class TableLookupDistribution

This class outputs one of the observations as the logProbability.

Inheritance:

Public Fields

int column

The column in the observation vector that corresponds to the logProbability

bool apply_log

do we apply a log transformation

real prior

do we normalize by a given prior

Public Methods

TableLookupDistribution(int column_ = 0, bool apply_log_ = true, real prior_ = 1.)

The column number corresponds to the logProbability which can be normalized by an eventual prior

Inherited from Distribution:

Public Fields

real log_probability

Sequence* log_probabilities

Public Methods

virtual real logProbability(Sequence* inputs)

virtual real viterbiLogProbability(Sequence* inputs)

virtual real frameLogProbability(int t, real* f_inputs)

virtual real viterbiFrameLogProbability(int t, real* f_inputs)

virtual void eMIterInitialize()

virtual void iterInitialize()

virtual void eMSequenceInitialize(Sequence* inputs)

virtual void sequenceInitialize(Sequence* inputs)

virtual void eMAccPosteriors(Sequence* inputs, real log_posterior)

virtual void frameEMAccPosteriors(int t, real* f_inputs, real log_posterior)

virtual void viterbiAccPosteriors(Sequence* inputs, real log_posterior)

virtual void frameViterbiAccPosteriors(int t, real* f_inputs, real log_posterior)

virtual void eMUpdate()

virtual void update()

virtual void decode(Sequence* inputs)

virtual void eMForward(Sequence* inputs)

virtual void viterbiForward(Sequence* inputs)

virtual void frameBackward(int t, real* f_inputs, real* beta_, real* f_outputs, real* alpha_)

virtual void viterbiBackward(Sequence* inputs, Sequence* alpha)

virtual void frameDecision(int t, real* decision)

Public Members

Returns the decision of the distribution

Inherited from GradientMachine:

Public Fields

int n_inputs

int n_outputs

Parameters* params

Parameters* der_params

Sequence* beta

Public Methods

virtual void forward(Sequence* inputs)

virtual void backward(Sequence* inputs, Sequence* alpha)

virtual void setPartialBackprop(bool flag=true)

virtual void frameForward(int t, real* f_inputs, real* f_outputs)

virtual void loadXFile(XFile* file)

virtual void saveXFile(XFile* file)

Inherited from Machine:

Public Fields

Sequence* outputs

Public Methods

virtual void reset()

virtual void setDataSet(DataSet* dataset_)

Inherited from Object:

Public Fields

Allocator* allocator

Public Methods

void addOption(const char* name, int size, void* ptr, const char* help="")

void addIOption(const char* name, int* ptr, int init_value, const char* help="")

void addROption(const char* name, real* ptr, real init_value, const char* help="")

void addBOption(const char* name, bool* ptr, bool init_value, const char* help="")

void addOOption(const char* name, Object** ptr, Object* init_value, const char* help="")

void setOption(const char* name, void* ptr)

void setIOption(const char* name, int option)

void setROption(const char* name, real option)

void setBOption(const char* name, bool option)

void setOOption(const char* name, Object* option)

void load(const char* filename)

void save(const char* filename)

void* operator new(size_t size, Allocator* allocator_=NULL)

void* operator new(size_t size, Allocator* allocator_, void* ptr_)

void operator delete(void* ptr)

Documentation

This class outputs one of the observations as the logProbability. It can eventually apply a log transformation and/or normalize by a given prior. It can therefore be used in conjunction with HMMs to implement the HMM/ANN hybrid model...

int column

The column in the observation vector that corresponds to the logProbability

bool apply_log

do we apply a log transformation

real prior

do we normalize by a given prior

TableLookupDistribution(int column_ = 0, bool apply_log_ = true, real prior_ = 1.)

The column number corresponds to the logProbability which can be normalized by an eventual prior

This class has no child classes.

Author:

Samy Bengio (bengio@idiap.ch)

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