Hopfield.TestUtil

Documentation

data Type

Constructors

H

BM

nonempty :: forall a. Gen [a] -> Gen [a]

mapMonad :: Monad m => (a -> b) -> m [a] -> m [b]

toGenVector :: Gen [a] -> Gen (Vector a)

Convert a list generator to a vector generator

signGen :: Gen Int

Generate a random sign (+/- 1)

binaryGen :: Gen Int

patternGen :: Type -> Int -> Gen Pattern

patternGen n: Generates patterns of size n

patternRangeGen :: Type -> (Int, Int) -> Gen Pattern

patternRangeGen (min, max) Generates patterns of size ranging between min and max

boundedListGen :: Gen a -> Int -> Gen [a]

boundedListGen g n: Generates lists (max length n) of the given Gen

patListGen :: Type -> Int -> Int -> Gen [Pattern]

patListGen t maxPatSize maxPatListSize Generates a list of patterns. The size of each pattern is less than maxPatSize. The size odf the list is less than maxPatListSize. The type is required in order to create types specific for Boltzmann, Hopfield etc.

patternsTupleGen :: Type -> Int -> Int -> Gen ([Pattern], [Pattern])

patternsTupleGen g m1 m2Generates a tuple of lists, as follows: Uses patListGen to generate 1 list of patterns with length less than m2. The list itself has to have length less than m1. The second element of a tuple is a list of patterns which have the same size as the patterns of the first list.

sameElemList :: a -> Gen [a]

sameElemVector :: a -> Gen (Vector a)

Generate vectors containing the same element replicated

allWeightsSame :: Int -> [[Double]]

Produces a matrix with 0's along the diagonal and 1's otherwise

boundedReplicateGen :: Int -> Gen a -> Gen [a]

boundedReplicateGen n g Generates lists containing g replicated. The list is bounded in size by n.

replaceAtN :: Int -> a -> [a] -> [a]

Replaces the nth element in the list with r

crosstalk :: HopfieldData -> Int -> Int -> Int

trainingPatsAreFixedPoints :: LearningType -> [Pattern] -> Gen Bool

Used as a property to check that patterns which are used to create the network are stable in respect to update

energyDecreasesAfterUpdate :: LearningType -> ([Pattern], [Pattern]) -> Gen Bool

Trains a network using training_pats and then updates each pattern in pats according to the weights of that network. The aim is to check that the energy decreases after each update.

repeatedUpdateCheck :: LearningType -> ([Pattern], [Pattern]) -> Gen Bool

boltzmannBuildGen :: Int -> Int -> Int -> Gen ([Pattern], Int)

boltzmannBuildGen maxPatSize maxPatListSize max_hidden Generates the structures required for creating a Boltzmann machine: a list of patterns together with the number of hidden layers, which has to be less than max_hidden.

buildBoltzmannCheck :: ([Pattern], Int) -> Gen Bool

boltzmannAndPatGen :: Int -> Int -> Int -> Gen ([Pattern], Int, Pattern)

Generates a list of patterns and the number of hidden layers used to train a Boltzmann machine, as well as a generic pattern to recognize on this machine.

probabilityCheck :: ([Pattern], Int, Pattern) -> Gen Bool

probabilityCheck (pats, nr_h, pat). Trains a Boltzmann machine using pats and nr_h and computes the activation probability for pat using this machine, and check if it is indeed a probability, ie in [0,1]

updateNeuronCheck :: Int -> ([Pattern], Int, Pattern) -> Gen Bool

buildIntTuple :: Gen (Int, Int)

binaryCheck :: (Int, Int) -> Bool

evalRandGen :: Rand StdGen a -> Gen a