border_length_ | SVMWrapper | [private] |
calculateGaussTable(UInt border_length, DoubleReal sigma, std::vector< DoubleReal > &gauss_table) | SVMWrapper | [static] |
computeKernelMatrix(svm_problem *problem1, svm_problem *problem2) | SVMWrapper | |
createRandomPartitions(svm_problem *problem, UInt number, std::vector< svm_problem * > &partitions) | SVMWrapper | [static] |
destroyProblem_(svm_problem *problem) | SVMWrapper | [protected] |
gauss_table_ | SVMWrapper | [private] |
gauss_tables_ | SVMWrapper | [private] |
getDecisionValues(svm_problem *data, std::vector< DoubleReal > &decision_values) | SVMWrapper | |
getDoubleParameter(SVM_parameter_type type) | SVMWrapper | |
getIntParameter(SVM_parameter_type type) | SVMWrapper | |
getLabels(svm_problem *problem, std::vector< DoubleReal > &labels) | SVMWrapper | [static] |
getNumberOfEnclosedPoints_(DoubleReal m1, DoubleReal m2, const std::vector< std::pair< DoubleReal, DoubleReal > > &points) | SVMWrapper | [private] |
getPValue(DoubleReal sigma1, DoubleReal sigma2, std::pair< DoubleReal, DoubleReal > point) | SVMWrapper | |
getSignificanceBorders(svm_problem *data, std::pair< DoubleReal, DoubleReal > &borders, DoubleReal confidence=0.95, UInt number_of_runs=10, UInt number_of_partitions=5, DoubleReal step_size=0.01, UInt max_iterations=1000000) | SVMWrapper | |
getSVCProbabilities(struct svm_problem *problem, std::vector< DoubleReal > &probabilities, std::vector< DoubleReal > &prediction_labels) | SVMWrapper | |
getSVRProbability() | SVMWrapper | |
initParameters_() | SVMWrapper | [private] |
kernel_type_ | SVMWrapper | [private] |
kernelOligo(const svm_node *x, const svm_node *y, const std::vector< DoubleReal > &gauss_table, DoubleReal sigma_square=0, UInt max_distance=50) | SVMWrapper | [static] |
loadModel(std::string modelFilename) | SVMWrapper | |
mergePartitions(const std::vector< svm_problem * > &problems, UInt except) | SVMWrapper | [static] |
model_ | SVMWrapper | [private] |
param_ | SVMWrapper | [private] |
performCrossValidation(svm_problem *problem, const std::map< SVM_parameter_type, DoubleReal > &start_values, const std::map< SVM_parameter_type, DoubleReal > &step_sizes, const std::map< SVM_parameter_type, DoubleReal > &end_values, UInt number_of_partitions, UInt number_of_runs, std::map< SVM_parameter_type, DoubleReal > &best_parameters, bool additive_step_size=true, bool output=false, String performances_file_name="performances.txt", bool mcc_as_performance_measure=false) | SVMWrapper | |
predict(struct svm_problem *predictProblem, std::vector< DoubleReal > &predicted_rts) | SVMWrapper | |
predict(const std::vector< svm_node * > &vectors, std::vector< DoubleReal > &predicted_rts) | SVMWrapper | |
saveModel(std::string modelFilename) const | SVMWrapper | |
scaleData(svm_problem *data, Int max_scale_value=-1) | SVMWrapper | |
setParameter(SVM_parameter_type type, Int value) | SVMWrapper | |
setParameter(SVM_parameter_type type, DoubleReal value) | SVMWrapper | |
setTrainingSample(svm_problem *training_sample) | SVMWrapper | |
setWeights(const std::vector< Int > &weight_labels, const std::vector< DoubleReal > &weights) | SVMWrapper | |
sigma_ | SVMWrapper | [private] |
sigmas_ | SVMWrapper | [private] |
SVMWrapper() | SVMWrapper | |
train(struct svm_problem *problem) | SVMWrapper | |
training_problem_ | SVMWrapper | [private] |
training_set_ | SVMWrapper | [private] |
~SVMWrapper() | SVMWrapper | |