > | descriptor_names_ |
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Eigen::MatrixXd | descriptor_transformations_ |
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Eigen::MatrixXd | y_transformations_ |
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Eigen::MatrixXd | Y_ |
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String | type_ |
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std::multiset< unsigned int > | descriptor_IDs_ |
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class for Naive Bayes
Definition at line 27 of file nBModel.h.