, 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] |