class FixedMachineDistribution

This class uses one of the outputs of a given pre-trained machine as an estimate of a probability (used in the method frameLogProbability.

Inheritance:


Public Fields

[more]Machine* machine
The (already trained) Machine that computes the probability.
[more]int output_number
which output should we take
[more]real prior
prior probability used to normalize the machine output
[more]List machine_inputs
temporary List used to compute machine forward

Public Methods

[more] FixedMachineDistribution(Machine* machine_, int output_number_ = 0, real prior_ = 1.)
this distribution is created with an already trained machine, the index of the output corresponding to this distribution, and 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 uses one of the outputs of a given pre-trained machine as an estimate of a probability (used in the method frameLogProbability. It can normalize it using an optional prior value. This class can therefore be used in conjunction with HMMs to implement the HMM/ANN hybrid model...

oMachine* machine
The (already trained) Machine that computes the probability. We suppose that the outputs of the machines are positive and sum to 1. One possibility is to use softmax outputs

oint output_number
which output should we take

oreal prior
prior probability used to normalize the machine output

oList machine_inputs
temporary List used to compute machine forward

o FixedMachineDistribution(Machine* machine_, int output_number_ = 0, real prior_ = 1.)
this distribution is created with an already trained machine, the index of the output corresponding to this distribution, and an eventual prior


This class has no child classes.
Author:
Samy Bengio (bengio@idiap.ch)

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