, including all inherited members.
_classname_() | PLearn::ParzenWindow | [static] |
_getOptionList_() | PLearn::ParzenWindow | [static] |
_getRemoteMethodMap_() | PLearn::ParzenWindow | [static] |
_isa_(const Object *o) | PLearn::ParzenWindow | [static] |
_new_instance_for_typemap_() | PLearn::ParzenWindow | [static] |
_static_initialize_() | PLearn::ParzenWindow | [static] |
_static_initializer_ | PLearn::ParzenWindow | [static] |
addToCovariance(const Vec &y, int j, const Mat &cov, real post) | PLearn::GaussMix | [protected] |
alpha | PLearn::GaussMix | |
alpha_min | PLearn::GaussMix | |
asString() const | PLearn::Object | [virtual] |
asStringRemoteTransmit() const | PLearn::Object | [virtual] |
b_costs | PLearn::PLearner | [mutable, protected] |
b_inputs | PLearn::PLearner | [mutable, protected] |
b_outputs | PLearn::PLearner | [mutable, protected] |
b_targets | PLearn::PLearner | [mutable, protected] |
b_weights | PLearn::PLearner | [mutable, protected] |
batchComputeOutputAndConfidence(VMat inputs, real probability, VMat outputs_and_confidence) const | PLearn::PLearner | [virtual] |
build() | PLearn::ParzenWindow | [virtual] |
build_() | PLearn::ParzenWindow | [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::GaussMix | [virtual] |
center | PLearn::GaussMix | |
center_y_x | PLearn::GaussMix | [mutable, protected] |
changeOption(const string &optionname, const string &value) | PLearn::Object | |
changeOptions(const map< string, string > &name_value) | PLearn::GaussMix | [virtual] |
chol_joint_cov | PLearn::GaussMix | [protected] |
cholesky_queue | PLearn::GaussMix | [mutable, protected] |
classname() const | PLearn::ParzenWindow | [virtual] |
clust_imputed_missing | PLearn::GaussMix | [protected] |
clusters_samp | PLearn::GaussMix | [protected] |
computeAllLogLikelihoods(const Vec &sample, const Vec &log_like) | PLearn::GaussMix | [protected] |
computeConfidenceFromOutput(const Vec &input, const Vec &output, real probability, TVec< pair< real, real > > &intervals) const | PLearn::PLearner | [virtual] |
computeCostsFromOutputs(const Vec &input, const Vec &output, const Vec &target, Vec &costs) const | PLearn::PDistribution | [virtual] |
computeCostsOnly(const Vec &input, const Vec &target, Vec &costs) const | PLearn::PLearner | [virtual] |
computeInputOutputConfMat(VMat inputs, real probability) const | PLearn::PLearner | |
computeInputOutputMat(VMat inputs) const | PLearn::PLearner | |
computeLogLikelihood(const Vec &y, int j, bool is_predictor=false) const | PLearn::GaussMix | [protected] |
computeMeansAndCovariances() | PLearn::GaussMix | [protected, virtual] |
computeMixtureWeights(bool allow_replace=true) | PLearn::GaussMix | [protected] |
computeOutput(const Vec &input, Vec &output) const | PLearn::PDistribution | [virtual] |
computeOutputAndCosts(const Vec &input, const Vec &target, Vec &output, Vec &costs) const | PLearn::PLearner | [virtual] |
computeOutputConfMat(VMat inputs, real probability) const | PLearn::PLearner | |
computeOutputCovMat(const Mat &inputs, Mat &outputs, TVec< Mat > &covariance_matrices) const | PLearn::PLearner | [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] |
computePosteriors() | PLearn::GaussMix | [protected, virtual] |
cond_var_inv_queue | PLearn::GaussMix | [mutable, protected] |
covariance | PLearn::GaussMix | [protected] |
current_cluster | PLearn::GaussMix | [protected] |
current_training_sample | PLearn::GaussMix | [protected] |
D | PLearn::GaussMix | [protected] |
declareMethods(RemoteMethodMap &rmm) | PLearn::PDistribution | [protected, static] |
declareOptions(OptionList &ol) | PLearn::ParzenWindow | [protected, static] |
declaringFile() | PLearn::ParzenWindow | [inline, static] |
deepCopy(CopiesMap &copies) const | PLearn::ParzenWindow | [virtual] |
deepCopyNoMap() | PLearn::Object | |
delta_curve | PLearn::PDistribution | [protected] |
density(const Vec &y) const | PLearn::PDistribution | [virtual] |
diags | PLearn::GaussMix | [protected] |
efficient_k_median | PLearn::GaussMix | |
efficient_k_median_iter | PLearn::GaussMix | |
efficient_missing | PLearn::GaussMix | |
eigenvalues | PLearn::GaussMix | [protected] |
eigenvalues_x | PLearn::GaussMix | [mutable, protected] |
eigenvalues_y_x | PLearn::GaussMix | [mutable, protected] |
eigenvectors | PLearn::GaussMix | [protected] |
eigenvectors_x | PLearn::GaussMix | [protected] |
eigenvectors_y_x | PLearn::GaussMix | [protected] |
epsilon | PLearn::GaussMix | |
error_covariance | PLearn::GaussMix | [protected] |
expdir | PLearn::PLearner | |
expectation(Vec &mu) const | PLearn::GaussMix | [virtual] |
f_eigen | PLearn::GaussMix | |
finalize() | PLearn::PLearner | [virtual] |
finalized | PLearn::PLearner | |
forget() | PLearn::GaussMix | [virtual] |
forget_when_training_set_changes | PLearn::PLearner | [protected] |
GaussMix() | PLearn::GaussMix | |
generate(Vec &s) const | PLearn::GaussMix | [virtual] |
generateFromGaussian(Vec &s, int given_gaussian) const | PLearn::GaussMix | [protected] |
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] |
getExperimentDirectory() const | PLearn::PLearner | [inline] |
getInitialWeightsFrom(const VMat &vmat) | PLearn::GaussMix | [protected] |
getNPredicted() const | PLearn::PDistribution | [inline] |
getNPredictor() const | PLearn::PDistribution | [inline] |
getOption(const string &optionname) const | PLearn::Object | |
getOptionList() const | PLearn::ParzenWindow | [virtual] |
getOptionMap() const | PLearn::ParzenWindow | [virtual] |
getOptionsToRemoteTransmit() const | PLearn::Object | [virtual] |
getOptionsToSave() const | PLearn::Object | [virtual] |
getOutputNames() const | PLearn::PLearner | [virtual] |
getRemoteMethodMap() const | PLearn::ParzenWindow | [virtual] |
getTestCostIndex(const string &costname) const | PLearn::PLearner | |
getTestCostNames() const | PLearn::PDistribution | [virtual] |
getTrainCostIndex(const string &costname) const | PLearn::PLearner | |
getTrainCostNames() const | PLearn::GaussMix | [virtual] |
getTrainingSet() const | PLearn::PLearner | [inline] |
getTrainStatsCollector() | PLearn::PLearner | [inline] |
getValidationSet() const | PLearn::PLearner | [inline] |
H3_inverse | PLearn::GaussMix | [protected] |
hasOption(const string &optionname) const | PLearn::Object | |
impute_missing | PLearn::GaussMix | |
imputed_missing | PLearn::GaussMix | [protected] |
indices_inv_queue | PLearn::GaussMix | [mutable, protected] |
indices_queue | PLearn::GaussMix | [mutable, protected] |
info() const | PLearn::Object | [virtual] |
inherited typedef | PLearn::ParzenWindow | [private] |
initial_weights | PLearn::GaussMix | [protected] |
initTrain() | PLearn::PLearner | [protected] |
inputsize() const | PLearn::PLearner | [virtual] |
inputsize_ | PLearn::PLearner | [protected] |
isotropic_sigma | PLearn::ParzenWindow | |
isStatefulLearner() const | PLearn::PLearner | [virtual] |
joint_cov | PLearn::GaussMix | [mutable, protected] |
joint_inv_cov | PLearn::GaussMix | [mutable, protected] |
kmeans(const VMat &samples, int nclust, TVec< int > &clust_idx, Mat &clust, int maxit=9999) | PLearn::GaussMix | [protected] |
kmeans_iterations | PLearn::GaussMix | |
L | PLearn::GaussMix | |
load(const PPath &filename) | PLearn::Object | [virtual] |
log_coeff | PLearn::GaussMix | [protected] |
log_coeff_x | PLearn::GaussMix | [protected] |
log_coeff_y_x | PLearn::GaussMix | [protected] |
log_density(const Vec &y) const | PLearn::GaussMix | [virtual] |
log_det_queue | PLearn::GaussMix | [mutable, protected] |
log_likelihood_dens | PLearn::GaussMix | [mutable, protected] |
log_likelihood_post_clust | PLearn::GaussMix | [protected] |
log_p_j_x | PLearn::GaussMix | [mutable, protected] |
lower_bound | PLearn::PDistribution | |
makeDeepCopyFromShallowCopy(CopiesMap &copies) | PLearn::ParzenWindow | [virtual] |
master_sends_testset_rows | PLearn::PLearner | |
max_samples_in_cluster | PLearn::GaussMix | |
mean_training | PLearn::GaussMix | [protected] |
min_samples_in_cluster | PLearn::GaussMix | |
missing_patterns | PLearn::GaussMix | [protected] |
missing_template | PLearn::GaussMix | [protected] |
missingExpectation(const Vec &input, Vec &mu) | PLearn::GaussMix | [virtual] |
n_curve_points | PLearn::PDistribution | |
n_eigen | PLearn::GaussMix | |
n_eigen_computed | PLearn::GaussMix | [protected] |
n_examples | PLearn::PLearner | [protected] |
n_predicted | PLearn::PDistribution | [mutable, protected] |
n_predictor | PLearn::PDistribution | [mutable, protected] |
need_recompute | PLearn::GaussMix | [protected] |
newread(PStream &in, unsigned int id=UINT_MAX) | PLearn::Object | |
newwrite(PStream &out) const | PLearn::Object | [virtual] |
no_missing_change | PLearn::GaussMix | [protected] |
nsamples | PLearn::GaussMix | [protected] |
nservers | PLearn::PLearner | |
nstages | PLearn::PLearner | |
nTestCosts() const | PLearn::PLearner | [virtual] |
nTrainCosts() const | PLearn::PLearner | [virtual] |
Object(bool call_build_=false) | PLearn::Object | |
oldread(istream &in) | PLearn::Object | [virtual] |
original_to_reordered | PLearn::GaussMix | [protected] |
outputs_def | PLearn::PDistribution | |
outputsize() const | PLearn::GaussMix | [virtual] |
p_j_x | PLearn::GaussMix | [mutable, protected] |
parallelize_here | PLearn::PLearner | |
parseOptionName(const string &optionname, Object *&final_object, OptionList::iterator &option_iter, string &option_index) | PLearn::Object | |
parseOptionName(const string &optionname, const Object *&final_object, OptionList::iterator &option_iter, string &option_index) const | PLearn::Object | |
ParzenWindow() | PLearn::ParzenWindow | |
ParzenWindow(real isotropic_sigma) | PLearn::ParzenWindow | |
PDistribution() | PLearn::PDistribution | |
PLearner() | PLearn::PLearner | |
posteriors | PLearn::GaussMix | [protected] |
PPointable() | PLearn::PPointable | [inline] |
PPointable(const PPointable &other) | PLearn::PPointable | [inline] |
precomputeAllGaussianLogCoefficients() | PLearn::GaussMix | [protected] |
precomputeGaussianLogCoefficient(const Vec &eigenvals, int dimension) const | PLearn::GaussMix | [protected] |
predicted_part | PLearn::PDistribution | [mutable, protected] |
predicted_size | PLearn::PDistribution | [protected] |
predictor_part | PLearn::PDistribution | [mutable, protected] |
predictor_size | PLearn::PDistribution | [protected] |
prepareToSendResults(PStream &out, int nres) | PLearn::Object | [static] |
previous_predictor_part_had_missing | PLearn::GaussMix | [mutable, protected] |
previous_training_sample | PLearn::GaussMix | [protected] |
processDataSet(VMat dataset) const | PLearn::PLearner | [virtual] |
ptimer | PLearn::GaussMix | [protected] |
random_gen | PLearn::PLearner | [mutable, protected] |
read(istream &in) | PLearn::Object | [virtual] |
readOptionVal(PStream &in, const string &optionname, unsigned int id=UINT_MAX) | PLearn::Object | |
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] |
replaceGaussian(int j) | PLearn::GaussMix | [protected] |
report_progress | PLearn::PLearner | |
resetGenerator(long g_seed) | PLearn::PDistribution | [virtual] |
resetInternalState() | PLearn::PLearner | [virtual] |
resizeDataBeforeTraining() | PLearn::GaussMix | [protected] |
resizeDataBeforeUsing() | PLearn::GaussMix | [protected] |
run() | PLearn::Object | [virtual] |
sample_to_path_index | PLearn::GaussMix | [protected] |
sample_to_template | PLearn::GaussMix | [protected] |
save(const PPath &filename) const | PLearn::Object | [virtual] |
save_trainingset_prefix | PLearn::PLearner | |
seed_ | PLearn::PLearner | |
setExperimentDirectory(const PPath &the_expdir) | PLearn::PLearner | [virtual] |
setOption(const string &optionname, const string &value) | PLearn::Object | |
setPredictor(const Vec &predictor, bool call_parent=true) const | PLearn::GaussMix | [virtual] |
setPredictorPredictedSizes(int the_predictor_size, int the_predicted_size, bool call_parent=true) | PLearn::GaussMix | [protected, virtual] |
setPredictorPredictedSizes_const() const | PLearn::GaussMix | [protected] |
setTrainingSet(VMat training_set, bool call_forget=true) | PLearn::GaussMix | [virtual] |
setTrainStatsCollector(PP< VecStatsCollector > statscol) | PLearn::PLearner | [virtual] |
setValidationSet(VMat validset) | PLearn::PLearner | [virtual] |
sigma | PLearn::GaussMix | |
sigma_min | PLearn::GaussMix | |
spanning_can_free | PLearn::GaussMix | [protected] |
spanning_path | PLearn::GaussMix | [protected] |
spanning_use_previous | PLearn::GaussMix | [protected] |
splitCond(const Vec &input) const | PLearn::PDistribution | [protected] |
stage | PLearn::PLearner | |
stage_joint_cov_computed | PLearn::GaussMix | [protected] |
stage_replaced | PLearn::GaussMix | [protected] |
stddev_training | PLearn::GaussMix | [protected] |
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] |
sum_of_posteriors | PLearn::GaussMix | [protected] |
survival_fn(const Vec &y) const | PLearn::GaussMix | [virtual] |
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] |
test_minibatch_size | PLearn::PLearner | |
train() | PLearn::ParzenWindow | [virtual] |
train_set | PLearn::PLearner | [protected] |
train_stats | PLearn::PLearner | [protected] |
traverse_tree(TVec< int > &path, TVec< bool > &span_can_free, TVec< bool > &span_use_previous, bool free_previous, bool use_previous, int index_node, int previous_node, const TVec< int > &parent, const TVec< TVec< int > > &children, const TVec< int > &message_up, const TVec< int > &message_down) | PLearn::GaussMix | [protected, static] |
type | PLearn::GaussMix | |
type_id | PLearn::GaussMix | [protected] |
unknownOutput(char def, const Vec &input, Vec &output, int &k) const | PLearn::GaussMix | [protected, virtual] |
unref() const | PLearn::PPointable | [inline] |
updateCholeskyFromPrevious(const Mat &chol_previous, Mat &chol_updated, const Mat &full_matrix, const TVec< int > &indices_previous, const TVec< int > &indices_updated) const | PLearn::GaussMix | [protected] |
updated_weights | PLearn::GaussMix | [protected] |
updateInverseVarianceFromPrevious(const Mat &src, Mat &dst, const Mat &full, const TVec< int > &ind_src, const TVec< int > &ind_dst, real *src_log_det=0, real *dst_log_det=0) const | PLearn::GaussMix | [protected] |
updateSampleWeights() | PLearn::GaussMix | [protected] |
upper_bound | PLearn::PDistribution | |
usage() const | PLearn::PPointable | [inline] |
use(VMat testset, VMat outputs) const | PLearn::PLearner | [virtual] |
use_a_separate_random_generator_for_testing | PLearn::PLearner | |
useOnTrain(Mat &outputs) const | PLearn::PLearner | [virtual] |
validation_set | PLearn::PLearner | [protected] |
variance(Mat &cov) const | PLearn::GaussMix | [virtual] |
verbosity | PLearn::PLearner | |
weightsize() const | PLearn::PLearner | [virtual] |
weightsize_ | PLearn::PLearner | [protected] |
write(ostream &out) const | PLearn::Object | [virtual] |
writeOptionVal(PStream &out, const string &optionname) const | PLearn::Object | |
y_centered | PLearn::GaussMix | [mutable, protected] |
y_x_mat | PLearn::GaussMix | [protected] |
~Object() | PLearn::Object | [virtual] |
~PPointable() | PLearn::PPointable | [inline, virtual] |