, including all inherited members.
_classname_() | PLearn::DenoisingRecurrentNet | [static] |
_getOptionList_() | PLearn::DenoisingRecurrentNet | [static] |
_getRemoteMethodMap_() | PLearn::DenoisingRecurrentNet | [static] |
_isa_(const Object *o) | PLearn::DenoisingRecurrentNet | [static] |
_new_instance_for_typemap_() | PLearn::DenoisingRecurrentNet | [static] |
_static_initialize_() | PLearn::DenoisingRecurrentNet | [static] |
_static_initializer_ | PLearn::DenoisingRecurrentNet | [static] |
acc_dynamic_connections_gr | PLearn::DenoisingRecurrentNet | [mutable, protected] |
acc_hidden_bias_gr | PLearn::DenoisingRecurrentNet | [mutable, protected] |
acc_input_connections_gr | PLearn::DenoisingRecurrentNet | [mutable, protected] |
acc_recons_bias_gr | PLearn::DenoisingRecurrentNet | [mutable, protected] |
acc_reconstruction_dynamic_connections_gr | PLearn::DenoisingRecurrentNet | [mutable, protected] |
acc_target_bias_gr | PLearn::DenoisingRecurrentNet | [mutable, protected] |
acc_target_connections_gr | PLearn::DenoisingRecurrentNet | [mutable, protected] |
applyMultipleSoftmaxToInputWindow(Vec input_reconstruction_activation, Vec input_reconstruction_prob) | PLearn::DenoisingRecurrentNet | [private] |
applyWeightPenalty(Mat &weights, Mat &acc_weights_gr, int &down_size, int &up_size, real &lr) | PLearn::DenoisingRecurrentNet | [private] |
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] |
bias_gradient | PLearn::DenoisingRecurrentNet | [mutable, protected] |
bpropUpdateConnection(const Vec &input, const Vec &output, Vec &input_gradient, const Vec &output_gradient, Mat &weights, Mat &acc_weights_gr, int &down_size, int &up_size, real &lr, bool accumulate, bool using_penalty_factor) | PLearn::DenoisingRecurrentNet | [private] |
bpropUpdateHiddenLayer(const Vec &input, const Vec &output, Vec &input_gradient, const Vec &output_gradient, Vec &bias, real &lr) | PLearn::DenoisingRecurrentNet | [private] |
build() | PLearn::DenoisingRecurrentNet | [virtual] |
build_() | PLearn::DenoisingRecurrentNet | [private] |
build_from_train_set() | PLearn::PLearner | [inline, protected, virtual] |
call(const string &methodname, int nargs, PStream &io) | PLearn::Object | [virtual] |
changeOption(const string &optionname, const string &value) | PLearn::Object | |
changeOptions(const map< string, string > &name_value) | PLearn::Object | [virtual] |
clamp_units(const Vec layer_vector, PP< RBMLayer > layer, TVec< int > symbol_sizes) const | PLearn::DenoisingRecurrentNet | |
clamp_units(const Vec layer_vector, PP< RBMLayer > layer, TVec< int > symbol_sizes, const Vec original_mask, Vec &formated_mask) const | PLearn::DenoisingRecurrentNet | |
classname() const | PLearn::DenoisingRecurrentNet | [virtual] |
clean_encoded_seq | PLearn::DenoisingRecurrentNet | [mutable, 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::DenoisingRecurrentNet | [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 | |
computeOutput(const Vec &input, Vec &output) const | PLearn::DenoisingRecurrentNet | [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] |
current_learning_rate | PLearn::DenoisingRecurrentNet | [mutable, protected] |
data | PLearn::DenoisingRecurrentNet | [protected] |
declareMethods(RemoteMethodMap &rmm) | PLearn::PLearner | [protected, static] |
declareOptions(OptionList &ol) | PLearn::DenoisingRecurrentNet | [protected, static] |
declaringFile() | PLearn::DenoisingRecurrentNet | [inline, static] |
deepCopy(CopiesMap &copies) const | PLearn::DenoisingRecurrentNet | [virtual] |
deepCopyNoMap() | PLearn::Object | |
DenoisingRecurrentNet() | PLearn::DenoisingRecurrentNet | |
duration_to_number_of_timeframes(int duration) | PLearn::DenoisingRecurrentNet | [static] |
dynamic_act_no_bias_contribution | PLearn::DenoisingRecurrentNet | [mutable, protected] |
dynamic_connections | PLearn::DenoisingRecurrentNet | |
dynamic_gradient_scale_factor | PLearn::DenoisingRecurrentNet | |
dynamic_reconstruction_connections | PLearn::DenoisingRecurrentNet | |
encode_artificialData(Mat seq) const | PLearn::DenoisingRecurrentNet | [private] |
encode_onehot_diffNote_duration(Mat sequence, Mat &encoded_sequence, bool use_silence, int duration_nbits=20) | PLearn::DenoisingRecurrentNet | [static] |
encode_onehot_note_octav_duration(Mat sequence, Mat &encoded_sequence, int prepend_zero_rows, bool use_silence, int octav_nbits, int duration_nbits=20) | PLearn::DenoisingRecurrentNet | [static] |
encode_onehot_timeframe(Mat sequence, Mat &encoded_sequence, int prepend_zero_rows, bool use_silence=false) | PLearn::DenoisingRecurrentNet | [static] |
encodeAndCreateSupervisedSequence(Mat seq) const | PLearn::DenoisingRecurrentNet | [private] |
encodeAndCreateSupervisedSequence2(Mat seq) const | PLearn::DenoisingRecurrentNet | [private] |
encoded_seq | PLearn::DenoisingRecurrentNet | [mutable, protected] |
encodeSequence(Mat sequence, Mat &encoded_seq) const | PLearn::DenoisingRecurrentNet | |
encodeSequenceAndPopulateLists(Mat seq, bool doNoise) const | PLearn::DenoisingRecurrentNet | [private] |
encoding | PLearn::DenoisingRecurrentNet | |
end_of_sequence_symbol | PLearn::DenoisingRecurrentNet | |
expdir | PLearn::PLearner | |
finalize() | PLearn::PLearner | [virtual] |
finalized | PLearn::PLearner | |
forget() | PLearn::DenoisingRecurrentNet | [virtual] |
forget_when_training_set_changes | PLearn::PLearner | [protected] |
fpropHiddenReconstructionFromLastHidden(Vec theInput, Vec hidden, Mat reconstruction_weights, Mat &acc_weights_gr, Vec &reconstruction_bias, Vec &reconstruction_bias2, Vec hidden_reconstruction_activation_grad, Vec &reconstruction_prob, Vec clean_input, Vec hidden_gradient, double hidden_reconstruction_cost_weight, double lr) | PLearn::DenoisingRecurrentNet | [private] |
fpropHiddenReconstructionFromLastHidden2(Vec theInput, Vec hidden, Mat reconstruction_weights, Mat &acc_weights_gr, Vec &reconstruction_bias, Vec &reconstruction_bias2, Vec hidden_reconstruction_activation_grad, Vec &reconstruction_prob, Vec clean_input, Vec hidden_gradient, double hidden_reconstruction_cost_weight, double lr) | PLearn::DenoisingRecurrentNet | [private] |
fpropHiddenSymmetricDynamicMatrix(Vec hidden, Mat reconstruction_weights, Vec &reconstruction_prob, Vec clean_input, Vec hidden_gradient, double hidden_reconstruction_cost_weight, double lr) | PLearn::DenoisingRecurrentNet | [private] |
fpropInputReconstructionFromHidden(Vec hidden, Mat reconstruction_weights, Vec &input_reconstruction_bias, Vec &input_reconstruction_prob, Vec clean_input) | PLearn::DenoisingRecurrentNet | [private] |
fpropUpdateInputReconstructionFromHidden(Vec hidden, Mat &reconstruction_weights, Mat &acc_weights_gr, Vec &input_reconstruction_bias, Vec &input_reconstruction_prob, Vec clean_input, Vec hidden_gradient, double input_reconstruction_cost_weight, double lr) | PLearn::DenoisingRecurrentNet | [private] |
generate(int t, int n) | PLearn::DenoisingRecurrentNet | |
generateArtificial() | PLearn::DenoisingRecurrentNet | |
getDurationBit(int duration) | PLearn::DenoisingRecurrentNet | [static] |
getDynamicConnectionsWeightMatrix() | PLearn::DenoisingRecurrentNet | [private] |
getDynamicReconstructionConnectionsWeightMatrix() | PLearn::DenoisingRecurrentNet | [private] |
getExperimentDirectory() const | PLearn::PLearner | [inline] |
getInputConnectionsWeightMatrix() | PLearn::DenoisingRecurrentNet | [private] |
getInputWindow(Mat sequence, int startpos, int winsize) | PLearn::DenoisingRecurrentNet | [inline, static] |
getNoteAndOctave(int midi_number, int ¬e, int &octave) | PLearn::DenoisingRecurrentNet | [inline, static] |
getOption(const string &optionname) const | PLearn::Object | |
getOptionList() const | PLearn::DenoisingRecurrentNet | [virtual] |
getOptionMap() const | PLearn::DenoisingRecurrentNet | [virtual] |
getOptionsToRemoteTransmit() const | PLearn::Object | [virtual] |
getOptionsToSave() const | PLearn::Object | [virtual] |
getOutputNames() const | PLearn::PLearner | [virtual] |
getRemoteMethodMap() const | PLearn::DenoisingRecurrentNet | [virtual] |
getSequence(int i, Mat &seq) const | PLearn::DenoisingRecurrentNet | |
getTargetConnectionsWeightMatrix(int tar) | PLearn::DenoisingRecurrentNet | [private] |
getTestCostIndex(const string &costname) const | PLearn::PLearner | |
getTestCostNames() const | PLearn::DenoisingRecurrentNet | [virtual] |
getTrainCostIndex(const string &costname) const | PLearn::PLearner | |
getTrainCostNames() const | PLearn::DenoisingRecurrentNet | [virtual] |
getTrainingSet() const | PLearn::PLearner | [inline] |
getTrainStatsCollector() | PLearn::PLearner | [inline] |
getValidationSet() const | PLearn::PLearner | [inline] |
hasOption(const string &optionname) const | PLearn::Object | |
hidden2_act_no_bias_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
hidden2_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
hidden_act_no_bias_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
hidden_connections | PLearn::DenoisingRecurrentNet | |
hidden_gradient | PLearn::DenoisingRecurrentNet | [mutable, protected] |
hidden_layer | PLearn::DenoisingRecurrentNet | |
hidden_layer2 | PLearn::DenoisingRecurrentNet | |
hidden_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
hidden_noise_prob | PLearn::DenoisingRecurrentNet | |
hidden_reconstruction_bias | PLearn::DenoisingRecurrentNet | |
hidden_reconstruction_bias2 | PLearn::DenoisingRecurrentNet | |
hidden_reconstruction_cost_weight | PLearn::DenoisingRecurrentNet | |
hidden_reconstruction_lr | PLearn::DenoisingRecurrentNet | |
hidden_reconstruction_prob | PLearn::DenoisingRecurrentNet | [mutable, protected] |
hidden_temporal_gradient | PLearn::DenoisingRecurrentNet | [mutable, protected] |
info() const | PLearn::Object | [virtual] |
inherited typedef | PLearn::DenoisingRecurrentNet | [private] |
initTrain() | PLearn::PLearner | [protected] |
inject_zero_forcing_noise(Mat sequence, double noise_prob) const | PLearn::DenoisingRecurrentNet | |
inject_zero_forcing_noise(Vec sequence, double noise_prob) const | PLearn::DenoisingRecurrentNet | |
input_connections | PLearn::DenoisingRecurrentNet | |
input_layer | PLearn::DenoisingRecurrentNet | |
input_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
input_noise_prob | PLearn::DenoisingRecurrentNet | |
input_reconstruction_bias | PLearn::DenoisingRecurrentNet | |
input_reconstruction_cost_weight | PLearn::DenoisingRecurrentNet | |
input_reconstruction_lr | PLearn::DenoisingRecurrentNet | |
input_reconstruction_prob | PLearn::DenoisingRecurrentNet | [mutable, protected] |
input_symbol_sizes | PLearn::DenoisingRecurrentNet | |
input_window_size | PLearn::DenoisingRecurrentNet | |
inputsize() const | PLearn::PLearner | [virtual] |
inputsize_ | PLearn::PLearner | [protected] |
isStatefulLearner() const | PLearn::PLearner | [virtual] |
L1_penalty_factor | PLearn::DenoisingRecurrentNet | |
L2_penalty_factor | PLearn::DenoisingRecurrentNet | |
load(const PPath &filename) | PLearn::Object | [virtual] |
locateSequenceBoundaries(VMat dataset, TVec< int > &boundaries, real end_of_sequence_symbol) | PLearn::DenoisingRecurrentNet | [static] |
makeDeepCopyFromShallowCopy(CopiesMap &copies) | PLearn::DenoisingRecurrentNet | [virtual] |
masks_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
master_sends_testset_rows | PLearn::PLearner | |
mean_encoded_vec | PLearn::DenoisingRecurrentNet | |
n_examples | PLearn::PLearner | [protected] |
nb_stage_reconstruction | PLearn::DenoisingRecurrentNet | |
nb_stage_target | PLearn::DenoisingRecurrentNet | |
newread(PStream &in, unsigned int id=UINT_MAX) | PLearn::Object | |
newwrite(PStream &out) const | PLearn::Object | [virtual] |
nll_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
noise | PLearn::DenoisingRecurrentNet | |
noisy_recurrent_lr | PLearn::DenoisingRecurrentNet | |
nSequences() const | PLearn::DenoisingRecurrentNet | [inline] |
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] |
outputsize() const | PLearn::DenoisingRecurrentNet | [virtual] |
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 | |
partition(TVec< double > part, TVec< double > periode, TVec< double > vel) const | PLearn::DenoisingRecurrentNet | |
PLearner() | PLearn::PLearner | |
PPointable() | PLearn::PPointable | [inline] |
PPointable(const PPointable &other) | PLearn::PPointable | [inline] |
prediction_cost_weight | PLearn::DenoisingRecurrentNet | |
prepareToSendResults(PStream &out, int nres) | PLearn::Object | [static] |
processDataSet(VMat dataset) const | PLearn::PLearner | [virtual] |
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 | |
recurrent_lr | PLearn::DenoisingRecurrentNet | |
recurrentFprop(Vec train_costs, Vec train_n_items, bool useDynamicConnections=true) const | PLearn::DenoisingRecurrentNet | [private] |
recurrentUpdate(real input_reconstruction_weight, real hidden_reconstruction_cost_weight, real temporal_gradient_contribution, real prediction_cost_weight, real inputAndDynamicPart, Vec train_costs, Vec train_n_items) | PLearn::DenoisingRecurrentNet | |
ref() const | PLearn::PPointable | [inline] |
remote_test(VMat testset, PP< VecStatsCollector > test_stats, bool rtestoutputs, bool rtestcosts) const | PLearn::PLearner | [virtual] |
remote_useOnTrain() const | PLearn::PLearner | [virtual] |
report_progress | PLearn::PLearner | |
resetInternalState() | PLearn::PLearner | [virtual] |
resize_lists(int l) const | PLearn::DenoisingRecurrentNet | [private] |
run() | PLearn::Object | [virtual] |
save(const PPath &filename) const | PLearn::Object | [virtual] |
save_trainingset_prefix | PLearn::PLearner | |
seed_ | PLearn::PLearner | |
seq | PLearn::DenoisingRecurrentNet | [mutable, protected] |
setExperimentDirectory(const PPath &the_expdir) | PLearn::PLearner | [virtual] |
setLearningRate(real the_learning_rate) | PLearn::DenoisingRecurrentNet | |
setOption(const string &optionname, const string &value) | PLearn::Object | |
setTrainingSet(VMat training_set, bool call_forget=true) | PLearn::DenoisingRecurrentNet | [virtual] |
setTrainStatsCollector(PP< VecStatsCollector > statscol) | PLearn::PLearner | [virtual] |
setValidationSet(VMat validset) | PLearn::PLearner | [virtual] |
splitRawMaskedSupervisedSequence(Mat seq, bool doNoise) const | PLearn::DenoisingRecurrentNet | [private] |
stage | PLearn::PLearner | |
sub_test(VMat testset, PP< VecStatsCollector > test_stats, bool rtestoutputs, bool rtestcosts) const | PLearn::PLearner | [virtual] |
target_connections | PLearn::DenoisingRecurrentNet | |
target_layers | PLearn::DenoisingRecurrentNet | |
target_layers_n_of_target_elements | PLearn::DenoisingRecurrentNet | |
target_layers_weights | PLearn::DenoisingRecurrentNet | |
target_prediction_act_no_bias_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
target_prediction_list | PLearn::DenoisingRecurrentNet | [mutable, protected] |
target_symbol_sizes | PLearn::DenoisingRecurrentNet | |
targets_list | PLearn::DenoisingRecurrentNet | [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::DenoisingRecurrentNet | [virtual] |
test_minibatch_size | PLearn::PLearner | |
testset_boundaries | PLearn::DenoisingRecurrentNet | [mutable, protected] |
tied_hidden_reconstruction_weights | PLearn::DenoisingRecurrentNet | |
tied_input_reconstruction_weights | PLearn::DenoisingRecurrentNet | |
train() | PLearn::DenoisingRecurrentNet | [virtual] |
train_set | PLearn::PLearner | [protected] |
train_stats | PLearn::PLearner | [protected] |
trainset_boundaries | PLearn::DenoisingRecurrentNet | [protected] |
trainUnconditionalPredictor() | PLearn::DenoisingRecurrentNet | [private] |
unconditionalFprop(Vec train_costs, Vec train_n_items) const | PLearn::DenoisingRecurrentNet | [private] |
unref() const | PLearn::PPointable | [inline] |
updateInputReconstructionFromHidden(Vec hidden, Mat &reconstruction_weights, Mat &acc_weights_gr, Vec &input_reconstruction_bias, Vec input_reconstruction_prob, Vec clean_input, Vec hidden_gradient, double input_reconstruction_cost_weight, double lr) | PLearn::DenoisingRecurrentNet | [private] |
updateTargetLayer(Vec &grad, Vec &bias, real &lr) | PLearn::DenoisingRecurrentNet | [private] |
usage() const | PLearn::PPointable | [inline] |
use(VMat testset, VMat outputs) const | PLearn::PLearner | [virtual] |
use_a_separate_random_generator_for_testing | PLearn::PLearner | |
use_target_layers_masks | PLearn::DenoisingRecurrentNet | |
useOnTrain(Mat &outputs) const | PLearn::PLearner | [virtual] |
validation_set | PLearn::PLearner | [protected] |
verbosity | PLearn::PLearner | |
visi_bias_gradient | PLearn::DenoisingRecurrentNet | [mutable, protected] |
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 | |
~Object() | PLearn::Object | [virtual] |
~PPointable() | PLearn::PPointable | [inline, virtual] |