This class can be used to model Multinomial Distributions.
Inheritance:
Public Fields
-
int n_values
- the number of different values that can take this discrete distribution
-
real prior_weights
- the prior weight given to each value.
-
List* initial_params
- optional initialization parameters
-
char* initial_file
- optional initialization file
-
real* log_weights
- the pointers to the parameters
-
real* dlog_weights
- the pointers to the d_parameters
-
real* weights_acc
- accumulators for EM
Public Fields
-
int n_observations
-
int tot_n_frames
-
int max_n_frames
-
real log_probability
-
real* log_probabilities
Public Methods
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virtual real logProbability(List* inputs)
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virtual real viterbiLogProbability(List* inputs)
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virtual real frameLogProbability(real* observations, real* inputs, int t)
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virtual void frameExpectation(real* observations, real* inputs, int t)
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virtual void eMIterInitialize()
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virtual void iterInitialize()
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virtual void eMSequenceInitialize(List* inputs)
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virtual void sequenceInitialize(List* inputs)
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virtual void eMAccPosteriors(List* inputs, real log_posterior)
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virtual void frameEMAccPosteriors(real* observations, real log_posterior, real* inputs, int t)
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virtual void viterbiAccPosteriors(List* inputs, real log_posterior)
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virtual void frameViterbiAccPosteriors(real* observations, real log_posterior, real* inputs, int t)
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virtual void eMUpdate()
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virtual void decode(List* inputs)
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virtual void eMForward(List* inputs)
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virtual void viterbiForward(List* inputs)
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virtual void frameBackward(real* observations, real* alpha, real* inputs, int t)
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virtual void viterbiBackward(List* inputs, real* alpha)
Public Fields
-
bool is_free
-
List* params
-
List* der_params
-
int n_params
-
real* beta
Public Methods
-
virtual void init()
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virtual int numberOfParams()
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virtual void backward(List* inputs, real* alpha)
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virtual void allocateMemory()
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virtual void freeMemory()
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virtual void loadFILE(FILE* file)
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virtual void saveFILE(FILE* file)
Inherited from Machine:
Public Fields
-
int n_inputs
-
int n_outputs
-
List* outputs
Public Methods
-
virtual void forward(List* inputs)
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virtual void reset()
Inherited from Object:
Public Methods
-
void addOption(const char* name, int size, void* ptr, const char* help="", bool is_allowed_after_init=false)
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void addIOption(const char* name, int* ptr, int init_value, const char* help="", bool is_allowed_after_init=false)
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void addROption(const char* name, real* ptr, real init_value, const char* help="", bool is_allowed_after_init=false)
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void addBOption(const char* name, bool* ptr, bool init_value, const char* help="", bool is_allowed_after_init=false)
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void setOption(const char* name, void* ptr)
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void setIOption(const char* name, int option)
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void setROption(const char* name, real option)
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void setBOption(const char* name, bool option)
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void load(const char* filename)
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void save(const char* filename)
Documentation
This class can be used to model Multinomial Distributions.
They can be trained using either EM (with EMTrainer) or gradient descent
(with GMTrainer).
int n_values
- the number of different values that can take this discrete distribution
real prior_weights
- the prior weight given to each value. kind of smoother
List* initial_params
- optional initialization parameters
char* initial_file
- optional initialization file
real* log_weights
- the pointers to the parameters
real* dlog_weights
- the pointers to the d_parameters
real* weights_acc
- accumulators for EM
- This class has no child classes.
- Author:
- Samy Bengio (bengio@idiap.ch)
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