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
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Machine* machine
- The (already trained) Machine that computes the probability.
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int output_number
- which output should we take
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real prior
- prior probability used to normalize the machine output
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List machine_inputs
- temporary List used to compute machine forward
Public Methods
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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
Public Fields
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int n_observations
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int tot_n_frames
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int max_n_frames
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real log_probability
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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
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bool is_free
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List* params
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List* der_params
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int n_params
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real* beta
Public Methods
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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
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int n_inputs
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int n_outputs
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List* outputs
Public Methods
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virtual void forward(List* inputs)
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virtual void reset()
Inherited from Object:
Public Methods
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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 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...
Machine* 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
int output_number
- which output should we take
real prior
- prior probability used to normalize the machine output
List machine_inputs
- temporary List used to compute machine forward
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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