Hopfield.Boltzmann.RestrictedBoltzmannMachine

Base Restricted Boltzmann machine. http:en.wikipedia.orgwikiRestricted_Boltzmann_machine

Documentation

learningRate :: Double

determines the rate in which the weights are changed in the training phase. http:en.wikipedia.orgwikiRestricted_Boltzmann_machine#Training_algorithm

data Mode

Constructors

Hidden

Visible

Instances

Eq Mode

Show Mode

data Phase

Constructors

Training

Matching

Instances

Eq Phase

Show Phase

data BoltzmannData

Constructors

BoltzmannData

Fields

weightsB :: Weights

the weights of the network

patternsB :: [Pattern]

the patterns which were used to train it

nr_hiddenB :: Int

number of neurons in the hidden layer

pattern_to_binaryB :: [(Pattern, [Int])]

the binary representation of the pattern index the pattern_to_binary field will not replace the patternsB field as it does not contain duplicated patterns, which might be required for statistical analysis in clustering and super attractors

Instances

Show BoltzmannData

getDimension :: Mode -> Weights -> Int

Retrieves the dimension of the weights matrix corresponding to the given mode. For hidden, it is the width of the matrix, and for visible it is the height.

notMode :: Mode -> Mode

buildBoltzmannData :: MonadRandom m => [Pattern] -> m BoltzmannData

buildBoltzmannData patterns trains a boltzmann network with patterns. The number of hidden neurons is set to the number of visible neurons.

buildBoltzmannData' :: MonadRandom m => [Pattern] -> Int -> m BoltzmannData

buildBoltzmannData' patterns nr_hidden: Takes a list of patterns and builds a Boltzmann network (by training) in which these patterns are stable states. The result of this function can be used to run a pattern against the network, by using matchPatternBoltzmann.

updateNeuron' :: Double -> Phase -> Mode -> Weights -> Pattern -> Int -> Int

getActivationProbability :: Phase -> Mode -> Weights -> Pattern -> Int -> Double

updateNeuron :: MonadRandom m => Phase -> Mode -> Weights -> Pattern -> Int -> m Int

updateNeuron mode ws pat index , given a vector pat of type mode updates the neuron with number index in the layer with opposite type.

getCounterPattern :: MonadRandom m => Phase -> Mode -> Weights -> Pattern -> m Pattern

getCounterPattern mode ws pat, given a vector pat of type mode computes the values of all the neurons in the layer of the opposite type.

updateWeights :: MonadRandom m => Weights -> Pattern -> m Weights

One step which updates the weights in the CD-n training process. The weights are changed according to one of the training patterns. http:en.wikipedia.orgwikiRestricted_Boltzmann_machine#Training_algorithm

trainBoltzmann :: MonadRandom m => [Pattern] -> Int -> m (Weights, [(Pattern, [Int])])

The training function for the Boltzmann Machine. We are using the contrastive divergence algorithm CD-1 TODO see if making the vis (we could extend to CD-n, but In practice, CD-1 has been shown to work surprisingly well. trainBoltzmann pats nr_hidden where pats are the training patterns and nr_hidden is the number of neurons to be created in the hidden layer. http:en.wikipedia.orgwikiRestricted_Boltzmann_machine#Training_algorithm

activation :: Double -> Double

The activation function for the network (the logistic sigmoid). http:en.wikipedia.orgwikiSigmoid_function

validPattern :: Phase -> Mode -> Weights -> Pattern -> Maybe String

validPattern mode weights pattern Returns an error string in a Just if the pattern is not compatible with weights and Nothing otherwise. mode gives the type of the pattern, which is checked (Visible or Hidden).

validWeights :: Weights -> Maybe String

updateBoltzmann :: MonadRandom m => Weights -> Pattern -> m Pattern

Updates a pattern using the Boltzmann machine

getFreeEnergy :: Weights -> Pattern -> Double

matchPatternBoltzmann :: MonadRandom m => BoltzmannData -> Pattern -> m Int

Matches a pattern against the a given network