Layer of bipolar units taking values in {1,1}. More...
#include <shark/Unsupervised/RBM/Neuronlayers/BipolarLayer.h>
Public Types  
typedef SymmetricBinarySpace  StateSpace 
the state space of this neuron is binary More...  
typedef RealVector  SufficientStatistics 
The sufficient statistics for the Binary Layer store the probability for a neuron to be on. More...  
typedef Batch< SufficientStatistics >::type  StatisticsBatch 
Sufficient statistics of a batch of data. More...  
Public Types inherited from shark::IParameterizable<>  
typedef RealVector  ParameterVectorType 
Public Member Functions  
const RealVector &  bias () const 
Returns the bias values of the units. More...  
RealVector &  bias () 
Returns the bias values of the units. More...  
void  resize (std::size_t newSize) 
Resizes this neuron layer. More...  
std::size_t  size () const 
Returns the number of neurons of this layer. More...  
template<class Input , class BetaVector >  
void  sufficientStatistics (Input const &input, StatisticsBatch &statistics, BetaVector const &beta) const 
Takes the input of the neuron and calculates the distribution of the neurons For binary neuronsthis is identical with the conditional probability for the neuron to be on given the state of the connected layer. More...  
template<class Matrix , class Rng >  
void  sample (StatisticsBatch const &statistics, Matrix &state, double alpha, Rng &rng) const 
Samples from the distribution using either Gibbs or flipthestate sampling. More...  
template<class Matrix >  
RealVector  logProbability (StatisticsBatch const &statistics, Matrix const &state) const 
Computes the log of the probability of the given states in the conditional distribution. More...  
template<class Matrix >  
Matrix const &  phi (Matrix const &state) const 
Transforms the current state of the neurons for the multiplication with the weight matrix of the RBM, i.e. calculates the value of the phifunction used in the interaction term. In the case of bipolar neurons the phifunction is just the identity. More...  
RealMatrix  expectedPhiValue (StatisticsBatch const &statistics) const 
Returns the conditional expectation of the phifunction given the state of the connected layer,. More...  
RealMatrix  mean (StatisticsBatch const &statistics) const 
Returns the mean of the distribution. More...  
template<class Matrix , class BetaVector >  
RealVector  energyTerm (Matrix const &state, BetaVector const &beta) const 
Returns the energy term this neuron adds to the energy function. More...  
template<class Input >  
double  logMarginalize (Input const &inputs, double beta) const 
Sums over all possible values of the terms of the energy function which depend on the this layer and returns the logarithmic result. More...  
template<class Vector , class SampleBatch >  
void  expectedParameterDerivative (Vector &derivative, SampleBatch const &samples) const 
Calculates the expectation of the derivatives of the energy term of this neuron layer with respect to it's parameters  the bias weights. The expectation is taken with respect to the conditional probability distribution of the layer given the state of the connected layer. More...  
template<class Vector , class SampleBatch , class WeightVector >  
void  expectedParameterDerivative (Vector &derivative, SampleBatch const &samples, WeightVector const &weights) const 
Calculates the expectation of the derivatives of the energy term of this neuron layer with respect to it's parameters  the bias weights. The expectation is taken with respect to the conditional probability distribution of the layer given the state of the connected layer. More...  
template<class Vector , class SampleBatch >  
void  parameterDerivative (Vector &derivative, SampleBatch const &samples) const 
Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters  the bias weights. More...  
template<class Vector , class SampleBatch , class WeightVector >  
void  parameterDerivative (Vector &derivative, SampleBatch const &samples, WeightVector const &weights) const 
Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters  the bias weights. More...  
RealVector  parameterVector () const 
Returns the vector with the parameters associated with the neurons in the layer, i.e. the bias vector. More...  
void  setParameterVector (RealVector const &newParameters) 
Sets the parameters associated with the neurons in the layer, i.e. the bias vector. More...  
std::size_t  numberOfParameters () const 
Returns the number of the parameters associated with the neurons in the layer. More...  
void  read (InArchive &archive) 
Reads the bias vector from an archive. More...  
void  write (OutArchive &archive) const 
Writes the bias vector to an archive. More...  
Public Member Functions inherited from shark::ISerializable  
virtual  ~ISerializable () 
Virtual d'tor. More...  
void  load (InArchive &archive, unsigned int version) 
Versioned loading of components, calls read(...). More...  
void  save (OutArchive &archive, unsigned int version) const 
Versioned storing of components, calls write(...). More...  
BOOST_SERIALIZATION_SPLIT_MEMBER ()  
Public Member Functions inherited from shark::IParameterizable<>  
virtual  ~IParameterizable () 
Layer of bipolar units taking values in {1,1}.
A neuron in a BipolarLayer takes values in {1,1} and the conditional probability to be 1 given the states of the neurons in the connected layer is determined by the sigmoid function and the input it gets from the connected layer.
Definition at line 45 of file BipolarLayer.h.
the state space of this neuron is binary
Definition at line 51 of file BipolarLayer.h.
typedef Batch<SufficientStatistics>::type shark::BipolarLayer::StatisticsBatch 
Sufficient statistics of a batch of data.
Definition at line 56 of file BipolarLayer.h.
typedef RealVector shark::BipolarLayer::SufficientStatistics 
The sufficient statistics for the Binary Layer store the probability for a neuron to be on.
Definition at line 54 of file BipolarLayer.h.

inline 
Returns the bias values of the units.
Definition at line 59 of file BipolarLayer.h.

inline 
Returns the bias values of the units.
Definition at line 64 of file BipolarLayer.h.

inline 
Returns the energy term this neuron adds to the energy function.
state  the state of the neuron layer 
beta  the inverse temperature of the ith state 
Definition at line 206 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inline 
Calculates the expectation of the derivatives of the energy term of this neuron layer with respect to it's parameters  the bias weights. The expectation is taken with respect to the conditional probability distribution of the layer given the state of the connected layer.
This function takes a batch of samples and extracts the required informations out of it.
derivative  the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives 
samples  the samples from which the informations can be extracted 
Definition at line 243 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inline 
Calculates the expectation of the derivatives of the energy term of this neuron layer with respect to it's parameters  the bias weights. The expectation is taken with respect to the conditional probability distribution of the layer given the state of the connected layer.
This function takes a batch of samples and weights the results
derivative  the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives 
samples  the samples from which the informations can be extracted 
weights  The weights for alle samples 
Definition at line 257 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inline 
Returns the conditional expectation of the phifunction given the state of the connected layer,.
statistics  the sufficient statistics of the layer 
Definition at line 185 of file BipolarLayer.h.

inline 
Sums over all possible values of the terms of the energy function which depend on the this layer and returns the logarithmic result.
This function is called by Energy when the unnormalized marginal probability of the connected layer is to be computed. This function calculates the part which depends on the neurons which are to be marginalized out. (In the case of the bipolar hidden neuron, this is the term \( \sum_h e^{\vec h^T W \vec v+ \vec h^T \vec c} \)). The rest is calculated by the energy function.
inputs  the inputs of the neurons they get from the other layer 
beta  the inverse temperature of the RBM 
Definition at line 225 of file BipolarLayer.h.
References size(), SIZE_CHECK, and shark::softPlus().

inline 
Computes the log of the probability of the given states in the conditional distribution.
Currently it is only possible to compute the case with alpha=0
statistics  the statistics of the conditional distribution 
state  the state to check 
Definition at line 155 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inline 
Returns the mean of the distribution.
statistics  the sufficient statistics of the layer for a whole batch 
Definition at line 193 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inlinevirtual 
Returns the number of the parameters associated with the neurons in the layer.
Reimplemented from shark::IParameterizable<>.
Definition at line 297 of file BipolarLayer.h.
References size().

inline 
Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters  the bias weights.
This function takes a batch of samples and extracts the required informations out of it.
derivative  the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives 
samples  the sample from which the informations can be extracted 
Definition at line 269 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inline 
Calculates the derivatives of the energy term of this neuron layer with respect to it's parameters  the bias weights.
This function takes a batch of samples and calculates a weighted derivative
derivative  the derivative with respect to the parameters, the result is added on top of it to accumulate derivatives 
samples  the sample from which the informations can be extracted 
weights  the weights for the single sample derivatives 
Definition at line 281 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inlinevirtual 
Returns the vector with the parameters associated with the neurons in the layer, i.e. the bias vector.
Reimplemented from shark::IParameterizable<>.
Definition at line 287 of file BipolarLayer.h.

inline 
Transforms the current state of the neurons for the multiplication with the weight matrix of the RBM, i.e. calculates the value of the phifunction used in the interaction term. In the case of bipolar neurons the phifunction is just the identity.
state  the state matrix of the neuron layer 
Definition at line 176 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inlinevirtual 
Reads the bias vector from an archive.
archive  the archive 
Reimplemented from shark::ISerializable.
Definition at line 304 of file BipolarLayer.h.

inline 
Resizes this neuron layer.
newSize  number of neurons in the layer 
Definition at line 71 of file BipolarLayer.h.

inline 
Samples from the distribution using either Gibbs or flipthestate sampling.
For alpha= 0 gibbs sampling is performed. That is the next state for neuron i is directly taken from the conditional distribution of the ith neuron. In the case of alpha=1, flipthestate sampling is performed, which takes the last state into account and tries to do deterministically jump into states with higher probability. This is counterbalanced by a higher chance to jump back into a lower probability state in later steps. For alpha between 0 and 1 a mixture of both is performed.
statistics  sufficient statistics containing the probabilities of the neurons to be one 
state  the state vector that shell hold the sampled states 
alpha  factor changing from gibbs to flipthe state sampling. 0<=alpha<=1 
rng  the random number generator used for sampling 
Definition at line 109 of file BipolarLayer.h.
References shark::random::coinToss(), SHARK_CRITICAL_REGION, size(), and SIZE_CHECK.

inlinevirtual 
Sets the parameters associated with the neurons in the layer, i.e. the bias vector.
Reimplemented from shark::IParameterizable<>.
Definition at line 292 of file BipolarLayer.h.

inline 
Returns the number of neurons of this layer.
Definition at line 76 of file BipolarLayer.h.
Referenced by energyTerm(), expectedParameterDerivative(), logMarginalize(), logProbability(), mean(), numberOfParameters(), parameterDerivative(), phi(), sample(), and sufficientStatistics().

inline 
Takes the input of the neuron and calculates the distribution of the neurons For binary neuronsthis is identical with the conditional probability for the neuron to be on given the state of the connected layer.
input  the batch of inputs of the neuron 
statistics  sufficient statistics containing the probabilities of the neurons to be one 
beta  the inverse Temperature of the RBM (typically 1) for the whole batch 
Definition at line 87 of file BipolarLayer.h.
References size(), and SIZE_CHECK.

inlinevirtual 
Writes the bias vector to an archive.
archive  the archive 
Reimplemented from shark::ISerializable.
Definition at line 311 of file BipolarLayer.h.