, 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] |