| _classname_() | PLearn::Object | [static] |
| _getOptionList_() | PLearn::Object | [static] |
| _isa_(Object *o) | PLearn::Object | [static] |
| _new_instance_for_typemap_() | PLearn::Object | [static] |
| _static_initialize_() | PLearn::Object | [static] |
| _static_initializer_ | PLearn::Object | [static] |
| alpha | PLearn::GaussianProcessRegressor | |
| already_sorted | PLearn::PDistribution | [protected] |
| BayesianCost() | PLearn::GaussianProcessRegressor | [protected] |
| build() | PLearn::GaussianProcessRegressor | [virtual] |
| build_() | PLearn::GaussianProcessRegressor | [private] |
| build_from_train_set() | PLearn::PLearner | [inline, protected, virtual] |
| call(const string &methodname, int nargs, PStream &in_parameters, PStream &out_results) | PLearn::Object | [virtual] |
| cdf(const Vec &y) const | PLearn::PDistribution | [virtual] |
| changeOption(const string &optionname, const string &value) | PLearn::Object | |
| changeOptions(const map< string, string > &name_value) | PLearn::Object | [virtual] |
| classname() const | PLearn::Object | [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] |
| 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] |
| cond_sort | PLearn::PDistribution | [protected] |
| cond_swap | PLearn::PDistribution | [protected] |
| conditional_flags | PLearn::PDistribution | |
| declareOptions(OptionList &ol) | PLearn::GaussianProcessRegressor | [protected, static] |
| deepCopy(CopiesMap &copies) const | PLearn::Object | [virtual] |
| delta_curve | PLearn::PDistribution | [protected] |
| density(const Vec &y) const | PLearn::PDistribution | [virtual] |
| eigenvalues | PLearn::GaussianProcessRegressor | |
| eigenvectors | PLearn::GaussianProcessRegressor | |
| ensureFullJointDistribution(TVec< int > &old_flags) | PLearn::PDistribution | [protected] |
| 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] |
| finishConditionalBuild() | PLearn::PDistribution | [protected] |
| forget() | PLearn::GaussianProcessRegressor | [virtual] |
| full_joint_distribution | PLearn::PDistribution | [protected] |
| GaussianProcessRegressor() | PLearn::GaussianProcessRegressor | |
| generate(Vec &y) const | PLearn::PDistribution | [virtual] |
| generateN(const Mat &Y) const | PLearn::PDistribution | |
| getExperimentDirectory() const | PLearn::PLearner | [inline] |
| getOption(const string &optionname) const | PLearn::Object | |
| getOptionList() const | PLearn::Object | [virtual] |
| getOptionsToSave() const | PLearn::Object | [virtual] |
| getTestCostIndex(const string &costname) const | PLearn::GaussianProcessRegressor | |
| getTestCostNames() const | PLearn::GaussianProcessRegressor | [virtual] |
| getTrainCostIndex(const string &costname) const | PLearn::GaussianProcessRegressor | |
| getTrainCostNames() const | PLearn::GaussianProcessRegressor | [virtual] |
| getTrainingSet() const | PLearn::PLearner | [inline] |
| getTrainStatsCollector() | PLearn::PLearner | [inline] |
| getValidationSet() const | PLearn::PLearner | [inline] |
| Gram_matrix_normalization | PLearn::GaussianProcessRegressor | |
| info() const | PLearn::Object | [virtual] |
| inherited typedef | PLearn::GaussianProcessRegressor | |
| input_part | PLearn::PDistribution | [mutable, protected] |
| input_part_size | PLearn::PConditionalDistribution | |
| inputsize() const | PLearn::PLearner | [virtual] |
| 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 string &filename) | PLearn::Object | [virtual] |
| log_density(const Vec &x) const | PLearn::GaussianProcessRegressor | [virtual] |
| lower_bound | PLearn::PDistribution | |
| makeDeepCopyFromShallowCopy(map< const void *, void * > &copies) | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::PLearner::makeDeepCopyFromShallowCopy(CopiesMap &copies) | PLearn::PLearner | [protected, virtual] |
| matlabSave(const string &matlab_subdir) | PLearn::PLearner | [inline, virtual] |
| max_nb_evectors | PLearn::GaussianProcessRegressor | |
| mean_allK | PLearn::GaussianProcessRegressor | |
| meanK | PLearn::GaussianProcessRegressor | |
| n_curve_points | PLearn::PDistribution | |
| n_examples | PLearn::PLearner | [protected] |
| n_input | PLearn::PDistribution | [protected] |
| n_margin | PLearn::PDistribution | [protected] |
| n_outputs | PLearn::GaussianProcessRegressor | |
| n_target | PLearn::PDistribution | [protected] |
| need_set_input | PLearn::PDistribution | [mutable, protected] |
| newread(PStream &in) | PLearn::Object | |
| newwrite(PStream &out) const | PLearn::Object | |
| noise_sd | PLearn::GaussianProcessRegressor | |
| nstages | PLearn::PLearner | |
| nTestCosts() const | PLearn::GaussianProcessRegressor | [inline, virtual] |
| nTrainCosts() const | PLearn::GaussianProcessRegressor | [inline, virtual] |
| Object() | PLearn::Object | |
| oldread(istream &in) | PLearn::Object | [virtual] |
| outputs_def | PLearn::PDistribution | |
| outputsize() const | PLearn::GaussianProcessRegressor | [virtual] |
| PConditionalDistribution() | PLearn::PConditionalDistribution | |
| PDistribution() | PLearn::PDistribution | |
| PLEARN_DECLARE_ABSTRACT_OBJECT(PLearner) | PLearn::PLearner | |
| PLEARN_DECLARE_OBJECT(GaussianProcessRegressor) | PLearn::GaussianProcessRegressor | |
| PLearn::PConditionalDistribution::PLEARN_DECLARE_OBJECT(PConditionalDistribution) | PLearn::PConditionalDistribution | |
| PLearn::PDistribution::PLEARN_DECLARE_OBJECT(PDistribution) | PLearn::PDistribution | |
| PLearner() | PLearn::PLearner | |
| PPointable() | PLearn::PPointable | [inline] |
| PPointable(const PPointable &other) | PLearn::PPointable | [inline] |
| prepareToSendResults(PStream &out, int nres) | PLearn::Object | [inline, protected] |
| print(ostream &out) const | PLearn::Object | [virtual] |
| provide_input | PLearn::PDistribution | |
| QFormInverse(real sigma2, Vec u) const | PLearn::GaussianProcessRegressor | [protected] |
| read(istream &in) | PLearn::Object | [virtual] |
| readOptionVal(PStream &in, const string &optionname) | PLearn::Object | |
| ref() const | PLearn::PPointable | [inline] |
| report_progress | PLearn::PLearner | |
| resetGenerator(long g_seed) const | PLearn::PDistribution | [virtual] |
| resetInternalState() | PLearn::PLearner | [virtual] |
| resizeParts() | PLearn::PDistribution | [protected] |
| run() | PLearn::Object | [virtual] |
| save(const string &filename) const | PLearn::Object | [virtual] |
| seed_ | PLearn::PLearner | |
| setConditionalFlags(TVec< int > &flags) | PLearn::PDistribution | |
| setExperimentDirectory(const string &the_expdir) | PLearn::PLearner | [virtual] |
| setInput(const Vec &input) | PLearn::GaussianProcessRegressor | [virtual] |
| PLearn::PConditionalDistribution::setInput(const Vec &input) const | PLearn::PConditionalDistribution | [virtual] |
| setInput_const(const Vec &input) const | PLearn::GaussianProcessRegressor | [virtual] |
| setOption(const string &optionname, const string &value) | PLearn::Object | |
| setTrainingSet(VMat training_set, bool call_forget=true) | PLearn::PDistribution | [virtual] |
| setTrainStatsCollector(PP< VecStatsCollector > statscol) | PLearn::PLearner | [virtual] |
| setValidationSet(VMat validset) | PLearn::PLearner | [virtual] |
| sortFromFlags(Vec &v) | PLearn::PDistribution | [protected] |
| sortFromFlags(Mat &m, bool sort_columns=true, bool sort_rows=false) | PLearn::PDistribution | [protected] |
| splitCond(const Vec &input) const | PLearn::PDistribution | [protected] |
| stage | PLearn::PLearner | |
| survival_fn(const Vec &y) const | PLearn::PDistribution | [virtual] |
| target_part | PLearn::PDistribution | [mutable, protected] |
| targetsize() const | PLearn::PLearner | [virtual] |
| targetsize_ | PLearn::PLearner | [protected] |
| test(VMat testset, PP< VecStatsCollector > test_stats, VMat testoutputs=0, VMat testcosts=0) const | PLearn::PLearner | [virtual] |
| train() | PLearn::GaussianProcessRegressor | [virtual] |
| train_set | PLearn::PLearner | [protected] |
| train_stats | PLearn::PLearner | [protected] |
| unref() const | PLearn::PPointable | [inline] |
| updateFromConditionalSorting() | PLearn::PDistribution | [protected, virtual] |
| upper_bound | PLearn::PDistribution | |
| usage() const | PLearn::PPointable | [inline] |
| use(VMat testset, VMat outputs) const | PLearn::PLearner | [virtual] |
| useOnTrain(Mat &outputs) const | PLearn::PLearner | [virtual] |
| 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] |
| verbosity | PLearn::PLearner | |
| weightsize_ | PLearn::PLearner | [protected] |
| write(ostream &out) const | PLearn::Object | [virtual] |
| writeOptionVal(PStream &out, const string &optionname) const | PLearn::Object | |
| ~GaussianProcessRegressor() | PLearn::GaussianProcessRegressor | [virtual] |
| ~Object() | PLearn::Object | [virtual] |
| ~PLearner() | PLearn::PLearner | [virtual] |
| ~PPointable() | PLearn::PPointable | [inline, virtual] |
