class TableLookupDistribution

This class outputs one of the observations as the logProbability.

Inheritance:


Public Fields

[more]int column
The column in the observation vector that corresponds to the logProbability
[more]bool apply_log
do we apply a log transformation
[more]real prior
do we normalize by a given prior

Public Methods

[more] 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

oint n_observations
oint tot_n_frames
oint max_n_frames
oreal log_probability
oreal* log_probabilities

Public Methods

ovirtual real logProbability(List* inputs)
ovirtual real viterbiLogProbability(List* inputs)
ovirtual real frameLogProbability(real* observations, real* inputs, int t)
ovirtual void frameExpectation(real* observations, real* inputs, int t)
ovirtual void eMIterInitialize()
ovirtual void iterInitialize()
ovirtual void eMSequenceInitialize(List* inputs)
ovirtual void sequenceInitialize(List* inputs)
ovirtual void eMAccPosteriors(List* inputs, real log_posterior)
ovirtual void frameEMAccPosteriors(real* observations, real log_posterior, real* inputs, int t)
ovirtual void viterbiAccPosteriors(List* inputs, real log_posterior)
ovirtual void frameViterbiAccPosteriors(real* observations, real log_posterior, real* inputs, int t)
ovirtual void eMUpdate()
ovirtual void decode(List* inputs)
ovirtual void eMForward(List* inputs)
ovirtual void viterbiForward(List* inputs)
ovirtual void frameBackward(real* observations, real* alpha, real* inputs, int t)
ovirtual void viterbiBackward(List* inputs, real* alpha)


Inherited from GradientMachine:

Public Fields

obool is_free
oList* params
oList* der_params
oint n_params
oreal* beta

Public Methods

ovirtual void init()
ovirtual int numberOfParams()
ovirtual void backward(List* inputs, real* alpha)
ovirtual void allocateMemory()
ovirtual void freeMemory()
ovirtual void loadFILE(FILE* file)
ovirtual void saveFILE(FILE* file)


Inherited from Machine:

Public Fields

oint n_inputs
oint n_outputs
oList* outputs

Public Methods

ovirtual void forward(List* inputs)
ovirtual void reset()


Inherited from Object:

Public Methods

ovoid addOption(const char* name, int size, void* ptr, const char* help="", bool is_allowed_after_init=false)
ovoid addIOption(const char* name, int* ptr, int init_value, const char* help="", bool is_allowed_after_init=false)
ovoid addROption(const char* name, real* ptr, real init_value, const char* help="", bool is_allowed_after_init=false)
ovoid addBOption(const char* name, bool* ptr, bool init_value, const char* help="", bool is_allowed_after_init=false)
ovoid setOption(const char* name, void* ptr)
ovoid setIOption(const char* name, int option)
ovoid setROption(const char* name, real option)
ovoid setBOption(const char* name, bool option)
ovoid load(const char* filename)
ovoid save(const char* filename)


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...

oint column
The column in the observation vector that corresponds to the logProbability

obool apply_log
do we apply a log transformation

oreal prior
do we normalize by a given prior

o 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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