, including all inherited members.
| _classname_() | PLearn::GaussianProcessRegressor | [static] |
| _classname_() | PLearn::GaussianProcessRegressor | [static] |
| _getOptionList_() | PLearn::GaussianProcessRegressor | [static] |
| _getOptionList_() | PLearn::GaussianProcessRegressor | [static] |
| _getRemoteMethodMap_() | PLearn::GaussianProcessRegressor | [static] |
| _getRemoteMethodMap_() | PLearn::GaussianProcessRegressor | [static] |
| _isa_(const Object *o) | PLearn::GaussianProcessRegressor | [static] |
| _isa_(const Object *o) | PLearn::GaussianProcessRegressor | [static] |
| _new_instance_for_typemap_() | PLearn::GaussianProcessRegressor | [static] |
| _new_instance_for_typemap_() | PLearn::GaussianProcessRegressor | [static] |
| _static_initialize_() | PLearn::GaussianProcessRegressor | [static] |
| _static_initialize_() | PLearn::GaussianProcessRegressor | [static] |
| _static_initializer_ | PLearn::GaussianProcessRegressor | [static] |
| AlgoExact enum value | PLearn::GaussianProcessRegressor | [protected] |
| AlgoProjectedProcess enum value | PLearn::GaussianProcessRegressor | [protected] |
| alpha | PLearn::GaussianProcessRegressor | |
| asString() const | PLearn::Object | [virtual] |
| asStringRemoteTransmit() const | PLearn::Object | [virtual] |
| PLearn::b_costs | PLearn::PLearner | [mutable, protected] |
| PLearn::PConditionalDistribution::b_costs | PLearn::PLearner | [mutable, protected] |
| PLearn::b_inputs | PLearn::PLearner | [mutable, protected] |
| PLearn::PConditionalDistribution::b_inputs | PLearn::PLearner | [mutable, protected] |
| PLearn::b_outputs | PLearn::PLearner | [mutable, protected] |
| PLearn::PConditionalDistribution::b_outputs | PLearn::PLearner | [mutable, protected] |
| PLearn::b_targets | PLearn::PLearner | [mutable, protected] |
| PLearn::PConditionalDistribution::b_targets | PLearn::PLearner | [mutable, protected] |
| PLearn::b_weights | PLearn::PLearner | [mutable, protected] |
| PLearn::PConditionalDistribution::b_weights | PLearn::PLearner | [mutable, protected] |
| batchComputeOutputAndConfidence(VMat inputs, real probability, VMat outputs_and_confidence) const | PLearn::PLearner | [virtual] |
| BayesianCost() | PLearn::GaussianProcessRegressor | [protected] |
| build() | PLearn::GaussianProcessRegressor | [virtual] |
| build() | PLearn::GaussianProcessRegressor | [virtual] |
| build_() | PLearn::GaussianProcessRegressor | [private] |
| build_() | PLearn::GaussianProcessRegressor | [private] |
| build_from_train_set() | PLearn::PLearner | [inline, protected, virtual] |
| call(const string &methodname, int nargs, PStream &io) | PLearn::Object | [virtual] |
| cdf(const Vec &y) const | PLearn::PDistribution | [virtual] |
| PLearn::changeOption(const string &optionname, const string &value) | PLearn::Object | |
| PLearn::PConditionalDistribution::changeOption(const string &optionname, const string &value) | PLearn::Object | |
| changeOptions(const map< string, string > &name_value) | PLearn::Object | [virtual] |
| classname() const | PLearn::GaussianProcessRegressor | [virtual] |
| classname() const | PLearn::GaussianProcessRegressor | [virtual] |
| computeConfidenceFromOutput(const Vec &input, const Vec &output, real probability, TVec< pair< real, real > > &intervals) const | PLearn::GaussianProcessRegressor | [virtual] |
| computeCostsFromOutputs(const Vec &input, const Vec &output, const Vec &target, Vec &costs) const | PLearn::GaussianProcessRegressor | [virtual] |
| computeCostsFromOutputs(const Vec &input, const Vec &output, const Vec &target, Vec &costs) const | PLearn::GaussianProcessRegressor | [virtual] |
| computeCostsOnly(const Vec &input, const Vec &target, Vec &costs) const | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::computeInputOutputConfMat(VMat inputs, real probability) const | PLearn::PLearner | |
| PLearn::PConditionalDistribution::computeInputOutputConfMat(VMat inputs, real probability) const | PLearn::PLearner | |
| PLearn::computeInputOutputMat(VMat inputs) const | PLearn::PLearner | |
| PLearn::PConditionalDistribution::computeInputOutputMat(VMat inputs) const | PLearn::PLearner | |
| computeOutput(const Vec &input, Vec &output) const | PLearn::GaussianProcessRegressor | [virtual] |
| computeOutput(const Vec &input, Vec &output) const | PLearn::GaussianProcessRegressor | [virtual] |
| computeOutputAndCosts(const Vec &input, const Vec &target, Vec &output, Vec &costs) const | PLearn::GaussianProcessRegressor | [virtual] |
| computeOutputAux(const Vec &input, Vec &output, Vec &kernel_evaluations) const | PLearn::GaussianProcessRegressor | [protected] |
| PLearn::computeOutputConfMat(VMat inputs, real probability) const | PLearn::PLearner | |
| PLearn::PConditionalDistribution::computeOutputConfMat(VMat inputs, real probability) const | PLearn::PLearner | |
| computeOutputCovMat(const Mat &inputs, Mat &outputs, TVec< Mat > &covariance_matrices) const | PLearn::GaussianProcessRegressor | [virtual] |
| computeOutputs(const Mat &input, Mat &output) const | PLearn::PLearner | [virtual] |
| computeOutputsAndCosts(const Mat &input, const Mat &target, Mat &output, Mat &costs) const | PLearn::PLearner | [virtual] |
| PLearn::declareMethods(RemoteMethodMap &rmm) | PLearn::PLearner | [protected, static] |
| PLearn::PConditionalDistribution::declareMethods(RemoteMethodMap &rmm) | PLearn::PDistribution | [protected, static] |
| declareOptions(OptionList &ol) | PLearn::GaussianProcessRegressor | [protected, static] |
| declareOptions(OptionList &ol) | PLearn::GaussianProcessRegressor | [protected, static] |
| declaringFile() | PLearn::GaussianProcessRegressor | [inline, static] |
| declaringFile() | PLearn::GaussianProcessRegressor | [inline, static] |
| deepCopy(CopiesMap &copies) const | PLearn::GaussianProcessRegressor | [virtual] |
| deepCopy(CopiesMap &copies) const | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::deepCopyNoMap() | PLearn::Object | |
| PLearn::PConditionalDistribution::deepCopyNoMap() | PLearn::Object | |
| delta_curve | PLearn::PDistribution | [protected] |
| density(const Vec &y) const | PLearn::PDistribution | [virtual] |
| eigenvalues | PLearn::GaussianProcessRegressor | |
| eigenvectors | PLearn::GaussianProcessRegressor | |
| PLearn::expdir | PLearn::PLearner | |
| PLearn::PConditionalDistribution::expdir | PLearn::PLearner | |
| expectation() const | PLearn::GaussianProcessRegressor | [virtual] |
| expectation(Vec expected_y) const | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::PConditionalDistribution::expectation(Vec &mu) const | PLearn::PDistribution | [virtual] |
| finalize() | PLearn::PLearner | [virtual] |
| PLearn::finalized | PLearn::PLearner | |
| PLearn::PConditionalDistribution::finalized | PLearn::PLearner | |
| forget() | PLearn::GaussianProcessRegressor | [virtual] |
| forget() | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::forget_when_training_set_changes | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::forget_when_training_set_changes | PLearn::PLearner | [protected] |
| GaussianProcessRegressor() | PLearn::GaussianProcessRegressor | |
| GaussianProcessRegressor() | PLearn::GaussianProcessRegressor | |
| generate(Vec &y) const | PLearn::PDistribution | [virtual] |
| generateJoint(Vec &xy) | PLearn::PDistribution | [virtual] |
| generateJoint(Vec &x, Vec &y) | PLearn::PDistribution | |
| generateN(const Mat &Y) const | PLearn::PDistribution | [virtual] |
| generatePredicted(Vec &y) | PLearn::PDistribution | [virtual] |
| generatePredictor(Vec &x) | PLearn::PDistribution | [virtual] |
| generatePredictorGivenPredicted(Vec &x, const Vec &y) | PLearn::PDistribution | [virtual] |
| PLearn::getExperimentDirectory() const | PLearn::PLearner | [inline] |
| PLearn::PConditionalDistribution::getExperimentDirectory() const | PLearn::PLearner | [inline] |
| getNPredicted() const | PLearn::PDistribution | [inline] |
| getNPredictor() const | PLearn::PDistribution | [inline] |
| PLearn::getOption(const string &optionname) const | PLearn::Object | |
| PLearn::PConditionalDistribution::getOption(const string &optionname) const | PLearn::Object | |
| getOptionList() const | PLearn::GaussianProcessRegressor | [virtual] |
| getOptionList() const | PLearn::GaussianProcessRegressor | [virtual] |
| getOptionMap() const | PLearn::GaussianProcessRegressor | [virtual] |
| getOptionMap() const | PLearn::GaussianProcessRegressor | [virtual] |
| getOptionsToRemoteTransmit() const | PLearn::Object | [virtual] |
| getOptionsToSave() const | PLearn::Object | [virtual] |
| getOutputNames() const | PLearn::PLearner | [virtual] |
| getRemoteMethodMap() const | PLearn::GaussianProcessRegressor | [virtual] |
| getRemoteMethodMap() const | PLearn::GaussianProcessRegressor | [virtual] |
| getTestCostIndex(const string &costname) const | PLearn::GaussianProcessRegressor | |
| getTestCostNames() const | PLearn::GaussianProcessRegressor | [virtual] |
| getTestCostNames() const | PLearn::GaussianProcessRegressor | [virtual] |
| getTrainCostIndex(const string &costname) const | PLearn::GaussianProcessRegressor | |
| getTrainCostNames() const | PLearn::GaussianProcessRegressor | [virtual] |
| getTrainCostNames() const | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::getTrainingSet() const | PLearn::PLearner | [inline] |
| PLearn::PConditionalDistribution::getTrainingSet() const | PLearn::PLearner | [inline] |
| PLearn::getTrainStatsCollector() | PLearn::PLearner | [inline] |
| PLearn::PConditionalDistribution::getTrainStatsCollector() | PLearn::PLearner | [inline] |
| PLearn::getValidationSet() const | PLearn::PLearner | [inline] |
| PLearn::PConditionalDistribution::getValidationSet() const | PLearn::PLearner | [inline] |
| Gram_matrix_normalization | PLearn::GaussianProcessRegressor | |
| PLearn::hasOption(const string &optionname) const | PLearn::Object | |
| PLearn::PConditionalDistribution::hasOption(const string &optionname) const | PLearn::Object | |
| hyperOptimize(const Mat &inputs, const Mat &targets, VarArray &hyperparam_vars) | PLearn::GaussianProcessRegressor | [protected] |
| info() const | PLearn::Object | [virtual] |
| inherited typedef | PLearn::GaussianProcessRegressor | |
| inherited typedef | PLearn::GaussianProcessRegressor | [private] |
| PLearn::initTrain() | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::initTrain() | PLearn::PLearner | [protected] |
| input_part_size | PLearn::PConditionalDistribution | |
| inputsize() const | PLearn::PLearner | [virtual] |
| PLearn::inputsize_ | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::inputsize_ | PLearn::PLearner | [protected] |
| inverseCovTimesVec(real sigma, Vec v, Vec Cinv_v) const | PLearn::GaussianProcessRegressor | [protected] |
| isStatefulLearner() const | PLearn::PLearner | [virtual] |
| K | PLearn::GaussianProcessRegressor | |
| kernel | PLearn::GaussianProcessRegressor | |
| Kxx | PLearn::GaussianProcessRegressor | [mutable] |
| Kxxi | PLearn::GaussianProcessRegressor | [mutable] |
| load(const PPath &filename) | PLearn::Object | [virtual] |
| log_density(const Vec &x) const | PLearn::GaussianProcessRegressor | [virtual] |
| lower_bound | PLearn::PDistribution | |
| m_active_set_indices | PLearn::GaussianProcessRegressor | |
| m_algorithm_enum | PLearn::GaussianProcessRegressor | [protected] |
| m_alpha | PLearn::GaussianProcessRegressor | [protected] |
| m_ARD_hyperprefix_initval | PLearn::GaussianProcessRegressor | |
| m_compute_confidence | PLearn::GaussianProcessRegressor | |
| m_confidence_epsilon | PLearn::GaussianProcessRegressor | |
| m_gram_inv_traintest_product | PLearn::GaussianProcessRegressor | [mutable, protected] |
| m_gram_inverse | PLearn::GaussianProcessRegressor | [protected] |
| m_gram_inverse_product | PLearn::GaussianProcessRegressor | [mutable, protected] |
| m_gram_traintest_inputs | PLearn::GaussianProcessRegressor | [mutable, protected] |
| m_hyperparameters | PLearn::GaussianProcessRegressor | |
| m_include_bias | PLearn::GaussianProcessRegressor | |
| m_intervals | PLearn::GaussianProcessRegressor | [mutable, protected] |
| m_kernel | PLearn::GaussianProcessRegressor | |
| m_kernel_evaluations | PLearn::GaussianProcessRegressor | [mutable, protected] |
| m_optimizer | PLearn::GaussianProcessRegressor | |
| m_save_gram_matrix | PLearn::GaussianProcessRegressor | |
| m_sigma_reductor | PLearn::GaussianProcessRegressor | [mutable, protected] |
| m_solution_algorithm | PLearn::GaussianProcessRegressor | |
| m_subgram_inverse | PLearn::GaussianProcessRegressor | [protected] |
| m_target_mean | PLearn::GaussianProcessRegressor | [protected] |
| m_training_inputs | PLearn::GaussianProcessRegressor | [protected] |
| m_weight_decay | PLearn::GaussianProcessRegressor | |
| makeDeepCopyFromShallowCopy(CopiesMap &copies) | PLearn::GaussianProcessRegressor | [virtual] |
| makeDeepCopyFromShallowCopy(CopiesMap &copies) | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::master_sends_testset_rows | PLearn::PLearner | |
| PLearn::PConditionalDistribution::master_sends_testset_rows | PLearn::PLearner | |
| max_nb_evectors | PLearn::GaussianProcessRegressor | |
| mean_allK | PLearn::GaussianProcessRegressor | |
| meanK | PLearn::GaussianProcessRegressor | |
| missingExpectation(const Vec &input, Vec &mu) | PLearn::PDistribution | [virtual] |
| n_curve_points | PLearn::PDistribution | |
| PLearn::n_examples | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::n_examples | PLearn::PLearner | [protected] |
| n_outputs | PLearn::GaussianProcessRegressor | |
| n_predicted | PLearn::PDistribution | [mutable, protected] |
| n_predictor | PLearn::PDistribution | [mutable, protected] |
| PLearn::newread(PStream &in, unsigned int id=UINT_MAX) | PLearn::Object | |
| PLearn::PConditionalDistribution::newread(PStream &in, unsigned int id=UINT_MAX) | PLearn::Object | |
| newwrite(PStream &out) const | PLearn::Object | [virtual] |
| noise_sd | PLearn::GaussianProcessRegressor | |
| PLearn::nservers | PLearn::PLearner | |
| PLearn::PConditionalDistribution::nservers | PLearn::PLearner | |
| PLearn::nstages | PLearn::PLearner | |
| PLearn::PConditionalDistribution::nstages | PLearn::PLearner | |
| nTestCosts() const | PLearn::GaussianProcessRegressor | [inline, virtual] |
| nTrainCosts() const | PLearn::GaussianProcessRegressor | [inline, virtual] |
| PLearn::Object(bool call_build_=false) | PLearn::Object | |
| PLearn::PConditionalDistribution::Object(bool call_build_=false) | PLearn::Object | |
| oldread(istream &in) | PLearn::Object | [virtual] |
| outputs_def | PLearn::PDistribution | |
| outputsize() const | PLearn::GaussianProcessRegressor | [virtual] |
| outputsize() const | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::parallelize_here | PLearn::PLearner | |
| PLearn::PConditionalDistribution::parallelize_here | PLearn::PLearner | |
| PLearn::parseOptionName(const string &optionname, Object *&final_object, OptionList::iterator &option_iter, string &option_index) | PLearn::Object | |
| PLearn::parseOptionName(const string &optionname, const Object *&final_object, OptionList::iterator &option_iter, string &option_index) const | PLearn::Object | |
| PLearn::PConditionalDistribution::parseOptionName(const string &optionname, Object *&final_object, OptionList::iterator &option_iter, string &option_index) | PLearn::Object | |
| PLearn::PConditionalDistribution::parseOptionName(const string &optionname, const Object *&final_object, OptionList::iterator &option_iter, string &option_index) const | PLearn::Object | |
| PConditionalDistribution() | PLearn::PConditionalDistribution | |
| PDistribution() | PLearn::PDistribution | |
| PLearn::PLearner() | PLearn::PLearner | |
| PLearn::PConditionalDistribution::PLearner() | PLearn::PLearner | |
| PLearn::PPointable() | PLearn::PPointable | [inline] |
| PLearn::PPointable(const PPointable &other) | PLearn::PPointable | [inline] |
| PLearn::PConditionalDistribution::PPointable() | PLearn::PPointable | [inline] |
| PLearn::PConditionalDistribution::PPointable(const PPointable &other) | PLearn::PPointable | [inline] |
| predicted_part | PLearn::PDistribution | [mutable, protected] |
| predicted_size | PLearn::PDistribution | [protected] |
| predictor_part | PLearn::PDistribution | [mutable, protected] |
| predictor_size | PLearn::PDistribution | [protected] |
| PLearn::prepareToSendResults(PStream &out, int nres) | PLearn::Object | [static] |
| PLearn::PConditionalDistribution::prepareToSendResults(PStream &out, int nres) | PLearn::Object | [static] |
| processDataSet(VMat dataset) const | PLearn::PLearner | [virtual] |
| QFormInverse(real sigma2, Vec u) const | PLearn::GaussianProcessRegressor | [protected] |
| PLearn::random_gen | PLearn::PLearner | [mutable, protected] |
| PLearn::PConditionalDistribution::random_gen | PLearn::PLearner | [mutable, protected] |
| read(istream &in) | PLearn::Object | [virtual] |
| PLearn::readOptionVal(PStream &in, const string &optionname, unsigned int id=UINT_MAX) | PLearn::Object | |
| PLearn::PConditionalDistribution::readOptionVal(PStream &in, const string &optionname, unsigned int id=UINT_MAX) | PLearn::Object | |
| PLearn::ref() const | PLearn::PPointable | [inline] |
| PLearn::PConditionalDistribution::ref() const | PLearn::PPointable | [inline] |
| remote_generate() | PLearn::PDistribution | |
| remote_test(VMat testset, PP< VecStatsCollector > test_stats, bool rtestoutputs, bool rtestcosts) const | PLearn::PLearner | [virtual] |
| remote_useOnTrain() const | PLearn::PLearner | [virtual] |
| PLearn::report_progress | PLearn::PLearner | |
| PLearn::PConditionalDistribution::report_progress | PLearn::PLearner | |
| resetGenerator(long g_seed) | PLearn::PDistribution | [virtual] |
| resetInternalState() | PLearn::PLearner | [virtual] |
| run() | PLearn::Object | [virtual] |
| save(const PPath &filename) const | PLearn::Object | [virtual] |
| PLearn::save_trainingset_prefix | PLearn::PLearner | |
| PLearn::PConditionalDistribution::save_trainingset_prefix | PLearn::PLearner | |
| PLearn::seed_ | PLearn::PLearner | |
| PLearn::PConditionalDistribution::seed_ | PLearn::PLearner | |
| setExperimentDirectory(const PPath &the_expdir) | PLearn::PLearner | [virtual] |
| setInput(const Vec &input) const | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::setOption(const string &optionname, const string &value) | PLearn::Object | |
| PLearn::PConditionalDistribution::setOption(const string &optionname, const string &value) | PLearn::Object | |
| setPredictor(const Vec &predictor, bool call_parent=true) const | PLearn::PDistribution | [virtual] |
| setPredictorPredictedSizes(int the_predictor_size, int the_predicted_size, bool call_parent=true) | PLearn::PDistribution | [virtual] |
| setTrainingSet(VMat training_set, bool call_forget=true) | PLearn::GaussianProcessRegressor | [virtual] |
| setTrainStatsCollector(PP< VecStatsCollector > statscol) | PLearn::PLearner | [virtual] |
| setValidationSet(VMat validset) | PLearn::PLearner | [virtual] |
| splitCond(const Vec &input) const | PLearn::PDistribution | [protected] |
| PLearn::stage | PLearn::PLearner | |
| PLearn::PConditionalDistribution::stage | PLearn::PLearner | |
| store_cov | PLearn::PDistribution | [mutable, protected] |
| store_expect | PLearn::PDistribution | [mutable, protected] |
| store_result | PLearn::PDistribution | [mutable, protected] |
| sub_test(VMat testset, PP< VecStatsCollector > test_stats, bool rtestoutputs, bool rtestcosts) const | PLearn::PLearner | [virtual] |
| survival_fn(const Vec &y) const | PLearn::PDistribution | [virtual] |
| targetsize() const | PLearn::PLearner | [virtual] |
| PLearn::targetsize_ | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::targetsize_ | PLearn::PLearner | [protected] |
| test(VMat testset, PP< VecStatsCollector > test_stats, VMat testoutputs=0, VMat testcosts=0) const | PLearn::PLearner | [virtual] |
| PLearn::test_minibatch_size | PLearn::PLearner | |
| PLearn::PConditionalDistribution::test_minibatch_size | PLearn::PLearner | |
| train() | PLearn::GaussianProcessRegressor | [virtual] |
| train() | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::train_set | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::train_set | PLearn::PLearner | [protected] |
| PLearn::train_stats | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::train_stats | PLearn::PLearner | [protected] |
| trainProjectedProcess(const Mat &all_training_inputs, const Mat &sub_training_inputs, const Mat &all_training_targets) | PLearn::GaussianProcessRegressor | [protected] |
| unknownOutput(char def, const Vec &input, Vec &output, int &k) const | PLearn::PDistribution | [protected, virtual] |
| PLearn::unref() const | PLearn::PPointable | [inline] |
| PLearn::PConditionalDistribution::unref() const | PLearn::PPointable | [inline] |
| upper_bound | PLearn::PDistribution | |
| PLearn::usage() const | PLearn::PPointable | [inline] |
| PLearn::PConditionalDistribution::usage() const | PLearn::PPointable | [inline] |
| use(VMat testset, VMat outputs) const | PLearn::PLearner | [virtual] |
| PLearn::use_a_separate_random_generator_for_testing | PLearn::PLearner | |
| PLearn::PConditionalDistribution::use_a_separate_random_generator_for_testing | PLearn::PLearner | |
| useOnTrain(Mat &outputs) const | PLearn::PLearner | [virtual] |
| PLearn::validation_set | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::validation_set | PLearn::PLearner | [protected] |
| variance() const | PLearn::GaussianProcessRegressor | [virtual] |
| variance(Vec diag_variances) const | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::PConditionalDistribution::variance(Mat &cov) const | PLearn::PDistribution | [virtual] |
| PLearn::verbosity | PLearn::PLearner | |
| PLearn::PConditionalDistribution::verbosity | PLearn::PLearner | |
| weightsize() const | PLearn::PLearner | [virtual] |
| PLearn::weightsize_ | PLearn::PLearner | [protected] |
| PLearn::PConditionalDistribution::weightsize_ | PLearn::PLearner | [protected] |
| write(ostream &out) const | PLearn::Object | [virtual] |
| PLearn::writeOptionVal(PStream &out, const string &optionname) const | PLearn::Object | |
| PLearn::PConditionalDistribution::writeOptionVal(PStream &out, const string &optionname) const | PLearn::Object | |
| ~GaussianProcessRegressor() | PLearn::GaussianProcessRegressor | [virtual] |
| ~Object() | PLearn::Object | [virtual] |
| ~PPointable() | PLearn::PPointable | [inline, virtual] |