Data.Datamining.Clustering.SOS

A Self-organising Set (SOS). An SOS maps input patterns onto a set, where each element in the set is a model of the input data. An SOS is like a Kohonen Self-organising Map (SOM), except:

• Instead of a grid, it uses a simple set of unconnected models. Since the models are unconnected, only the model that best matches the input is ever updated. This makes it faster, however, topological relationships within the input data are not preserved.
• New models are created on-the-fly when no existing model is similar enough to an input pattern. If the SOS is at capacity, the least useful model will be deleted.

This implementation supports the use of non-numeric patterns.

In layman's terms, a SOS can be useful when you you want to build a set of models on some data. A tutorial is available at https://github.com/mhwombat/som/wiki.

References:

• de Buitléir, Amy, Russell, Michael and Daly, Mark. (2012). Wains: A pattern-seeking artificial life species. Artificial Life, 18 (4), 399-423.
• Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43 (1), 59–69.

Construction

data SOS t x k p

A Simplified Self-Organising Map (SOS). t is the type of the counter. x is the type of the learning rate and the difference metric. k is the type of the model indices. p is the type of the input patterns and models.

Constructors

SOS

Fields

toMap :: Map k (p, t)

Maps patterns and match counts to nodes.

learningRate :: t -> x

A function which determines the learning rate for a node. The input parameter indicates how many patterns (or pattern batches) have previously been presented to the classifier. Typically this is used to make the learning rate decay over time. The output is the learning rate for that node (the amount by which the node's model should be updated to match the target). The learning rate should be between zero and one.

maxSize :: Int

The maximum number of models this SOS can hold.

diffThreshold :: x

The threshold that triggers creation of a new model.

allowDeletion :: Bool

Delete existing models to make room for new ones? The least useful (least frequently matched) models will be deleted first.

difference :: p -> p -> x

A function which compares two patterns and returns a non-negative number representing how different the patterns are. A result of 0 indicates that the patterns are identical.

makeSimilar :: p -> x -> p -> p

A function which updates models. For example, if this function is f, then f target amount pattern returns a modified copy of pattern that is more similar to target than pattern is. The magnitude of the adjustment is controlled by the amount parameter, which should be a number between 0 and 1. Larger values for amount permit greater adjustments. If amount=1, the result should be identical to the target. If amount=0, the result should be the unmodified pattern.

nextIndex :: k

Index for the next node to add to the SOS.

Instances

Generic (SOS t x k p)

(NFData t, NFData x, NFData k, NFData p) => NFData (SOS t x k p)

type Rep (SOS t x k p)

makeSOS :: Bounded k => (t -> x) -> Int -> x -> Bool -> (p -> p -> x) -> (p -> x -> p -> p) -> SOS t x k p

Deconstruction

time :: Num t => SOS t x k p -> t

The current "time" (number of times the SOS has been trained).

isEmpty :: SOS t x k p -> Bool

Returns true if the SOS has no models, false otherwise.

numModels :: SOS t x k p -> Int

Returns the number of models the SOS currently contains.

modelMap :: SOS t x k p -> Map k p

Returns a map from node ID to model.

counterMap :: SOS t x k p -> Map k t

Returns a map from node ID to counter (number of times the node's model has been the closest match to an input pattern).

Learning and classification

exponential :: (Floating a, Integral t) => a -> a -> t -> a

A typical learning function for classifiers. exponential r0 d t returns the learning rate at time t. When t = 0, the learning rate is r0. Over time the learning rate decays exponentially; the decay rate is d. Normally the parameters are chosen such that:

• 0 < r0 < 1
• 0 < d

classify :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SOS t x k p -> p -> (k, x, [(k, x)], SOS t x k p)

classify s p identifies the model s that most closely matches the pattern p. If necessary, it will create a new node and model. Returns the ID of the node with the best matching model, the difference between the best matching model and the pattern, the differences between the input and each model in the SOS, and the (possibly updated) SOS.

train :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SOS t x k p -> p -> SOS t x k p

train s p identifies the model in s that most closely matches p, and updates it to be a somewhat better match.

trainBatch :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SOS t x k p -> [p] -> SOS t x k p

For each pattern p in ps, trainBatch s ps identifies the model in s that most closely matches p, and updates it to be a somewhat better match.