Hopfield.Boltzmann.ClassificationBoltzmannMachine

Base Restricted 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

Classification

Instances

Eq Mode

Show Mode

data BoltzmannData

Constructors

BoltzmannData

Fields

weightsB :: Weights

the weights of the network

classificationWeights :: Weights

biasB :: Bias

biasC :: Bias

biasD :: Bias

patternsB :: [Pattern]

the patterns which were used to train it can be decuded from weights, maybe should be remove now

hiddenCount :: Int

number of neurons in the hidden layer

pattern_to_class :: [(Pattern, Int)]

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. One has to ensure that the appropriate weights matrix is passed with this function.

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

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

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

buildCBoltzmannData' patterns nrHidden: 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.

getActivationSum :: Weights -> Bias -> Pattern -> Int -> Double

getActivationProbability ws bias pat index can be used to compute the activation probability for a neuron in the visible layer, or for parts of the sums requires for the probability of the classifications

getActivationProbabilityVisible :: Weights -> Bias -> Pattern -> Int -> Double

getActivationProbabilityVisible ws bias h index returns the activation probability for a neuron index in a visible pattern, given the weights matrix ws, the vector of biases bias. Applies the activation function to the activation sum, in order to obtain the probability.

getActivationSumHidden :: Weights -> Weights -> Bias -> Pattern -> Pattern -> Int -> Double

getActivationSumHidden ws bias h index returns the activation sum for a neuron index in a hidden pattern, given the weights matrix ws, the vector of biases bias.

getHiddenSums :: Weights -> Weights -> Bias -> Pattern -> Pattern -> Vector Double

getActivationSumHidden ws bias h index returns the activation sum for all neurons in the hidden pattern, given the weights matrix ws, the vector of biases bias.

getActivationProbabilityHidden :: Weights -> Weights -> Bias -> Pattern -> Pattern -> Int -> Double

getActivationProbabilityVisible ws u bias v index returns the activation probability for a neuron index in a hidden pattern, given the weights matrices ws and u, the vector of biases bias. Applies the activation function to the activation sum, in order to obtain the probability.

updateNeuronVisible :: MonadRandom m => Weights -> Bias -> Pattern -> Int -> m Int

updateNeuronVisible ws bias h index updates a neuron in the visible layer by using gibbsSampling, according to the activation probability

updateNeuronHidden :: MonadRandom m => Weights -> Weights -> Bias -> Pattern -> Pattern -> Int -> m Int

Updates a neuron in the hidden layer by using gibbsSampling, according to the activation probability

updateVisible :: MonadRandom m => Weights -> Bias -> Pattern -> m Pattern

Updates the entire visible layer by using gibbsSampling, according to the activation probability

updateHidden :: MonadRandom m => Weights -> Weights -> Bias -> Pattern -> Pattern -> m Pattern

Updates the entire visible layer by using gibbsSampling, according to the activation probability

updateClassification :: Weights -> Bias -> Pattern -> Pattern

Updates a classification vector given the current state of the network ( the u matrix and the vector of biases d, together with a hidden vector h)

getClassificationVector :: [(Pattern, Int)] -> Pattern -> Pattern

oneTrainingStep :: MonadRandom m => BoltzmannData -> Pattern -> m BoltzmannData

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 oneTrainingStep bm visible updates the parameters of bm (the 2 weight matrices and the biases) according to the training instance v and its classification, obtained by looking in the map kept in bm

trainBoltzmann :: MonadRandom m => [Pattern] -> Int -> m BoltzmannData

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 pratice, CD-1 has been shown to work surprisingly well. trainBoltzmann pats nrHidden where pats are the training patterns and nrHidden is the number of neurons to be created in the hidden layer. http:en.wikipedia.orgwikiRestricted_Boltzmann_machine#Training_algorithm

matchPatternCBoltzmann :: BoltzmannData -> Pattern -> Int

matchPatternBoltzmann bm pat given the Boltzmann trained network bm recognizes pat, by classifying it to one of the patterns the network was trained with. This is done by computing the free energy of pat with every possible classification, and choosing the classification with lowest energy. http:uai.sis.pitt.edupapers11/p463-louradour.pdf

getFreeEnergy :: BoltzmannData -> Pattern -> Pattern -> Double

getFreeEnergy bm visible classification_vector Computes the free energy of v with classification_vector, according to the trained Boltzmann network bm. It is used for classifying a given visible vector according to the classes used for training the network bm.

activation :: Double -> Double

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

softplus :: Double -> Double

The function used to compute the free energy http:uai.sis.pitt.edupapers11/p463-louradour.pdf

validClassificationVector :: Pattern -> Int -> Maybe String

validPattern :: 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

validWeights ws checks that a weight matrix is well formed.